System

Abstract

An object of the system according to the embodiment is to allow a user to generate a unique artwork based on a theme or a keyword and to exhibit the artwork online.SOLUTION: A system includes a reception part, an analysis part, a generation part, and a display part. The reception unit receives an input of a theme or a keyword from a user. The analysis unit analyzes the theme or the keyword received by the reception unit and collects related data. The generation unit generates an artwork based on the data collected by the analysis unit. The exhibition unit exhibits the artwork generated by the generation unit on an online platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawback of making it difficult for users to create original artwork based on a theme or keyword and exhibit it online.

[0005] The system according to the embodiment aims to enable users to create original artworks based on themes and keywords and exhibit them online. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and an exhibition unit. The reception unit receives input of a theme or keyword from a user. The analysis unit analyzes the theme or keyword received by the reception unit and collects related data. The generation unit generates an artwork based on the data collected by the analysis unit. The exhibition unit exhibits the artwork generated by the generation unit on an online platform. [Effects of the Invention]

[0007] The system according to the embodiment allows users to create their own artwork based on a theme or keyword and exhibit it online. [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) An art generation system according to an embodiment of the present invention uses a generation AI to generate unique artworks based on themes and keywords entered by users and exhibit them online. The art generation system analyzes the themes and keywords entered by users, and the generation AI generates artworks and exhibits them on an online platform. For example, the user inputs a theme such as "nature" or "future." The art generation system then uses the generation AI to analyze the input theme and keywords and collect related data. For example, the generation AI collects data such as images, colors, and shapes related to "nature" and combines them to generate artworks. The generated artworks are exhibited on an online platform, allowing users to explore artworks that match their sensibilities. For example, if a user views artworks generated under the theme "future," artworks with futuristic designs and colors are displayed. Furthermore, users can propose their own themes to create new art. For example, if a user proposes the keyword "movement," the generation AI generates artworks that express movement. This allows the art generation system to easily generate artworks that match their sensibilities and exhibit them online. This allows the art generation system to allow users to easily create artwork that matches their sensibilities and exhibit it online. Users can also gain new inspiration by viewing artworks created by other users. For example, if a user views artworks created using the keyword "light," they can gain new perspectives on how light is expressed and how colors are used.

[0029] An art generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and an exhibition unit. The reception unit receives input of a theme or keyword from a user. The user can input, for example, themes such as "nature," "future," or "abstract," or keywords such as "blue," "light," or "movement." The analysis unit analyzes the theme or keyword received by the reception unit and collects related data. The analysis unit analyzes the theme or keyword using, for example, natural language processing technology, and collects related image data and text data. The generation unit generates an artwork based on the data collected by the analysis unit. The generation unit combines the collected data to generate an artwork using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the artwork based on the input theme or keyword. The exhibition unit exhibits the artwork generated by the generation unit on an online platform. The exhibition unit displays the generated artwork on, for example, a website or a mobile app, allowing users to view it. As a result, the art generation system according to an embodiment can generate artwork based on the theme or keyword input by a user and exhibit it online.

[0030] The art creation system includes a search unit for searching for artworks created by a user. The search unit provides a function for efficiently searching for artworks created by a user. The search unit, for example, includes a keyword search function, allowing a user to search for related artworks by entering a specific keyword. The search unit also includes a filtering function, allowing a user to narrow down the artworks by specifying conditions such as the category of the artwork or the creation date and time. Furthermore, the search unit includes a function for recommending artworks that match the user's preferences based on the user's browsing history and rating history. For example, the search unit preferentially displays artworks similar to artworks that the user has previously given high ratings. This allows a user to efficiently search for created artworks.

[0031] The art generation system includes a suggestion unit that allows a user to propose a new theme. The suggestion unit provides a function that allows a user to propose a new theme and generate an artwork. The suggestion unit, for example, provides an interface that allows a user to freely input a theme or keywords. The user can propose a new theme based on their own interests and concerns. The suggestion unit analyzes the theme and keywords input by the user and collects related data. For example, if the user inputs the keyword "movement," the suggestion unit collects image data and color data to express movement. Based on the collected data, the suggestion unit starts the process by which the generation AI generates an artwork. This allows a user to propose a new theme and generate an artwork.

[0032] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays themes and keywords that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and keywords that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0033] The reception unit can present input candidates based on the user's current areas of interest when the user inputs a theme or keyword. The reception unit can suggest related themes and keywords based on, for example, the user's recent searches or browsing history. The reception unit can also analyze topics in online communities and forums in which the user participates and suggest related themes and keywords. Furthermore, the reception unit can also suggest related themes and keywords based on the genres of books the user recently purchased or movies the user watched. This makes it possible to present appropriate input candidates based on the user's current areas of interest.

[0034] When a user inputs a theme or keyword, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the theme or keyword using voice recognition technology. If the user selects text input, the reception unit can also input the theme or keyword using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input the theme or keyword using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0035] When inputting a theme or keyword, the reception unit can preferentially receive themes and keywords that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially display themes and keywords related to that area. Furthermore, if the user is traveling, the reception unit can also preferentially display themes and keywords related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can also preferentially display themes and keywords related to the event. This makes it possible to preferentially receive themes and keywords that are highly relevant based on the user's geographical location information.

[0036] When a theme or keyword is input, the reception unit can analyze the user's online platform activity and suggest related themes and keywords. The reception unit can suggest related themes and keywords based on, for example, hashtags frequently used by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related themes and keywords. Furthermore, the reception unit can also suggest related themes and keywords based on the activity of the user's friends on social media. This makes it possible to suggest related themes and keywords based on the user's social media activity.

