System

Abstract

The system according to the embodiment aims to generate a new work based on the style and thought of a deceased artist.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collector collects past works of the artist. The analysis unit analyzes the data collected by the collection unit and learns the style and thinking of the artist. The generation unit adjusts a new work concept based on the data analyzed by the analysis unit. The providing unit provides a generation platform based on the concept 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] Previous technology was unable to generate new works by deceased artists, limiting the opportunities for fans to discover new works.

[0005] The system of the embodiment aims to generate new works based on the style and thoughts of deceased artists. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past works by an artist. The analysis unit analyzes the data collected by the collection unit and learns the artist's style and thoughts. The generation unit adjusts a new work concept based on the data analyzed by the analysis unit. The provision unit provides a generation platform based on the concept generated by the generation unit. [Effects of the Invention]

[0007] An embodiment of the system can generate new works based on the style and thinking of deceased artists. [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 system according to an embodiment of the present invention generates new artworks in line with an artist's thinking. This system collects the artist's past works, analyzes them using AI, and fine-tunes a new artwork concept based on the analyzed data using AI to generate new artworks in line with the artist's style and thinking. It also supports the artist's creative process by providing a generation platform. For example, the system collects the artist's past works. This involves collecting artworks in various formats, such as paintings and music. For example, in the case of paintings, image data of the past works is collected, and in the case of music, music data is collected. The collected data is then analyzed using AI. The AI ​​analyzes the collected data to learn the artist's style and thinking. For example, in the case of paintings, characteristics such as color use, composition, and brushstrokes are analyzed, and in the case of music, characteristics such as melody, rhythm, and instrument usage are analyzed. Furthermore, the AI ​​fine-tunes a new artwork concept based on the analyzed data. The AI ​​generates a new artwork concept based on the analyzed data. For example, in the case of paintings, it suggests new themes, color use, and composition, and in the case of music, it suggests new melodies, rhythms, and instrument usage. This generates a new artwork in line with the artist's style and thinking. The system also supports artists in creating artworks by providing them with a generative platform. Artists can use the generative platform to generate new works that fit their own style and ideas. For example, in the case of paintings, artists can use the generative platform to try out new themes, color usage, and composition, while in the case of songs, artists can try out new melodies, rhythms, and instrument usage. This allows artists to receive support in creating new works. The system can generate new works that fit the artists' ideas and support their work creation. For example, fans can enjoy new paintings and songs by deceased artists, and living artists can use the generative platform to create new works that fit their own style and ideas.

[0029] A creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past works by an artist. The artist's past works include, but are not limited to, paintings, music, sculptures, and the like. The collection unit, for example, collects digital data. The collection unit can also collect physical works. For example, the collection unit collects image data of paintings and audio data of music. The analysis unit analyzes the data collected by the collection unit to learn the artist's style and thoughts. The analysis unit, for example, analyzes the color usage and composition of paintings using image analysis technology. The analysis unit can also analyze the melody and rhythm of music using audio analysis technology. For example, the analysis unit analyzes the color palette and composition patterns of paintings, and the melodic progression and rhythm patterns of music. The generation unit fine-tunes the concept of a new work based on the data analyzed by the analysis unit. The generation unit, for example, generates new themes and melodies using algorithms. The generation unit can also perform manual adjustments. For example, the generation unit proposes new themes and color usage, and proposes new melodies and rhythms based on the analyzed data. The provision unit provides a generation platform based on the concept generated by the generation unit. The provision unit provides, for example, an online platform. The provision unit can also hold physical exhibitions. For example, the provision unit provides artists with opportunities to try out new themes, color usage, and compositions through the generation platform, and opportunities to try out new melodies and rhythms. In this way, the generation system according to the embodiment can generate new works that are in line with the artists' ideas and support their creation.

[0030] The collection unit can collect works in multiple formats, such as paintings or songs. The collection unit collects works in multiple formats, such as paintings, music, sculptures, photographs, and digital art. For example, the collection unit collects image data of paintings and audio data of music. The collection unit can also collect 3D data of sculptures. For example, the collection unit collects files of digital art and image data of photographs. By collecting works in various formats, the artist's style and thoughts can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the collected data into AI and have the AI ​​classify and organize the data.

[0031] The analysis unit can analyze the collected data and learn characteristics of color usage, composition, melody, or rhythm. The analysis unit can, for example, use image analysis technology to analyze the color usage and composition of a painting. For example, the analysis unit can analyze the color palette and composition patterns of a painting. The analysis unit can also analyze the melody and rhythm of music using audio analysis technology. For example, the analysis unit can analyze the progression of a musical melody and rhythm patterns. The analysis unit can also analyze the content of poetry or lyrics using text analysis technology. For example, the analysis unit can analyze the theme and expression method of a poem. This makes it possible to learn the style and thoughts of an artist in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The generation unit can adjust a new theme or melody based on the analyzed data. The generation unit, for example, uses an algorithm to generate a new theme or melody. For example, the generation unit suggests a new theme or color scheme based on the analyzed data. The generation unit can also make manual adjustments. For example, the generation unit suggests a new melody or rhythm. The generation unit can also fine-tune a new work concept using AI. For example, the generation unit makes further adjustments based on a theme or melody generated by AI. This makes it possible to generate a new work that is in line with the artist's style and ideas. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the analyzed data into AI and have the AI ​​generate a new work concept.

[0033] The providing unit can provide artists with a generation platform and support them in creating works. The providing unit, for example, provides an online platform. For example, the providing unit provides artists with opportunities to try out new themes, color usage, and composition through the generation platform. The providing unit can also hold physical exhibitions. For example, the providing unit provides opportunities to try out new melodies and rhythms. This allows artists to receive support in creating new works. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can manage the generation platform with AI and have the AI ​​provide support to artists.

[0034] When collecting, the collection unit can filter based on the artist's creative period or a specific theme. For example, the collection unit distinguishes between an artist's early works and later works when collecting them. For example, the collection unit prioritizes collecting an artist's early works. The collection unit can also filter works based on a specific theme. For example, the collection unit prioritizes collecting nature-themed works. The collection unit can also collect works based on the artist's creative period. For example, the collection unit prioritizes collecting works created during wartime. In this way, by filtering works based on a specific theme or creative period, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI perform the filtering of works to be collected.

[0035] The collection unit may classify works based on the technique or material used by the artist when collecting them. The collection unit may classify works based on technique, such as oil painting, acrylic painting, digital art, etc. For example, the collection unit may preferentially collect oil paintings. The collection unit may also classify works based on material, such as canvas, paper, or digital media. For example, the collection unit may preferentially collect works painted on canvas. The collection unit may also classify works based on a particular technique. For example, the collection unit may preferentially collect works using pointillism. This allows for more detailed analysis by classifying works based on technique or material. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may have AI perform the classification of the collected works.

