system

Abstract

The system according to this embodiment aims to enable artists to obtain new art creation ideas based on data from contemporary art. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data on contemporary art. The analysis unit analyzes the data collected by the collection unit. The provision unit provides artists with new art creation ideas based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for an artist to effectively utilize modern art data to obtain new ideas.

[0005] The system according to the embodiment aims to enable an artist to obtain new ideas for art production based on modern art data.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects modern art data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides an artist with new ideas for art production based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows artists to obtain new art creation ideas based on data from contemporary art. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An art production support system according to an embodiment of the present invention is a system that uses generative AI to analyze a vast amount of data on contemporary art and provides artists with new art production ideas based on the results. This art production support system provides artists with new art production ideas by having the generative AI collect and analyze data on contemporary art. For example, the art production support system collects image data and text data of artworks, artist profile information, etc. For example, this includes image data of past artworks, related critical articles, and artist career information. This allows the art production support system to grasp the overall picture of contemporary art. Next, the art production support system analyzes the collected data. The generative AI analyzes trends and currents in contemporary art based on the collected data. For example, it can analyze art styles that were popular during a particular period, or trends in works related to a particular theme. This allows the art production support system to grasp trends in contemporary art and provide artists with useful information. Furthermore, the art production support system provides artists with new art production ideas based on the analysis results. For example, based on the data analyzed by the generative AI, it can propose new themes and styles to artists. This allows artists to respond sensitively to trends and currents in contemporary art and pursue new forms of expression. This system makes it easier for artists to grasp trends and currents in contemporary art and discover new forms of expression. Furthermore, artists can create innovative and meaningful artworks based on ideas provided by the generative AI. For example, by incorporating new themes and styles suggested by the generative AI, artists can add a new perspective to their work. In this way, using generative AI allows artists to efficiently analyze data on contemporary art and gain new ideas for art creation. This enables artists to gain a deep understanding of existing trends and currents, and to create innovative and meaningful artworks based on that knowledge. Thus, art production support systems can provide artists with new ideas for art creation.

[0029] The art production support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data on contemporary art. The collection unit collects, for example, image data and text data of artworks, and artist profile information. For example, the collection unit can collect image data of past artworks. The collection unit can also collect critical articles on artworks. Furthermore, the collection unit can collect artist biographical information. For example, the collection unit can collect an artist's exhibition history and award history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. For example, the analysis unit can analyze art styles that were popular during a particular period. Furthermore, the analysis unit can analyze trends in works related to a particular theme. Furthermore, the analysis unit can also analyze the techniques and methods of expression of artworks. For example, the analysis unit can analyze the colors and composition of artworks. The provision unit provides artists with new art production ideas based on the analysis results obtained by the analysis unit. The service provider can, for example, propose new themes and styles to artists based on the analysis results. For instance, the service provider can propose new themes to artists. It can also propose new styles to artists. Furthermore, the service provider can propose new techniques and methods of expression to artists. For example, the service provider can propose new colors and compositions to artists. As a result, the art production support system according to this embodiment can efficiently collect and analyze data on contemporary art and provide new ideas for art production.

[0030] The data collection department collects data on contemporary art. This includes, for example, image and text data of artworks, and artist profile information. Specifically, the department obtains image data of artworks from online art gallery and museum databases. This includes high-resolution images and detailed descriptions of the works. It also collects critical articles from art criticism websites and specialized magazines, accumulating evaluations and interpretations of artworks in the database. Furthermore, it collects biographical information from artists' official websites and social media, gathering detailed profile information such as exhibition history, awards, and educational background. This allows the data collection department to comprehensively collect diverse data on contemporary art and centralize it in a database. The collected data is stored in cloud storage, making it accessible to the analysis and provision departments. Data collection and updating frequency may be real-time or periodic. This allows the data collection department to always maintain the latest art information, improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. Specifically, it uses AI to analyze image data and identify art styles and techniques that were popular during specific periods. For example, it uses deep learning-based image recognition technology to extract features such as the color, composition, and texture of artworks and analyzes them as time-series data. It also analyzes text data using natural language processing technology to grasp the changes in trends and themes from critical articles and artist biographies. For example, it can analyze when works on a particular theme were produced in large numbers and what kind of evaluations they received. Furthermore, the analysis unit can also analyze the techniques and methods of expression of artworks. For example, it can analyze the frequency of color use and compositional patterns to reveal common characteristics of specific artists or periods. In this way, the analysis unit can comprehensively analyze diverse aspects of contemporary art and grasp art trends and currents in detail. The analysis results are stored in a database and made accessible to the provision unit. This allows the analysis unit to analyze the collected data efficiently and effectively and improve the overall system performance.

[0032] The service provider department offers artists new art creation ideas based on the analysis results obtained by the analysis department. Specifically, it proposes new themes and styles to artists based on the analysis results. For example, the service provider department might suggest, based on past data, that a particular theme is likely to gain renewed attention, and propose that the artist create a work incorporating that theme. It also introduces new styles and techniques derived from the analysis results to artists, supporting them in broadening their creative scope. For example, the service provider department can suggest new color combinations and compositions to artists. Furthermore, the service provider department can also propose new techniques and methods of expression to artists. For example, it might introduce the latest digital art technologies and interactive art methods derived from the analysis results, providing artists with opportunities to try new methods of expression. The service provider department customizes these suggestions individually for each artist, supporting their creative activities. The suggestions are optimized based on the artist's past works, career, and current creative situation. This allows the service provider department to provide artists with concrete and practical advice, supporting them in generating new art creation ideas.

