System

Abstract

A system according to an embodiment aims to streamline the process by which a user materializes an idea of a work of art and obtains feedback.SOLUTION: A system includes a reception unit, an analysis unit, a production unit, and an evaluation unit. The reception unit receives an idea of an art work from a user. The analysis unit analyzes the idea received by the reception unit and provides feedback. The production portion produces the artwork based on the feedback provided by the analysis portion. The evaluation unit evaluates the artwork produced by the production unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of inefficient processes for users to concretize their ideas for artwork and receive feedback.

[0005] The system according to the embodiment aims to streamline the process by which users materialize ideas for artwork and receive feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a production unit, and an evaluation unit. The reception unit receives ideas for artworks from users. The analysis unit analyzes the ideas received by the reception unit and provides feedback. The production unit produces artworks based on the feedback provided by the analysis unit. The evaluation unit evaluates the artworks produced by the production unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the process by which users materialize their ideas for artwork and get feedback. [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) In an embodiment of the present invention, an AI art assistant system allows a user to submit an idea for an artwork and receive feedback from the AI. In this system, the user inputs an idea for an artwork, and the AI ​​analyzes the idea and provides feedback on related artwork, techniques, and color usage. The user then creates the artwork based on the feedback, and the AI ​​monitors the user's progress and provides additional feedback as needed. Finally, the user has the AI ​​evaluate the completed artwork and receive advice on improvements and future projects. For example, if a user inputs "I want to paint a seascape," the AI ​​analyzes the idea and provides feedback on related artwork, techniques, and color usage. The user then receives feedback from the AI ​​and uses it to create the artwork. For example, if the AI ​​provides feedback such as "blue and green are effective colors for seascapes," the user incorporates that advice into their creation. The AI ​​also monitors the user's progress and provides additional feedback as needed. Furthermore, the user can have the AI ​​evaluate the completed artwork. The AI ​​evaluates the composition, color usage, and technique of the artwork and provides improvements and advice on future projects. This allows the AI ​​art assistant system to gain inspiration by allowing users to submit ideas for artwork and receive feedback. This allows users to experience dynamic collaboration with the AI, resulting in more creative artworks. For example, the system can support the user's creative process by quickly and accurately analyzing ideas written by the user and providing appropriate feedback. Users can also learn specific areas for improvement in their artwork, helping them to improve their skills.

[0029] An AI art assistant system according to an embodiment includes a reception unit, an analysis unit, a production unit, and an evaluation unit. The reception unit receives an idea for an artwork from a user. Ideas input by the user include, but are not limited to, visual art, music, literature, and the like. For example, if the user inputs the idea in text format, the reception unit may use text analysis technology to receive the idea. Alternatively, if the user inputs the idea in voice format, the reception unit may use voice recognition technology to receive the idea. Furthermore, if the user inputs the idea in image format, the reception unit may use image analysis technology to receive the idea. The analysis unit uses AI to analyze the idea received by the reception unit and provide feedback. The analysis may be performed using, for example, text analysis, image analysis, or voice analysis, but is not limited to these examples. For example, the analysis unit may learn about past artworks and the artist's style and provide optimal feedback based on the user's idea. The production unit creates the artwork based on the feedback provided by the analysis unit. The creation may be performed using, for example, digital art production, hand-drawn art production, or the like, but is not limited to these examples. For example, the production unit monitors the user's progress and provides additional feedback as needed. The evaluation unit evaluates the artwork created by the production unit. The evaluation is based on criteria such as, but not limited to, technical evaluation, aesthetic evaluation, and user satisfaction. For example, the evaluation unit evaluates the composition, color usage, technique, etc. of the completed artwork and provides advice on areas for improvement and for the next production. In this way, the AI ​​art assistant system according to the embodiment allows the user to experience dynamic collaboration with the AI ​​and produce more creative artwork.

[0030] The reception unit can analyze the user's past idea submission history and select the optimal reception method. The reception unit can, for example, analyze trends in ideas submitted by the user in the past and suggest the optimal reception method. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past idea submission history data into the generation AI and have the generation AI select the optimal reception method.

[0031] When receiving ideas, the reception unit can filter the ideas based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives ideas related to the project the user is currently working on. The reception unit can also filter related ideas based on the user's area of ​​interest. The reception unit can also filter optimal ideas by referring to the user's past project history. This makes it possible to preferentially receive ideas related to the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project history data into a generation AI and have the generation AI perform filtering.