[0036] When collecting, the collection unit can adjust the collection priority based on the rating or popularity of the artist's works. For example, the collection unit prioritizes collecting highly rated works. For example, the collection unit prioritizes collecting works with high review scores. The collection unit can also prioritize collecting popular works. For example, the collection unit prioritizes collecting works that have received a lot of attention on social media. The collection unit can also collect low-rated but important works in a balanced manner. For example, the collection unit prioritizes collecting historically important works. In this way, important works can be preferentially collected by adjusting the collection priority based on rating or popularity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to adjust the priority of works to be collected.

[0037] When collecting, the collection unit can prioritize collecting highly relevant works by taking into account the artist's geographical and cultural background. For example, the collection unit prioritizes collecting works related to the artist's hometown. For example, the collection unit prioritizes collecting works created in the artist's hometown. The collection unit can also collect works based on the culture or historical background that influenced the artist. For example, the collection unit prioritizes collecting works related to a particular cultural sphere. The collection unit can also prioritize collecting works related to the region or country where the artist's work was exhibited. For example, the collection unit prioritizes collecting works exhibited in a particular region. This makes it possible to collect more relevant works by taking into account the geographical and cultural background. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI select the works to collect.

[0038] When collecting data, the collection unit may analyze the artist's social media activities and public interviews to collect related works. For example, the collection unit may collect works mentioned by the artist on social media. For example, the collection unit may prioritize collecting works introduced by the artist on social media. The collection unit may also collect data based on works or themes mentioned in public interviews. For example, the collection unit may prioritize collecting works mentioned in interviews. The collection unit may also analyze fan reactions on social media to collect popular works. For example, the collection unit may prioritize collecting works that receive a lot of reactions on social media. In this way, highly relevant works can be collected by analyzing social media activities and public interviews. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input social media and interview data into AI and have the AI ​​collect related works.

[0039] When collecting, the collection unit can select works to collect by referring to records of the artist's past exhibitions and concerts. The collection unit, for example, collects works exhibited at past exhibitions. For example, the collection unit collects works based on catalogs of past exhibitions. The collection unit can also collect songs performed at past concerts. For example, the collection unit collects songs based on programs of past concerts. The collection unit can also preferentially collect works that have received particularly high praise based on records of exhibitions and concerts. For example, the collection unit preferentially collects works that have received high praise at exhibitions and concerts. In this way, by referring to records of past exhibitions and concerts, highly rated works can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input records of exhibitions and concerts into AI and have the AI ​​select works to collect.

[0040] During the analysis, the analysis unit can perform analysis to identify an artist's creative process or source of inspiration. For example, the analysis unit analyzes related literature and interviews to identify sources of inspiration in the artist's work. For example, the analysis unit identifies events and people that influenced the artist. The analysis unit can also analyze the production process of a work to identify the artist's creative process. For example, the analysis unit analyzes the artist's sketches and notes to identify the creative process. The analysis unit can also analyze related data to identify other artists and works that influenced the artist. For example, the analysis unit identifies works by other artists that influenced the artist. This allows for a deeper understanding of the artist's style and thoughts by identifying the creative process and source of inspiration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input related literature and interview data into AI and have the AI ​​identify sources of inspiration.

[0041] During the analysis, the analysis unit can identify repetitive patterns or unique techniques in the artist's works. For example, the analysis unit can identify patterns of color use in the artist's works. For example, the analysis unit can identify specific color combinations or color scheme patterns. The analysis unit can also identify patterns of composition in the artist's works. For example, the analysis unit can identify specific patterns of compositional arrangement or perspective. The analysis unit can also identify patterns of brushstrokes or techniques in the artist's works. For example, the analysis unit can identify specific patterns of brushstrokes or painting techniques. This allows the artist's style to be understood in more detail by identifying repetitive patterns or unique techniques. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify repetitive patterns or unique techniques.

[0042] During the analysis, the analysis unit can perform a detailed analysis of trends in color or sound combinations in the artist's work. The analysis unit, for example, analyzes color combinations in the artist's work. For example, the analysis unit analyzes specific color combinations or color patterns. The analysis unit can also analyze sound combinations in the artist's work. For example, the analysis unit analyzes specific sound harmonies or rhythmic combinations. The analysis unit can also analyze changes in color or sound in the artist's work. For example, the analysis unit analyzes changes in color or sound for each period in which the work was created. This allows for a detailed analysis of trends in color and sound combinations to provide a deeper understanding of the artist's style. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input collected data into AI and have the AI ​​analyze trends in color and sound combinations.

[0043] During the analysis, the analysis unit can identify the influence of other artists related to the artist's work. The analysis unit, for example, identifies other artists who influenced the artist. For example, the analysis unit can identify the influence of other artists' styles and techniques on the artist's work. The analysis unit can also identify the era or culture that influenced the artist. For example, the analysis unit can identify a specific historical or cultural background. By identifying the influence of other artists, it is possible to gain a deeper understanding of the artist's style and thought. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the influence of other artists.

[0044] The analysis unit may take into account the social or historical background of the artist's work during the analysis. For example, the analysis unit may consider the social background of the era in which the artist's work was created. For example, the analysis unit may consider specific social issues or events. The analysis unit may also consider the historical background of the region in which the artist's work was created. For example, the analysis unit may consider historical events in a specific region. The analysis unit may also consider social and historical events that influenced the artist's work. For example, the analysis unit may consider specific events or occurrences. This allows for a deeper understanding of the artist's work by considering the social and historical background. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input collected data into AI and have the AI ​​perform an analysis of the social and historical background.

[0045] During the analysis, the analysis unit can identify technological evolution or changes in the artist's work. For example, the analysis unit can identify technological evolution in the artist's work. For example, the analysis unit can identify the evolutionary stage of a particular technology. The analysis unit can also identify changes in techniques in the artist's work. For example, the analysis unit can identify changes in a particular technique. The analysis unit can also identify technological innovations in the artist's work. For example, the analysis unit can identify particular technological innovations. In this way, by identifying technological evolution or changes, it is possible to understand the changes in the artist's work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the identification of technological evolution or changes.

[0046] During generation, the generation unit can generate a new work by combining specific elements of the artist's past works. For example, the generation unit generates a new painting by combining the color palette and composition of the artist's past works. For example, the generation unit combines a specific color palette and composition pattern. The generation unit can also generate a new song by combining the melody and rhythm of the artist's past works. For example, the generation unit combines a specific melody line and rhythm pattern. The generation unit can also generate a new work by combining the theme and style of the artist's past works. For example, the generation unit combines a specific theme and art style. In this way, a new work can be generated by combining elements of past works. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​execute a combination of specific elements.

