system

Abstract

The system according to this embodiment aims to efficiently identify areas for improvement in digital illustrations and support the user in improving their drawing skills. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives the user's digital illustration. The analysis unit analyzes the digital illustration received by the reception unit. The suggestion unit suggests areas for improvement in the illustration based on the results of the analysis by the analysis unit. The provision unit provides the user with the areas for improvement suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the improvement points of digital illustrations have not been sufficiently pointed out efficiently to support the improvement of the user's drawing ability.

[0005] The system according to the embodiment aims to efficiently point out the improvement points of digital illustrations and support the improvement of the user's drawing ability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives the user's digital illustration. The analysis unit analyzes the digital illustration received by the reception unit. The suggestion unit points out areas for improvement in the illustration based on the results of the analysis by the analysis unit. The provision unit provides the user with the improvements pointed out by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently identify areas for improvement in digital illustrations and support the user in improving their drawing skills. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The illustration critique system according to an embodiment of the present invention is a system in which a user shows their digital illustration to a generating AI, which then analyzes it and points out areas for improvement from various perspectives, such as "coloring" and "composition." The illustration critique system critiques the user's digital illustration and helps the user improve their drawing skills. Furthermore, by having a fictional character handle the critique, the system lowers the psychological barrier for the user and makes them feel more comfortable with the generating AI. For example, a user shows their digital illustration to the generating AI. In this case, the user only needs to upload the illustration. For example, the user uploads an illustration of a character they have drawn. This information is input into the generating AI. Next, the generating AI analyzes the input illustration. The generating AI evaluates the illustration from perspectives such as coloring and composition and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. The generating AI provides the user with the identified areas for improvement. For example, it provides specific advice such as "adjust the color balance" or "change the composition." This allows the user to improve their illustration and enhance their drawing skills. Furthermore, by having a fictional character handle the critique, users can lower their psychological barriers and become more familiar with the generating AI. For example, if the character provides advice in a friendly tone, users can receive critiques of their illustrations in an enjoyable way without feeling fear or anxiety towards the generating AI. This allows the illustration critique system to efficiently receive, analyze, point out areas for improvement, and provide feedback on the user's digital illustrations.

[0029] The illustration critique system according to this embodiment comprises a reception unit, an analysis unit, a feedback unit, and a provision unit. The reception unit receives digital illustrations from users. User digital illustrations include, but are not limited to, JPEG, PNG, and vector images. The reception unit provides, for example, an interface for uploading digital illustrations drawn by users. The reception unit can also estimate the user's emotions when receiving digital illustrations and adjust the timing of illustration acceptance based on the estimated emotions. For example, if the user is feeling stressed, the system may accept the illustration at a time when the user can relax. The analysis unit analyzes the digital illustrations received by the reception unit using a generation AI. The generation AI evaluates the illustrations from perspectives such as coloring and composition, and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. The feedback unit points out areas for improvement in the illustrations based on the results of the analysis by the analysis unit. The feedback unit points out specific areas for improvement, such as color balance or unnatural composition. For example, it provides specific advice on adjusting the color balance. The providing unit provides the user with the improvements pointed out by the feedback unit. The providing unit includes, for example, a character display unit that provides advice to the user in friendly language. For example, by having a character provide advice to the user in friendly language, the user can receive feedback on their illustrations in an enjoyable way without feeling fear or anxiety towards the generating AI. As a result, the illustration feedback system according to this embodiment can efficiently receive, analyze, point out, and provide feedback on the user's digital illustrations.

[0030] The reception desk accepts digital illustrations from users. These illustrations may include, but are not limited to, JPEG, PNG, and vector images. The reception desk provides an interface for users to upload their digital illustrations, specifically allowing them to easily upload illustrations via a web browser or mobile application. Uploaded illustrations are immediately stored in the system's database and used for subsequent processing. Furthermore, the reception desk can estimate the user's emotions when accepting digital illustrations and adjust the timing of the submission based on this estimation. For example, if a user is stressed, the reception desk will accept the illustration at a time when the user is relaxed. This emotion estimation utilizes AI technology that analyzes data such as the user's facial expressions, voice, and input speed. For instance, it analyzes the user's facial expressions and voice tone via a webcam and microphone to estimate their stress and relaxation levels. This allows users to submit illustrations in the most relaxed state, leading to better feedback. Additionally, the reception desk can refer to the user's past submission history and feedback to accept illustrations at the optimal time and in the most appropriate manner for each individual user. This allows the reception department to respond flexibly to user needs, thereby improving the overall user experience of the system.

[0031] The analysis department uses generative AI to analyze digital illustrations received by the reception department. The generative AI evaluates illustrations from perspectives such as coloring and composition, and identifies areas for improvement. Specifically, the generative AI uses deep learning technology to comprehensively evaluate multiple elements such as color balance, light and dark contrast, compositional stability, and line smoothness. For example, it points out specific areas for improvement, such as poor color balance or unnatural composition. Based on a large amount of illustration data it has learned from in the past, the generative AI extracts the characteristics of excellent illustrations and evaluates the received illustration by comparing it to these characteristics. Furthermore, the generative AI can provide feedback tailored to the user's skill level. For example, it can advise beginners on basic color usage and composition, and point out more advanced techniques and methods of expression to advanced users. This allows the analysis department to support the improvement of users' skills and provide appropriate feedback tailored to individual needs. The generative AI can also evaluate illustrations according to their style and theme. For example, it can apply appropriate evaluation criteria to different styles, such as anime-style illustrations or realistic landscape paintings, and provide specific advice to help users get closer to their desired style. This allows the analytics department to respond to diverse user needs and provide more personalized feedback.