[0033] The data collection unit can collect image data or text data of artworks, as well as artist profile information. For example, the data collection unit can collect image data of artworks. For example, the data collection unit can collect photographs of past artworks. The data collection unit can also collect image data of digital art. Furthermore, the data collection unit can collect text data related to artworks. For example, the data collection unit can collect descriptive texts of artworks. The data collection unit can also collect comments from artists. Furthermore, the data collection unit can collect artist profile information. For example, the data collection unit can collect the artist's career history. The data collection unit can also collect the artist's awards history. Furthermore, the data collection unit can also collect the artist's exhibition history. In this way, the data collection unit can grasp the overall picture of contemporary art by collecting diverse data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input image data of artworks into a generative AI and have the generative AI perform the collection of image data.

[0034] The analysis unit can analyze trends and currents in contemporary art based on the collected data. For example, the analysis unit can analyze trends in contemporary art based on the collected data. For example, the analysis unit can analyze art styles that were popular during a particular period. The analysis unit can also analyze trends in works related to a particular theme. Furthermore, the analysis unit can analyze the techniques and methods of expression of artworks. For example, the analysis unit can analyze the colors and composition of artworks. Furthermore, the analysis unit can analyze the materials and techniques of artworks. Furthermore, the analysis unit can analyze the themes and messages of artworks. For example, the analysis unit can analyze the themes and messages of artworks and grasp their trends. In this way, the analysis unit can grasp trends and currents in contemporary art. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0035] The service provider can propose new themes and styles to artists based on the analysis results. For example, the service provider can propose new themes to artists based on the analysis results. For example, the service provider can propose works to artists on the theme of environmental issues. The service provider can also propose works to artists on the theme of social issues. Furthermore, the service provider can also propose new styles to artists. For example, the service provider can propose abstract painting styles to artists. The service provider can also propose figurative painting styles to artists. Furthermore, the service provider can propose installation styles to artists. In this way, the service provider can pursue new forms of expression by proposing new themes and styles to artists. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the analysis results into a generative AI and have the generative AI execute the proposal of new themes and styles.

[0036] The data collection unit can collect data on cultural and social backgrounds. For example, the data collection unit can collect data on historical backgrounds. It can also collect data on social events. Furthermore, the data collection unit can collect data on cultural trends. For example, the data collection unit can collect data on cultural trends that were popular during a particular period. It can also collect data on the cultural background of a particular region. This allows the data collection unit to perform deeper analysis by collecting data on cultural and social backgrounds. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input cultural and social background data into a generative AI and have the generative AI perform the data collection.

[0037] The analysis unit can specify a particular analysis method, such as an analysis method using a machine learning algorithm. For example, the analysis unit can classify data using a clustering algorithm. It can also classify data using a classification algorithm. Furthermore, the analysis unit can analyze data using regression analysis. For example, the analysis unit can classify artworks by theme using a clustering algorithm. It can also classify artworks by style using a classification algorithm. Furthermore, the analysis unit can analyze trends in artworks using regression analysis. This improves the transparency of the analysis by specifying a particular analysis method. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a machine learning algorithm into a generative AI and have the generative AI specify the analysis method.

[0038] The provider can specify, for example, the means by which it presents ideas provided by the generative AI to artists, such as presenting ideas through a user interface. The provider can specify, for example, the means by which it presents ideas through a user interface. For example, the provider can present ideas through a web application. The provider can also present ideas through a mobile application. Furthermore, the provider can present ideas through a desktop application. For example, the provider can propose new themes and styles to artists through a web application. The provider can also propose new techniques and methods of expression to artists through a mobile application. Furthermore, the provider can propose new colors and compositions to artists through a desktop application. In this way, by specifying specific means, the provider clarifies how ideas are provided. Some or all of the above-described processes in the provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the provider can input a user interface into a generative AI and have the generative AI perform the presentation of ideas.

[0039] The data collection unit can analyze an artist's past work history and select an appropriate data collection method. For example, the data collection unit can use a generative AI to collect data on similar themes based on the artist's past work history. The data collection unit can also analyze an artist's past work history and collect data on different styles. Furthermore, the data collection unit can collect data on specific techniques based on the artist's past work history. In this way, the data collection unit can select the optimal data collection method by analyzing an artist's past work history. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without a generative AI. For example, the data collection unit can input an artist's past work history into a generative AI and have the generative AI select a data collection method.

[0040] The data collection unit can filter data based on the artist's current projects and areas of interest. For example, the data collection unit can use a generating AI to collect data related to the artist's current projects. The data collection unit can also use a generating AI to collect data on specific themes based on the artist's areas of interest. Furthermore, the data collection unit can use a generating AI to collect necessary data according to the progress of the artist's current projects. This allows the data collection unit to collect highly relevant data by filtering based on the artist's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or not. For example, the data collection unit can input the artist's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the artist's geographical location. For example, if the artist is in a specific region, the data collection unit can use a generating AI to collect data on artworks related to that region. If the artist is traveling, the data collection unit can also use a generating AI to collect data related to the culture and history of the place they are visiting. Furthermore, if the artist is in a specific city, the data collection unit can use a generating AI to collect data related to the art scene of that city. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the artist's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or not. For example, the data collection unit can input the artist's geographical location into a generating AI and have the generating AI perform the data collection.