[0047] During generation, the generation unit can generate a new work based on the artist's unpublished sketches or notes. For example, the generation unit generates a new painting based on the artist's unpublished sketches. For example, the generation unit generates a new painting based on an unpublished design. The generation unit can also generate a new song based on the artist's unpublished melody. For example, the generation unit generates a new song based on an unpublished melody line. The generation unit can also generate a new work based on the artist's unpublished notes. For example, the generation unit generates a new song based on unpublished lyrics. In this way, by generating a new work based on unpublished sketches or notes, the artist's unpublished ideas can be utilized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected unpublished sketches and notes into AI and have the AI ​​generate a new work.

[0048] During generation, the generation unit can generate a new work by emphasizing a particular technique or style in the artist's work. For example, the generation unit can generate a new painting by emphasizing a particular color palette in the artist's work. For example, the generation unit can emphasize a particular color palette. The generation unit can also generate a new song by emphasizing a particular rhythm in the artist's work. For example, the generation unit can emphasize a particular rhythmic pattern. The generation unit can also generate a new work by emphasizing a particular composition in the artist's work. For example, the generation unit can emphasize a particular compositional arrangement. In this way, by emphasizing a particular technique or style, a new work that makes the most of the artist's characteristics can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the emphasis on a particular technique or style.

[0049] During generation, the generation unit can generate a new work by fusing different genres or styles of an artist's work. The generation unit, for example, generates a multimedia work that fuses painting and music. For example, the generation unit fuses a specific painting style with a musical genre. The generation unit can also generate a new piece of music that fuses classical music with contemporary music. For example, the generation unit fuses specific classical music elements with contemporary music elements. The generation unit can also generate a new painting that fuses abstract painting with figurative painting. For example, the generation unit fuses a specific abstract painting style with a figurative painting style. This allows a new work to be generated by fusing different genres or styles. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the fusing of different genres or styles.

[0050] During generation, the generation unit can generate a new work by emphasizing a particular theme or message in the artist's work. For example, the generation unit can generate a new painting by emphasizing a peace theme in the artist's work. For example, the generation unit can emphasize a particular message of peace. The generation unit can also generate a new song by emphasizing a love message in the artist's work. For example, the generation unit can emphasize a particular love theme. The generation unit can also generate a new poem by emphasizing a nature theme in the artist's work. For example, the generation unit can emphasize a particular nature theme. In this way, a new work that reflects the artist's intentions can be generated by emphasizing a particular theme or message. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the emphasis on a particular theme or message.

[0051] During generation, the generation unit can change a specific color or sound combination in the artist's work to generate a new work. For example, the generation unit can change the color combination in the artist's work to generate a new painting. For example, the generation unit can change the specific color palette. The generation unit can also change the sound combination in the artist's work to generate a new song. For example, the generation unit can change the harmony of specific sounds. The generation unit can also generate a new multimedia work by changing the color or sound combination in the artist's work. For example, the generation unit can change the specific color and sound combination. In this way, a new work can be generated by changing the color or sound combination. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​change the color or sound combination.

[0052] When providing the platform, the providing unit can design the platform by referring to the artist's past exhibition or presentation methods. For example, the providing unit designs the platform by referring to the layout of the artist's past exhibitions. For example, the providing unit refers to the layout of a particular exhibition. The providing unit can also design the platform by referring to the artist's past presentation methods. For example, the providing unit refers to a particular presentation method. The providing unit can also design the platform by referring to the artist's past exhibition methods to make it easier for users to understand the works. For example, the providing unit refers to a particular exhibition method. In this way, by referring to past exhibition or presentation methods, a platform can be provided that makes it easier for users to understand the works. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data from past exhibitions and presentations into AI and have the AI ​​execute the platform design.

[0053] The providing unit can collect user opinions on the artists' works at the time of providing them and improve the platform's functions. The providing unit, for example, improves the platform's interface based on user feedback. For example, the providing unit changes the interface design based on user feedback. The providing unit can also add or remove functions to be provided based on user feedback. For example, the providing unit adds new functions based on user feedback. The providing unit can also improve the way the works are displayed based on user feedback. For example, the providing unit changes the display method based on user feedback. This can improve the platform's functions based on user feedback, thereby improving the user experience. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input user opinions into AI and have the AI ​​improve the platform's functions.

[0054] The providing unit can enrich the platform by adding educational content or commentary related to the artist's work during provision. The providing unit, for example, adds educational content related to the artist's work. For example, the providing unit adds background information about the work. The providing unit can also add commentary about the artist's work. For example, the providing unit adds explanations about techniques. The providing unit can also add interviews or documentaries related to the artist's work. For example, the providing unit adds interview videos or documentaries. By adding educational content or commentary, users can gain a deeper understanding of the work. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can have AI create the educational content or commentary.

[0055] The providing unit can add a function to introduce works by other artists related to the artist's work when providing the data. The providing unit, for example, automatically suggests works by other artists related to the artist's work. For example, the providing unit suggests works by other artists related to a specific theme or style. The providing unit can also introduce works by other artists influenced by the artist's work. For example, the providing unit can introduce works by artists who have a specific influence. The providing unit can also introduce works by other artists with the same theme or style as the artist's work. For example, the providing unit can introduce works related to a specific theme or style. This allows the user to encounter new works by introducing works by other artists. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on works by other artists into AI and have the AI ​​suggest related works.

[0056] The providing unit can add a function of holding an event or workshop related to the artist's work at the time of providing. The providing unit, for example, provides information on exhibitions and concerts related to the artist's work. For example, the providing unit provides information on a specific exhibition or concert. The providing unit can also provide information on workshops and seminars related to the artist's work. For example, the providing unit provides information on a specific workshop or seminar. The providing unit can also provide information on online events related to the artist's work. For example, the providing unit provides information on a specific online event. This can provide an opportunity for users to deepen their understanding of the work by holding an event or workshop. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information on events and workshops into AI and cause the AI ​​to provide related information.

[0057] The providing unit can add a function to sell goods or products related to the artist's works at the time of providing. The providing unit, for example, sells posters and prints related to the artist's works. For example, the providing unit sells specific posters and prints. The providing unit can also sell books and catalogs related to the artist's works. For example, the providing unit sells specific books and catalogs. The providing unit can also sell accessories and goods related to the artist's works. For example, the providing unit sells specific accessories and goods. In this way, by selling goods and products, users can obtain items related to the works. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the goods and products to be sold into AI and have the AI ​​execute the sale of related products.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can collect social media posts and comments related to the artist's work and use them for analysis by the analysis unit. For example, the collection unit can collect fan reactions and opinions about the artist's work. The collection unit can also collect trends and topics on social media and use them for analysis by the analysis unit. This can help understand social reactions and trends to the artist's work and aid in the analysis by the analysis unit. Some or all of the above-mentioned processing by the collection unit can be performed using AI, or can be performed without AI. For example, the collection unit can input social media data into AI and have the AI ​​collect and organize the data.

