System

Abstract

An object of a system according to an embodiment is to analyze a tendency and a trend of art, generate a new idea, and provide the new idea to an artist.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data of an art. The analysis unit analyzes the data collected by the collection unit and specifies a tendency and a tidal current of the art. The generation unit generates a new idea based on the analysis result obtained by the analysis unit. The providing unit provides the idea generated by the generating unit to the artist.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of being inefficient in the process of understanding trends and trends in art and generating new ideas.

[0005] The system according to the embodiment aims to analyze trends and trends in art, generate new ideas, and provide them to artists. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects art data. The analysis unit analyzes the data collected by the collection unit and identifies trends and trends in art. The generation unit generates new ideas based on the analysis results obtained by the analysis unit. The provision unit provides the ideas generated by the generation unit to artists. [Effects of the Invention]

[0007] The system according to the embodiment can analyze trends and trends in art, generate new ideas, and provide them to artists. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An art production support system according to an embodiment of the present invention collects art data, analyzes it using a generative AI, generates new ideas, and provides them to artists. The art production support system collects art data, analyzes it using a generative AI, and identifies trends and trends in art, thereby generating new ideas and providing them to artists. For example, the art production support system collects data such as information on contemporary art works and exhibitions, and the artist's activity history. For example, the information can be obtained from databases of online galleries and art events. The art production support system then analyzes the collected data using a generative AI to identify trends and trends in art. The generative AI can analyze the collected data and analyze how specific themes and styles are evolving over time. The art production support system then generates new ideas based on the analysis results using the generative AI. Based on the analysis results, the generative AI can generate innovative and meaningful ideas for the artist. For example, it can propose new expression techniques and concepts. Finally, the art production support system provides the generated ideas to the artist. The ideas generated by the generative AI are provided to the artist, who can use them as a reference to create new artworks using their own creativity. In this way, the art production support system can promote artists' creativity and support innovation in the art world. In this way, the art production support system can promote artists' creativity and support innovation in the art world. For example, artists can use their own creativity to explore new expression methods and concepts while referring to ideas provided by AI. This allows artists to create more attractive and inspirational artworks.

[0029] An art production support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects art data. Examples of art data include, but are not limited to, paintings, sculptures, and digital art. The collection unit acquires information from, for example, online galleries or art event databases. The collection unit can also collect art data from the Internet using web scraping technology. For example, the collection unit automatically acquires art data from specific websites or platforms. The analysis unit analyzes the data collected by the collection unit to identify trends and trends in art. The analysis can be performed using, for example, but is not limited to, data mining or machine learning algorithms. For example, the analysis unit can analyze the collected data to identify how a specific theme or style has evolved over time. The generation unit generates new ideas based on the analysis results obtained by the analysis unit. The generation can be performed using, for example, but is not limited to, a generative AI. For example, the generation unit generates ideas that propose new expression techniques or concepts based on the analysis results. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The providing unit provides the idea generated by the generation unit to the artist. The provision is performed, for example, based on a notification method or a provision format, but is not limited to these examples. For example, the providing unit notifies the artist of the generated idea and supports the artist in using it as a reference to create a new artwork. This enables the art production support system according to the embodiment to efficiently collect, analyze, generate, and provide art data.

[0030] The collection unit can acquire information from an online gallery or an art event database. Online galleries include, but are not limited to, specific websites or platforms. The collection unit can acquire, for example, information about artworks from online galleries. The collection unit can also acquire information from an art event database. Art event databases include, but are not limited to, specific event databases and collection methods. For example, the collection unit acquires exhibition information and artist activity history from the art event database. This allows the latest art data to be collected by acquiring information from online gallery and art event databases. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input information acquired from the online gallery or art event database into a generation AI, causing the generation AI to collect data.

[0031] The analysis unit can analyze the collected data and identify changes in a particular theme or style. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can identify how a particular theme or style has changed over time. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can analyze the frequency of a theme or the characteristics of a style to identify art trends. The analysis unit can also classify the collected data using clustering technology to identify changes in a particular theme or style. For example, the analysis unit can cluster the collected data and analyze the characteristics of each cluster. This can identify changes in a particular theme or style to identify art trends. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The generation unit can generate ideas that propose new expression techniques or concepts based on the analysis results. The generation unit, for example, uses a generation AI to generate ideas that propose new expression techniques or concepts based on the analysis results. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the generation unit proposes new expression techniques or concepts based on the analysis results. The generation unit can also generate creative ideas using the generation AI. For example, the generation unit generates innovative and meaningful ideas based on the analysis results. Furthermore, the generation unit can also generate ideas that are useful to artists using the generation AI. For example, the generation unit generates ideas that help artists gain new inspiration based on the analysis results. This can provide new inspiration to artists by proposing new expression techniques or concepts. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate ideas.

[0033] The providing unit can provide the generated idea to the artist and support the artist in creating a new work of art using the idea as a reference. The providing unit, for example, notifies the artist of the generated idea. Notification methods include, but are not limited to, email, push notification, and a web application. For example, the providing unit can send the generated idea to the artist by email. The providing unit can also notify the artist of the generated idea via push notification. Furthermore, the providing unit can provide the generated idea to the artist through a web application. For example, the providing unit can display the generated idea on a web application and support the artist in creating a new work of art using the idea as a reference. This can promote creativity by supporting the artist in creating a new work of art using the generated idea as a reference. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input the generated idea into a generation AI and cause the generation AI to provide the idea.

[0034] When collecting art data, the collection unit can filter the art data based on the user's past artworks and areas of interest. For example, the collection unit prioritizes collecting related data based on the style of artworks the user has created in the past. For example, the collection unit analyzes the style of artworks the user has created in the past and filters related art data. The collection unit can also collect data on specific themes or genres based on the user's art fields of interest. For example, the collection unit identifies the art field in which the user is interested and prioritizes collecting data related to that field. Furthermore, the collection unit can collect data related to highly rated artworks based on the user's ratings of past artworks. For example, the collection unit analyzes the ratings of the user's past artworks and filters data related to highly rated artworks. This allows highly relevant data to be collected by filtering based on the user's past artworks and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's past artworks and areas of interest into a generation AI and have the generation AI perform filtering.