[0032] The feedback section points out areas for improvement in the illustration based on the analysis conducted by the analysis section. The feedback section identifies specific areas for improvement, such as color balance or unnatural composition. Specifically, it provides concrete advice on adjusting color balance. For example, if the color balance is poor, it will show specific color combinations and color schemes, indicating which colors should be adjusted and by how much. If the composition is unnatural, it will provide concrete advice on how to guide the viewer's eye and the placement of elements. The feedback section includes a function to add marks and comments directly to the illustration to clearly and visually indicate the areas for improvement identified by the generating AI. This makes it easier for users to intuitively understand specific areas for improvement. Furthermore, the feedback section can provide feedback tailored to the user's skill level and desired style. For example, it will advise beginners on basic techniques and knowledge, while providing feedback on more advanced techniques and expression methods to advanced users. In this way, the feedback section can support the user's skill improvement and provide appropriate feedback tailored to individual needs. The feedback section also refers to feedback and improvements the user has received in the past to support continuous skill development. For example, it evaluates the extent to which previously identified areas for improvement have been addressed and provides further advice. This allows the feedback department to continuously support the user's growth and provide more effective feedback.

[0033] The provision unit provides users with the improvements pointed out by the feedback unit. The provision unit includes, for example, a character display unit that provides advice to users in friendly language. Specifically, by having a character provide advice to users in friendly language, users can receive illustration critiques in an enjoyable way without feeling fear or anxiety towards the generating AI. The character display unit can be customized according to the user's age and preferences, with a friendly character providing advice. For example, cute animal characters can be prepared for children, and characters with a calm design can be prepared for adults. Furthermore, the provision unit has a function to add marks and comments directly to the illustration to make the advice visually easy to understand. This makes it easier for users to intuitively understand specific areas for improvement. The provision unit also provides specific procedures and reference materials to make it easier for users to implement the advice. For example, it shows specific procedures for improving color balance and examples of reference color schemes. This makes it easier for users to implement the advice and effectively supports skill improvement. Furthermore, the provision unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, feedback and suggestions for improvement from users after they receive advice can be collected and incorporated into future advice. This allows the service provider to provide more effective feedback to users and improve the overall user experience of the system.

[0034] The analysis unit can evaluate illustrations from perspectives such as coloring and composition. For example, the analysis unit evaluates illustrations using specific evaluation criteria and methods for coloring. For example, it evaluates color selection, gradation, and shading. The analysis unit can also evaluate illustrations using specific evaluation criteria and methods for composition. For example, it evaluates eye guidance, balance, and focal point. By evaluating illustrations from perspectives such as coloring and composition, more specific areas for improvement can be pointed out. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input evaluations of coloring and composition into a generation AI, and the generation AI can output evaluation results.

[0035] The feedback section can point out specific areas for improvement, such as color balance or unnatural composition. For example, it can use specific evaluation criteria and methods to point out areas for improvement regarding color balance. For example, it might point out the use of complementary colors, color harmony, and contrast. It can also use specific evaluation criteria and methods to point out areas for improvement regarding unnatural composition. For example, it might point out the flow of the viewer's gaze, the placement of elements, and the use of space. This makes it easier for users to improve their illustrations by pointing out specific areas for improvement. Some or all of the above processing in the feedback section may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback section can input feedback on color balance and unnatural composition into a generation AI, and the generation AI can output areas for improvement.

[0036] The service provider may include a character display unit that provides advice to the user in friendly language. The service provider may provide advice using specific criteria and examples of friendly language, such as approachable expressions and positive feedback. By providing advice in friendly language, the service provider can lower the user's psychological barrier and make them feel more comfortable with the generating AI. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider may input advice in friendly language to the generating AI, and the generating AI may output the advice.

[0037] The service provider can provide users with specific advice to improve their illustrations. The service provider provides advice using specific content and format, for example, technical guidance or step-by-step guides for improvement. This allows users to effectively improve their illustrations by providing specific advice. Some or all of the above processing in the service provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the service provider can input specific advice into a generating AI, and the generating AI can output the advice.

[0038] The reception department can analyze a user's past illustration submission history and select the optimal reception method. For example, the reception department can analyze the frequency of illustrations the user has submitted in the past and suggest the optimal reception timing. For example, it can analyze the types of illustrations the user has submitted in the past and select an appropriate reception method. The reception department can also suggest the optimal reception interface based on the user's past submission history. In this way, by analyzing past submission history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past submission history data into AI, which can then select the optimal reception method.

[0039] The reception unit can filter illustrations based on the user's current projects and areas of interest when they are submitted. For example, the reception unit can prioritize illustrations related to the user's current projects. For example, it can filter and accept relevant illustrations based on the user's areas of interest. The reception unit can also accept appropriate illustrations according to the user's project progress. This allows for the priority of receiving highly relevant illustrations by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's projects and areas of interest into an AI, which can then perform the filtering.

[0040] The reception desk can prioritize accepting illustrations that are highly relevant to the user's geographical location when they are submitted. For example, if the user is in a specific region, the reception desk will prioritize accepting illustrations related to that region. For example, based on the user's location, it will prioritize accepting illustrations related to local events or scenery. The reception desk can also prioritize accepting illustrations related to the user's travel destination if the user is traveling. In this way, by considering the user's geographical location, it is possible to prioritize accepting illustrations that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then select highly relevant illustrations.