[0032] When receiving an idea, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user inputs the idea by voice, the reception unit can receive the idea using voice recognition. Furthermore, if the user inputs the idea by text, the reception unit can also receive the idea using text analysis. Furthermore, if the user inputs the idea by image, the reception unit can also receive the idea using image analysis. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0033] The analysis unit can learn about past artworks and artist styles and provide appropriate feedback based on the user's ideas. The analysis unit, for example, learns about past artworks and artist styles and provides optimal feedback based on the user's ideas. Past artworks include, but are not limited to, digital art, paintings, sculptures, etc. Artist styles include, but are not limited to, color usage, composition, technique, etc. This allows optimal feedback to be provided based on the user's ideas. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input data about past artworks into a generation AI and cause the generation AI to generate feedback.

[0034] The analysis unit can adjust the level of detail of the feedback based on the importance of the idea during analysis. For example, the analysis unit can provide detailed feedback for ideas with high importance. The analysis unit can also provide concise feedback for ideas with low importance. The analysis unit can also gradually adjust the level of detail of the feedback depending on the importance of the idea. This makes it possible to provide appropriate feedback depending on the importance of the idea. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input idea importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea. For example, the analysis unit can apply a color analysis algorithm to a painting idea. The analysis unit can also apply a shape analysis algorithm to a sculpture idea. The analysis unit can also apply a composition analysis algorithm to a photography idea. This allows for appropriate analysis depending on the category of the idea. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input idea category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0036] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past feedback results. The analysis unit improves the accuracy of the analysis, for example, based on feedback received by the user in the past. The analysis unit can also analyze the user's past feedback history and adjust the analysis algorithm. The analysis unit can also gradually improve the accuracy of the analysis by referring to the user's feedback results. This makes it possible to improve the accuracy of the analysis by referring to the user's past feedback results. 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 user's past feedback data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0037] The production department can monitor the user's progress and provide additional feedback as needed. For example, the production department can periodically check the user's progress and provide additional feedback as needed. The production department can also provide appropriate feedback when the user reaches a specific step. The production department can also provide advice on how to proceed to the next step based on the user's progress. This makes it possible to provide appropriate feedback according to the user's progress. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input the user's progress data into a generation AI and have the generation AI provide additional feedback.

[0038] During production, the production department can analyze the user's past production history and select the optimal production method. For example, the production department can suggest the optimal method based on production methods the user has used in the past. The production department can also analyze the user's past production history and suggest the optimal production procedure. The production department can also select the optimal production method by referring to the user's past production results. This makes it possible to provide the optimal production method based on the user's past production history. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the user's past production history data into a generation AI and have the generation AI select the optimal production method.

[0039] During production, the production department can customize the production means based on the user's current living situation. For example, if the user is busy, the production department can suggest a means that can be produced in a short amount of time. Furthermore, if the user has time, the production department can also suggest detailed production procedures. The production department can also customize the production means according to the user's living situation. This makes it possible to provide an appropriate production means according to the user's living situation. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the user's living situation data into the generation AI and have the generation AI customize the production means.

[0040] During production, the production department can improve the production method by reflecting user feedback. For example, the production department improves the production method based on feedback provided by the user. The production department can also analyze the user's feedback history and improve the production procedure. The production department can also improve the production method step by step based on user feedback. In this way, the production method can be improved based on user feedback. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input user feedback data into a generation AI and have the generation AI improve the production method.

[0041] The evaluation unit can evaluate the composition, color usage, technique, etc. of the completed artwork and provide areas for improvement and advice for the next production. The evaluation unit, for example, evaluates the composition of the completed artwork. The evaluation unit can also evaluate the color usage of the completed artwork. The evaluation unit can also evaluate the technique used in the completed artwork. This makes it possible to evaluate the completed artwork and provide advice for the next production. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without using AI. For example, the evaluation unit can input data of the completed artwork into the generation AI and have the generation AI perform the evaluation and provide advice.