[0035] When collecting art data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects art data using voice recognition technology. For example, the collection unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect related art data using text analysis technology. For example, the collection unit analyzes the text entered by the user and collects related art data. Furthermore, if the user uses image input, the collection unit can also collect art data using image recognition technology. For example, the collection unit analyzes images uploaded by the user and collects related art data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the user's input data into a generation AI, causing the generation AI to select the optimal collection means.

[0036] When collecting art data, the collection unit can determine the priority of the data to be collected, taking into account the ratings and popularity of the artworks. The collection unit determines the priority of the data to be collected, for example, taking into account the ratings and popularity of the artworks. For example, the collection unit prioritizes collecting highly rated artworks and providing them to the user. The collection unit can also prioritize collecting popular artworks and providing them to the user. Furthermore, the collection unit can dynamically adjust the priority of the data to be collected based on the ratings and popularity. For example, the collection unit analyzes the ratings and popularity of the artworks in real time and adjusts the priority of the data to be collected. This allows important data to be collected preferentially by determining the priority of the data to be collected, taking into account the ratings and popularity of the artworks. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the ratings and popularity of the artworks into a generation AI, and have the generation AI determine the priority of the data to be collected.

[0037] When collecting art data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data on art events and exhibitions in that area. Also, if the user is traveling, the collection unit can collect data related to the art scene of the destination. Furthermore, the collection unit can collect art data specific to the area based on the user's geographical location information. For example, the collection unit prioritizes collecting art data for the area based on the user's geographical location information. This makes it possible to provide art data specific to the area by prioritizing collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, causing the generation AI to prioritize collecting highly relevant data.

[0038] When collecting art data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities and collects related data. For example, the collection unit collects works by artists the user follows on social media. The collection unit can also analyze the user's social media posts and collect related art data. Furthermore, the collection unit can collect related art data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the posts of the user's friends on social media and collects related art data. In this way, by analyzing the user's social media activities and collecting related data, it is possible to provide data that matches the user's interests. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI, causing the generation AI to collect related data.

[0039] When collecting art data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting art data that the user has previously rated highly. The collection unit can also adjust the type and amount of data to be collected based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and propose an optimal collection method. For example, the collection unit analyzes the user's past feedback and customizes the collection method. This allows the user to be provided with optimal data by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's past feedback data into a generation AI, allowing the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the art data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the art data. For example, the analysis unit performs a detailed analysis on art data with high importance. The analysis unit can also perform a simplified analysis on art data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the art data. For example, the analysis unit evaluates the value and influence of the art data and adjusts the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the art data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the art data to a generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the art data. For example, the analysis unit applies different analysis algorithms depending on the category of the art data. For example, the analysis unit applies a color analysis algorithm to painting data. The analysis unit can also apply a shape analysis algorithm to sculpture data. The analysis unit can also apply a pixel analysis algorithm to digital art data. For example, the analysis unit selects and applies an optimal analysis algorithm based on the category of the art data. This enables more accurate analysis by applying different analysis algorithms depending on the category of the art data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the art data into a generation AI, causing the generation AI to apply the optimal analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also propose an optimal analysis method based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into a generation AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the art data was collected. The analysis unit determines the analysis priority based on, for example, the time when the art data was collected. For example, the analysis unit prioritizes analyzing the most recent art data. The analysis unit can also postpone analyzing older art data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of collection. For example, the analysis unit evaluates the time when the art data was collected and prioritizes analyzing the most recent data. In this way, by determining the analysis priority based on the time when the art data was collected, the most recent data can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the art data was collected into the generation AI, and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the art data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the art data. For example, the analysis unit prioritizes analysis of highly relevant art data. The analysis unit can also postpone analysis of less relevant art data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the art data. For example, the analysis unit evaluates the degree of similarity of the art data's theme or the correlation of the data and adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the art data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the art data into a generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit may explain the analysis results in simple terms. Furthermore, if the user is an expert, the analysis unit may explain the analysis results using extensive technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit may evaluate the user's occupation and educational background and adjust the use of technical terms in the analysis. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terms in the analysis.

[0046] The generation unit can adjust the level of detail of the generated idea based on the importance of the analysis result during generation. The generation unit adjusts the level of detail of the generated idea based on, for example, the importance of the analysis result. For example, the generation unit generates detailed ideas based on analysis results with high importance. The generation unit can also generate simple ideas based on analysis results with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the generated idea based on the importance of the analysis result. For example, the generation unit evaluates the value and impact of the analysis result and adjusts the level of detail of the generated idea. In this way, ideas can be generated efficiently by adjusting the level of detail of the generated idea based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the idea.

[0047] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis result. For example, the generation unit applies different generation algorithms depending on the category of the analysis result. For example, the generation unit generates color ideas based on painting data. The generation unit can also generate shape ideas based on sculpture data. Furthermore, the generation unit can generate pixel ideas based on digital art data. For example, the generation unit selects and applies an optimal generation algorithm based on the category of the analysis result. In this way, by applying different generation algorithms depending on the category of the analysis result, more accurate ideas can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the category of the analysis result into a generation AI, causing the generation AI to apply the optimal generation algorithm.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm by referring to generation results that the user has previously rated highly. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. Furthermore, the generation unit can also propose an optimal generation method based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and proposes an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into a generation AI, and the generation AI can improve the accuracy of generation.