[0037] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or keyword. The reception unit customizes input candidates based on, for example, themes and keywords that the user has liked in the past. The reception unit can also customize the input interface based on feedback provided by the user in the past. Furthermore, the reception unit can suggest related themes and keywords based on artworks that the user has rated in the past. This makes it possible to customize the input method based on the user's past feedback.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of themes and keywords. For example, the analysis unit performs detailed data analysis on themes and keywords with high importance. The analysis unit can also perform simplified data analysis on themes and keywords with low importance. Furthermore, the analysis unit can also focus data analysis on themes and keywords in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of themes and keywords.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of themes and keywords. For example, the analysis unit can apply a natural image analysis algorithm to themes and keywords related to nature. The analysis unit can also apply an abstract image analysis algorithm to abstract themes and keywords. Furthermore, the analysis unit can apply a futuristic design analysis algorithm to themes and keywords related to the future. This makes it possible to apply an appropriate analysis algorithm depending on the category of themes and keywords.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on data of artworks created by the user in the past. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. Furthermore, the analysis unit can also improve the accuracy of the analysis based on data of artworks rated by the user in the past. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of themes and keywords. For example, the analysis unit prioritizes analysis of recently submitted themes and keywords. Furthermore, if a user submits a theme or keyword related to a specific event, the analysis unit can also determine the priority of analysis based on the time of the event. Furthermore, if a user submits a theme or keyword related to a specific season, the analysis unit can also determine the priority of analysis based on the season. This makes it possible to determine the priority of analysis based on the time of submission of themes and keywords.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of themes and keywords. For example, the analysis unit prioritizes analysis of highly relevant themes and keywords. The analysis unit can also postpone analysis of less relevant themes and keywords. Furthermore, the analysis unit can prioritize analysis of themes and keywords in which the user is particularly interested. This makes it possible to adjust the order of analysis based on the relevance of themes and keywords.

[0043] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. In addition, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0044] The generation unit can adjust the level of detail of the generation based on the importance of the theme or keyword during generation. For example, the generation unit generates a detailed artwork for a theme or keyword with a high level of importance. The generation unit can also generate a simplified artwork for a theme or keyword with a low level of importance. Furthermore, the generation unit can generate an artwork with a focus on a theme or keyword in which the user is particularly interested. This allows the level of detail of the generation to be adjusted based on the importance of the theme or keyword.

[0045] During generation, the generation unit can apply different generation algorithms depending on the category of the theme or keyword. For example, the generation unit applies a natural image generation algorithm to a theme or keyword related to nature. The generation unit can also apply an abstract image generation algorithm to an abstract theme or keyword. Furthermore, the generation unit can also apply a futuristic design generation algorithm to a theme or keyword related to the future. This makes it possible to apply an appropriate generation algorithm depending on the category of the theme or keyword.

[0046] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation based on data of artworks previously generated by the user. The generation unit can also adjust the generation algorithm based on feedback previously provided by the user. Furthermore, the generation unit can also improve the accuracy of generation based on data of artworks previously rated by the user. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0047] At the time of generation, the generation unit can determine the priority of generation based on the time of submission of themes and keywords. For example, the generation unit generates the most recently submitted themes and keywords with priority. Furthermore, if a user submits a theme or keywords related to a specific event, the generation unit can also determine the priority of generation based on the time of the event. Furthermore, if a user submits a theme or keywords related to a specific season, the generation unit can also determine the priority of generation based on the season. In this way, the priority of generation can be determined based on the time of submission of themes and keywords.

[0048] The generation unit can adjust the order of generation based on the relevance of themes and keywords during generation. For example, the generation unit generates themes and keywords with high relevance first. The generation unit can also generate themes and keywords with low relevance later. Furthermore, the generation unit can also generate themes and keywords in which the user is particularly interested with high relevance first. This makes it possible to adjust the order of generation based on the relevance of themes and keywords.

[0049] The generation unit may adjust the use of technical terms in the generated artwork according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate an artwork that uses a lot of technical terms. Alternatively, if the user is a beginner, the generation unit may generate an artwork that avoids technical terms. Furthermore, the generation unit may adjust the level of detail in the artwork according to the user's level of expertise. This allows the use of technical terms in the generated artwork to be adjusted according to the user's level of expertise.

[0050] The exhibition unit can adjust the level of detail of the exhibition based on the importance of the artwork during the exhibition. For example, the exhibition unit can display artworks with high importance including detailed information. The exhibition unit can also display artworks with low importance including simplified information. Furthermore, the exhibition unit can provide focused information on artworks in which the user is particularly interested. This allows the level of detail of the exhibition to be adjusted based on the importance of the artwork.

[0051] The exhibition unit can apply different exhibition algorithms depending on the category of the artwork during exhibition. For example, the exhibition unit can apply a nature image exhibition algorithm to nature-related artworks. The exhibition unit can also apply an abstract image exhibition algorithm to abstract artworks. Furthermore, the exhibition unit can apply a futuristic design exhibition algorithm to future-related artworks. In this way, an appropriate exhibition algorithm can be applied depending on the category of the artwork.

[0052] The exhibition unit can improve the accuracy of the exhibition by referring to the user's past exhibition results when exhibiting. For example, the exhibition unit improves the accuracy of the exhibition based on data on artworks that the user has viewed in the past. The exhibition unit can also adjust the exhibition algorithm based on feedback that the user has provided in the past. Furthermore, the exhibition unit can improve the accuracy of the exhibition based on data on artworks that the user has evaluated in the past. This makes it possible to improve the accuracy of the exhibition based on the user's past exhibition results.

[0053] The exhibition unit can determine the exhibition priority based on the time of submission of the artworks during the exhibition. For example, the exhibition unit prioritizes the exhibition of recently submitted artworks. Furthermore, if a user submits artworks related to a specific event, the exhibition unit can determine the exhibition priority based on the time of the event. Furthermore, if a user submits artworks related to a specific season, the exhibition unit can determine the exhibition priority based on the season. In this way, the exhibition priority can be determined based on the time of submission of the artworks.