[0042] At the time of evaluation, the evaluation unit can analyze the user's past evaluation history and select the optimal evaluation method. The evaluation unit can, for example, suggest the optimal evaluation method based on evaluations the user has received in the past. The evaluation unit can also analyze the user's past evaluation history and customize the evaluation method. The evaluation unit can also improve the evaluation method by referring to the user's past evaluation results. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's past evaluation history data into the generation AI and have the generation AI select the optimal evaluation method.

[0043] The evaluation unit can customize the evaluation means based on the user's current living situation at the time of evaluation. For example, if the user is busy, the evaluation unit can suggest a means that can be evaluated in a short time. Furthermore, if the user has time, the evaluation unit can also suggest a detailed evaluation means. Furthermore, the evaluation unit can customize the evaluation means according to the user's living situation. This makes it possible to provide an appropriate evaluation means according to the user's living situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's living situation data into the generation AI and have the generation AI customize the evaluation means.

[0044] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit can improve the evaluation method, for example, based on feedback provided by the user. The evaluation unit can also analyze the user's feedback history and improve the evaluation means. The evaluation unit can also improve the evaluation method step by step based on the user's feedback. This allows the evaluation method to be improved based on the user's feedback. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input user feedback data into the generation AI and have the generation AI improve the evaluation method.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The reception unit can analyze the user's rating history of past artworks and provide optimal feedback for new ideas submitted by the user. For example, the reception unit can customize feedback for new ideas by referring to the style and technique of artworks that the user previously received high ratings for. The reception unit can also provide feedback for new ideas by taking into account areas for improvement in artworks that the user previously received low ratings for. Furthermore, the reception unit can prioritize feedback related to specific artists or artworks based on the user's rating history. This allows for more personalized feedback to be provided based on the user's past rating history.

[0047] The production department can monitor the user's progress and provide real-time feedback as needed. For example, if the user is having difficulty with a particular step, they can provide specific advice for that step. If the user is making good progress, they can provide guidelines for moving on to the next step. Furthermore, if the user comes up with a new idea during production, they can provide immediate feedback on that idea. This allows them to support the user's production process in real time.

[0048] The reception unit can adjust the idea reception method based on the user's current mood and physical condition. For example, if the user is tired, it can receive ideas in the form of simple questions. If the user is energetic, it can provide an interface for inputting detailed ideas. Furthermore, if the user is concentrating, it can receive multiple ideas at once. This makes it possible to provide the optimal idea reception method according to the user's mood and physical condition.

[0049] The production department can analyze the user's past production history and suggest new production methods based on the techniques and styles that the user has used successfully in the past. For example, it can suggest techniques that the user has used highly in the past. It can also suggest new style combinations based on styles that the user has used in the past. It can also suggest improvements to specific techniques and styles by referring to the user's past production history. This makes it possible to provide the optimal production method based on the user's past production history.

[0050] The analysis unit can adjust the content of the feedback based on the user's current project progress. For example, in the early stages of a project, it can provide feedback on the overall concept and direction of the idea. In the middle of a project, it can provide feedback on specific techniques and color usage. Furthermore, in the final stages of a project, it can provide feedback on finishing details and final adjustments. This makes it possible to provide optimal feedback depending on the project's progress.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The reception unit receives ideas for artworks from users. Ideas input by users can include visual art, music, literature, etc. The reception unit can receive ideas in any format: text, audio, or image, and uses the corresponding analysis technology (text analysis technology, audio recognition technology, image analysis technology). Step 2: The analysis unit uses AI to analyze the ideas received by the reception unit and provide feedback. Analysis is performed using methods such as text analysis, image analysis, and audio analysis. The analysis unit learns from past artworks and the artist's style, and provides optimal feedback based on the user's idea. Step 3: The production team creates artwork based on the feedback provided by the analysis team. The creation can be digital, hand-drawn, or other methods. The production team monitors the user's progress and provides additional feedback as needed. Step 4: The evaluation department evaluates the artworks created by the production department. Evaluation is based on criteria such as technical evaluation, aesthetic evaluation, and user satisfaction. The evaluation department evaluates the composition, color usage, technique, etc. of the completed artworks, and provides advice on areas for improvement and for the next production.