[0041] The reception desk can analyze the user's social media activity when receiving illustrations and accept relevant illustrations. For example, the reception desk may prioritize accepting illustrations that the user has shared on social media. For example, it may accept relevant illustrations based on the user's social media activity. The reception desk may also prioritize accepting illustrations related to the style of artists that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant illustrations. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity data into AI, and the AI ​​may select relevant illustrations.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the illustrations. For example, the analysis unit will perform a detailed analysis for important illustrations, a standard analysis for general illustrations, and a concise analysis for simple illustrations. By adjusting the level of detail of the analysis based on the importance of the illustrations, the analysis unit can provide appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input illustration importance data into the AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the illustration during analysis. For example, in the case of character illustrations, the analysis unit analyzes pose and facial expression. For example, in the case of landscape illustrations, it analyzes perspective and color. In addition, in the case of abstract paintings, the analysis unit can analyze composition and color balance. By applying different analysis algorithms depending on the category of the illustration, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input illustration category data into AI, and the AI ​​can select an appropriate analysis algorithm.

[0044] The analysis department can determine the priority of the analysis based on the submission date of the illustrations. For example, the analysis department might prioritize the analysis of recently submitted illustrations, while delaying the analysis of older illustrations. The analysis department can also determine the appropriate order of analysis based on the submission date. This allows for efficient analysis by prioritizing the analysis based on the submission date of the illustrations. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the illustration submission date data into an AI, which can then determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the illustrations during the analysis process. For example, the analysis unit may group illustrations on the same theme together for analysis. For example, it may prioritize the analysis of highly relevant illustrations. The analysis unit can also postpone the analysis of less relevant illustrations. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the illustrations. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input illustration relevance data into an AI, which can then adjust the order of analysis.

[0046] The feedback function can adjust the level of detail of its feedback based on the importance of the illustration. For example, it provides detailed feedback for important illustrations, standard feedback for general illustrations, and concise feedback for simple illustrations. By adjusting the level of detail based on the importance of the illustration, it can provide appropriate feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration importance data into the AI, which can then adjust the level of detail of the feedback.

[0047] The feedback function can apply different feedback algorithms depending on the category of the illustration. For example, in the case of character illustrations, it will provide feedback on pose and facial expression. For example, in the case of landscape illustrations, it will provide feedback on perspective and color. Furthermore, in the case of abstract paintings, the feedback function can also provide feedback on composition and color balance. By applying different feedback algorithms depending on the category of the illustration, it is possible to provide more accurate feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration category data into AI, and the AI ​​can select an appropriate feedback algorithm.

[0048] The feedback system can prioritize feedback based on the submission date of the illustrations. For example, it might prioritize recently submitted illustrations and postpone older ones. The feedback system can also determine an appropriate order of feedback based on the submission date. This allows for efficient feedback by prioritizing feedback based on the illustration submission date. Some or all of the above processing in the feedback system may be performed using AI or not. For example, the feedback system can input illustration submission date data into an AI, which can then determine the priority.

[0049] The feedback function can adjust the order of feedback based on the relevance of the illustrations. For example, it may group illustrations on the same theme together for feedback. For example, it may prioritize feedback on highly relevant illustrations. It can also postpone feedback on less relevant illustrations. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the illustrations. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration relevance data into AI, which can then adjust the order of feedback.

[0050] The service provider can select the most appropriate advice by referring to the user's past feedback when providing advice. For example, the service provider can provide the most appropriate advice based on the advice the user has received in the past. For example, it can analyze the user's past feedback and select appropriate advice. The service provider can also suggest the most appropriate advice from the user's past advice history. In this way, the service provider can provide the most appropriate advice by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past feedback data into AI, and the AI ​​can select the most appropriate advice.

[0051] The service provider can adjust the level of detail of the advice based on the user's current skill level. For example, it might provide basic advice to beginners, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail according to the user's skill level, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's skill level data into the AI, which can then adjust the level of detail of the advice.

[0052] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. For instance, based on the user's location, it can provide advice related to local events or scenery. Furthermore, if the user is traveling, the service provider can provide advice related to their travel destination. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location into the AI, which can then select the most appropriate advice.

[0053] The service provider can analyze the user's social media activity and provide relevant advice when offering advice. For example, it can provide advice related to illustrations the user has shared on social media. For example, it can provide relevant advice based on the user's social media activity. The service provider can also provide advice related to the style of artists the user follows on social media. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can select relevant advice.

[0054] The character display unit can select the optimal character representation by referring to the user's past reactions when displaying characters. For example, the character display unit may prioritize displaying characters that the user has liked in the past. For example, it may analyze the user's past reactions and select the optimal character. The character display unit can also adjust the representation method of a particular character based on the user's past reactions. In this way, it can provide the optimal character representation by referring to the user's past reactions. Some or all of the above processing in the character display unit may be performed using AI or not. For example, the character display unit can input the user's past reaction data into AI, and the AI ​​can select the optimal character representation.