[0049] The generation unit can determine the priority of ideas to be generated based on the time when the analysis results were collected during generation. The generation unit determines the priority of ideas to be generated based on, for example, the time when the analysis results were collected. For example, the generation unit generates ideas preferentially based on the latest analysis results. The generation unit can also generate ideas later based on older analysis results. Furthermore, the generation unit can dynamically adjust the priority of ideas to be generated based on the time when the analysis results were collected. For example, the generation unit evaluates the time when the analysis results were collected and generates ideas based on the latest data. In this way, by determining the priority of ideas to be generated based on the time when the analysis results were collected, the latest ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and have the generation AI determine the priority of ideas.

[0050] The generation unit can adjust the order of ideas to be generated based on the relevance of the analysis results during generation. The generation unit adjusts the order of ideas to be generated based on, for example, the relevance of the analysis results. For example, the generation unit prioritizes generating ideas based on highly relevant analysis results. The generation unit can also postpone generating ideas based on less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order of ideas to be generated based on the relevance of the analysis results. For example, the generation unit evaluates the degree of agreement of the themes of the analysis results or the correlation of the data and adjusts the order of ideas to be generated. In this way, by adjusting the order of ideas to be generated based on the relevance of the analysis results, highly relevant ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI adjust the order of ideas.

[0051] The generation unit can adjust the use of technical terms in the generated idea according to the user's level of expertise during generation. The generation unit, for example, adjusts the use of technical terms in the generated idea according to the user's level of expertise. For example, if the user is a beginner, the generation unit generates an idea using simple language. Also, if the user is an expert, the generation unit can generate an idea using a lot of technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the generated idea based on the user's level of expertise. For example, the generation unit evaluates the user's occupation and educational background and adjusts the use of technical terms in the generated idea. In this way, by adjusting the use of technical terms in the generated idea according to the user's level of expertise, ideas that are easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the idea.

[0052] The providing unit can adjust the level of detail of the provided information based on the importance of the generated idea when providing the idea. The providing unit adjusts the level of detail of the provided information based on, for example, the importance of the generated idea. For example, the providing unit provides a detailed explanation for an idea with a high importance. The providing unit can also provide a simplified explanation for an idea with a low importance. Furthermore, the providing unit can dynamically adjust the level of detail of the provided information based on the importance of the idea. For example, the providing unit evaluates the specificity and depth of the explanation of the idea and adjusts the level of detail of the provided information. In this way, ideas can be provided efficiently by adjusting the level of detail of the provided information based on the importance of the generated idea. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the generated idea to the generation AI, causing the generation AI to adjust the level of detail of the provided information.

[0053] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. The providing unit applies different providing algorithms depending on, for example, the category of the generated idea. For example, the providing unit can provide a detailed description of colors for an idea related to painting. The providing unit can also provide a detailed description of shapes for an idea related to sculpture. Furthermore, the providing unit can provide a detailed description of pixels for an idea related to digital art. For example, the providing unit selects and applies an optimal providing algorithm based on the category of the generated idea. This allows for more accurate ideas to be provided by applying different providing algorithms depending on the category of the generated idea. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the generated idea into the generation AI, causing the generation AI to apply the optimal providing algorithm.

[0054] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit, for example, improves the accuracy of the provision by referring to the user's past provision results. For example, the providing unit adjusts the provision algorithm by referring to provision results that the user has previously rated highly. The providing unit can also analyze the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can also suggest an optimal provision method based on the user's past provision results. For example, the providing unit analyzes the user's past provision results and suggests an optimal provision method. In this way, the accuracy of the provision can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into a generation AI, and the generation AI can improve the accuracy of the provision.

[0055] The providing unit can determine the priority of providing the generated ideas based on the time when the ideas were collected at the time of providing. The providing unit determines the priority of providing the generated ideas based on, for example, the time when the ideas were collected. For example, the providing unit provides the most recent ideas preferentially. The providing unit can also provide older ideas later. Furthermore, the providing unit can dynamically adjust the priority of providing the ideas based on the time when the ideas were collected. For example, the providing unit evaluates the time when the ideas were collected and provides the ideas based on the most recent data. In this way, by determining the priority of providing the ideas based on the time when the generated ideas were collected, the most recent ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the time when the generated ideas were collected into the generation AI, and causes the generation AI to determine the priority of providing the ideas.

[0056] The providing unit can adjust the order of providing the generated ideas based on the relevance of the ideas when providing them. The providing unit adjusts the order of providing the ideas based on, for example, the relevance of the generated ideas. For example, the providing unit prioritizes providing highly relevant ideas. The providing unit can also postpone providing less relevant ideas. Furthermore, the providing unit can dynamically adjust the order of providing the ideas based on the relevance of the ideas. For example, the providing unit evaluates the degree of agreement of the ideas' themes or the correlation of data and adjusts the order of providing. In this way, by adjusting the order of providing the ideas based on the relevance of the generated ideas, highly relevant ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the generated ideas to the generation AI and cause the generation AI to adjust the order of providing.

[0057] The providing unit can adjust the use of technical terms in the ideas to be provided according to the user's level of expertise when providing the ideas. The providing unit, for example, adjusts the use of technical terms in the ideas to be provided according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide ideas in simple language. Also, if the user is an expert, the providing unit can provide ideas using a lot of technical terms. Furthermore, the providing unit can dynamically adjust the use of technical terms in the ideas to be provided based on the user's level of expertise. For example, the providing unit evaluates the user's occupation and educational background and adjusts the use of technical terms in the ideas to be provided. In this way, by adjusting the use of technical terms in the ideas to be provided according to the user's level of expertise, ideas that are easy for the user to understand can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the ideas.

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

[0059] The collection unit can prioritize collection of related art data based on the user's past ratings of artworks. For example, the collection unit can analyze the style and theme of artworks that the user has previously rated highly, and prioritize collection of art data with similar styles and themes. The collection unit can also filter to avoid art works with styles and themes that the user has previously rated poorly. Furthermore, the collection unit can prioritize collection of data on specific artists or exhibitions based on the user's rating history. This allows for efficient collection of art data that matches the user's preferences.