[0054] The exhibition unit can adjust the order of display based on the relevance of the artworks during display. For example, the exhibition unit prioritizes displaying highly relevant artworks. The exhibition unit can also display less relevant artworks later. Furthermore, the exhibition unit can prioritize displaying artworks in which the user is particularly interested. This allows the order of display to be adjusted based on the relevance of the artworks.

[0055] The exhibition unit can adjust the use of technical terms in the exhibition according to the user's level of expertise during the exhibition. For example, if the user has technical expertise, the exhibition unit can create an exhibition that uses a lot of technical terms. Also, if the user is a beginner, the exhibition unit can create an exhibition that avoids technical terms. Furthermore, the exhibition unit can adjust the level of detail in the exhibition according to the user's level of expertise. This allows the use of technical terms in the exhibition to be adjusted according to the user's level of expertise.

[0056] During a search, the search unit can adjust the level of detail of the search based on the importance of the artwork. For example, the search unit performs a search including detailed information for artworks with high importance. The search unit can also perform a search including simplified information for artworks with low importance. Furthermore, the search unit can provide information focused on artworks in which the user is particularly interested. This allows the level of detail of the search to be adjusted based on the importance of the artwork.

[0057] During the search, the search unit can apply different search algorithms depending on the category of the artwork. For example, the search unit can apply a natural image search algorithm to artworks related to nature. The search unit can also apply an abstract image search algorithm to abstract artworks. Furthermore, the search unit can apply a futuristic design search algorithm to artworks related to the future. This makes it possible to apply an appropriate search algorithm depending on the category of the artwork.

[0058] During a search, the search unit can improve the accuracy of the search by referring to the user's past search results. The search unit can improve the accuracy of the search, for example, based on data on artworks that the user has viewed in the past. The search unit can also adjust the search algorithm based on feedback that the user has provided in the past. Furthermore, the search unit can improve the accuracy of the search based on data on artworks that the user has rated in the past. This makes it possible to improve the accuracy of the search based on the user's past search results.

[0059] During a search, the search unit can determine search priorities based on the time of submission of the artwork. For example, the search unit prioritizes searches for artworks that have been submitted recently. In addition, if a user submits artwork related to a specific event, the search unit can also determine search priorities based on the time of the event. Furthermore, if a user submits artwork related to a specific season, the search unit can also determine search priorities based on the season. In this way, search priorities can be determined based on the time of submission of the artwork.

[0060] During a search, the search unit can adjust the order of searches based on the relevance of the artworks. For example, the search unit prioritizes searching for highly relevant artworks. The search unit can also postpone searching for less relevant artworks. Furthermore, the search unit can prioritize searching for artworks in which the user is particularly interested. This allows the order of searches to be adjusted based on the relevance of the artworks.

[0061] The search unit can adjust the use of technical terms in a search according to the user's level of expertise during a search. For example, if the user has technical expertise, the search unit can provide search results that use a lot of technical terms. In addition, if the user is a beginner, the search unit can provide search results that avoid technical terms. Furthermore, the search unit can adjust the level of detail of the search results according to the user's level of expertise. This allows the use of technical terms in a search to be adjusted according to the user's level of expertise.

[0062] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the theme or keyword when making a suggestion. For example, the suggestion unit makes detailed suggestions for themes or keywords with high importance. The suggestion unit can also make simplified suggestions for themes or keywords with low importance. Furthermore, the suggestion unit can also make suggestions with a focus on themes or keywords in which the user is particularly interested. This makes it possible to adjust the level of detail of the suggestion based on the importance of themes or keywords.

[0063] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the theme or keyword. For example, the suggestion unit can apply a natural image suggestion algorithm to a theme or keyword related to nature. The suggestion unit can also apply an abstract image suggestion algorithm to an abstract theme or keyword. Furthermore, the suggestion unit can also apply a futuristic design suggestion algorithm to a theme or keyword related to the future. This makes it possible to apply an appropriate suggestion algorithm depending on the category of the theme or keyword.

[0064] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion based on, for example, data on themes and keywords previously suggested by the user. The suggestion unit can also adjust the suggestion algorithm based on feedback previously provided by the user. Furthermore, the suggestion unit can also improve the accuracy of the suggestion based on data on proposals previously evaluated by the user. This makes it possible to improve the accuracy of the suggestion based on the user's past suggestion results.

[0065] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the themes and keywords. For example, the suggestion unit preferentially suggests themes and keywords that have been submitted recently. Furthermore, if a user submits a theme or keyword related to a specific event, the suggestion unit can also determine the priority of the suggestions based on the time of the event. Furthermore, if a user submits a theme or keyword related to a specific season, the suggestion unit can also determine the priority of the suggestions based on the season. In this way, the priority of the suggestions can be determined based on the time of submission of the theme or keyword.

[0066] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the themes and keywords. For example, the suggestion unit prioritizes suggesting highly relevant themes and keywords. The suggestion unit can also postpone suggesting less relevant themes and keywords. Furthermore, the suggestion unit can prioritize suggesting themes and keywords in which the user is particularly interested. This makes it possible to adjust the order of suggestions based on the relevance of themes and keywords.

[0067] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a suggestion that uses a lot of technical terms. Also, if the user is a beginner, the suggestion unit can make a suggestion that avoids technical terms. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the suggestion according to the user's level of expertise.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The art generation system can further include a generation unit that automatically generates artworks that match the user's preferences based on the user's past browsing history and rating history. For example, the system can analyze the style and theme of artworks that the user has previously given high ratings and generate similar artworks. It can also generate artworks in categories that the user frequently browses. Furthermore, if the user likes the work of a particular artist, it can generate artworks that imitate that artist's style. This allows for efficient generation of artworks that match the user's preferences.