[0042] The data collection unit can analyze an artist's social media activity and collect relevant data. For example, the data collection unit can use a generating AI to collect data related to works shared by the artist on social media. The data collection unit can also use a generating AI to collect data on works by other artists that the artist follows. Furthermore, the data collection unit can use a generating AI to collect data related to themes of interest from the artist's social media activity. In this way, the data collection unit can collect relevant data by analyzing the artist's social media activity. Some or all of the above processing in the data collection unit may be performed using a generating AI, for example, or without a generating AI. For example, the data collection unit can input the artist's social media activity into a generating AI and have the generating AI perform the data collection.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the artwork. For example, the analysis unit can use the generative AI to perform a detailed analysis of important artworks. The analysis unit can also use the generative AI to perform a concise analysis of general artworks. Furthermore, the analysis unit can use the generative AI to perform a deep analysis of important works related to a specific theme. In this way, the analysis unit can perform a detailed analysis of important works by adjusting the level of detail of the analysis based on the importance of the artworks. Some or all of the above processing in the analysis unit may be performed using the generative AI, or not. For example, the analysis unit can input the importance of the artworks into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the artwork. For example, the analysis unit can apply a color analysis algorithm to a painting using a generating AI. Similarly, the analysis unit can apply a shape analysis algorithm to a sculpture using a generating AI. Furthermore, the analysis unit can apply a pixel analysis algorithm to a digital artwork using a generating AI. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the artwork. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or without one. For example, the analysis unit can input the category of the artwork into the generating AI and have the generating AI perform the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the creation date of the artwork. For example, the analysis unit can have the generating AI prioritize the analysis of the latest artwork. The analysis unit can also have the generating AI perform a detailed analysis of important past artworks. Furthermore, the analysis unit can have the generating AI prioritize the analysis of artworks that were popular during a particular period. In this way, by determining the priority of analysis based on the creation date of the artwork, the analysis unit can prioritize the analysis of the latest and important works. Some or all of the above processing in the analysis unit may be performed using the generating AI, or not. For example, the analysis unit can input the creation date of the artwork into the generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the artworks. For example, the analysis unit can have the generating AI prioritize the analysis of highly relevant artworks. The analysis unit can also have the generating AI postpone the analysis of less relevant artworks. Furthermore, the analysis unit can have the generating AI prioritize the analysis of artworks related to a specific theme. In this way, the analysis unit can prioritize the analysis of highly relevant works by adjusting the order of analysis based on the relevance of the artworks. Some or all of the above processing in the analysis unit may be performed using the generating AI, or not. For example, the analysis unit can input the relevance of the artworks into the generating AI and have the generating AI adjust the order of analysis.

[0047] The service provider can adjust the level of detail of the ideas it provides based on the importance of the artwork. For example, the service provider can have the generative AI provide detailed ideas for important artworks. The service provider can also have the generative AI provide concise ideas for general artworks. Furthermore, the service provider can have the generative AI provide in-depth ideas for important works related to a specific theme. In this way, the service provider can provide detailed ideas for important works by adjusting the level of detail of the ideas it provides based on the importance of the artwork. Some or all of the above processing in the service provider may be performed using the generative AI, or not. For example, the service provider can input the importance of the artwork into the generative AI and have the generative AI perform the adjustment of the level of detail of the ideas.

[0048] The providing unit can apply different providing algorithms depending on the category of the artwork. For example, the providing unit can have the generative AI provide ideas regarding color for a painting. It can also have the generative AI provide ideas regarding shape for a sculpture. Furthermore, it can have the generative AI provide ideas regarding pixels for a digital artwork. This allows the providing unit to provide more appropriate ideas by applying different providing algorithms depending on the category of the artwork. Some or all of the above processing in the providing unit may be performed using the generative AI, or not. For example, the providing unit can input the category of the artwork into the generative AI and have the generative AI apply the providing algorithm.

[0049] The idea generation unit can adjust the order in which ideas are presented based on the creation date of the artwork. For example, the idea generation unit can have the generative AI prioritize providing ideas for the most recent artwork. The idea generation unit can also have the generative AI provide detailed ideas for important past artworks. Furthermore, the idea generation unit can have the generative AI prioritize providing ideas for artworks that were popular at a particular time. In this way, the idea generation unit can prioritize providing ideas for the most recent and important works by adjusting the order in which ideas are presented based on the creation date of the artwork. Some or all of the above processing in the idea generation unit may be performed using the generative AI, or not. For example, the idea generation unit can input the creation date of the artwork into the generative AI and have the generative AI perform the adjustment of the order of ideas.

[0050] The idea provider can adjust the order in which ideas are presented based on the relevance of the artworks. For example, the idea provider can have the generative AI prioritize providing ideas to highly relevant artworks. The idea provider can also have the generative AI postpone providing ideas to less relevant artworks. Furthermore, the idea provider can have the generative AI prioritize providing ideas to artworks related to a specific theme. In this way, the idea provider can prioritize providing ideas to highly relevant artworks by adjusting the order in which ideas are presented based on the relevance of the artworks. Some or all of the above processing in the idea provider can be performed using the generative AI, or not using the generative AI. For example, the idea provider can input the relevance of the artworks into the generative AI and have the generative AI adjust the order of ideas.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The art production support system can also be equipped with an inspiration collection unit. This unit can record the inspiration artists experience in their daily lives and collect it as data. For example, artists can record inspiration from scenes they see in the city or from everyday events through a smartphone app. The inspiration collection unit can also collect information from places artists have visited and events they have attended. Furthermore, it can record inspiration from books artists have read or movies they have watched. This allows the art production support system to provide new art production ideas based on inspiration gained from the artist's daily life.

[0053] The art production support system can also include a collaboration promotion department. This department provides functions to facilitate collaboration between artists. For example, it can recommend compatible artists based on an artist's profile information and past works. It can also provide a platform for artists to collaborate on projects. Furthermore, it can offer opportunities for artists to collaborate with experts in other fields. This allows the art production support system to generate new art production ideas through collaboration among artists.

[0054] The art production support system can also be equipped with a trend prediction unit. This unit provides the functionality to predict future art trends based on contemporary art data. For example, it can analyze data from past artworks to predict art styles and themes that are likely to become popular in the future. Furthermore, it can predict future trends for individual artists based on their activities and the tendencies of their work. In addition, it can analyze trends in the art market and propose works to artists that meet market needs. This allows the art production support system to provide artists with ideas for creating works that respond to future trends.