[0060] The collection unit can collect works by other artists related to the artist's works and use them for analysis by the analysis unit. For example, the collection unit can collect works by other artists who have influenced the artist. The collection unit can also collect works by other artists related to the artist's works and use them for analysis by the analysis unit. This makes it possible to understand the influence of other artists on the artist's works and use this information for analysis by the analysis unit. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on works by other artists into AI and have the AI ​​collect and organize the data.

[0061] The analysis unit can identify the influence of other artists on the artist's work and reflect this in the analysis results. For example, the analysis unit can identify the styles and techniques of other artists who influenced the artist. The analysis unit can also identify the influence of other artists and reflect this in the analysis results. This allows for a deeper understanding by providing analysis results that take into account the influence of other artists. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input data on other artists' works into AI and have the AI ​​identify the influence and reflect it in the analysis results.

[0062] The generation unit can generate new works that reflect the influence of other artists on the artist's work. For example, the generation unit generates new works that incorporate the styles and techniques of other artists who influenced the artist. The generation unit can also generate new works that reflect the influence of other artists. This makes it possible to provide a wider variety of works by providing new works that reflect the influence of other artists. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input data on other artists' works into AI and have the AI ​​reflect the influence and generate new works.

[0063] The providing unit can provide an exhibition method or presentation method that reflects the influence of other artists on the artist's work. For example, the providing unit can provide an exhibition method that reflects the influence of other artists. The providing unit can also provide a presentation method that reflects the influence of other artists. By providing exhibition methods or presentation methods that reflect the influence of other artists, a wider variety of exhibition methods or presentation methods can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on other artists' works into AI and have the AI ​​reflect the influence and provide exhibition methods or presentation methods.

[0064] The providing unit can provide educational content and commentary that reflects the influence of other artists on the artist's work. For example, the providing unit can provide educational content that reflects the influence of other artists. The providing unit can also provide commentary that reflects the influence of other artists. This can provide a deeper understanding by providing educational content and commentary that reflects the influence of other artists. Some or all of the above-described processing in the providing unit can be performed using AI, or can be performed without AI. For example, the providing unit can input data on other artists' works into AI and have the AI ​​reflect the influence and provide educational content and commentary.

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

[0066] Step 1: The collection department collects the artist's past works. Collections include paintings, music, sculptures, etc. The collection department collects digital data, but can also collect physical works. For example, they collect image data of paintings and audio data of music. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the artist's style and thinking. The analysis unit uses image analysis technology to analyze the color palette and composition of the painting, and audio analysis technology to analyze the melody and rhythm of the music. For example, it analyzes the color palette and composition patterns of the painting, and the progression and rhythm patterns of the music. Step 3: The generator adjusts the concept of the new work based on the data analyzed by the analyzer. The generator uses algorithms to generate new themes and melodies, and also makes manual adjustments. For example, it proposes new themes, color schemes, melodies, and rhythms based on the analyzed data. Step 4: The provider provides a generation platform based on the concepts generated by the generator. The provider provides an online platform as well as a physical exhibition. For example, the generation platform provides artists with the opportunity to experiment with new themes, color palettes, compositions, melodies, and rhythms.

[0067] (Example 2) A system according to an embodiment of the present invention generates new artworks in line with an artist's thinking. This system collects the artist's past works, analyzes them using AI, and fine-tunes a new artwork concept based on the analyzed data using AI to generate new artworks in line with the artist's style and thinking. It also supports the artist's creative process by providing a generation platform. For example, the system collects the artist's past works. This involves collecting artworks in various formats, such as paintings and music. For example, in the case of paintings, image data of the past works is collected, and in the case of music, music data is collected. The collected data is then analyzed using AI. The AI ​​analyzes the collected data to learn the artist's style and thinking. For example, in the case of paintings, characteristics such as color use, composition, and brushstrokes are analyzed, and in the case of music, characteristics such as melody, rhythm, and instrument usage are analyzed. Furthermore, the AI ​​fine-tunes a new artwork concept based on the analyzed data. The AI ​​generates a new artwork concept based on the analyzed data. For example, in the case of paintings, it suggests new themes, color use, and composition, and in the case of music, it suggests new melodies, rhythms, and instrument usage. This generates a new artwork in line with the artist's style and thinking. The system also supports artists in creating artworks by providing them with a generative platform. Artists can use the generative platform to generate new works that fit their own style and ideas. For example, in the case of paintings, artists can use the generative platform to try out new themes, color usage, and composition, while in the case of songs, artists can try out new melodies, rhythms, and instrument usage. This allows artists to receive support in creating new works. The system can generate new works that fit the artists' ideas and support their work creation. For example, fans can enjoy new paintings and songs by deceased artists, and living artists can use the generative platform to create new works that fit their own style and ideas.

[0068] A creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past works by an artist. The artist's past works include, but are not limited to, paintings, music, sculptures, and the like. The collection unit, for example, collects digital data. The collection unit can also collect physical works. For example, the collection unit collects image data of paintings and audio data of music. The analysis unit analyzes the data collected by the collection unit to learn the artist's style and thoughts. The analysis unit, for example, analyzes the color usage and composition of paintings using image analysis technology. The analysis unit can also analyze the melody and rhythm of music using audio analysis technology. For example, the analysis unit analyzes the color palette and composition patterns of paintings, and the melodic progression and rhythm patterns of music. The generation unit fine-tunes the concept of a new work based on the data analyzed by the analysis unit. The generation unit, for example, generates new themes and melodies using algorithms. The generation unit can also perform manual adjustments. For example, the generation unit proposes new themes and color usage, and proposes new melodies and rhythms based on the analyzed data. The provision unit provides a generation platform based on the concept generated by the generation unit. The provision unit provides, for example, an online platform. The provision unit can also hold physical exhibitions. For example, the provision unit provides artists with opportunities to try out new themes, color usage, and compositions through the generation platform, and opportunities to try out new melodies and rhythms. In this way, the generation system according to the embodiment can generate new works that are in line with the artists' ideas and support their creation.

[0069] The collection unit can collect works in multiple formats, such as paintings or songs. The collection unit collects works in multiple formats, such as paintings, music, sculptures, photographs, and digital art. For example, the collection unit collects image data of paintings and audio data of music. The collection unit can also collect 3D data of sculptures. For example, the collection unit collects files of digital art and image data of photographs. By collecting works in various formats, the artist's style and thoughts can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the collected data into AI and have the AI ​​classify and organize the data.