[0053] (Example 2) In an embodiment of the present invention, an AI art assistant system allows a user to submit an idea for an artwork and receive feedback from the AI. In this system, the user inputs an idea for an artwork, and the AI ​​analyzes the idea and provides feedback on related artwork, techniques, and color usage. The user then creates the artwork based on the feedback, and the AI ​​monitors the user's progress and provides additional feedback as needed. Finally, the user has the AI ​​evaluate the completed artwork and receive advice on improvements and future projects. For example, if a user inputs "I want to paint a seascape," the AI ​​analyzes the idea and provides feedback on related artwork, techniques, and color usage. The user then receives feedback from the AI ​​and uses it to create the artwork. For example, if the AI ​​provides feedback such as "blue and green are effective colors for seascapes," the user incorporates that advice into their creation. The AI ​​also monitors the user's progress and provides additional feedback as needed. Furthermore, the user can have the AI ​​evaluate the completed artwork. The AI ​​evaluates the composition, color usage, and technique of the artwork and provides improvements and advice on future projects. This allows the AI ​​art assistant system to gain inspiration by allowing users to submit ideas for artwork and receive feedback. This allows users to experience dynamic collaboration with the AI, resulting in more creative artworks. For example, the system can support the user's creative process by quickly and accurately analyzing ideas written by the user and providing appropriate feedback. Users can also learn specific areas for improvement in their artwork, helping them to improve their skills.

[0054] An AI art assistant system according to an embodiment includes a reception unit, an analysis unit, a production unit, and an evaluation unit. The reception unit receives an idea for an artwork from a user. Ideas input by the user include, but are not limited to, visual art, music, literature, and the like. For example, if the user inputs the idea in text format, the reception unit may use text analysis technology to receive the idea. Alternatively, if the user inputs the idea in voice format, the reception unit may use voice recognition technology to receive the idea. Furthermore, if the user inputs the idea in image format, the reception unit may use image analysis technology to receive the idea. The analysis unit uses AI to analyze the idea received by the reception unit and provide feedback. The analysis may be performed using, for example, text analysis, image analysis, or voice analysis, but is not limited to these examples. For example, the analysis unit may learn about past artworks and the artist's style and provide optimal feedback based on the user's idea. The production unit creates the artwork based on the feedback provided by the analysis unit. The creation may be performed using, for example, digital art production, hand-drawn art production, or the like, but is not limited to these examples. For example, the production unit monitors the user's progress and provides additional feedback as needed. The evaluation unit evaluates the artwork created by the production unit. The evaluation is based on criteria such as, but not limited to, technical evaluation, aesthetic evaluation, and user satisfaction. For example, the evaluation unit evaluates the composition, color usage, technique, etc. of the completed artwork and provides advice on areas for improvement and for the next production. In this way, the AI ​​art assistant system according to the embodiment allows the user to experience dynamic collaboration with the AI ​​and produce more creative artwork.

[0055] The reception unit can estimate the user's emotions and adjust the timing of receiving ideas based on the estimated user emotions. For example, if the user is relaxed, the reception unit can immediately receive ideas. If the user is stressed, the reception unit can also wait a short time before receiving ideas. If the user is excited, the reception unit can also immediately receive ideas. This allows ideas to be received at an appropriate time depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0056] The reception unit can analyze the user's past idea submission history and select the optimal reception method. The reception unit can, for example, analyze trends in ideas submitted by the user in the past and suggest the optimal reception method. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past idea submission history data into the generation AI and have the generation AI select the optimal reception method.

[0057] When receiving ideas, the reception unit can filter the ideas based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives ideas related to the project the user is currently working on. The reception unit can also filter related ideas based on the user's area of ​​interest. The reception unit can also filter optimal ideas by referring to the user's past project history. This makes it possible to preferentially receive ideas related to the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project history data into a generation AI and have the generation AI perform filtering.

[0058] When receiving an idea, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user inputs the idea by voice, the reception unit can receive the idea using voice recognition. Furthermore, if the user inputs the idea by text, the reception unit can also receive the idea using text analysis. Furthermore, if the user inputs the idea by image, the reception unit can also receive the idea using image analysis. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0059] The analysis unit can learn about past artworks and artist styles and provide appropriate feedback based on the user's ideas. The analysis unit, for example, learns about past artworks and artist styles and provides optimal feedback based on the user's ideas. Past artworks include, but are not limited to, digital art, paintings, sculptures, etc. Artist styles include, but are not limited to, color usage, composition, technique, etc. This allows optimal feedback to be provided based on the user's ideas. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input data about past artworks into a generation AI and cause the generation AI to generate feedback.