[0070] The art generation system can further include a suggestion unit that automatically suggests themes and keywords related to a region based on the user's geographical location information. For example, if the user is in a specific city, the system can suggest artworks themed around the history and culture of that city. If the user is in a region rich in nature, the system can suggest artworks themed around the natural scenery of that region. Furthermore, if the user is traveling, the system can suggest artworks themed around tourist attractions and scenery at the travel destination. This makes it possible to suggest highly relevant themes based on the user's geographical location information.

[0071] The art generation system may further include a suggestion unit that automatically suggests themes and keywords that the user likes based on the user's past input history. For example, the system may prioritize suggestions of themes and keywords that the user has frequently input in the past. Also, if the user inputs a theme or keyword related to a particular season or event, the system may suggest new themes and keywords related to that season or event. Furthermore, if the user likes a particular artist or style, the system may suggest themes and keywords related to that artist or style. This allows the system to suggest optimal themes and keywords based on the user's past input history.

[0072] The art generation system may further include a suggestion unit that automatically suggests related themes and keywords based on the user's online platform activities. For example, related themes and keywords may be suggested based on hashtags frequently used by the user on social media. Related themes and keywords may also be suggested by analyzing the content of the user's social media posts. Furthermore, related themes and keywords may also be suggested based on the activities of the user's friends on social media. In this way, related themes and keywords can be suggested based on the user's online platform activities.

[0073] The art generation system may further include a generation unit that adjusts the use of technical terms in the generated artwork according to the user's level of expertise. For example, if the user has technical expertise, the system may generate an artwork that uses a lot of technical terms. Alternatively, if the user is a beginner, the system may generate an artwork that avoids technical terms. Furthermore, the level of detail in the artwork may be adjusted according to the user's level of expertise. This allows the use of technical terms in the generated artwork to be adjusted according to the user's level of expertise.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The reception unit receives input of a theme or keyword from the user. For example, the user can input themes such as "nature," "future," or "abstract," or keywords such as "blue," "light," or "movement." Step 2: The analysis unit analyzes the theme or keywords received by the reception unit and collects related data. For example, the analysis unit analyzes the theme or keywords using natural language processing technology and collects related image data and text data. Step 3: The generation unit generates an artwork based on the data collected by the analysis unit. The generation unit uses, for example, a generation AI to combine the collected data and generate an artwork. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an artwork based on the input theme or keywords. Step 4: The exhibition unit exhibits the artwork generated by the generation unit on an online platform. For example, the exhibition unit displays the generated artwork on a website or mobile app so that users can view it.

[0076] (Example 2) An art generation system according to an embodiment of the present invention uses a generation AI to generate unique artworks based on themes and keywords entered by users and exhibit them online. The art generation system analyzes the themes and keywords entered by users, and the generation AI generates artworks and exhibits them on an online platform. For example, the user inputs a theme such as "nature" or "future." The art generation system then uses the generation AI to analyze the input theme and keywords and collect related data. For example, the generation AI collects data such as images, colors, and shapes related to "nature" and combines them to generate artworks. The generated artworks are exhibited on an online platform, allowing users to explore artworks that match their sensibilities. For example, if a user views artworks generated under the theme "future," artworks with futuristic designs and colors are displayed. Furthermore, users can propose their own themes to create new art. For example, if a user proposes the keyword "movement," the generation AI generates artworks that express movement. This allows the art generation system to easily generate artworks that match their sensibilities and exhibit them online. This allows the art generation system to allow users to easily create artwork that matches their sensibilities and exhibit it online. Users can also gain new inspiration by viewing artworks created by other users. For example, if a user views artworks created using the keyword "light," they can gain new perspectives on how light is expressed and how colors are used.

[0077] An art generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and an exhibition unit. The reception unit receives input of a theme or keyword from a user. The user can input, for example, themes such as "nature," "future," or "abstract," or keywords such as "blue," "light," or "movement." The analysis unit analyzes the theme or keyword received by the reception unit and collects related data. The analysis unit analyzes the theme or keyword using, for example, natural language processing technology, and collects related image data and text data. The generation unit generates an artwork based on the data collected by the analysis unit. The generation unit combines the collected data to generate an artwork using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the artwork based on the input theme or keyword. The exhibition unit exhibits the artwork generated by the generation unit on an online platform. The exhibition unit displays the generated artwork on, for example, a website or a mobile app, allowing users to view it. As a result, the art generation system according to an embodiment can generate artwork based on the theme or keyword input by a user and exhibit it online.

[0078] The art creation system includes a search unit for searching for artworks created by a user. The search unit provides a function for efficiently searching for artworks created by a user. The search unit, for example, includes a keyword search function, allowing a user to search for related artworks by entering a specific keyword. The search unit also includes a filtering function, allowing a user to narrow down the artworks by specifying conditions such as the category of the artwork or the creation date and time. Furthermore, the search unit includes a function for recommending artworks that match the user's preferences based on the user's browsing history and rating history. For example, the search unit preferentially displays artworks similar to artworks that the user has previously given high ratings. This allows a user to efficiently search for created artworks.

[0079] The art generation system includes a suggestion unit that allows a user to propose a new theme. The suggestion unit provides a function that allows a user to propose a new theme and generate an artwork. The suggestion unit, for example, provides an interface that allows a user to freely input a theme or keywords. The user can propose a new theme based on their own interests and concerns. The suggestion unit analyzes the theme and keywords input by the user and collects related data. For example, if the user inputs the keyword "movement," the suggestion unit collects image data and color data to express movement. Based on the collected data, the suggestion unit starts the process by which the generation AI generates an artwork. This allows a user to propose a new theme and generate an artwork.