[0055] The art production support system can also include a user feedback unit. This unit collects user feedback on works created by artists and uses that data to provide ideas for future art production. For example, the user feedback unit can collect audience comments and evaluations of works exhibited by artists. It can also collect comments and evaluations of works on online platforms. Furthermore, the user feedback unit can analyze the collected feedback and suggest improvements and new ideas to artists. This allows the art production support system to provide better art production ideas based on user feedback.

[0056] The art production support system can also include a cultural background analysis unit. This unit analyzes the cultural background of an artwork and provides art production ideas based on the results. For example, it can analyze the cultural background of the era and region in which the artwork was created. It can also analyze historical events and social circumstances that influenced the artwork. Furthermore, based on the cultural background of the artwork, it can propose new themes and styles. This allows the art production support system to provide ideas that take the cultural background of the artwork into consideration.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection department collects data on contemporary art. This includes, for example, image data and text data of artworks, and artist profile information. Specifically, it collects image data of past artworks, critical articles about artworks, and biographical information such as artists' exhibition history and awards. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. Specifically, it analyzes art styles that were popular during a particular period, trends in works related to specific themes, and techniques and methods of expression in artworks (such as color and composition). Step 3: The provision department provides the artist with new art creation ideas based on the analysis results obtained by the analysis department. For example, the provision department may suggest new themes, styles, techniques, and methods of expression (such as color and composition) to the artist based on the analysis results.

[0059] (Example of form 2) An art production support system according to an embodiment of the present invention is a system that uses generative AI to analyze a vast amount of data on contemporary art and provides artists with new art production ideas based on the results. This art production support system provides artists with new art production ideas by having the generative AI collect and analyze data on contemporary art. For example, the art production support system collects image data and text data of artworks, artist profile information, etc. For example, this includes image data of past artworks, related critical articles, and artist career information. This allows the art production support system to grasp the overall picture of contemporary art. Next, the art production support system analyzes the collected data. The generative AI analyzes trends and currents in contemporary art based on the collected data. For example, it can analyze art styles that were popular during a particular period, or trends in works related to a particular theme. This allows the art production support system to grasp trends in contemporary art and provide artists with useful information. Furthermore, the art production support system provides artists with new art production ideas based on the analysis results. For example, based on the data analyzed by the generative AI, it can propose new themes and styles to artists. This allows artists to respond sensitively to trends and currents in contemporary art and pursue new forms of expression. This system makes it easier for artists to grasp trends and currents in contemporary art and discover new forms of expression. Furthermore, artists can create innovative and meaningful artworks based on ideas provided by the generative AI. For example, by incorporating new themes and styles suggested by the generative AI, artists can add a new perspective to their work. In this way, using generative AI allows artists to efficiently analyze data on contemporary art and gain new ideas for art creation. This enables artists to gain a deep understanding of existing trends and currents, and to create innovative and meaningful artworks based on that knowledge. Thus, art production support systems can provide artists with new ideas for art creation.

[0060] The art production support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data on contemporary art. The collection unit collects, for example, image data and text data of artworks, and artist profile information. For example, the collection unit can collect image data of past artworks. The collection unit can also collect critical articles on artworks. Furthermore, the collection unit can collect artist biographical information. For example, the collection unit can collect an artist's exhibition history and award history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. For example, the analysis unit can analyze art styles that were popular during a particular period. Furthermore, the analysis unit can analyze trends in works related to a particular theme. Furthermore, the analysis unit can also analyze the techniques and methods of expression of artworks. For example, the analysis unit can analyze the colors and composition of artworks. The provision unit provides artists with new art production ideas based on the analysis results obtained by the analysis unit. The service provider can, for example, propose new themes and styles to artists based on the analysis results. For instance, the service provider can propose new themes to artists. It can also propose new styles to artists. Furthermore, the service provider can propose new techniques and methods of expression to artists. For example, the service provider can propose new colors and compositions to artists. As a result, the art production support system according to this embodiment can efficiently collect and analyze data on contemporary art and provide new ideas for art production.

[0061] The data collection department collects data on contemporary art. This includes, for example, image and text data of artworks, and artist profile information. Specifically, the department obtains image data of artworks from online art gallery and museum databases. This includes high-resolution images and detailed descriptions of the works. It also collects critical articles from art criticism websites and specialized magazines, accumulating evaluations and interpretations of artworks in the database. Furthermore, it collects biographical information from artists' official websites and social media, gathering detailed profile information such as exhibition history, awards, and educational background. This allows the data collection department to comprehensively collect diverse data on contemporary art and centralize it in a database. The collected data is stored in cloud storage, making it accessible to the analysis and provision departments. Data collection and updating frequency may be real-time or periodic. This allows the data collection department to always maintain the latest art information, improving the accuracy and reliability of the entire system.

[0062] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. Specifically, it uses AI to analyze image data and identify art styles and techniques that were popular during specific periods. For example, it uses deep learning-based image recognition technology to extract features such as the color, composition, and texture of artworks and analyzes them as time-series data. It also analyzes text data using natural language processing technology to grasp the changes in trends and themes from critical articles and artist biographies. For example, it can analyze when works on a particular theme were produced in large numbers and what kind of evaluations they received. Furthermore, the analysis unit can also analyze the techniques and methods of expression of artworks. For example, it can analyze the frequency of color use and compositional patterns to reveal common characteristics of specific artists or periods. In this way, the analysis unit can comprehensively analyze diverse aspects of contemporary art and grasp art trends and currents in detail. The analysis results are stored in a database and made accessible to the provision unit. This allows the analysis unit to analyze the collected data efficiently and effectively and improve the overall system performance.