[0070] The analysis unit can analyze the collected data and learn characteristics of color usage, composition, melody, or rhythm. The analysis unit can, for example, use image analysis technology to analyze the color usage and composition of a painting. For example, the analysis unit can analyze the color palette and composition patterns of a painting. The analysis unit can also analyze the melody and rhythm of music using audio analysis technology. For example, the analysis unit can analyze the progression of a musical melody and rhythm patterns. The analysis unit can also analyze the content of poetry or lyrics using text analysis technology. For example, the analysis unit can analyze the theme and expression method of a poem. This makes it possible to learn the style and thoughts of an artist in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0071] The generation unit can adjust a new theme or melody based on the analyzed data. The generation unit, for example, uses an algorithm to generate a new theme or melody. For example, the generation unit suggests a new theme or color scheme based on the analyzed data. The generation unit can also make manual adjustments. For example, the generation unit suggests a new melody or rhythm. The generation unit can also fine-tune a new work concept using AI. For example, the generation unit makes further adjustments based on a theme or melody generated by AI. This makes it possible to generate a new work that is in line with the artist's style and ideas. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the analyzed data into AI and have the AI ​​generate a new work concept.

[0072] The providing unit can provide artists with a generation platform and support them in creating works. The providing unit, for example, provides an online platform. For example, the providing unit provides artists with opportunities to try out new themes, color usage, and composition through the generation platform. The providing unit can also hold physical exhibitions. For example, the providing unit provides opportunities to try out new melodies and rhythms. This allows artists to receive support in creating new works. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can manage the generation platform with AI and have the AI ​​provide support to artists.

[0073] The collection unit can estimate the user's emotions and determine the priority of works to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using an emotion engine. For example, the collection unit captures the user's facial expressions with a camera and analyzes them with the emotion engine. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions based on survey results. For example, the collection unit analyzes the user's responses to estimate the emotions. The collection unit determines the priority of works to be collected based on the estimated user's emotions. For example, if the user is sad, the collection unit can prioritize collecting comforting works. If the user is excited, the collection unit can prioritize collecting stimulating works. If the user is relaxed, the collection unit can prioritize collecting calming works. This makes it possible to prioritize collecting appropriate works according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​determine the priority of the works to be collected.

[0074] When collecting, the collection unit can filter based on the artist's creative period or a specific theme. For example, the collection unit distinguishes between an artist's early works and later works when collecting them. For example, the collection unit prioritizes collecting an artist's early works. The collection unit can also filter works based on a specific theme. For example, the collection unit prioritizes collecting nature-themed works. The collection unit can also collect works based on the artist's creative period. For example, the collection unit prioritizes collecting works created during wartime. In this way, by filtering works based on a specific theme or creative period, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI perform the filtering of works to be collected.

[0075] The collection unit may classify works based on the technique or material used by the artist when collecting them. The collection unit may classify works based on technique, such as oil painting, acrylic painting, digital art, etc. For example, the collection unit may preferentially collect oil paintings. The collection unit may also classify works based on material, such as canvas, paper, or digital media. For example, the collection unit may preferentially collect works painted on canvas. The collection unit may also classify works based on a particular technique. For example, the collection unit may preferentially collect works using pointillism. This allows for more detailed analysis by classifying works based on technique or material. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may have AI perform the classification of the collected works.

[0076] When collecting, the collection unit can adjust the collection priority based on the rating or popularity of the artist's works. For example, the collection unit prioritizes collecting highly rated works. For example, the collection unit prioritizes collecting works with high review scores. The collection unit can also prioritize collecting popular works. For example, the collection unit prioritizes collecting works that have received a lot of attention on social media. The collection unit can also collect low-rated but important works in a balanced manner. For example, the collection unit prioritizes collecting historically important works. In this way, important works can be preferentially collected by adjusting the collection priority based on rating or popularity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to adjust the priority of works to be collected.

[0077] The collection unit can estimate the user's emotions and select the type of artwork to collect based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using an emotion engine. For example, the collection unit captures the user's facial expressions with a camera and analyzes them with an emotion engine. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions based on survey results. For example, the collection unit analyzes the user's answers to estimate the emotions. The collection unit selects the type of artwork to collect based on the estimated user's emotions. For example, the collection unit can prioritize collecting comforting music if the user is sad. Also, the collection unit can prioritize collecting stimulating paintings if the user is excited. Also, the collection unit can prioritize collecting calming music if the user is relaxed. This makes it possible to collect artworks in an appropriate format according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​select the format of the works to be collected.

[0078] When collecting, the collection unit can prioritize collecting highly relevant works by taking into account the artist's geographical and cultural background. For example, the collection unit prioritizes collecting works related to the artist's hometown. For example, the collection unit prioritizes collecting works created in the artist's hometown. The collection unit can also collect works based on the culture or historical background that influenced the artist. For example, the collection unit prioritizes collecting works related to a particular cultural sphere. The collection unit can also prioritize collecting works related to the region or country where the artist's work was exhibited. For example, the collection unit prioritizes collecting works exhibited in a particular region. This makes it possible to collect more relevant works by taking into account the geographical and cultural background. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have AI select the works to collect.

[0079] When collecting data, the collection unit may analyze the artist's social media activities and public interviews to collect related works. For example, the collection unit may collect works mentioned by the artist on social media. For example, the collection unit may prioritize collecting works introduced by the artist on social media. The collection unit may also collect data based on works or themes mentioned in public interviews. For example, the collection unit may prioritize collecting works mentioned in interviews. The collection unit may also analyze fan reactions on social media to collect popular works. For example, the collection unit may prioritize collecting works that receive a lot of reactions on social media. In this way, highly relevant works can be collected by analyzing social media activities and public interviews. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input social media and interview data into AI and have the AI ​​collect related works.

[0080] When collecting, the collection unit can select works to collect by referring to records of the artist's past exhibitions and concerts. The collection unit, for example, collects works exhibited at past exhibitions. For example, the collection unit collects works based on catalogs of past exhibitions. The collection unit can also collect songs performed at past concerts. For example, the collection unit collects songs based on programs of past concerts. The collection unit can also preferentially collect works that have received particularly high praise based on records of exhibitions and concerts. For example, the collection unit preferentially collects works that have received high praise at exhibitions and concerts. In this way, by referring to records of past exhibitions and concerts, highly rated works can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input records of exhibitions and concerts into AI and have the AI ​​select works to collect.

[0081] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using an emotion engine. For example, the analysis unit captures the user's facial expressions with a camera and analyzes them using an emotion engine. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions based on survey results. For example, the analysis unit analyzes the user's answers to estimate the emotions. The analysis unit adjusts the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. In this way, by adjusting the level of detail of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​adjust the level of analysis detail.

[0082] During the analysis, the analysis unit can perform analysis to identify an artist's creative process or source of inspiration. For example, the analysis unit analyzes related literature and interviews to identify sources of inspiration in the artist's work. For example, the analysis unit identifies events and people that influenced the artist. The analysis unit can also analyze the production process of a work to identify the artist's creative process. For example, the analysis unit analyzes the artist's sketches and notes to identify the creative process. The analysis unit can also analyze related data to identify other artists and works that influenced the artist. For example, the analysis unit identifies works by other artists that influenced the artist. This allows for a deeper understanding of the artist's style and thoughts by identifying the creative process and source of inspiration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input related literature and interview data into AI and have the AI ​​identify sources of inspiration.