[0060] The generator can adjust the variety of ideas to be generated by referring to the user's past generation results. For example, the generator can analyze the style or theme of generation results that the user has previously rated highly, and generate ideas with similar styles or themes. The generator can also adjust the ideas to avoid styles or themes of generation results that the user has previously rated poorly. Furthermore, the generator can generate ideas related to specific artists or exhibitions based on the user's rating history. This makes it possible to provide a variety of ideas that match the user's preferences.

[0061] When collecting art data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect works by artists the user follows on social media. The collection unit can also analyze the content of the user's social media posts to collect related art data. Furthermore, the collection unit can collect related art data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities and collecting related data, it is possible to provide data that matches the user's interests.

[0062] The generation unit can adjust the use of technical terms in the generated ideas according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit can generate ideas using simple language. Alternatively, if the user is an expert, the generation unit can generate ideas using a lot of technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the generated ideas based on the user's level of expertise. In this way, by adjusting the use of technical terms in the generated ideas according to the user's level of expertise, it is possible to provide ideas that are easy for the user to understand.

[0063] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the ideas. For example, the providing unit can provide a detailed description of colors for an idea related to a painting. The providing unit can also provide a detailed description of shapes for an idea related to a sculpture. Furthermore, the providing unit can provide a detailed description of pixels for an idea related to digital art. In this way, by applying different providing algorithms depending on the category of the generated idea, more accurate ideas can be provided.

[0064] When collecting art data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data on art events and exhibitions in that area. Also, if the user is traveling, the collection unit can collect data related to the art scene of the destination. Furthermore, the collection unit can collect art data specific to the area based on the user's geographical location information. This makes it possible to provide art data specific to the area by prioritized collection of highly relevant data by taking into account the user's geographical location information.

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

[0066] Step 1: The collection unit collects art data. Art data includes paintings, sculptures, digital art, etc. The collection unit obtains information from databases of online galleries and art events. It can also use web scraping technology to collect art data from the Internet. For example, it can automatically obtain art data from specific websites or platforms. Step 2: The analysis department analyzes the data collected by the collection department to identify trends and trends in art. This analysis is done using data mining and machine learning algorithms. For example, the collected data can be analyzed to identify how certain themes or styles are evolving over time. Step 3: The generation unit generates new ideas based on the analysis results obtained by the analysis unit. This is done using generative AI. For example, ideas that propose new expression methods or concepts are generated based on the analysis results. Generative AI can be text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the idea generated by the generating unit to the artist. The provision is based on the notification method and the provision format. For example, the generated idea may be notified to the artist, and the artist may use it as a reference to create a new artwork.

[0067] (Example 2) An art production support system according to an embodiment of the present invention collects art data, analyzes it using a generative AI, generates new ideas, and provides them to artists. The art production support system collects art data, analyzes it using a generative AI, and identifies trends and trends in art, thereby generating new ideas and providing them to artists. For example, the art production support system collects data such as information on contemporary art works and exhibitions, and the artist's activity history. For example, the information can be obtained from databases of online galleries and art events. The art production support system then analyzes the collected data using a generative AI to identify trends and trends in art. The generative AI can analyze the collected data and analyze how specific themes and styles are evolving over time. The art production support system then generates new ideas based on the analysis results using the generative AI. Based on the analysis results, the generative AI can generate innovative and meaningful ideas for the artist. For example, it can propose new expression techniques and concepts. Finally, the art production support system provides the generated ideas to the artist. The ideas generated by the generative AI are provided to the artist, who can use them as a reference to create new artworks using their own creativity. In this way, the art production support system can promote artists' creativity and support innovation in the art world. In this way, the art production support system can promote artists' creativity and support innovation in the art world. For example, artists can use their own creativity to explore new expression methods and concepts while referring to ideas provided by AI. This allows artists to create more attractive and inspirational artworks.

[0068] An art production support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects art data. Examples of art data include, but are not limited to, paintings, sculptures, and digital art. The collection unit acquires information from, for example, online galleries or art event databases. The collection unit can also collect art data from the Internet using web scraping technology. For example, the collection unit automatically acquires art data from specific websites or platforms. The analysis unit analyzes the data collected by the collection unit to identify trends and trends in art. The analysis can be performed using, for example, but is not limited to, data mining or machine learning algorithms. For example, the analysis unit can analyze the collected data to identify how a specific theme or style has evolved over time. The generation unit generates new ideas based on the analysis results obtained by the analysis unit. The generation can be performed using, for example, but is not limited to, a generative AI. For example, the generation unit generates ideas that propose new expression techniques or concepts based on the analysis results. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The providing unit provides the idea generated by the generation unit to the artist. The provision is performed, for example, based on a notification method or a provision format, but is not limited to these examples. For example, the providing unit notifies the artist of the generated idea and supports the artist in using it as a reference to create a new artwork. This enables the art production support system according to the embodiment to efficiently collect, analyze, generate, and provide art data.

[0069] The collection unit can acquire information from an online gallery or an art event database. Online galleries include, but are not limited to, specific websites or platforms. The collection unit can acquire, for example, information about artworks from online galleries. The collection unit can also acquire information from an art event database. Art event databases include, but are not limited to, specific event databases and collection methods. For example, the collection unit acquires exhibition information and artist activity history from the art event database. This allows the latest art data to be collected by acquiring information from online gallery and art event databases. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input information acquired from the online gallery or art event database into a generation AI, causing the generation AI to collect data.