[0063] The service provider department offers artists new art creation ideas based on the analysis results obtained by the analysis department. Specifically, it proposes new themes and styles to artists based on the analysis results. For example, the service provider department might suggest, based on past data, that a particular theme is likely to gain renewed attention, and propose that the artist create a work incorporating that theme. It also introduces new styles and techniques derived from the analysis results to artists, supporting them in broadening their creative scope. For example, the service provider department can suggest new color combinations and compositions to artists. Furthermore, the service provider department can also propose new techniques and methods of expression to artists. For example, it might introduce the latest digital art technologies and interactive art methods derived from the analysis results, providing artists with opportunities to try new methods of expression. The service provider department customizes these suggestions individually for each artist, supporting their creative activities. The suggestions are optimized based on the artist's past works, career, and current creative situation. This allows the service provider department to provide artists with concrete and practical advice, supporting them in generating new art creation ideas.

[0064] The data collection unit can collect image data or text data of artworks, as well as artist profile information. For example, the data collection unit can collect image data of artworks. For example, the data collection unit can collect photographs of past artworks. The data collection unit can also collect image data of digital art. Furthermore, the data collection unit can collect text data related to artworks. For example, the data collection unit can collect descriptive texts of artworks. The data collection unit can also collect comments from artists. Furthermore, the data collection unit can collect artist profile information. For example, the data collection unit can collect the artist's career history. The data collection unit can also collect the artist's awards history. Furthermore, the data collection unit can also collect the artist's exhibition history. In this way, the data collection unit can grasp the overall picture of contemporary art by collecting diverse data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input image data of artworks into a generative AI and have the generative AI perform the collection of image data.

[0065] The analysis unit can analyze trends and currents in contemporary art based on the collected data. For example, the analysis unit can analyze trends in contemporary art based on the collected data. For example, the analysis unit can analyze art styles that were popular during a particular period. The analysis unit can also analyze trends in works related to a particular theme. Furthermore, the analysis unit can analyze the techniques and methods of expression of artworks. For example, the analysis unit can analyze the colors and composition of artworks. Furthermore, the analysis unit can analyze the materials and techniques of artworks. Furthermore, the analysis unit can analyze the themes and messages of artworks. For example, the analysis unit can analyze the themes and messages of artworks and grasp their trends. In this way, the analysis unit can grasp trends and currents in contemporary art. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0066] The service provider can propose new themes and styles to artists based on the analysis results. For example, the service provider can propose new themes to artists based on the analysis results. For example, the service provider can propose works to artists on the theme of environmental issues. The service provider can also propose works to artists on the theme of social issues. Furthermore, the service provider can also propose new styles to artists. For example, the service provider can propose abstract painting styles to artists. The service provider can also propose figurative painting styles to artists. Furthermore, the service provider can propose installation styles to artists. In this way, the service provider can pursue new forms of expression by proposing new themes and styles to artists. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the analysis results into a generative AI and have the generative AI execute the proposal of new themes and styles.

[0067] The data collection unit can collect data on cultural and social backgrounds. For example, the data collection unit can collect data on historical backgrounds. It can also collect data on social events. Furthermore, the data collection unit can collect data on cultural trends. For example, the data collection unit can collect data on cultural trends that were popular during a particular period. It can also collect data on the cultural background of a particular region. This allows the data collection unit to perform deeper analysis by collecting data on cultural and social backgrounds. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input cultural and social background data into a generative AI and have the generative AI perform the data collection.

[0068] The analysis unit can specify a particular analysis method, such as an analysis method using a machine learning algorithm. For example, the analysis unit can classify data using a clustering algorithm. It can also classify data using a classification algorithm. Furthermore, the analysis unit can analyze data using regression analysis. For example, the analysis unit can classify artworks by theme using a clustering algorithm. It can also classify artworks by style using a classification algorithm. Furthermore, the analysis unit can analyze trends in artworks using regression analysis. This improves the transparency of the analysis by specifying a particular analysis method. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a machine learning algorithm into a generative AI and have the generative AI specify the analysis method.

[0069] The provider can specify, for example, the means by which it presents ideas provided by the generative AI to artists, such as presenting ideas through a user interface. The provider can specify, for example, the means by which it presents ideas through a user interface. For example, the provider can present ideas through a web application. The provider can also present ideas through a mobile application. Furthermore, the provider can present ideas through a desktop application. For example, the provider can propose new themes and styles to artists through a web application. The provider can also propose new techniques and methods of expression to artists through a mobile application. Furthermore, the provider can propose new colors and compositions to artists through a desktop application. In this way, by specifying specific means, the provider clarifies how ideas are provided. Some or all of the above-described processes in the provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the provider can input a user interface into a generative AI and have the generative AI perform the presentation of ideas.

[0070] The data collection unit can estimate the artist's emotions and adjust the type of data collected based on the estimated emotions. For example, if the artist is relaxed, the data collection unit can have the generative AI prioritize collecting data on past successful works. If the artist is stressed, the data collection unit can have the generative AI collect data on artworks with relaxing effects. Furthermore, if the artist is excited, the data collection unit can have the generative AI collect data on artworks with challenging themes. This allows the data collection unit to collect more appropriate data by adjusting the type of data collected according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the artist's emotional data into a generating AI, allowing the AI ​​to perform the data collection.

[0071] The data collection unit can analyze an artist's past work history and select an appropriate data collection method. For example, the data collection unit can use a generative AI to collect data on similar themes based on the artist's past work history. The data collection unit can also analyze an artist's past work history and collect data on different styles. Furthermore, the data collection unit can collect data on specific techniques based on the artist's past work history. In this way, the data collection unit can select the optimal data collection method by analyzing an artist's past work history. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without a generative AI. For example, the data collection unit can input an artist's past work history into a generative AI and have the generative AI select a data collection method.