[0083] During the analysis, the analysis unit can identify repetitive patterns or unique techniques in the artist's works. For example, the analysis unit can identify patterns of color use in the artist's works. For example, the analysis unit can identify specific color combinations or color scheme patterns. The analysis unit can also identify patterns of composition in the artist's works. For example, the analysis unit can identify specific patterns of compositional arrangement or perspective. The analysis unit can also identify patterns of brushstrokes or techniques in the artist's works. For example, the analysis unit can identify specific patterns of brushstrokes or painting techniques. This allows the artist's style to be understood in more detail by identifying repetitive patterns or unique techniques. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify repetitive patterns or unique techniques.

[0084] During the analysis, the analysis unit can perform a detailed analysis of trends in color or sound combinations in the artist's work. The analysis unit, for example, analyzes color combinations in the artist's work. For example, the analysis unit analyzes specific color combinations or color patterns. The analysis unit can also analyze sound combinations in the artist's work. For example, the analysis unit analyzes specific sound harmonies or rhythmic combinations. The analysis unit can also analyze changes in color or sound in the artist's work. For example, the analysis unit analyzes changes in color or sound for each period in which the work was created. This allows for a detailed analysis of trends in color and sound combinations to provide a deeper understanding of the artist's style. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input collected data into AI and have the AI ​​analyze trends in color and sound combinations.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using an emotion engine. For example, the analysis unit captures the user's facial expressions with a camera and analyzes them using an emotion engine. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions based on survey results. For example, the analysis unit analyzes the user's answers to estimate the emotions. The analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit provides a simple, highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. The analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0086] During the analysis, the analysis unit can identify the influence of other artists related to the artist's work. The analysis unit, for example, identifies other artists who influenced the artist. For example, the analysis unit can identify the influence of other artists' styles and techniques on the artist's work. The analysis unit can also identify the era or culture that influenced the artist. For example, the analysis unit can identify a specific historical or cultural background. By identifying the influence of other artists, it is possible to gain a deeper understanding of the artist's style and thought. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the influence of other artists.

[0087] The analysis unit may take into account the social or historical background of the artist's work during the analysis. For example, the analysis unit may consider the social background of the era in which the artist's work was created. For example, the analysis unit may consider specific social issues or events. The analysis unit may also consider the historical background of the region in which the artist's work was created. For example, the analysis unit may consider historical events in a specific region. The analysis unit may also consider social and historical events that influenced the artist's work. For example, the analysis unit may consider specific events or occurrences. This allows for a deeper understanding of the artist's work by considering the social and historical background. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input collected data into AI and have the AI ​​perform an analysis of the social and historical background.

[0088] During the analysis, the analysis unit can identify technological evolution or changes in the artist's work. For example, the analysis unit can identify technological evolution in the artist's work. For example, the analysis unit can identify the evolutionary stage of a particular technology. The analysis unit can also identify changes in techniques in the artist's work. For example, the analysis unit can identify changes in a particular technique. The analysis unit can also identify technological innovations in the artist's work. For example, the analysis unit can identify particular technological innovations. In this way, by identifying technological evolution or changes, it is possible to understand the changes in the artist's work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the identification of technological evolution or changes.

[0089] The generation unit can estimate the user's emotion and adjust the theme or style of the generated artwork based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion using an emotion engine. For example, the generation unit captures the user's facial expression with a camera and analyzes it with an emotion engine. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion based on the results of a survey. For example, the generation unit analyzes the user's answers to estimate the emotion. The generation unit adjusts the theme and style of the generated artwork based on the estimated user's emotion. For example, the generation unit generates artwork with a calm theme and style when the user is relaxed. The generation unit can also generate artwork with a stimulating theme and style when the user is excited. The generation unit can also generate artwork with a comforting theme and style when the user is sad. This makes it possible to generate artwork with an appropriate theme and style according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input user emotion data into the AI ​​and have the AI ​​adjust the theme and style of the work to be generated.

[0090] During generation, the generation unit can generate a new work by combining specific elements of the artist's past works. For example, the generation unit generates a new painting by combining the color palette and composition of the artist's past works. For example, the generation unit combines a specific color palette and composition pattern. The generation unit can also generate a new song by combining the melody and rhythm of the artist's past works. For example, the generation unit combines a specific melody line and rhythm pattern. The generation unit can also generate a new work by combining the theme and style of the artist's past works. For example, the generation unit combines a specific theme and art style. In this way, a new work can be generated by combining elements of past works. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​execute a combination of specific elements.

[0091] During generation, the generation unit can generate a new work based on the artist's unpublished sketches or notes. For example, the generation unit generates a new painting based on the artist's unpublished sketches. For example, the generation unit generates a new painting based on an unpublished design. The generation unit can also generate a new song based on the artist's unpublished melody. For example, the generation unit generates a new song based on an unpublished melody line. The generation unit can also generate a new work based on the artist's unpublished notes. For example, the generation unit generates a new song based on unpublished lyrics. In this way, by generating a new work based on unpublished sketches or notes, the artist's unpublished ideas can be utilized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected unpublished sketches and notes into AI and have the AI ​​generate a new work.

[0092] During generation, the generation unit can generate a new work by emphasizing a particular technique or style in the artist's work. For example, the generation unit can generate a new painting by emphasizing a particular color palette in the artist's work. For example, the generation unit can emphasize a particular color palette. The generation unit can also generate a new song by emphasizing a particular rhythm in the artist's work. For example, the generation unit can emphasize a particular rhythmic pattern. The generation unit can also generate a new work by emphasizing a particular composition in the artist's work. For example, the generation unit can emphasize a particular compositional arrangement. In this way, by emphasizing a particular technique or style, a new work that makes the most of the artist's characteristics can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the emphasis on a particular technique or style.

[0093] The generation unit can estimate the user's emotions and select the format of the artwork to be generated based on the estimated user's emotions. The generation unit, for example, uses an emotion engine to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and analyzes them with an emotion engine. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the user's emotions based on survey results. For example, the generation unit analyzes the user's answers to estimate the emotions. The generation unit selects the format of the artwork to be generated based on the estimated user's emotions. For example, the generation unit can generate a calming song if the user is relaxed. The generation unit can also generate a stimulating painting if the user is excited. The generation unit can also generate a comforting poem if the user is sad. This makes it possible to generate an artwork in an appropriate format depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the AI ​​and have the AI ​​select the format of the work to be generated.