[0070] The analysis unit can analyze the collected data and identify changes in a particular theme or style. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can identify how a particular theme or style has changed over time. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can analyze the frequency of a theme or the characteristics of a style to identify art trends. The analysis unit can also classify the collected data using clustering technology to identify changes in a particular theme or style. For example, the analysis unit can cluster the collected data and analyze the characteristics of each cluster. This can identify changes in a particular theme or style to identify art trends. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0071] The generation unit can generate ideas that propose new expression techniques or concepts based on the analysis results. The generation unit, for example, uses a generation AI to generate ideas that propose new expression techniques or concepts based on the analysis results. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the generation unit proposes new expression techniques or concepts based on the analysis results. The generation unit can also generate creative ideas using the generation AI. For example, the generation unit generates innovative and meaningful ideas based on the analysis results. Furthermore, the generation unit can also generate ideas that are useful to artists using the generation AI. For example, the generation unit generates ideas that help artists gain new inspiration based on the analysis results. This can provide new inspiration to artists by proposing new expression techniques or concepts. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate ideas.

[0072] The providing unit can provide the generated idea to the artist and support the artist in creating a new work of art using the idea as a reference. The providing unit, for example, notifies the artist of the generated idea. Notification methods include, but are not limited to, email, push notification, and a web application. For example, the providing unit can send the generated idea to the artist by email. The providing unit can also notify the artist of the generated idea via push notification. Furthermore, the providing unit can provide the generated idea to the artist through a web application. For example, the providing unit can display the generated idea on a web application and support the artist in creating a new work of art using the idea as a reference. This can promote creativity by supporting the artist in creating a new work of art using the generated idea as a reference. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input the generated idea into a generation AI and cause the generation AI to provide the idea.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting art data based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using emotion recognition technology. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice to calculate an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. The collection unit adjusts the timing of collecting art data based on the estimated user's emotions. For example, if the user feels motivated to create, the collection unit can immediately collect art data to provide inspiration. If the user is tired, the collection unit can delay the collection timing and provide data when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can adjust the collection timing to provide data at the optimal timing. This allows data to be provided at optimal timing by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI, and have the generation AI adjust the collection timing.

[0074] When collecting art data, the collection unit can filter the art data based on the user's past artworks and areas of interest. For example, the collection unit prioritizes collecting related data based on the style of artworks the user has created in the past. For example, the collection unit analyzes the style of artworks the user has created in the past and filters related art data. The collection unit can also collect data on specific themes or genres based on the user's art fields of interest. For example, the collection unit identifies the art field in which the user is interested and prioritizes collecting data related to that field. Furthermore, the collection unit can collect data related to highly rated artworks based on the user's ratings of past artworks. For example, the collection unit analyzes the ratings of the user's past artworks and filters data related to highly rated artworks. This allows highly relevant data to be collected by filtering based on the user's past artworks and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's past artworks and areas of interest into a generation AI and have the generation AI perform filtering.

[0075] When collecting art data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects art data using voice recognition technology. For example, the collection unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect related art data using text analysis technology. For example, the collection unit analyzes the text entered by the user and collects related art data. Furthermore, if the user uses image input, the collection unit can also collect art data using image recognition technology. For example, the collection unit analyzes images uploaded by the user and collects related art data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the user's input data into a generation AI, causing the generation AI to select the optimal collection means.

[0076] When collecting art data, the collection unit can determine the priority of the data to be collected, taking into account the ratings and popularity of the artworks. The collection unit determines the priority of the data to be collected, for example, taking into account the ratings and popularity of the artworks. For example, the collection unit prioritizes collecting highly rated artworks and providing them to the user. The collection unit can also prioritize collecting popular artworks and providing them to the user. Furthermore, the collection unit can dynamically adjust the priority of the data to be collected based on the ratings and popularity. For example, the collection unit analyzes the ratings and popularity of the artworks in real time and adjusts the priority of the data to be collected. This allows important data to be collected preferentially by determining the priority of the data to be collected, taking into account the ratings and popularity of the artworks. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the ratings and popularity of the artworks into a generation AI, and have the generation AI determine the priority of the data to be collected.

[0077] The collection unit can estimate the user's emotion and determine the priority of art data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using emotion recognition technology. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. The collection unit determines the priority of art data to be collected based on the estimated user's emotion. For example, if the user is excited, the collection unit can prioritize collecting stimulating art data. If the user is relaxed, the collection unit can prioritize collecting calm art data. Furthermore, if the user is focused, the collection unit can prioritize collecting art data that provides deep insights. This allows optimal data to be provided by determining the priority of art data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI, causing the generation AI to determine the priority of the art data to be collected.

[0078] When collecting art data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data on art events and exhibitions in that area. Also, if the user is traveling, the collection unit can collect data related to the art scene of the destination. Furthermore, the collection unit can collect art data specific to the area based on the user's geographical location information. For example, the collection unit prioritizes collecting art data for the area based on the user's geographical location information. This makes it possible to provide art data specific to the area by prioritizing collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI, causing the generation AI to prioritize collecting highly relevant data.

[0079] When collecting art data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities and collects related data. For example, the collection unit collects works by artists the user follows on social media. The collection unit can also analyze the user's social media posts and collect related art data. Furthermore, the collection unit can collect related art data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the posts of the user's friends on social media and collects related art data. In this way, by analyzing the user's social media activities and collecting related data, it is possible to provide data that matches the user's interests. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI, causing the generation AI to collect related data.