[0072] The data collection unit can filter data based on the artist's current projects and areas of interest. For example, the data collection unit can use a generating AI to collect data related to the artist's current projects. The data collection unit can also use a generating AI to collect data on specific themes based on the artist's areas of interest. Furthermore, the data collection unit can use a generating AI to collect necessary data according to the progress of the artist's current projects. This allows the data collection unit to collect highly relevant data by filtering based on the artist's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or not. For example, the data collection unit can input the artist's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0073] The data collection unit can estimate the artist's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the artist is relaxed, the data collection unit can have the generative AI prioritize collecting data from past successful works. If the artist is stressed, the data collection unit can have the generative AI prioritize collecting data from relaxing artworks. Furthermore, if the artist is excited, the data collection unit can have the generative AI prioritize collecting data from challenging themes. This allows the data collection unit to collect more appropriate data by prioritizing data according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input artist emotional data into a generating AI and have the generating AI determine the priority of the data.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the artist's geographical location. For example, if the artist is in a specific region, the data collection unit can use a generating AI to collect data on artworks related to that region. If the artist is traveling, the data collection unit can also use a generating AI to collect data related to the culture and history of the place they are visiting. Furthermore, if the artist is in a specific city, the data collection unit can use a generating AI to collect data related to the art scene of that city. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the artist's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or not. For example, the data collection unit can input the artist's geographical location into a generating AI and have the generating AI perform the data collection.

[0075] The data collection unit can analyze an artist's social media activity and collect relevant data. For example, the data collection unit can use a generating AI to collect data related to works shared by the artist on social media. The data collection unit can also use a generating AI to collect data on works by other artists that the artist follows. Furthermore, the data collection unit can use a generating AI to collect data related to themes of interest from the artist's social media activity. In this way, the data collection unit can collect relevant data by analyzing the artist's social media activity. Some or all of the above processing in the data collection unit may be performed using a generating AI, for example, or without a generating AI. For example, the data collection unit can input the artist's social media activity into a generating AI and have the generating AI perform the data collection.

[0076] The analysis unit can estimate the artist's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the artist is relaxed, the generation AI can provide detailed analysis results. If the artist is stressed, the generation AI can provide concise and to-the-point analysis results. Furthermore, if the artist is excited, the generation AI can provide visually stimulating analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the artist's emotional data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the artwork. For example, the analysis unit can use the generative AI to perform a detailed analysis of important artworks. The analysis unit can also use the generative AI to perform a concise analysis of general artworks. Furthermore, the analysis unit can use the generative AI to perform a deep analysis of important works related to a specific theme. In this way, the analysis unit can perform a detailed analysis of important works by adjusting the level of detail of the analysis based on the importance of the artworks. Some or all of the above processing in the analysis unit may be performed using the generative AI, or not. For example, the analysis unit can input the importance of the artworks into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the artwork. For example, the analysis unit can apply a color analysis algorithm to a painting using a generating AI. Similarly, the analysis unit can apply a shape analysis algorithm to a sculpture using a generating AI. Furthermore, the analysis unit can apply a pixel analysis algorithm to a digital artwork using a generating AI. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the artwork. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or without one. For example, the analysis unit can input the category of the artwork into the generating AI and have the generating AI perform the application of the analysis algorithm.

[0079] The analysis unit can estimate the artist's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the artist is relaxed, the generation AI can provide a detailed analysis. If the artist is stressed, the generation AI can provide a concise analysis. Furthermore, if the artist is excited, the generation AI can provide a visually stimulating analysis. By adjusting the length of the analysis according to the artist's emotions, the analysis unit can provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit can input the artist's emotion data into a generation AI and have the generation AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the creation date of the artwork. For example, the analysis unit can have the generating AI prioritize the analysis of the latest artwork. The analysis unit can also have the generating AI perform a detailed analysis of important past artworks. Furthermore, the analysis unit can have the generating AI prioritize the analysis of artworks that were popular during a particular period. In this way, by determining the priority of analysis based on the creation date of the artwork, the analysis unit can prioritize the analysis of the latest and important works. Some or all of the above processing in the analysis unit may be performed using the generating AI, or not. For example, the analysis unit can input the creation date of the artwork into the generating AI and have the generating AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the artworks. For example, the analysis unit can have the generating AI prioritize the analysis of highly relevant artworks. The analysis unit can also have the generating AI postpone the analysis of less relevant artworks. Furthermore, the analysis unit can have the generating AI prioritize the analysis of artworks related to a specific theme. In this way, the analysis unit can prioritize the analysis of highly relevant works by adjusting the order of analysis based on the relevance of the artworks. Some or all of the above processing in the analysis unit may be performed using the generating AI, or not. For example, the analysis unit can input the relevance of the artworks into the generating AI and have the generating AI adjust the order of analysis.

[0082] The service provider can estimate the artist's emotions and adjust the way the ideas are presented based on the estimated emotions. For example, if the artist is relaxed, the service provider's generative AI can provide detailed ideas. If the artist is stressed, the service provider's generative AI can provide concise and to-the-point ideas. Furthermore, if the artist is excited, the service provider's generative AI can provide visually stimulating ideas. In this way, the service provider can provide more appropriate ideas by adjusting the way the ideas are presented according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the artist's emotional data into a generating AI and have the AI ​​adjust the way the idea is expressed.