[0094] During generation, the generation unit can generate a new work by fusing different genres or styles of an artist's work. The generation unit, for example, generates a multimedia work that fuses painting and music. For example, the generation unit fuses a specific painting style with a musical genre. The generation unit can also generate a new piece of music that fuses classical music with contemporary music. For example, the generation unit fuses specific classical music elements with contemporary music elements. The generation unit can also generate a new painting that fuses abstract painting with figurative painting. For example, the generation unit fuses a specific abstract painting style with a figurative painting style. This allows a new work to be generated by fusing different genres or styles. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the fusing of different genres or styles.

[0095] During generation, the generation unit can generate a new work by emphasizing a particular theme or message in the artist's work. For example, the generation unit can generate a new painting by emphasizing a peace theme in the artist's work. For example, the generation unit can emphasize a particular message of peace. The generation unit can also generate a new song by emphasizing a love message in the artist's work. For example, the generation unit can emphasize a particular love theme. The generation unit can also generate a new poem by emphasizing a nature theme in the artist's work. For example, the generation unit can emphasize a particular nature theme. In this way, a new work that reflects the artist's intentions can be generated by emphasizing a particular theme or message. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input collected data into AI and have the AI ​​execute the emphasis on a particular theme or message.

[0096] During generation, the generation unit can change a specific color or sound combination in the artist's work to generate a new work. For example, the generation unit can change the color combination in the artist's work to generate a new painting. For example, the generation unit can change the specific color palette. The generation unit can also change the sound combination in the artist's work to generate a new song. For example, the generation unit can change the harmony of specific sounds. The generation unit can also generate a new multimedia work by changing the color or sound combination in the artist's work. For example, the generation unit can change the specific color and sound combination. In this way, a new work can be generated by changing the color or sound combination. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input collected data into AI and have the AI ​​change the color or sound combination.

[0097] The providing unit can estimate the user's emotions and adjust the platform functions provided based on the estimated user emotions. The providing unit, for example, uses an emotion engine to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and analyzes them with an emotion engine. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also estimate the user's emotions based on survey results. For example, the providing unit can analyze the user's responses to estimate the emotions. The providing unit adjusts the platform functions provided based on the estimated user emotions. For example, the providing unit can provide detailed functions when the user is relaxed. The providing unit can also provide simple functions when the user is in a hurry. The providing unit can also provide visually stimulating functions when the user is excited. This can improve the user experience by providing appropriate functions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI and cause the AI ​​to adjust the functions of the platform it provides.

[0098] When providing the platform, the providing unit can design the platform by referring to the artist's past exhibition or presentation methods. For example, the providing unit designs the platform by referring to the layout of the artist's past exhibitions. For example, the providing unit refers to the layout of a particular exhibition. The providing unit can also design the platform by referring to the artist's past presentation methods. For example, the providing unit refers to a particular presentation method. The providing unit can also design the platform by referring to the artist's past exhibition methods to make it easier for users to understand the works. For example, the providing unit refers to a particular exhibition method. In this way, by referring to past exhibition or presentation methods, a platform can be provided that makes it easier for users to understand the works. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data from past exhibitions and presentations into AI and have the AI ​​execute the platform design.

[0099] The providing unit can collect user opinions on the artists' works at the time of providing them and improve the platform's functions. The providing unit, for example, improves the platform's interface based on user feedback. For example, the providing unit changes the interface design based on user feedback. The providing unit can also add or remove functions to be provided based on user feedback. For example, the providing unit adds new functions based on user feedback. The providing unit can also improve the way the works are displayed based on user feedback. For example, the providing unit changes the display method based on user feedback. This can improve the platform's functions based on user feedback, thereby improving the user experience. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input user opinions into AI and have the AI ​​improve the platform's functions.

[0100] The providing unit can enrich the platform by adding educational content or commentary related to the artist's work during provision. The providing unit, for example, adds educational content related to the artist's work. For example, the providing unit adds background information about the work. The providing unit can also add commentary about the artist's work. For example, the providing unit adds explanations about techniques. The providing unit can also add interviews or documentaries related to the artist's work. For example, the providing unit adds interview videos or documentaries. By adding educational content or commentary, users can gain a deeper understanding of the work. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can have AI create the educational content or commentary.

[0101] The providing unit can estimate the user's emotions and adjust the platform interface provided based on the estimated user emotions. The providing unit, for example, uses an emotion engine to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and analyzes them with an emotion engine. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also estimate the user's emotions based on survey results. For example, the providing unit can analyze the user's responses to estimate the emotions. The providing unit adjusts the platform interface provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide an interface with subdued colors. If the user is having fun, the providing unit can provide an interface with bright colors. If the user is tired, the providing unit can provide a simple, highly visible interface. This improves the user experience by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the interface.

[0102] The providing unit can add a function to introduce works by other artists related to the artist's work when providing the data. The providing unit, for example, automatically suggests works by other artists related to the artist's work. For example, the providing unit suggests works by other artists related to a specific theme or style. The providing unit can also introduce works by other artists influenced by the artist's work. For example, the providing unit can introduce works by artists who have a specific influence. The providing unit can also introduce works by other artists with the same theme or style as the artist's work. For example, the providing unit can introduce works related to a specific theme or style. This allows the user to encounter new works by introducing works by other artists. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on works by other artists into AI and have the AI ​​suggest related works.

[0103] The providing unit can add a function of holding an event or workshop related to the artist's work at the time of providing. The providing unit, for example, provides information on exhibitions and concerts related to the artist's work. For example, the providing unit provides information on a specific exhibition or concert. The providing unit can also provide information on workshops and seminars related to the artist's work. For example, the providing unit provides information on a specific workshop or seminar. The providing unit can also provide information on online events related to the artist's work. For example, the providing unit provides information on a specific online event. This can provide an opportunity for users to deepen their understanding of the work by holding an event or workshop. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information on events and workshops into AI and cause the AI ​​to provide related information.

[0104] The providing unit can add a function to sell goods or products related to the artist's works at the time of providing. The providing unit, for example, sells posters and prints related to the artist's works. For example, the providing unit sells specific posters and prints. The providing unit can also sell books and catalogs related to the artist's works. For example, the providing unit sells specific books and catalogs. The providing unit can also sell accessories and goods related to the artist's works. For example, the providing unit sells specific accessories and goods. In this way, by selling goods and products, users can obtain items related to the works. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the goods and products to be sold into AI and have the AI ​​execute the sale of related products. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the artist's past works using the camera 42 and microphone 38B of the smart device 14 and processes the data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the artist's style and thoughts. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, fine-tunes a new work concept based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the artist with a generation platform. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the artist's past works using the camera 42 and microphone 238 of the smart glasses 214 and processes the data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the artist's style and thoughts. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, fine-tunes a new work concept based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the artist with a generation platform. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects past works of an artist using the camera 42 and microphone 238 of the headset terminal 314 and processes the data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn the artist's style and thoughts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and fine-tunes a new work concept based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314, and provides a generation platform to the artist. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the artist's past works using the camera 42 and microphone 238 of the robot 414 and processes the data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the artist's style and thoughts. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, fine-tunes a new work concept based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides the artist with a generation platform.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can collect social media posts and comments related to the artist's work and use them for analysis by the analysis unit. For example, the collection unit can collect fan reactions and opinions about the artist's work. The collection unit can also collect trends and topics on social media and use them for analysis by the analysis unit. This can help understand social reactions and trends to the artist's work and aid in the analysis by the analysis unit. Some or all of the above-mentioned processing by the collection unit can be performed using AI, or can be performed without AI. For example, the collection unit can input social media data into AI and have the AI ​​collect and organize the data.