[0080] When collecting art data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, customizes the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting art data that the user has previously rated highly. The collection unit can also adjust the type and amount of data to be collected based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and propose an optimal collection method. For example, the collection unit analyzes the user's past feedback and customizes the collection method. This allows the user to be provided with optimal data by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's past feedback data into a generation AI, allowing the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, emotion recognition technology. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. The analysis unit adjusts the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit displays the analysis result in a calm tone when the user is relaxed. The analysis unit can also display visually stimulating analysis results when the user is excited. The analysis unit can also display analysis results that provide detailed and deep insights when the user is focused. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing optimal analysis results. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the generation AI, and have the generation AI adjust the analysis presentation method.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the art data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the art data. For example, the analysis unit performs a detailed analysis on art data with high importance. The analysis unit can also perform a simplified analysis on art data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the art data. For example, the analysis unit evaluates the value and influence of the art data and adjusts the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the art data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the art data to a generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the art data. For example, the analysis unit applies different analysis algorithms depending on the category of the art data. For example, the analysis unit applies a color analysis algorithm to painting data. The analysis unit can also apply a shape analysis algorithm to sculpture data. The analysis unit can also apply a pixel analysis algorithm to digital art data. For example, the analysis unit selects and applies an optimal analysis algorithm based on the category of the art data. This enables more accurate analysis by applying different analysis algorithms depending on the category of the art data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the art data into a generation AI, causing the generation AI to apply the optimal analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also propose an optimal analysis method based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into a generation AI, which can improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, emotion recognition technology. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. The analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can perform a short and to-the-point analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform an analysis that provides deeper insights when the user is concentrating. This allows the analysis length to be adjusted according to the user's emotion, thereby providing optimal analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the generation AI, and have the generation AI adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time when the art data was collected. The analysis unit determines the analysis priority based on, for example, the time when the art data was collected. For example, the analysis unit prioritizes analyzing the most recent art data. The analysis unit can also postpone analyzing older art data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of collection. For example, the analysis unit evaluates the time when the art data was collected and prioritizes analyzing the most recent data. In this way, by determining the analysis priority based on the time when the art data was collected, the most recent data can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the art data was collected into the generation AI, and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the art data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the art data. For example, the analysis unit prioritizes analysis of highly relevant art data. The analysis unit can also postpone analysis of less relevant art data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the art data. For example, the analysis unit evaluates the degree of similarity of the art data's theme or the correlation of the data and adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the art data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the art data into a generation AI and have the generation AI adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit may explain the analysis results in simple terms. Furthermore, if the user is an expert, the analysis unit may explain the analysis results using extensive technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit may evaluate the user's occupation and educational background and adjust the use of technical terms in the analysis. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terms in the analysis.

[0089] The generation unit can estimate the user's emotion and adjust the expression style of the generated idea based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, emotion recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. The generation unit adjusts the expression style of the generated idea based on the estimated user's emotion. For example, if the user is relaxed, the generation unit expresses the idea in a calm tone. If the user is excited, the generation unit can also express a visually stimulating idea. Furthermore, if the user is focused, the generation unit can express an idea that provides detailed and deep insight. This allows the user to adjust the expression method of the generated idea according to the user's emotion, thereby providing optimal ideas. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data into the generation AI, causing the generation AI to adjust the expression method of the idea.

[0090] The generation unit can adjust the level of detail of the generated idea based on the importance of the analysis result during generation. The generation unit adjusts the level of detail of the generated idea based on, for example, the importance of the analysis result. For example, the generation unit generates detailed ideas based on analysis results with high importance. The generation unit can also generate simple ideas based on analysis results with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the generated idea based on the importance of the analysis result. For example, the generation unit evaluates the value and impact of the analysis result and adjusts the level of detail of the generated idea. In this way, ideas can be generated efficiently by adjusting the level of detail of the generated idea based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the idea.

[0091] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis result. For example, the generation unit applies different generation algorithms depending on the category of the analysis result. For example, the generation unit generates color ideas based on painting data. The generation unit can also generate shape ideas based on sculpture data. Furthermore, the generation unit can generate pixel ideas based on digital art data. For example, the generation unit selects and applies an optimal generation algorithm based on the category of the analysis result. In this way, by applying different generation algorithms depending on the category of the analysis result, more accurate ideas can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the category of the analysis result into a generation AI, causing the generation AI to apply the optimal generation algorithm.

[0092] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm by referring to generation results that the user has previously rated highly. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. Furthermore, the generation unit can also propose an optimal generation method based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and proposes an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into a generation AI, and the generation AI can improve the accuracy of generation.

[0093] The generation unit can estimate the user's emotion and adjust the length of the generated idea based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, emotion recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on heart rate fluctuations. The generation unit adjusts the length of the generated idea based on the estimated user's emotion. For example, the generation unit generates a short and to-the-point idea when the user is in a hurry. The generation unit can also generate a longer idea with detailed explanations when the user is relaxed. The generation unit can also generate a longer idea that provides deeper insights when the user is focused. This allows the length of the idea to be adjusted according to the user's emotion, thereby providing optimal ideas. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data into the generation AI, causing the generation AI to adjust the length of the idea.

[0094] The generation unit can determine the priority of ideas to be generated based on the time when the analysis results were collected during generation. The generation unit determines the priority of ideas to be generated based on, for example, the time when the analysis results were collected. For example, the generation unit generates ideas preferentially based on the latest analysis results. The generation unit can also generate ideas later based on older analysis results. Furthermore, the generation unit can dynamically adjust the priority of ideas to be generated based on the time when the analysis results were collected. For example, the generation unit evaluates the time when the analysis results were collected and generates ideas based on the latest data. In this way, by determining the priority of ideas to be generated based on the time when the analysis results were collected, the latest ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and have the generation AI determine the priority of ideas.

[0095] The generation unit can adjust the order of ideas to be generated based on the relevance of the analysis results during generation. The generation unit adjusts the order of ideas to be generated based on, for example, the relevance of the analysis results. For example, the generation unit prioritizes generating ideas based on highly relevant analysis results. The generation unit can also postpone generating ideas based on less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order of ideas to be generated based on the relevance of the analysis results. For example, the generation unit evaluates the degree of agreement of the themes of the analysis results or the correlation of the data and adjusts the order of ideas to be generated. In this way, by adjusting the order of ideas to be generated based on the relevance of the analysis results, highly relevant ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI adjust the order of ideas.