[0080] The reception unit can estimate the user's emotions and customize the theme or keyword input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is excited, the reception unit can provide an interface with a visually stimulating design to make input tasks more enjoyable. This makes it possible to provide an input interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays themes and keywords that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and keywords that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0082] The reception unit can present input candidates based on the user's current areas of interest when the user inputs a theme or keyword. The reception unit can suggest related themes and keywords based on, for example, the user's recent searches or browsing history. The reception unit can also analyze topics in online communities and forums in which the user participates and suggest related themes and keywords. Furthermore, the reception unit can also suggest related themes and keywords based on the genres of books the user recently purchased or movies the user watched. This makes it possible to present appropriate input candidates based on the user's current areas of interest.

[0083] When a user inputs a theme or keyword, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the theme or keyword using voice recognition technology. If the user selects text input, the reception unit can also input the theme or keyword using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input the theme or keyword using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0084] The reception unit can estimate the user's emotions and determine the priority of the input themes and keywords based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize visually stimulating themes and keywords. Also, if the user is relaxed, the reception unit can prioritize calm themes and keywords. Furthermore, if the user is stressed, the reception unit can prioritize simple and intuitive themes and keywords. This makes it possible to determine the priority of themes and keywords based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When inputting a theme or keyword, the reception unit can preferentially receive themes and keywords that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially display themes and keywords related to that area. Furthermore, if the user is traveling, the reception unit can also preferentially display themes and keywords related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can also preferentially display themes and keywords related to the event. This makes it possible to preferentially receive themes and keywords that are highly relevant based on the user's geographical location information.

[0086] When a theme or keyword is input, the reception unit can analyze the user's online platform activity and suggest related themes and keywords. The reception unit can suggest related themes and keywords based on, for example, hashtags frequently used by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related themes and keywords. Furthermore, the reception unit can also suggest related themes and keywords based on the activity of the user's friends on social media. This makes it possible to suggest related themes and keywords based on the user's social media activity.

[0087] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or keyword. The reception unit customizes input candidates based on, for example, themes and keywords that the user has liked in the past. The reception unit can also customize the input interface based on feedback provided by the user in the past. Furthermore, the reception unit can suggest related themes and keywords based on artworks that the user has rated in the past. This makes it possible to customize the input method based on the user's past feedback.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prioritize analyzing data related to calm themes and keywords. Furthermore, if the user is excited, the analysis unit can prioritize analyzing data related to visually stimulating themes and keywords. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing data related to simple and intuitive themes and keywords. This allows the analysis algorithm to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of themes and keywords. For example, the analysis unit performs detailed data analysis on themes and keywords with high importance. The analysis unit can also perform simplified data analysis on themes and keywords with low importance. Furthermore, the analysis unit can also focus data analysis on themes and keywords in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of themes and keywords.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of themes and keywords. For example, the analysis unit can apply a natural image analysis algorithm to themes and keywords related to nature. The analysis unit can also apply an abstract image analysis algorithm to abstract themes and keywords. Furthermore, the analysis unit can apply a futuristic design analysis algorithm to themes and keywords related to the future. This makes it possible to apply an appropriate analysis algorithm depending on the category of themes and keywords.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on data of artworks created by the user in the past. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. Furthermore, the analysis unit can also improve the accuracy of the analysis based on data of artworks rated by the user in the past. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0092] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing visually stimulating themes and keywords. Furthermore, if the user is relaxed, the analysis unit can also prioritize analyzing calm themes and keywords. Furthermore, if the user is stressed, the analysis unit can also prioritize analyzing simple and intuitive themes and keywords. This allows the analysis priorities to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of themes and keywords. For example, the analysis unit prioritizes analysis of recently submitted themes and keywords. Furthermore, if a user submits a theme or keyword related to a specific event, the analysis unit can also determine the priority of analysis based on the time of the event. Furthermore, if a user submits a theme or keyword related to a specific season, the analysis unit can also determine the priority of analysis based on the season. This makes it possible to determine the priority of analysis based on the time of submission of themes and keywords.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of themes and keywords. For example, the analysis unit prioritizes analysis of highly relevant themes and keywords. The analysis unit can also postpone analysis of less relevant themes and keywords. Furthermore, the analysis unit can prioritize analysis of themes and keywords in which the user is particularly interested. This makes it possible to adjust the order of analysis based on the relevance of themes and keywords.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. In addition, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0096] The generation unit can estimate the user's emotions and adjust the style of the generated artwork based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate artwork with calming colors and designs. If the user is excited, the generation unit can also generate artwork with visually stimulating colors and designs. Furthermore, if the user is stressed, the generation unit can also generate artwork with a simple and intuitive design. This allows the style of the artwork to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The generation unit can adjust the level of detail of the generation based on the importance of the theme or keyword during generation. For example, the generation unit generates a detailed artwork for a theme or keyword with a high level of importance. The generation unit can also generate a simplified artwork for a theme or keyword with a low level of importance. Furthermore, the generation unit can generate an artwork with a focus on a theme or keyword in which the user is particularly interested. This allows the level of detail of the generation to be adjusted based on the importance of the theme or keyword.

[0098] During generation, the generation unit can apply different generation algorithms depending on the category of the theme or keyword. For example, the generation unit applies a natural image generation algorithm to a theme or keyword related to nature. The generation unit can also apply an abstract image generation algorithm to an abstract theme or keyword. Furthermore, the generation unit can also apply a futuristic design generation algorithm to a theme or keyword related to the future. This makes it possible to apply an appropriate generation algorithm depending on the category of the theme or keyword.

[0099] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation based on data of artworks previously generated by the user. The generation unit can also adjust the generation algorithm based on feedback previously provided by the user. Furthermore, the generation unit can also improve the accuracy of generation based on data of artworks previously rated by the user. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0100] The generation unit can estimate the user's emotions and adjust the length of the generated artwork based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point artwork. If the user is relaxed, the generation unit can also generate a longer artwork with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate an artwork with visually stimulating effects. This allows the length of the artwork to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] At the time of generation, the generation unit can determine the priority of generation based on the time of submission of themes and keywords. For example, the generation unit generates the most recently submitted themes and keywords with priority. Furthermore, if a user submits a theme or keywords related to a specific event, the generation unit can also determine the priority of generation based on the time of the event. Furthermore, if a user submits a theme or keywords related to a specific season, the generation unit can also determine the priority of generation based on the season. In this way, the priority of generation can be determined based on the time of submission of themes and keywords.