[0083] The service provider can adjust the level of detail of the ideas it provides based on the importance of the artwork. For example, the service provider can have the generative AI provide detailed ideas for important artworks. The service provider can also have the generative AI provide concise ideas for general artworks. Furthermore, the service provider can have the generative AI provide in-depth ideas for important works related to a specific theme. In this way, the service provider can provide detailed ideas for important works by adjusting the level of detail of the ideas it provides based on the importance of the artwork. Some or all of the above processing in the service provider may be performed using the generative AI, or not. For example, the service provider can input the importance of the artwork into the generative AI and have the generative AI perform the adjustment of the level of detail of the ideas.

[0084] The providing unit can apply different providing algorithms depending on the category of the artwork. For example, the providing unit can have the generative AI provide ideas regarding color for a painting. It can also have the generative AI provide ideas regarding shape for a sculpture. Furthermore, it can have the generative AI provide ideas regarding pixels for a digital artwork. This allows the providing unit to provide more appropriate ideas by applying different providing algorithms depending on the category of the artwork. Some or all of the above processing in the providing unit may be performed using the generative AI, or not. For example, the providing unit can input the category of the artwork into the generative AI and have the generative AI apply the providing algorithm.

[0085] The service provider can estimate the artist's emotions and prioritize the ideas to offer based on the estimated emotions. For example, if the artist is relaxed, the service provider can have the generative AI prioritize offering detailed ideas. If the artist is stressed, the service provider can have the generative AI prioritize offering concise ideas. Furthermore, if the artist is excited, the service provider can have the generative AI prioritize offering visually stimulating ideas. This allows the service provider to offer more appropriate ideas by prioritizing the ideas offered according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the artist's emotional data into a generating AI and have the AI ​​determine the priority of ideas.

[0086] The idea generation unit can adjust the order in which ideas are presented based on the creation date of the artwork. For example, the idea generation unit can have the generative AI prioritize providing ideas for the most recent artwork. The idea generation unit can also have the generative AI provide detailed ideas for important past artworks. Furthermore, the idea generation unit can have the generative AI prioritize providing ideas for artworks that were popular at a particular time. In this way, the idea generation unit can prioritize providing ideas for the most recent and important works by adjusting the order in which ideas are presented based on the creation date of the artwork. Some or all of the above processing in the idea generation unit may be performed using the generative AI, or not. For example, the idea generation unit can input the creation date of the artwork into the generative AI and have the generative AI perform the adjustment of the order of ideas.

[0087] The idea provider can adjust the order in which ideas are presented based on the relevance of the artworks. For example, the idea provider can have the generative AI prioritize providing ideas to highly relevant artworks. The idea provider can also have the generative AI postpone providing ideas to less relevant artworks. Furthermore, the idea provider can have the generative AI prioritize providing ideas to artworks related to a specific theme. In this way, the idea provider can prioritize providing ideas to highly relevant artworks by adjusting the order in which ideas are presented based on the relevance of the artworks. Some or all of the above processing in the idea provider can be performed using the generative AI, or not using the generative AI. For example, the idea provider can input the relevance of the artworks into the generative AI and have the generative AI adjust the order of ideas.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The art production support system can also be equipped with an inspiration collection unit. This unit can record the inspiration artists experience in their daily lives and collect it as data. For example, artists can record inspiration from scenes they see in the city or from everyday events through a smartphone app. The inspiration collection unit can also collect information from places artists have visited and events they have attended. Furthermore, it can record inspiration from books artists have read or movies they have watched. This allows the art production support system to provide new art production ideas based on inspiration gained from the artist's daily life.

[0090] The art production support system can also include a collaboration promotion department. This department provides functions to facilitate collaboration between artists. For example, it can recommend compatible artists based on an artist's profile information and past works. It can also provide a platform for artists to collaborate on projects. Furthermore, it can offer opportunities for artists to collaborate with experts in other fields. This allows the art production support system to generate new art production ideas through collaboration among artists.

[0091] The art production support system can also be equipped with an emotional feedback unit. This unit records the emotions the artist feels during the creative process and provides feedback based on that data. For example, it can record emotions such as joy, excitement, and stress the artist experiences during the creative process. Furthermore, it can suggest improvements to the creative process based on the artist's emotions. Additionally, the emotional feedback unit can provide a function for artists to share their emotions with other artists. This allows the art production support system to provide better art production ideas based on the artist's emotions.

[0092] The art production support system can also be equipped with a trend prediction unit. This unit provides the functionality to predict future art trends based on contemporary art data. For example, it can analyze data from past artworks to predict art styles and themes that are likely to become popular in the future. Furthermore, it can predict future trends for individual artists based on their activities and the tendencies of their work. In addition, it can analyze trends in the art market and propose works to artists that meet market needs. This allows the art production support system to provide artists with ideas for creating works that respond to future trends.

[0093] The art production support system can also be equipped with an emotion analysis unit. This unit analyzes the artist's emotions and provides art production ideas based on the results. For example, it can analyze the emotions the artist felt during the creative process and suggest new themes or styles based on those emotions. It can also analyze changes in the artist's emotions and suggest improvements to the production process in response to those changes. Furthermore, it can compare the artist's emotions with those of other artists and suggest collaborations based on those emotions. This allows the art production support system to provide more appropriate art production ideas based on the artist's emotions.

[0094] The art production support system can also include a user feedback unit. This unit collects user feedback on works created by artists and uses that data to provide ideas for future art production. For example, the user feedback unit can collect audience comments and evaluations of works exhibited by artists. It can also collect comments and evaluations of works on online platforms. Furthermore, the user feedback unit can analyze the collected feedback and suggest improvements and new ideas to artists. This allows the art production support system to provide better art production ideas based on user feedback.

[0095] The art production support system can also be equipped with an emotion monitoring unit. This unit monitors the artist's emotions in real time and provides art production ideas based on that data. For example, the emotion monitoring unit can record the emotions the artist feels during the creative process in real time and analyze that data. It can also track changes in the artist's emotions in real time and provide ideas corresponding to those changes. Furthermore, the emotion monitoring unit can share the artist's emotional data with other artists and propose collaborations based on those emotions. This allows the art production support system to provide more appropriate art production ideas in real time, based on the artist's emotions.