[0055] The character display unit can select the optimal display method when displaying a character, taking into account the user's device information. For example, if the user is using a smartphone, the character display unit will display a character that is sized to fit the screen. For example, if the user is using a tablet, the character display unit will display a character optimized for a larger screen. Furthermore, if the user is using a smartwatch, the character display unit can display a concise and highly visible character. In this way, the optimal character display can be provided by taking into account the user's device information. Some or all of the above processing in the character display unit may be performed using AI, or it may be performed without AI. For example, the character display unit can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception department can analyze a user's past illustration submission history and select the optimal reception method. For example, the reception department can analyze the frequency of illustrations the user has submitted in the past and suggest the optimal reception timing. For example, it can analyze the types of illustrations the user has submitted in the past and select an appropriate reception method. The reception department can also suggest the optimal reception interface based on the user's past submission history. In this way, by analyzing past submission history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past submission history data into AI, which can then select the optimal reception method.

[0058] The service provider can select the most appropriate advice by referring to the user's past feedback when providing advice. For example, the service provider can provide the most appropriate advice based on the advice the user has received in the past. For example, it can analyze the user's past feedback and select appropriate advice. The service provider can also suggest the most appropriate advice from the user's past advice history. In this way, the service provider can provide the most appropriate advice by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past feedback data into AI, and the AI ​​can select the most appropriate advice.

[0059] The service provider can adjust the level of detail of the advice based on the user's current skill level. For example, it might provide basic advice to beginners, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail according to the user's skill level, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's skill level data into the AI, which can then adjust the level of detail of the advice.

[0060] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. For instance, based on the user's location, it can provide advice related to local events or scenery. Furthermore, if the user is traveling, the service provider can provide advice related to their travel destination. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location into the AI, which can then select the most appropriate advice.

[0061] The character display unit can select the optimal display method when displaying a character, taking into account the user's device information. For example, if the user is using a smartphone, the character display unit will display a character that is sized to fit the screen. For example, if the user is using a tablet, the character display unit will display a character optimized for a larger screen. Furthermore, if the user is using a smartwatch, the character display unit can display a concise and highly visible character. In this way, the optimal character display can be provided by taking into account the user's device information. Some or all of the above processing in the character display unit may be performed using AI, or it may be performed without AI. For example, the character display unit can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts the user's digital illustration. This includes, for example, JPEG, PNG, and vector images. The reception desk provides an interface for users to upload their digital illustrations. Furthermore, when accepting a digital illustration, the reception desk can estimate the user's emotions and adjust the timing of the illustration's acceptance based on that estimation. For example, if the user is feeling stressed, the reception desk might wait until the user is able to relax before accepting the illustration. Step 2: The analysis department uses a generation AI to analyze the digital illustrations received by the reception department. The generation AI evaluates the illustrations from perspectives such as coloring and composition, and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. Step 3: The feedback team points out areas for improvement in the illustration based on the analysis conducted by the analysis team. The feedback team will point out specific areas for improvement, such as color balance or unnatural composition. For example, they may provide specific advice on adjusting the color balance. Step 4: The providing unit provides the user with the improvements pointed out by the feedback unit. The providing unit includes a character display unit that provides advice to the user in friendly language. For example, by having a character provide advice to the user in friendly language, the user can receive feedback on their illustrations in a fun way without feeling fear or anxiety about the generating AI.

[0064] (Example of form 2) The illustration critique system according to an embodiment of the present invention is a system in which a user shows their digital illustration to a generating AI, which then analyzes it and points out areas for improvement from various perspectives, such as "coloring" and "composition." The illustration critique system critiques the user's digital illustration and helps the user improve their drawing skills. Furthermore, by having a fictional character handle the critique, the system lowers the psychological barrier for the user and makes them feel more comfortable with the generating AI. For example, a user shows their digital illustration to the generating AI. In this case, the user only needs to upload the illustration. For example, the user uploads an illustration of a character they have drawn. This information is input into the generating AI. Next, the generating AI analyzes the input illustration. The generating AI evaluates the illustration from perspectives such as coloring and composition and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. The generating AI provides the user with the identified areas for improvement. For example, it provides specific advice such as "adjust the color balance" or "change the composition." This allows the user to improve their illustration and enhance their drawing skills. Furthermore, by having a fictional character handle the critique, users can lower their psychological barriers and become more familiar with the generating AI. For example, if the character provides advice in a friendly tone, users can receive critiques of their illustrations in an enjoyable way without feeling fear or anxiety towards the generating AI. This allows the illustration critique system to efficiently receive, analyze, point out areas for improvement, and provide feedback on the user's digital illustrations.

[0065] The illustration critique system according to this embodiment comprises a reception unit, an analysis unit, a feedback unit, and a provision unit. The reception unit receives digital illustrations from users. User digital illustrations include, but are not limited to, JPEG, PNG, and vector images. The reception unit provides, for example, an interface for uploading digital illustrations drawn by users. The reception unit can also estimate the user's emotions when receiving digital illustrations and adjust the timing of illustration acceptance based on the estimated emotions. For example, if the user is feeling stressed, the system may accept the illustration at a time when the user can relax. The analysis unit analyzes the digital illustrations received by the reception unit using a generation AI. The generation AI evaluates the illustrations from perspectives such as coloring and composition, and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. The feedback unit points out areas for improvement in the illustrations based on the results of the analysis by the analysis unit. The feedback unit points out specific areas for improvement, such as color balance or unnatural composition. For example, it provides specific advice on adjusting the color balance. The providing unit provides the user with the improvements pointed out by the feedback unit. The providing unit includes, for example, a character display unit that provides advice to the user in friendly language. For example, by having a character provide advice to the user in friendly language, the user can receive feedback on their illustrations in an enjoyable way without feeling fear or anxiety towards the generating AI. As a result, the illustration feedback system according to this embodiment can efficiently receive, analyze, point out, and provide feedback on the user's digital illustrations.