[0102] The generation unit can adjust the order of generation based on the relevance of themes and keywords during generation. For example, the generation unit generates themes and keywords with high relevance first. The generation unit can also generate themes and keywords with low relevance later. Furthermore, the generation unit can also generate themes and keywords in which the user is particularly interested with high relevance first. This makes it possible to adjust the order of generation based on the relevance of themes and keywords.

[0103] The generation unit may adjust the use of technical terms in the generated artwork according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate an artwork that uses a lot of technical terms. Alternatively, if the user is a beginner, the generation unit may generate an artwork that avoids technical terms. Furthermore, the generation unit may adjust the level of detail in the artwork according to the user's level of expertise. This allows the use of technical terms in the generated artwork to be adjusted according to the user's level of expertise.

[0104] The exhibition unit can estimate the user's emotions and adjust the display method of the exhibits based on the estimated user emotions. For example, if the user is nervous, the exhibition unit can provide a simple, highly visible display method. If the user is relaxed, the exhibition unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the exhibition unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the exhibits based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The exhibition unit can adjust the level of detail of the exhibition based on the importance of the artwork during the exhibition. For example, the exhibition unit can display artworks with high importance including detailed information. The exhibition unit can also display artworks with low importance including simplified information. Furthermore, the exhibition unit can provide focused information on artworks in which the user is particularly interested. This allows the level of detail of the exhibition to be adjusted based on the importance of the artwork.

[0106] The exhibition unit can apply different exhibition algorithms depending on the category of the artwork during exhibition. For example, the exhibition unit can apply a nature image exhibition algorithm to nature-related artworks. The exhibition unit can also apply an abstract image exhibition algorithm to abstract artworks. Furthermore, the exhibition unit can apply a futuristic design exhibition algorithm to future-related artworks. In this way, an appropriate exhibition algorithm can be applied depending on the category of the artwork.

[0107] The exhibition unit can improve the accuracy of the exhibition by referring to the user's past exhibition results when exhibiting. For example, the exhibition unit improves the accuracy of the exhibition based on data on artworks that the user has viewed in the past. The exhibition unit can also adjust the exhibition algorithm based on feedback that the user has provided in the past. Furthermore, the exhibition unit can improve the accuracy of the exhibition based on data on artworks that the user has evaluated in the past. This makes it possible to improve the accuracy of the exhibition based on the user's past exhibition results.

[0108] The exhibition unit can estimate the user's emotions and determine the priority of the exhibitions based on the estimated user's emotions. For example, if the user is excited, the exhibition unit can prioritize displaying visually stimulating artworks. Furthermore, if the user is relaxed, the exhibition unit can prioritize displaying calming artworks. Furthermore, if the user is stressed, the exhibition unit can prioritize displaying simple and intuitive artworks. This makes it possible to determine the priority of the exhibitions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The exhibition unit can determine the exhibition priority based on the time of submission of the artworks during the exhibition. For example, the exhibition unit prioritizes the exhibition of recently submitted artworks. Furthermore, if a user submits artworks related to a specific event, the exhibition unit can determine the exhibition priority based on the time of the event. Furthermore, if a user submits artworks related to a specific season, the exhibition unit can determine the exhibition priority based on the season. In this way, the exhibition priority can be determined based on the time of submission of the artworks.

[0110] The exhibition unit can adjust the order of display based on the relevance of the artworks during display. For example, the exhibition unit prioritizes displaying highly relevant artworks. The exhibition unit can also display less relevant artworks later. Furthermore, the exhibition unit can prioritize displaying artworks in which the user is particularly interested. This allows the order of display to be adjusted based on the relevance of the artworks.

[0111] The exhibition unit can adjust the use of technical terms in the exhibition according to the user's level of expertise during the exhibition. For example, if the user has technical expertise, the exhibition unit can create an exhibition that uses a lot of technical terms. Also, if the user is a beginner, the exhibition unit can create an exhibition that avoids technical terms. Furthermore, the exhibition unit can adjust the level of detail in the exhibition according to the user's level of expertise. This allows the use of technical terms in the exhibition to be adjusted according to the user's level of expertise.

[0112] The search unit can estimate the user's emotions and adjust the display method of the search based on the estimated user's emotions. For example, if the user is nervous, the search unit provides a simple, highly visible display method. If the user is relaxed, the search unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the search unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the search based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] During a search, the search unit can adjust the level of detail of the search based on the importance of the artwork. For example, the search unit performs a search including detailed information for artworks with high importance. The search unit can also perform a search including simplified information for artworks with low importance. Furthermore, the search unit can provide information focused on artworks in which the user is particularly interested. This allows the level of detail of the search to be adjusted based on the importance of the artwork.

[0114] During the search, the search unit can apply different search algorithms depending on the category of the artwork. For example, the search unit can apply a natural image search algorithm to artworks related to nature. The search unit can also apply an abstract image search algorithm to abstract artworks. Furthermore, the search unit can apply a futuristic design search algorithm to artworks related to the future. This makes it possible to apply an appropriate search algorithm depending on the category of the artwork.

[0115] During a search, the search unit can improve the accuracy of the search by referring to the user's past search results. The search unit can improve the accuracy of the search, for example, based on data on artworks that the user has viewed in the past. The search unit can also adjust the search algorithm based on feedback that the user has provided in the past. Furthermore, the search unit can improve the accuracy of the search based on data on artworks that the user has rated in the past. This makes it possible to improve the accuracy of the search based on the user's past search results.