[0096] The generation unit can adjust the use of technical terms in the generated idea according to the user's level of expertise during generation. The generation unit, for example, adjusts the use of technical terms in the generated idea according to the user's level of expertise. For example, if the user is a beginner, the generation unit generates an idea using simple language. Also, if the user is an expert, the generation unit can generate an idea using a lot of technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the generated idea based on the user's level of expertise. For example, the generation unit evaluates the user's occupation and educational background and adjusts the use of technical terms in the generated idea. In this way, by adjusting the use of technical terms in the generated idea according to the user's level of expertise, ideas that are easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the idea.

[0097] The providing unit can estimate the user's emotion and adjust the presentation method of the provided idea based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using emotion recognition technology. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. The providing unit adjusts the presentation method of the provided idea based on the estimated user's emotion. For example, if the user is relaxed, the providing unit provides ideas in a calm tone. If the user is excited, the providing unit can provide visually stimulating ideas. Furthermore, if the user is focused, the providing unit can provide ideas that provide detailed and deep insights. This allows the optimal idea to be provided by adjusting the way the idea is expressed in accordance with the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI, causing the generation AI to adjust the way the idea is expressed.

[0098] The providing unit can adjust the level of detail of the provided information based on the importance of the generated idea when providing the idea. The providing unit adjusts the level of detail of the provided information based on, for example, the importance of the generated idea. For example, the providing unit provides a detailed explanation for an idea with a high importance. The providing unit can also provide a simplified explanation for an idea with a low importance. Furthermore, the providing unit can dynamically adjust the level of detail of the provided information based on the importance of the idea. For example, the providing unit evaluates the specificity and depth of the explanation of the idea and adjusts the level of detail of the provided information. In this way, ideas can be provided efficiently by adjusting the level of detail of the provided information based on the importance of the generated idea. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the generated idea to the generation AI, causing the generation AI to adjust the level of detail of the provided information.

[0099] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the idea. The providing unit applies different providing algorithms depending on, for example, the category of the generated idea. For example, the providing unit can provide a detailed description of colors for an idea related to painting. The providing unit can also provide a detailed description of shapes for an idea related to sculpture. Furthermore, the providing unit can provide a detailed description of pixels for an idea related to digital art. For example, the providing unit selects and applies an optimal providing algorithm based on the category of the generated idea. This allows for more accurate ideas to be provided by applying different providing algorithms depending on the category of the generated idea. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the generated idea into the generation AI, causing the generation AI to apply the optimal providing algorithm.

[0100] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit, for example, improves the accuracy of the provision by referring to the user's past provision results. For example, the providing unit adjusts the provision algorithm by referring to provision results that the user has previously rated highly. The providing unit can also analyze the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can also suggest an optimal provision method based on the user's past provision results. For example, the providing unit analyzes the user's past provision results and suggests an optimal provision method. In this way, the accuracy of the provision can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into a generation AI, and the generation AI can improve the accuracy of the provision.

[0101] The providing unit can estimate the user's emotion and adjust the length of the idea to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using emotion recognition technology. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. The providing unit adjusts the length of the idea to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit can provide a short, to-the-point idea. If the user is relaxed, the providing unit can provide a longer idea with detailed explanations. If the user is focused, the providing unit can provide a longer idea with deeper insights. This allows the length of the idea to be provided to be adjusted according to the user's emotion, thereby providing optimal ideas. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generation AI, causing the generation AI to adjust the length of the idea.

[0102] The providing unit can determine the priority of providing the generated ideas based on the time when the ideas were collected at the time of providing. The providing unit determines the priority of providing the generated ideas based on, for example, the time when the ideas were collected. For example, the providing unit provides the most recent ideas preferentially. The providing unit can also provide older ideas later. Furthermore, the providing unit can dynamically adjust the priority of providing the ideas based on the time when the ideas were collected. For example, the providing unit evaluates the time when the ideas were collected and provides the ideas based on the most recent data. In this way, by determining the priority of providing the ideas based on the time when the generated ideas were collected, the most recent ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the time when the generated ideas were collected into the generation AI, and causes the generation AI to determine the priority of providing the ideas.

[0103] The providing unit can adjust the order of providing the generated ideas based on the relevance of the ideas when providing them. The providing unit adjusts the order of providing the ideas based on, for example, the relevance of the generated ideas. For example, the providing unit prioritizes providing highly relevant ideas. The providing unit can also postpone providing less relevant ideas. Furthermore, the providing unit can dynamically adjust the order of providing the ideas based on the relevance of the ideas. For example, the providing unit evaluates the degree of agreement of the ideas' themes or the correlation of data and adjusts the order of providing. In this way, by adjusting the order of providing the ideas based on the relevance of the generated ideas, highly relevant ideas can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the generated ideas to the generation AI and cause the generation AI to adjust the order of providing.

[0104] The providing unit can adjust the use of technical terms in the ideas to be provided according to the user's level of expertise when providing the ideas. The providing unit, for example, adjusts the use of technical terms in the ideas to be provided according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide ideas in simple language. Also, if the user is an expert, the providing unit can provide ideas using a lot of technical terms. Furthermore, the providing unit can dynamically adjust the use of technical terms in the ideas to be provided based on the user's level of expertise. For example, the providing unit evaluates the user's occupation and educational background and adjusts the use of technical terms in the ideas to be provided. In this way, by adjusting the use of technical terms in the ideas to be provided according to the user's level of expertise, ideas that are easy for the user to understand can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the ideas. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect art data using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and identifies trends and trends in art. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12, which generates new ideas based on the analysis results. For example, the provision unit can be realized by the control unit 46A of the smart device 14, which provides the generated ideas to artists. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect art data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the collected data to identify trends and trends in art. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and generate new ideas based on the analysis results. For example, the provision unit can be realized by the control unit 46A of the smart glasses 214 and provide the generated ideas to the artist. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect art data using the camera 42 or microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and trends in art. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates new ideas based on the analysis results. For example, the provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated ideas to artists. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect art data using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and trends in art. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates new ideas based on the analysis results. For example, the provision unit is realized by the control unit 46A of the robot 414 and provides the generated ideas to the artist.