[0066] The reception desk accepts digital illustrations from users. These illustrations may include, but are not limited to, JPEG, PNG, and vector images. The reception desk provides an interface for users to upload their digital illustrations, specifically allowing them to easily upload illustrations via a web browser or mobile application. Uploaded illustrations are immediately stored in the system's database and used for subsequent processing. Furthermore, the reception desk can estimate the user's emotions when accepting digital illustrations and adjust the timing of the submission based on this estimation. For example, if a user is stressed, the reception desk will accept the illustration at a time when the user is relaxed. This emotion estimation utilizes AI technology that analyzes data such as the user's facial expressions, voice, and input speed. For instance, it analyzes the user's facial expressions and voice tone via a webcam and microphone to estimate their stress and relaxation levels. This allows users to submit illustrations in the most relaxed state, leading to better feedback. Additionally, the reception desk can refer to the user's past submission history and feedback to accept illustrations at the optimal time and in the most appropriate manner for each individual user. This allows the reception department to respond flexibly to user needs, thereby improving the overall user experience of the system.

[0067] The analysis department uses generative AI to analyze digital illustrations received by the reception department. The generative AI evaluates illustrations from perspectives such as coloring and composition, and identifies areas for improvement. Specifically, the generative AI uses deep learning technology to comprehensively evaluate multiple elements such as color balance, light and dark contrast, compositional stability, and line smoothness. For example, it points out specific areas for improvement, such as poor color balance or unnatural composition. Based on a large amount of illustration data it has learned from in the past, the generative AI extracts the characteristics of excellent illustrations and evaluates the received illustration by comparing it to these characteristics. Furthermore, the generative AI can provide feedback tailored to the user's skill level. For example, it can advise beginners on basic color usage and composition, and point out more advanced techniques and methods of expression to advanced users. This allows the analysis department to support the improvement of users' skills and provide appropriate feedback tailored to individual needs. The generative AI can also evaluate illustrations according to their style and theme. For example, it can apply appropriate evaluation criteria to different styles, such as anime-style illustrations or realistic landscape paintings, and provide specific advice to help users get closer to their desired style. This allows the analytics department to respond to diverse user needs and provide more personalized feedback.

[0068] The feedback section points out areas for improvement in the illustration based on the analysis conducted by the analysis section. The feedback section identifies specific areas for improvement, such as color balance or unnatural composition. Specifically, it provides concrete advice on adjusting color balance. For example, if the color balance is poor, it will show specific color combinations and color schemes, indicating which colors should be adjusted and by how much. If the composition is unnatural, it will provide concrete advice on how to guide the viewer's eye and the placement of elements. The feedback section includes a function to add marks and comments directly to the illustration to clearly and visually indicate the areas for improvement identified by the generating AI. This makes it easier for users to intuitively understand specific areas for improvement. Furthermore, the feedback section can provide feedback tailored to the user's skill level and desired style. For example, it will advise beginners on basic techniques and knowledge, while providing feedback on more advanced techniques and expression methods to advanced users. In this way, the feedback section can support the user's skill improvement and provide appropriate feedback tailored to individual needs. The feedback section also refers to feedback and improvements the user has received in the past to support continuous skill development. For example, it evaluates the extent to which previously identified areas for improvement have been addressed and provides further advice. This allows the feedback department to continuously support the user's growth and provide more effective feedback.

[0069] The provision unit provides users with the improvements pointed out by the feedback unit. The provision unit includes, for example, a character display unit that provides advice to users in friendly language. Specifically, by having a character provide advice to users in friendly language, users can receive illustration critiques in an enjoyable way without feeling fear or anxiety towards the generating AI. The character display unit can be customized according to the user's age and preferences, with a friendly character providing advice. For example, cute animal characters can be prepared for children, and characters with a calm design can be prepared for adults. Furthermore, the provision unit has a function to add marks and comments directly to the illustration to make the advice visually easy to understand. This makes it easier for users to intuitively understand specific areas for improvement. The provision unit also provides specific procedures and reference materials to make it easier for users to implement the advice. For example, it shows specific procedures for improving color balance and examples of reference color schemes. This makes it easier for users to implement the advice and effectively supports skill improvement. Furthermore, the provision unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, feedback and suggestions for improvement from users after they receive advice can be collected and incorporated into future advice. This allows the service provider to provide more effective feedback to users and improve the overall user experience of the system.

[0070] The analysis unit can evaluate illustrations from perspectives such as coloring and composition. For example, the analysis unit evaluates illustrations using specific evaluation criteria and methods for coloring. For example, it evaluates color selection, gradation, and shading. The analysis unit can also evaluate illustrations using specific evaluation criteria and methods for composition. For example, it evaluates eye guidance, balance, and focal point. By evaluating illustrations from perspectives such as coloring and composition, more specific areas for improvement can be pointed out. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input evaluations of coloring and composition into a generation AI, and the generation AI can output evaluation results.