[0060] The analysis unit can estimate the user's emotions and adjust the way feedback is presented based on the estimated user emotions. For example, the analysis unit can provide detailed feedback when the user is relaxed. The analysis unit can also provide concise feedback when the user is stressed. The analysis unit can also provide visually stimulating feedback when the user is excited. This allows appropriate feedback to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] The analysis unit can adjust the level of detail of the feedback based on the importance of the idea during analysis. For example, the analysis unit can provide detailed feedback for ideas with high importance. The analysis unit can also provide concise feedback for ideas with low importance. The analysis unit can also gradually adjust the level of detail of the feedback depending on the importance of the idea. This makes it possible to provide appropriate feedback depending on the importance of the idea. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input idea importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0062] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea. For example, the analysis unit can apply a color analysis algorithm to a painting idea. The analysis unit can also apply a shape analysis algorithm to a sculpture idea. The analysis unit can also apply a composition analysis algorithm to a photography idea. This allows for appropriate analysis depending on the category of the idea. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input idea category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0063] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past feedback results. The analysis unit improves the accuracy of the analysis, for example, based on feedback received by the user in the past. The analysis unit can also analyze the user's past feedback history and adjust the analysis algorithm. The analysis unit can also gradually improve the accuracy of the analysis by referring to the user's feedback results. This makes it possible to improve the accuracy of the analysis by referring to the user's past feedback results. 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 user's past feedback data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0064] The production department can monitor the user's progress and provide additional feedback as needed. For example, the production department can periodically check the user's progress and provide additional feedback as needed. The production department can also provide appropriate feedback when the user reaches a specific step. The production department can also provide advice on how to proceed to the next step based on the user's progress. This makes it possible to provide appropriate feedback according to the user's progress. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input the user's progress data into a generation AI and have the generation AI provide additional feedback.

[0065] The production department can estimate the user's emotions and adjust the production method based on the estimated user emotions. For example, if the user is relaxed, the production department can proceed with production at a leisurely pace. Furthermore, if the user is stressed, the production department can proceed with production in simple steps. Furthermore, if the user is excited, the production department can proceed with production in a visually stimulating manner. This allows for providing an appropriate production method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the production department can be performed using AI, or without AI. For example, the production department can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0066] During production, the production department can analyze the user's past production history and select the optimal production method. For example, the production department can suggest the optimal method based on production methods the user has used in the past. The production department can also analyze the user's past production history and suggest the optimal production procedure. The production department can also select the optimal production method by referring to the user's past production results. This makes it possible to provide the optimal production method based on the user's past production history. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the user's past production history data into a generation AI and have the generation AI select the optimal production method.

[0067] During production, the production department can customize the production means based on the user's current living situation. For example, if the user is busy, the production department can suggest a means that can be produced in a short amount of time. Furthermore, if the user has time, the production department can also suggest detailed production procedures. The production department can also customize the production means according to the user's living situation. This makes it possible to provide an appropriate production means according to the user's living situation. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the user's living situation data into the generation AI and have the generation AI customize the production means.

[0068] During production, the production department can improve the production method by reflecting user feedback. For example, the production department improves the production method based on feedback provided by the user. The production department can also analyze the user's feedback history and improve the production procedure. The production department can also improve the production method step by step based on user feedback. In this way, the production method can be improved based on user feedback. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input user feedback data into a generation AI and have the generation AI improve the production method.

[0069] The evaluation unit can evaluate the composition, color usage, technique, etc. of the completed artwork and provide areas for improvement and advice for the next production. The evaluation unit, for example, evaluates the composition of the completed artwork. The evaluation unit can also evaluate the color usage of the completed artwork. The evaluation unit can also evaluate the technique used in the completed artwork. This makes it possible to evaluate the completed artwork and provide advice for the next production. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without using AI. For example, the evaluation unit can input data of the completed artwork into the generation AI and have the generation AI perform the evaluation and provide advice.