[0116] The search unit can estimate the user's emotions and determine search priorities based on the estimated user emotions. For example, if the user is excited, the search unit can prioritize searching for visually stimulating artworks. Furthermore, if the user is relaxed, the search unit can prioritize searching for calming artworks. Furthermore, if the user is stressed, the search unit can prioritize searching for simple and intuitive artworks. This makes it possible to determine search priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] During a search, the search unit can determine search priorities based on the time of submission of the artwork. For example, the search unit prioritizes searches for artworks that have been submitted recently. In addition, if a user submits artwork related to a specific event, the search unit can also determine search priorities based on the time of the event. Furthermore, if a user submits artwork related to a specific season, the search unit can also determine search priorities based on the season. In this way, search priorities can be determined based on the time of submission of the artwork.

[0118] During a search, the search unit can adjust the order of searches based on the relevance of the artworks. For example, the search unit prioritizes searching for highly relevant artworks. The search unit can also postpone searching for less relevant artworks. Furthermore, the search unit can prioritize searching for artworks in which the user is particularly interested. This allows the order of searches to be adjusted based on the relevance of the artworks.

[0119] The search unit can adjust the use of technical terms in a search according to the user's level of expertise during a search. For example, if the user has technical expertise, the search unit can provide search results that use a lot of technical terms. In addition, if the user is a beginner, the search unit can provide search results that avoid technical terms. Furthermore, the search unit can adjust the level of detail of the search results according to the user's level of expertise. This allows the use of technical terms in a search to be adjusted according to the user's level of expertise.

[0120] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit provides a simple, highly visible display method. If the user is relaxed, the suggestion unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the theme or keyword when making a suggestion. For example, the suggestion unit makes detailed suggestions for themes or keywords with high importance. The suggestion unit can also make simplified suggestions for themes or keywords with low importance. Furthermore, the suggestion unit can also make suggestions with a focus on themes or keywords in which the user is particularly interested. This makes it possible to adjust the level of detail of the suggestion based on the importance of themes or keywords.

[0122] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the theme or keyword. For example, the suggestion unit can apply a natural image suggestion algorithm to a theme or keyword related to nature. The suggestion unit can also apply an abstract image suggestion algorithm to an abstract theme or keyword. Furthermore, the suggestion unit can also apply a futuristic design suggestion algorithm to a theme or keyword related to the future. This makes it possible to apply an appropriate suggestion algorithm depending on the category of the theme or keyword.

[0123] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion based on, for example, data on themes and keywords previously suggested by the user. The suggestion unit can also adjust the suggestion algorithm based on feedback previously provided by the user. Furthermore, the suggestion unit can also improve the accuracy of the suggestion based on data on proposals previously evaluated by the user. This makes it possible to improve the accuracy of the suggestion based on the user's past suggestion results.

[0124] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, if the user is excited, the suggestion unit can prioritize visually stimulating themes and keywords. Also, if the user is relaxed, the suggestion unit can prioritize calm themes and keywords. Furthermore, if the user is stressed, the suggestion unit can prioritize simple and intuitive themes and keywords. This makes it possible to determine the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the themes and keywords. For example, the suggestion unit preferentially suggests themes and keywords that have been submitted recently. Furthermore, if a user submits a theme or keyword related to a specific event, the suggestion unit can also determine the priority of the suggestions based on the time of the event. Furthermore, if a user submits a theme or keyword related to a specific season, the suggestion unit can also determine the priority of the suggestions based on the season. In this way, the priority of the suggestions can be determined based on the time of submission of the theme or keyword.

[0126] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the themes and keywords. For example, the suggestion unit prioritizes suggesting highly relevant themes and keywords. The suggestion unit can also postpone suggesting less relevant themes and keywords. Furthermore, the suggestion unit can prioritize suggesting themes and keywords in which the user is particularly interested. This makes it possible to adjust the order of suggestions based on the relevance of themes and keywords.

[0127] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a suggestion that uses a lot of technical terms. Also, if the user is a beginner, the suggestion unit can make a suggestion that avoids technical terms. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the suggestion according to the user's level of expertise. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, exhibition unit, search unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives input of a theme or keywords from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the input theme or keywords and collects related data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates artworks using a generation AI based on the collected data. The exhibition unit exhibits the generated artworks on an online platform via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. The search unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides a function for efficiently searching for artworks generated by users. The suggestion unit provides an interface for the user to suggest a new theme via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12 . === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, exhibition unit, search unit, and suggestion 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 reception unit receives input of a theme or keyword from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input theme or keyword and collects related data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an artwork using a generation AI based on the collected data. The exhibition unit exhibits the generated artwork on an online platform via the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The search unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides a function for efficiently searching for artworks generated by a user. The suggestion unit provides an interface for the user to suggest a new theme via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12 . === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, exhibition unit, search unit, and proposal unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives input of a theme or keywords from a user via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input theme or keywords and collects related data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an artwork using a generation AI based on the collected data. The exhibition unit exhibits the generated artwork on an online platform via the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The search unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides a function for efficiently searching for artworks generated by users. The suggestion unit provides an interface for the user to suggest a new theme via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12 . === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, exhibition unit, search unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input of a theme or keywords from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input theme or keywords and collects related data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an artwork using a generation AI based on the collected data. The exhibition unit exhibits the generated artwork on an online platform via the speaker 240 of the robot 414 and the communication I / F 26 of the data processing device 12. The search unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides a function for efficiently searching for artworks generated by users. The suggestion unit provides an interface for the user to suggest a new theme via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12 .

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] The art generation system may further include a suggestion unit that estimates the user's emotions and automatically suggests a theme for the artwork to be generated based on the estimated emotions. For example, if the user is feeling stressed, a theme with a relaxing effect may be suggested. Alternatively, if the user is excited, an energetic theme may be suggested. Furthermore, if the user is sad, a comforting or encouraging theme may be suggested. This allows the system to suggest themes according to the user's emotions and generate more personalized artwork.