[0071] The feedback section can point out specific areas for improvement, such as color balance or unnatural composition. For example, it can use specific evaluation criteria and methods to point out areas for improvement regarding color balance. For example, it might point out the use of complementary colors, color harmony, and contrast. It can also use specific evaluation criteria and methods to point out areas for improvement regarding unnatural composition. For example, it might point out the flow of the viewer's gaze, the placement of elements, and the use of space. This makes it easier for users to improve their illustrations by pointing out specific areas for improvement. Some or all of the above processing in the feedback section may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback section can input feedback on color balance and unnatural composition into a generation AI, and the generation AI can output areas for improvement.

[0072] The service provider may include a character display unit that provides advice to the user in friendly language. The service provider may provide advice using specific criteria and examples of friendly language, such as approachable expressions and positive feedback. By providing advice in friendly language, the service provider can lower the user's psychological barrier and make them feel more comfortable with the generating AI. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider may input advice in friendly language to the generating AI, and the generating AI may output the advice.

[0073] The service provider can provide users with specific advice to improve their illustrations. The service provider provides advice using specific content and format, for example, technical guidance or step-by-step guides for improvement. This allows users to effectively improve their illustrations by providing specific advice. Some or all of the above processing in the service provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the service provider can input specific advice into a generating AI, and the generating AI can output the advice.

[0074] The reception unit can estimate the user's emotions and adjust the timing of illustration requests based on the estimated emotions. For example, if the user is stressed, the reception unit will request illustrations at a time when the user can relax. For example, if the user is concentrating, the reception unit will request illustrations immediately to begin analysis quickly. The reception unit can also request illustrations after the user has taken a break if the user is tired. By adjusting the timing of illustration requests according to the user's emotions, the system can reduce user stress and efficiently receive illustrations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of requests.

[0075] The reception department can analyze a user's past illustration submission history and select the optimal reception method. For example, the reception department can analyze the frequency of illustrations the user has submitted in the past and suggest the optimal reception timing. For example, it can analyze the types of illustrations the user has submitted in the past and select an appropriate reception method. The reception department can also suggest the optimal reception interface based on the user's past submission history. In this way, by analyzing past submission history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past submission history data into AI, which can then select the optimal reception method.

[0076] The reception unit can filter illustrations based on the user's current projects and areas of interest when they are submitted. For example, the reception unit can prioritize illustrations related to the user's current projects. For example, it can filter and accept relevant illustrations based on the user's areas of interest. The reception unit can also accept appropriate illustrations according to the user's project progress. This allows for the priority of receiving highly relevant illustrations by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's projects and areas of interest into an AI, which can then perform the filtering.

[0077] The reception unit can estimate the user's emotions and determine the priority of illustrations to receive based on the estimated emotions. For example, if the user is excited, the reception unit will immediately receive the illustration and prioritize its analysis. For example, if the user is relaxed, it will receive the illustration with the same priority as other illustrations. The reception unit can also prioritize illustrations to provide reassurance if the user is feeling anxious. By prioritizing illustrations according to the user's emotions, illustrations can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority.

[0078] The reception desk can prioritize accepting illustrations that are highly relevant to the user's geographical location when they are submitted. For example, if the user is in a specific region, the reception desk will prioritize accepting illustrations related to that region. For example, based on the user's location, it will prioritize accepting illustrations related to local events or scenery. The reception desk can also prioritize accepting illustrations related to the user's travel destination if the user is traveling. In this way, by considering the user's geographical location, it is possible to prioritize accepting illustrations that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then select highly relevant illustrations.

[0079] The reception desk can analyze the user's social media activity when receiving illustrations and accept relevant illustrations. For example, the reception desk may prioritize accepting illustrations that the user has shared on social media. For example, it may accept relevant illustrations based on the user's social media activity. The reception desk may also prioritize accepting illustrations related to the style of artists that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant illustrations. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity data into AI, and the AI ​​may select relevant illustrations.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, it can provide concise analysis results. The analysis unit can also provide visually appealing analysis results if the user is excited. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the illustrations. For example, the analysis unit will perform a detailed analysis for important illustrations, a standard analysis for general illustrations, and a concise analysis for simple illustrations. By adjusting the level of detail of the analysis based on the importance of the illustrations, the analysis unit can provide appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input illustration importance data into the AI, which can then adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the illustration during analysis. For example, in the case of character illustrations, the analysis unit analyzes pose and facial expression. For example, in the case of landscape illustrations, it analyzes perspective and color. In addition, in the case of abstract paintings, the analysis unit can analyze composition and color balance. By applying different analysis algorithms depending on the category of the illustration, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input illustration category data into AI, and the AI ​​can select an appropriate analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, it will provide a detailed analysis. The analysis unit can also provide a visually appealing analysis if the user is excited. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the analysis.

[0084] The analysis department can determine the priority of the analysis based on the submission date of the illustrations. For example, the analysis department might prioritize the analysis of recently submitted illustrations, while delaying the analysis of older illustrations. The analysis department can also determine the appropriate order of analysis based on the submission date. This allows for efficient analysis by prioritizing the analysis based on the submission date of the illustrations. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the illustration submission date data into an AI, which can then determine the priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the illustrations during the analysis process. For example, the analysis unit may group illustrations on the same theme together for analysis. For example, it may prioritize the analysis of highly relevant illustrations. The analysis unit can also postpone the analysis of less relevant illustrations. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the illustrations. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input illustration relevance data into an AI, which can then adjust the order of analysis.