[0070] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. For example, the evaluation unit can provide a detailed evaluation when the user is relaxed. The evaluation unit can also provide a concise evaluation when the user is stressed. The evaluation unit can also provide a visually stimulating evaluation when the user is excited. This makes it possible to provide an appropriate evaluation method depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] At the time of evaluation, the evaluation unit can analyze the user's past evaluation history and select the optimal evaluation method. The evaluation unit can, for example, suggest the optimal evaluation method based on evaluations the user has received in the past. The evaluation unit can also analyze the user's past evaluation history and customize the evaluation method. The evaluation unit can also improve the evaluation method by referring to the user's past evaluation results. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's past evaluation history data into the generation AI and have the generation AI select the optimal evaluation method.

[0072] The evaluation unit can customize the evaluation means based on the user's current living situation at the time of evaluation. For example, if the user is busy, the evaluation unit can suggest a means that can be evaluated in a short time. Furthermore, if the user has time, the evaluation unit can also suggest a detailed evaluation means. Furthermore, the evaluation unit can customize the evaluation means according to the user's living situation. This makes it possible to provide an appropriate evaluation means according to the user's living situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's living situation data into the generation AI and have the generation AI customize the evaluation means.

[0073] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit can improve the evaluation method, for example, based on feedback provided by the user. The evaluation unit can also analyze the user's feedback history and improve the evaluation means. The evaluation unit can also improve the evaluation method step by step based on the user's feedback. This allows the evaluation method to be improved based on the user's feedback. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input user feedback data into the generation AI and have the generation AI improve the evaluation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, production unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive an idea for an artwork from a user using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the idea received by the reception unit and provides feedback. For example, the production unit is realized by the control unit 46A of the smart device 14 and produces an artwork based on the feedback provided by the analysis unit. For example, the evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the artwork produced by the production unit and provides improvements and advice for the next production. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, production unit, and evaluation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive an idea for an artwork from a user using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the idea received by the reception unit and provides feedback. For example, the production unit is realized by the control unit 46A of the smart glasses 214 and produces an artwork based on the feedback provided by the analysis unit. For example, the evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the artwork produced by the production unit and provides advice on improvements and for the next production. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, production unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive an idea for an artwork from a user using the 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, analyzes the idea received by the reception unit, and provides feedback. For example, the production unit is realized by the control unit 46A of the headset-type terminal 314, and produces an artwork based on the feedback provided by the analysis unit. For example, the evaluation unit is realized by the specific processing unit 290 of the data processing device 12, evaluates the artwork produced by the production unit, and provides advice on improvements and for the next production. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, production unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive an idea for an artwork from a user using the 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, analyzes the idea received by the reception unit, and provides feedback. For example, the production unit is realized by the control unit 46A of the robot 414, and produces an artwork based on the feedback provided by the analysis unit. For example, the evaluation unit is realized by the specific processing unit 290 of the data processing device 12, evaluates the artwork produced by the production unit, and provides advice on improvements and for the next production.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The reception unit can analyze the user's rating history of past artworks and provide optimal feedback for new ideas submitted by the user. For example, the reception unit can customize feedback for new ideas by referring to the style and technique of artworks that the user previously received high ratings for. The reception unit can also provide feedback for new ideas by taking into account areas for improvement in artworks that the user previously received low ratings for. Furthermore, the reception unit can prioritize feedback related to specific artists or artworks based on the user's rating history. This allows for more personalized feedback to be provided based on the user's past rating history.

[0076] The analysis unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, detailed technical feedback can be provided. If the user is stressed, concise positive feedback can be provided. Furthermore, if the user is excited, inspirational feedback can be provided. In this way, optimal feedback can be provided depending on the user's emotions. Emotions are estimated by analyzing the user's facial expressions and voice data.

[0077] The production department can monitor the user's progress and provide real-time feedback as needed. For example, if the user is having difficulty with a particular step, they can provide specific advice for that step. If the user is making good progress, they can provide guidelines for moving on to the next step. Furthermore, if the user comes up with a new idea during production, they can provide immediate feedback on that idea. This allows them to support the user's production process in real time.

[0078] The evaluation unit can estimate the user's emotions and adjust the evaluation feedback based on the estimated emotions. For example, if the user is relaxed, a detailed evaluation and points for improvement can be provided. If the user is stressed, it can provide mainly positive feedback. Furthermore, if the user is excited, it can provide challenging advice for the next production. This makes it possible to provide optimal evaluation feedback according to the user's emotions. Emotions are estimated by analyzing the user's facial expressions and voice data.