[0107] The analysis unit can estimate fans' emotions toward an artist's work and adjust the analysis results based on the estimated emotions. For example, the analysis unit can analyze social media comments and reviews to estimate fans' emotions. The analysis unit can also adjust the analysis results based on fans' emotions to provide analysis results that are more in line with their emotions. This allows for analysis results that take fans' emotions into consideration, thereby providing analysis results that are more relatable. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input social media data into AI and have the AI ​​perform emotion estimation and adjustment of the analysis results.

[0108] The generation unit can estimate fans' emotions toward the artist's work and adjust the theme and style of the new work based on the estimated emotions. For example, the generation unit can analyze social media comments and reviews to estimate fans' emotions. The generation unit can also adjust the theme and style of the new work based on fans' emotions to generate a work that is more in line with fans' emotions. This allows for the generation of new works that take fans' emotions into consideration, thereby providing works that are more likely to resonate with fans. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input social media data into AI and have the AI ​​estimate emotions and adjust the theme and style of the work.

[0109] The providing unit can estimate fans' emotions toward an artist's work and adjust the platform functions provided based on the estimated emotions. For example, the providing unit can analyze social media comments and reviews to estimate fans' emotions. The providing unit can also adjust the platform functions provided based on fans' emotions to provide functions that are more in line with fans' emotions. This allows for the provision of platform functions that take fans' emotions into consideration, thereby providing a more empathetic user experience. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input social media data into AI and have the AI ​​perform emotion estimation and adjust platform functions.

[0110] The providing unit can estimate fans' emotions toward an artist's work and adjust the platform interface provided based on the estimated emotions. For example, the providing unit can analyze social media comments and reviews to estimate fans' emotions. The providing unit can also adjust the platform interface provided based on fans' emotions to provide an interface that is more in line with fans' emotions. By providing an interface that takes fans' emotions into consideration, it is possible to provide a more empathetic user experience. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input social media data into AI and have the AI ​​perform emotion estimation and interface adjustment.

[0111] The collection unit can collect works by other artists related to the artist's works and use them for analysis by the analysis unit. For example, the collection unit can collect works by other artists who have influenced the artist. The collection unit can also collect works by other artists related to the artist's works and use them for analysis by the analysis unit. This makes it possible to understand the influence of other artists on the artist's works and use this information for analysis by the analysis unit. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on works by other artists into AI and have the AI ​​collect and organize the data.

[0112] The analysis unit can identify the influence of other artists on the artist's work and reflect this in the analysis results. For example, the analysis unit can identify the styles and techniques of other artists who influenced the artist. The analysis unit can also identify the influence of other artists and reflect this in the analysis results. This allows for a deeper understanding by providing analysis results that take into account the influence of other artists. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input data on other artists' works into AI and have the AI ​​identify the influence and reflect it in the analysis results.

[0113] The generation unit can generate new works that reflect the influence of other artists on the artist's work. For example, the generation unit generates new works that incorporate the styles and techniques of other artists who influenced the artist. The generation unit can also generate new works that reflect the influence of other artists. This makes it possible to provide a wider variety of works by providing new works that reflect the influence of other artists. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input data on other artists' works into AI and have the AI ​​reflect the influence and generate new works.

[0114] The providing unit can provide an exhibition method or presentation method that reflects the influence of other artists on the artist's work. For example, the providing unit can provide an exhibition method that reflects the influence of other artists. The providing unit can also provide a presentation method that reflects the influence of other artists. By providing exhibition methods or presentation methods that reflect the influence of other artists, a wider variety of exhibition methods or presentation methods can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on other artists' works into AI and have the AI ​​reflect the influence and provide exhibition methods or presentation methods.

[0115] The providing unit can provide educational content and commentary that reflects the influence of other artists on the artist's work. For example, the providing unit can provide educational content that reflects the influence of other artists. The providing unit can also provide commentary that reflects the influence of other artists. This can provide a deeper understanding by providing educational content and commentary that reflects the influence of other artists. Some or all of the above-described processing in the providing unit can be performed using AI, or can be performed without AI. For example, the providing unit can input data on other artists' works into AI and have the AI ​​reflect the influence and provide educational content and commentary.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection department collects the artist's past works. Collections include paintings, music, sculptures, etc. The collection department collects digital data, but can also collect physical works. For example, they collect image data of paintings and audio data of music. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the artist's style and thinking. The analysis unit uses image analysis technology to analyze the color palette and composition of the painting, and audio analysis technology to analyze the melody and rhythm of the music. For example, it analyzes the color palette and composition patterns of the painting, and the progression and rhythm patterns of the music. Step 3: The generator adjusts the concept of the new work based on the data analyzed by the analyzer. The generator uses algorithms to generate new themes and melodies, and also makes manual adjustments. For example, it proposes new themes, color schemes, melodies, and rhythms based on the analyzed data. Step 4: The provider provides a generation platform based on the concepts generated by the generator. The provider provides an online platform as well as a physical exhibition. For example, the generation platform provides artists with the opportunity to experiment with new themes, color palettes, compositions, melodies, and rhythms.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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. The collection department collects past works by artists, an analysis unit that analyzes the data collected by the collection unit and learns the style and thoughts of the artist; a generation unit that adjusts a new work concept based on the data analyzed by the analysis unit; a providing unit that provides a creation platform based on the concept created by the creating unit; Equipped with A system characterized by:

2. The collecting unit Collecting works in multiple formats, such as paintings or songs 2. The system of claim 1.

3. The analysis unit Analyze the collected data and learn about the characteristics of color, composition, melody, and rhythm.

2. The system of claim 1.

4. The generation unit Tailor a new theme or melody based on the analyzed data 2. The system of claim 1.

5. The providing unit Providing a generative platform for artists and supporting their work 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the works to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting, filter based on the period of an artist's creation or a specific theme.

2. The system of claim 1.

8. The collecting unit When collecting, classification is based on the techniques or materials used by the artist.

2. The system of claim 1.

Images