[0086] The feedback function can estimate the user's emotions and adjust the way it expresses its feedback based on those emotions. For example, if the user is relaxed, the feedback function will provide detailed feedback. If the user is in a hurry, it will provide concise feedback. Furthermore, if the user is excited, the feedback function can provide visually appealing feedback. This allows for more appropriate feedback by adjusting the expression of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback function may be performed using or without a generative AI. For example, the feedback function can input user emotion data into a generative AI, which can then estimate the emotion and adjust its expression.

[0087] The feedback function can adjust the level of detail of its feedback based on the importance of the illustration. For example, it provides detailed feedback for important illustrations, standard feedback for general illustrations, and concise feedback for simple illustrations. By adjusting the level of detail based on the importance of the illustration, it can provide appropriate feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration importance data into the AI, which can then adjust the level of detail of the feedback.

[0088] The feedback function can apply different feedback algorithms depending on the category of the illustration. For example, in the case of character illustrations, it will provide feedback on pose and facial expression. For example, in the case of landscape illustrations, it will provide feedback on perspective and color. Furthermore, in the case of abstract paintings, the feedback function can also provide feedback on composition and color balance. By applying different feedback algorithms depending on the category of the illustration, it is possible to provide more accurate feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration category data into AI, and the AI ​​can select an appropriate feedback algorithm.

[0089] The feedback function can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback function will provide short, to-the-point feedback. If the user is relaxed, it will provide detailed feedback. The feedback function can also provide visually appealing feedback if the user is excited. By adjusting the length of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback function may be performed using generative AI or not. For example, the feedback function can input user emotion data into a generative AI, which can estimate the emotion and adjust the length of the feedback.

[0090] The feedback system can prioritize feedback based on the submission date of the illustrations. For example, it might prioritize recently submitted illustrations and postpone older ones. The feedback system can also determine an appropriate order of feedback based on the submission date. This allows for efficient feedback by prioritizing feedback based on the illustration submission date. Some or all of the above processing in the feedback system may be performed using AI or not. For example, the feedback system can input illustration submission date data into an AI, which can then determine the priority.

[0091] The feedback function can adjust the order of feedback based on the relevance of the illustrations. For example, it may group illustrations on the same theme together for feedback. For example, it may prioritize feedback on highly relevant illustrations. It can also postpone feedback on less relevant illustrations. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the illustrations. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function can input illustration relevance data into AI, which can then adjust the order of feedback.

[0092] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is in a hurry, it can provide concise advice. The service provider can also provide visually appealing advice if the user is excited. This allows for more appropriate advice to be provided by adjusting the way advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the way advice is presented.

[0093] The service provider can select the most appropriate advice by referring to the user's past feedback when providing advice. For example, the service provider can provide the most appropriate advice based on the advice the user has received in the past. For example, it can analyze the user's past feedback and select appropriate advice. The service provider can also suggest the most appropriate advice from the user's past advice history. In this way, the service provider can provide the most appropriate advice by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past feedback data into AI, and the AI ​​can select the most appropriate advice.

[0094] The service provider can adjust the level of detail of the advice based on the user's current skill level. For example, it might provide basic advice to beginners, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail according to the user's skill level, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's skill level data into the AI, which can then adjust the level of detail of the advice.

[0095] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is excited, the service provider will provide advice immediately. For example, if the user is relaxed, it will provide advice with the same priority as other advice. The service provider can also prioritize advice to reassure the user if they are feeling anxious. This allows for more appropriate timing of advice by determining the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of advice.

[0096] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. For instance, based on the user's location, it can provide advice related to local events or scenery. Furthermore, if the user is traveling, the service provider can provide advice related to their travel destination. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location into the AI, which can then select the most appropriate advice.

[0097] The service provider can analyze the user's social media activity and provide relevant advice when offering advice. For example, it can provide advice related to illustrations the user has shared on social media. For example, it can provide relevant advice based on the user's social media activity. The service provider can also provide advice related to the style of artists the user follows on social media. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can select relevant advice.

[0098] The character display unit can estimate the user's emotions and adjust the character's expression based on the estimated emotions. For example, if the user is relaxed, the character display unit can display a friendly and approachable character. For example, if the user is tense, it can display a calm character. The character display unit can also display an energetic and lively character if the user is excited. In this way, by adjusting the character's expression according to the user's emotions, a more approachable character can be displayed. Emotion estimation is achieved using an emotion estimation function with an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the character display unit may be performed using the generative AI or not. For example, the character display unit can input the user's emotion data into the generative AI, which can estimate the emotion and adjust the character's expression.

[0099] The character display unit can select the optimal character representation by referring to the user's past reactions when displaying characters. For example, the character display unit may prioritize displaying characters that the user has liked in the past. For example, it may analyze the user's past reactions and select the optimal character. The character display unit can also adjust the representation method of a particular character based on the user's past reactions. In this way, it can provide the optimal character representation by referring to the user's past reactions. Some or all of the above processing in the character display unit may be performed using AI or not. For example, the character display unit can input the user's past reaction data into AI, and the AI ​​can select the optimal character representation.

[0100] The character display unit can estimate the user's emotions and adjust the display order of characters based on the estimated emotions. For example, if the user is relaxed, the character display unit can display friendly characters first. For example, if the user is tense, it can display calm characters first. The character display unit can also display energetic characters first if the user is excited. In this way, by adjusting the display order of characters according to the user's emotions, more approachable characters can be displayed. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the character display unit may be performed using the generative AI or not. For example, the character display unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the display order of characters.