[0130] The art generation system can further include a generation unit that automatically generates artworks that match the user's preferences based on the user's past browsing history and rating history. For example, the system can analyze the style and theme of artworks that the user has previously given high ratings and generate similar artworks. It can also generate artworks in categories that the user frequently browses. Furthermore, if the user likes the work of a particular artist, it can generate artworks that imitate that artist's style. This allows for efficient generation of artworks that match the user's preferences.

[0131] The art generation system may further include a generation unit that estimates the user's emotions and adjusts the color and design of the artwork based on the estimated emotions. For example, if the user is relaxed, calm colors and designs may be used. If the user is excited, vivid and stimulating colors and designs may be used. Furthermore, if the user is sad, colors and designs that evoke a sense of comfort and healing may be used. In this way, an artwork can be generated that corresponds to the user's emotions.

[0132] The art generation system can further include a suggestion unit that automatically suggests themes and keywords related to a region based on the user's geographical location information. For example, if the user is in a specific city, the system can suggest artworks themed around the history and culture of that city. If the user is in a region rich in nature, the system can suggest artworks themed around the natural scenery of that region. Furthermore, if the user is traveling, the system can suggest artworks themed around tourist attractions and scenery at the travel destination. This makes it possible to suggest highly relevant themes based on the user's geographical location information.

[0133] The art generation system may further include an exhibition unit that estimates the user's emotions and adjusts the display method of the artwork based on the estimated emotions. For example, if the user is nervous, a simple, highly visible exhibition method may be provided. If the user is relaxed, an exhibition method including detailed information may be provided. Furthermore, if the user is in a hurry, an exhibition method that focuses on the main points may be provided. In this way, the display method of the artwork may be adjusted based on the user's emotions.

[0134] The art generation system may further include a suggestion unit that automatically suggests themes and keywords that the user likes based on the user's past input history. For example, the system may prioritize suggestions of themes and keywords that the user has frequently input in the past. Also, if the user inputs a theme or keyword related to a particular season or event, the system may suggest new themes and keywords related to that season or event. Furthermore, if the user likes a particular artist or style, the system may suggest themes and keywords related to that artist or style. This allows the system to suggest optimal themes and keywords based on the user's past input history.

[0135] The art generation system may further include a generation unit that estimates the user's emotions and adjusts the artwork generation process based on the estimated emotions. For example, if the user is relaxed, the generation process may proceed slowly to generate a detailed artwork. Alternatively, if the user is excited, the generation process may proceed quickly to generate a visually stimulating artwork. Furthermore, if the user is stressed, a simple and intuitive artwork may be generated. This allows a generation process to be provided that corresponds to the user's emotions.

[0136] The art generation system may further include a suggestion unit that automatically suggests related themes and keywords based on the user's online platform activities. For example, related themes and keywords may be suggested based on hashtags frequently used by the user on social media. Related themes and keywords may also be suggested by analyzing the content of the user's social media posts. Furthermore, related themes and keywords may also be suggested based on the activities of the user's friends on social media. In this way, related themes and keywords can be suggested based on the user's online platform activities.

[0137] The art generation system may further include an evaluation unit that estimates the user's emotions and adjusts the evaluation criteria for artworks based on the estimated emotions. For example, if the user is relaxed, artworks with calm colors and designs may be rated highly. If the user is excited, visually stimulating artworks may be rated highly. Furthermore, if the user is sad, artworks that evoke a sense of comfort or healing may be rated highly. In this way, the evaluation criteria for artworks can be adjusted based on the user's emotions.

[0138] The art generation system may further include a generation unit that adjusts the use of technical terms in the generated artwork according to the user's level of expertise. For example, if the user has technical expertise, the system may generate an artwork that uses a lot of technical terms. Alternatively, if the user is a beginner, the system may generate an artwork that avoids technical terms. Furthermore, the level of detail in the artwork may be adjusted according to the user's level of expertise. This allows the use of technical terms in the generated artwork to be adjusted according to the user's level of expertise.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The reception unit receives input of a theme or keyword from the user. For example, the user can input themes such as "nature," "future," or "abstract," or keywords such as "blue," "light," or "movement." Step 2: The analysis unit analyzes the theme or keywords received by the reception unit and collects related data. For example, the analysis unit analyzes the theme or keywords using natural language processing technology and collects related image data and text data. Step 3: The generation unit generates an artwork based on the data collected by the analysis unit. The generation unit uses, for example, a generation AI to combine the collected data and generate an artwork. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an artwork based on the input theme or keywords. Step 4: The exhibition unit exhibits the artwork generated by the generation unit on an online platform. For example, the exhibition unit displays the generated artwork on a website or mobile app so that users can view it.

[0141] 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.

[0142] 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.

[0143] 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.

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

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 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.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the 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.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 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.

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

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 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.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The 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.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a theme or keyword from a user; an analysis unit that analyzes the theme or keyword received by the reception unit and collects related data; a generation unit that generates an artwork based on the data collected by the analysis unit; an exhibition unit that exhibits the artwork generated by the generation unit on an online platform; A system characterized by:

2. A search unit for searching user-generated artworks 2. The system of claim 1.

3. A suggestion section is provided for users to suggest new themes.

2. The system of claim 1.

4. The reception unit To estimate a user's emotion and customize a theme or keyword input interface based on the estimated user's emotion.

2. The system of claim 1.

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

6. The reception unit When typing topics or keywords, suggestions are provided based on the user's current interests 2. The system of claim 1.

7. The reception unit When entering a topic or keyword, select the appropriate input method according to the user's input method.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the input themes and keywords based on the estimated user emotions.

2. The system of claim 1.

Images