[0096] The art production support system can also include a cultural background analysis unit. This unit analyzes the cultural background of an artwork and provides art production ideas based on the results. For example, it can analyze the cultural background of the era and region in which the artwork was created. It can also analyze historical events and social circumstances that influenced the artwork. Furthermore, based on the cultural background of the artwork, it can propose new themes and styles. This allows the art production support system to provide ideas that take the cultural background of the artwork into consideration.

[0097] The art production support system can also be equipped with an emotion simulation unit. This unit simulates the emotions an artist might experience during the creative process and provides art production ideas based on the results. For example, the emotion simulation unit can simulate the emotions an artist might feel when creating a specific theme or style. It can also simulate the emotions an artist might feel in different creative environments. Furthermore, based on the simulation results, the emotion simulation unit can suggest a production process tailored to the artist's emotions. This allows the art production support system to provide ideas that take into account the emotions an artist might experience.

[0098] The art production support system can also be equipped with an emotion sharing function. This function allows artists to share the emotions they feel during the creative process with other artists and provides art production ideas based on that data. For example, the emotion sharing function can record the emotions an artist feels during the creative process and share that data with other artists. Furthermore, the emotion sharing function can facilitate collaboration between artists based on the shared emotional data. In addition, the emotion sharing function can analyze the emotional data and suggest new themes and styles to artists based on those emotions. Thus, the art production support system can share artists' emotions and provide ideas based on those emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection department collects data on contemporary art. This includes, for example, image data and text data of artworks, and artist profile information. Specifically, it collects image data of past artworks, critical articles about artworks, and biographical information such as artists' exhibition history and awards. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends and currents in contemporary art based on the collected data. Specifically, it analyzes art styles that were popular during a particular period, trends in works related to specific themes, and techniques and methods of expression in artworks (such as color and composition). Step 3: The provision department provides the artist with new art creation ideas based on the analysis results obtained by the analysis department. For example, the provision department may suggest new themes, styles, techniques, and methods of expression (such as color and composition) to the artist based on the analysis results.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] For example, the collection unit can collect image data and text data of artworks using the camera 42 and communication I / F 44 of the smart device 14. The collection unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and can collect artist profile information, etc. The analysis unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and analyzes trends and currents in contemporary art based on the collected data. The provision unit can also be implemented by, for example, the control unit 46A of the smart device 14, and provides artists with new art creation ideas based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] For example, the collection unit can collect image data and text data of artworks using the camera 42 and communication I / F 44 of the smart glasses 214. The collection unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and can collect artist profile information, etc. The analysis unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and analyzes trends and currents in contemporary art based on the collected data. The provision unit can also be implemented by, for example, the control unit 46A of the smart glasses 214, and provides artists with new art creation ideas based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] For example, the collection unit can collect image data and text data of artworks using the camera 42 and communication I / F 44 of the headset terminal 314. The collection unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and can collect artist profile information, etc. The analysis unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and analyzes trends and currents in contemporary art based on the collected data. The provision unit can also be implemented by, for example, the control unit 46A of the headset terminal 314, and provides artists with new art creation ideas based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] For example, the collection unit can collect image data and text data of artworks using the camera 42 and communication I / F 44 of the robot 414. The collection unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and can collect artist profile information, etc. The analysis unit can also be implemented by, for example, the specific processing unit 290 of the data processing device 12, and analyzes trends and currents in contemporary art based on the collected data. The provision unit can also be implemented by, for example, the control unit 46A of the robot 414, and provides artists with new art creation ideas based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The collection department collects data on contemporary art, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides artists with new art creation ideas based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect image data or text data of artworks, as well as artist profile information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we will analyze trends and currents in contemporary art. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results, we propose new themes and styles to artists. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect data on cultural and social backgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, As a specific analysis method, for example, specify an analysis method using a machine learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, As a concrete means of presenting ideas generated by AI to artists, specify, for example, the means of presenting ideas through a user interface. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the artist's emotions and adjust the type of data collected based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the artist's past work history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Filter based on the artist's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate the artist's emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Prioritize the collection of highly relevant data, taking into account the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze artists' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the artist's emotions and adjust the method of expression of the analysis based on the estimated emotions of the artist. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the artist's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Prioritize analysis based on the creation date of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the artworks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the artist's emotions and adjusts the way the ideas are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Adjust the level of detail of the ideas provided based on the importance of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, A different distribution algorithm is applied depending on the category of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate the artist's emotions and prioritize the ideas we offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Adjust the order of the ideas you present based on when the artwork was created. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Adjust the order of the ideas presented based on the relevance of the artworks. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects data on contemporary art, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides artists with new art creation ideas based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect image data or text data of artworks, as well as artist profile information. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we will analyze trends and currents in contemporary art. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results, we propose new themes and styles to artists. The system according to feature 1.

5. The aforementioned collection unit is Collect data on cultural and social backgrounds. The system according to feature 1.

6. The aforementioned analysis unit, As a specific analysis method, for example, specify an analysis method using a machine learning algorithm. The system according to feature 1.

7. The aforementioned supply unit is, As a concrete means of presenting ideas generated by AI to artists, for example, specify the means of presenting ideas through a user interface. The system according to feature 1.

8. The aforementioned collection unit is We estimate the artist's emotions and adjust the type of data collected based on the estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the artist's past work history and select the appropriate data collection method. The system according to feature 1.

10. The aforementioned collection unit is Filter based on the artist's current projects and areas of interest. The system according to feature 1.

Images