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

[0106] The collection unit can prioritize collection of related art data based on the user's past ratings of artworks. For example, the collection unit can analyze the style and theme of artworks that the user has previously rated highly, and prioritize collection of art data with similar styles and themes. The collection unit can also filter to avoid art works with styles and themes that the user has previously rated poorly. Furthermore, the collection unit can prioritize collection of data on specific artists or exhibitions based on the user's rating history. This allows for efficient collection of art data that matches the user's preferences.

[0107] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display the analysis results using calm colors and soft fonts. If the user is excited, the analysis unit can display the analysis results using vivid colors and bold fonts. Furthermore, if the user is focused, the analysis results can be visually presented using detailed graphs and charts. This can facilitate understanding of the analysis results by providing visual presentations that correspond to the user's emotions.

[0108] The generator can adjust the variety of ideas to be generated by referring to the user's past generation results. For example, the generator can analyze the style or theme of generation results that the user has previously rated highly, and generate ideas with similar styles or themes. The generator can also adjust the ideas to avoid styles or themes of generation results that the user has previously rated poorly. Furthermore, the generator can generate ideas related to specific artists or exhibitions based on the user's rating history. This makes it possible to provide a variety of ideas that match the user's preferences.

[0109] The providing unit can estimate the user's emotions and adjust the order of ideas to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide calm ideas first. Also, if the user is excited, the providing unit can provide stimulating ideas first. Furthermore, if the user is focused, the providing unit can provide ideas that provide deep insight first. In this way, by providing ideas in an order according to the user's emotions, optimal ideas can be provided.

[0110] When collecting art data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect works by artists the user follows on social media. The collection unit can also analyze the content of the user's social media posts to collect related art data. Furthermore, the collection unit can collect related art data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities and collecting related data, it is possible to provide data that matches the user's interests.

[0111] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Also, the analysis unit can provide concise analysis results when the user is excited. Furthermore, the analysis unit can provide analysis results that provide deep insights when the user is focused. In this way, the analysis results can be provided with a level of detail according to the user's emotions, thereby providing optimal analysis results.

[0112] The generation unit can adjust the use of technical terms in the generated ideas according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit can generate ideas using simple language. Alternatively, if the user is an expert, the generation unit can generate ideas using a lot of technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the generated ideas based on the user's level of expertise. In this way, by adjusting the use of technical terms in the generated ideas according to the user's level of expertise, it is possible to provide ideas that are easy for the user to understand.

[0113] The providing unit can apply different providing algorithms depending on the category of the generated idea when providing the ideas. For example, the providing unit can provide a detailed description of colors for an idea related to a painting. The providing unit can also provide a detailed description of shapes for an idea related to a sculpture. Furthermore, the providing unit can provide a detailed description of pixels for an idea related to digital art. In this way, by applying different providing algorithms depending on the category of the generated idea, more accurate ideas can be provided.

[0114] When collecting art data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data on art events and exhibitions in that area. Also, if the user is traveling, the collection unit can collect data related to the art scene of the destination. Furthermore, the collection unit can collect art data specific to the area based on the user's geographical location information. This makes it possible to provide art data specific to the area by prioritized collection of highly relevant data by taking into account the user's geographical location information.

[0115] The providing unit can estimate the user's emotions and adjust the length of the ideas to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point ideas. If the user is relaxed, the providing unit can also provide longer ideas with detailed explanations. Furthermore, if the user is focused, the providing unit can also provide longer ideas with deeper insights. In this way, optimal ideas can be provided by adjusting the length of the ideas to be provided according to the user's emotions.

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

[0117] Step 1: The collection unit collects art data. Art data includes paintings, sculptures, digital art, etc. The collection unit obtains information from databases of online galleries and art events. It can also use web scraping technology to collect art data from the Internet. For example, it can automatically obtain art data from specific websites or platforms. Step 2: The analysis department analyzes the data collected by the collection department to identify trends and trends in art. This analysis is done using data mining and machine learning algorithms. For example, the collected data can be analyzed to identify how certain themes or styles are evolving over time. Step 3: The generation unit generates new ideas based on the analysis results obtained by the analysis unit. This is done using generative AI. For example, ideas that propose new expression methods or concepts are generated based on the analysis results. Generative AI can be text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the idea generated by the generating unit to the artist. The provision is based on the notification method and the provision format. For example, the generated idea may be notified to the artist, and the artist may use it as a reference to create a new artwork.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects art data; an analysis unit that analyzes the data collected by the collection unit and identifies trends and trends in art; a generation unit that generates new ideas based on the analysis results obtained by the analysis unit; a providing unit that provides the ideas generated by the generating unit to artists; Equipped with A system characterized by:

2. The collecting unit Get information from online gallery and art event databases 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify specific themes and stylistic shifts 2. The system of claim 1.

4. The generation unit Generate ideas that suggest new expression methods and concepts based on the analysis results 2. The system of claim 1.

5. The providing unit Provide generated ideas to artists, helping them to create new artworks based on them 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust the timing of art data collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting art data, it filters it based on the user's past artwork and areas of interest.

2. The system of claim 1.

8. The collecting unit When collecting art data, select the optimal collection method according to the user's input method.

2. The system of claim 1.

9. The collecting unit When collecting art data, prioritize the data to be collected by taking into account the ratings and popularity of the artworks.

2. The system of claim 1.

10. The collecting unit Estimate user emotions and prioritize art data to be collected based on the estimated user emotions.

2. The system of claim 1.

Images