[0079] The reception unit can adjust the idea reception method based on the user's current mood and physical condition. For example, if the user is tired, it can receive ideas in the form of simple questions. If the user is energetic, it can provide an interface for inputting detailed ideas. Furthermore, if the user is concentrating, it can receive multiple ideas at once. This makes it possible to provide the optimal idea reception method according to the user's mood and physical condition.

[0080] The analysis unit can estimate the user's emotions and adjust the tone of the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback can be provided in a calm tone. If the user is stressed, the feedback can be provided in an encouraging tone. Furthermore, if the user is excited, the feedback can be provided in an energetic tone. In this way, it is possible to provide feedback in the optimal tone depending on the user's emotions. Emotions are estimated by analyzing the user's facial expressions and voice data.

[0081] The production department can analyze the user's past production history and suggest new production methods based on the techniques and styles that the user has used successfully in the past. For example, it can suggest techniques that the user has used highly in the past. It can also suggest new style combinations based on styles that the user has used in the past. It can also suggest improvements to specific techniques and styles by referring to the user's past production history. This makes it possible to provide the optimal production method based on the user's past production history.

[0082] The evaluation unit can estimate the user's emotions and customize the evaluation feedback based on the estimated emotions. For example, if the user is relaxed, a detailed technical evaluation can be provided. If the user is stressed, positive feedback can be provided. If the user is excited, challenging advice for the next production can be provided. This makes it possible to provide optimal evaluation feedback according to the user's emotions. Emotions are estimated by analyzing the user's facial expressions and voice data.

[0083] The analysis unit can adjust the content of the feedback based on the user's current project progress. For example, in the early stages of a project, it can provide feedback on the overall concept and direction of the idea. In the middle of a project, it can provide feedback on specific techniques and color usage. Furthermore, in the final stages of a project, it can provide feedback on finishing details and final adjustments. This makes it possible to provide optimal feedback depending on the project's progress.

[0084] The production department can estimate the user's emotions and adjust the pace of production based on the estimated emotions. For example, if the user is relaxed, the production can proceed at a leisurely pace. If the user is stressed, steps that can be completed in a short time can be suggested. Furthermore, if the user is excited, the production can proceed at an energetic pace. This makes it possible to provide the optimal production pace according to the user's emotions. Emotions are estimated by analyzing the user's facial expressions and voice data.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The reception unit receives ideas for artworks from users. Ideas input by users can include visual art, music, literature, etc. The reception unit can receive ideas in any format: text, audio, or image, and uses the corresponding analysis technology (text analysis technology, audio recognition technology, image analysis technology). Step 2: The analysis unit uses AI to analyze the ideas received by the reception unit and provide feedback. Analysis is performed using methods such as text analysis, image analysis, and audio analysis. The analysis unit learns from past artworks and the artist's style, and provides optimal feedback based on the user's idea. Step 3: The production team creates artwork based on the feedback provided by the analysis team. The creation can be digital, hand-drawn, or other methods. The production team monitors the user's progress and provides additional feedback as needed. Step 4: The evaluation department evaluates the artworks created by the production department. Evaluation is based on criteria such as technical evaluation, aesthetic evaluation, and user satisfaction. The evaluation department evaluates the composition, color usage, technique, etc. of the completed artworks, and provides advice on areas for improvement and for the next production.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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, in order to avoid confusion and to 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.

[0157] 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.

[0158] [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives ideas for artworks from users; an analysis unit that analyzes the idea received by the reception unit and provides feedback; a production unit that produces artwork based on the feedback provided by the analysis unit; and an evaluation unit that evaluates the artwork created by the creation unit. A system characterized by:

2. The reception unit Estimate user emotions and adjust the timing of idea acceptance based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze users' past idea submission history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit Filter ideas based on your current projects and interests 2. The system of claim 1.

5. The reception unit When accepting ideas, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The analysis unit Learn from past artworks and artists' styles to provide appropriate feedback based on your ideas 2. The system of claim 1.

7. The analysis unit Inferring user emotions and adjusting feedback presentation based on the estimated user emotions 2. The system of claim 1.

8. The analysis unit During analysis, adjust the level of detail in your feedback based on the importance of the idea 2. The system of claim 1.

Images