[0101] The character display unit can select the optimal display method when displaying a character, taking into account the user's device information. For example, if the user is using a smartphone, the character display unit will display a character that is sized to fit the screen. For example, if the user is using a tablet, the character display unit will display a character optimized for a larger screen. Furthermore, if the user is using a smartwatch, the character display unit can display a concise and highly visible character. In this way, the optimal character display can be provided by taking into account the user's device information. Some or all of the above processing in the character display unit may be performed using AI, or it may be performed without AI. For example, the character display unit can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception department can analyze a user's past illustration submission history and select the optimal reception method. For example, the reception department can analyze the frequency of illustrations the user has submitted in the past and suggest the optimal reception timing. For example, it can analyze the types of illustrations the user has submitted in the past and select an appropriate reception method. The reception department can also suggest the optimal reception interface based on the user's past submission history. In this way, by analyzing past submission history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past submission history data into AI, which can then select the optimal reception method.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, it can provide concise analysis results. The analysis unit can also provide visually appealing analysis results if the user is excited. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation.

[0105] The feedback function can estimate the user's emotions and adjust the way it expresses its feedback based on those emotions. For example, if the user is relaxed, the feedback function will provide detailed feedback. If the user is in a hurry, it will provide concise feedback. Furthermore, if the user is excited, the feedback function can provide visually appealing feedback. This allows for more appropriate feedback by adjusting the expression of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback function may be performed using or without a generative AI. For example, the feedback function can input user emotion data into a generative AI, which can then estimate the emotion and adjust its expression.

[0106] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is in a hurry, it can provide concise advice. The service provider can also provide visually appealing advice if the user is excited. This allows for more appropriate advice to be provided by adjusting the way advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the way advice is presented.

[0107] The service provider can select the most appropriate advice by referring to the user's past feedback when providing advice. For example, the service provider can provide the most appropriate advice based on the advice the user has received in the past. For example, it can analyze the user's past feedback and select appropriate advice. The service provider can also suggest the most appropriate advice from the user's past advice history. In this way, the service provider can provide the most appropriate advice by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past feedback data into AI, and the AI ​​can select the most appropriate advice.

[0108] The service provider can adjust the level of detail of the advice based on the user's current skill level. For example, it might provide basic advice to beginners, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail according to the user's skill level, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's skill level data into the AI, which can then adjust the level of detail of the advice.

[0109] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is excited, the service provider will provide advice immediately. For example, if the user is relaxed, it will provide advice with the same priority as other advice. The service provider can also prioritize advice to reassure the user if they are feeling anxious. This allows for more appropriate timing of advice by determining the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of advice.

[0110] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. For instance, based on the user's location, it can provide advice related to local events or scenery. Furthermore, if the user is traveling, the service provider can provide advice related to their travel destination. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location into the AI, which can then select the most appropriate advice.

[0111] The character display unit can estimate the user's emotions and adjust the character's expression based on the estimated emotions. For example, if the user is relaxed, the character display unit can display a friendly and approachable character. For example, if the user is tense, it can display a calm character. The character display unit can also display an energetic and lively character if the user is excited. In this way, by adjusting the character's expression according to the user's emotions, a more approachable character can be displayed. Emotion estimation is achieved using an emotion estimation function with an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the character display unit may be performed using the generative AI or not. For example, the character display unit can input the user's emotion data into the generative AI, which can estimate the emotion and adjust the character's expression.

[0112] The character display unit can select the optimal display method when displaying a character, taking into account the user's device information. For example, if the user is using a smartphone, the character display unit will display a character that is sized to fit the screen. For example, if the user is using a tablet, the character display unit will display a character optimized for a larger screen. Furthermore, if the user is using a smartwatch, the character display unit can display a concise and highly visible character. In this way, the optimal character display can be provided by taking into account the user's device information. Some or all of the above processing in the character display unit may be performed using AI, or it may be performed without AI. For example, the character display unit can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk accepts the user's digital illustration. This includes, for example, JPEG, PNG, and vector images. The reception desk provides an interface for users to upload their digital illustrations. Furthermore, when accepting a digital illustration, the reception desk can estimate the user's emotions and adjust the timing of the illustration's acceptance based on that estimation. For example, if the user is feeling stressed, the reception desk might wait until the user is able to relax before accepting the illustration. Step 2: The analysis department uses a generation AI to analyze the digital illustrations received by the reception department. The generation AI evaluates the illustrations from perspectives such as coloring and composition, and identifies areas for improvement. For example, it points out specific areas for improvement, such as poor color balance or an unnatural composition. Step 3: The feedback team points out areas for improvement in the illustration based on the analysis conducted by the analysis team. The feedback team will point out specific areas for improvement, such as color balance or unnatural composition. For example, they may provide specific advice on adjusting the color balance. Step 4: The providing unit provides the user with the improvements pointed out by the feedback unit. The providing unit includes a character display unit that provides advice to the user in friendly language. For example, by having a character provide advice to the user in friendly language, the user can receive feedback on their illustrations in a fun way without feeling fear or anxiety about the generating AI.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, feedback unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for uploading digital illustrations drawn by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the digital illustration using a generation AI. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and points out areas for improvement in the illustration based on the analysis results. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and includes a character display unit that provides advice in friendly language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for uploading digital illustrations drawn by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the digital illustration using a generation AI. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests areas for improvement in the illustration based on the analysis results. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and includes a character display unit that provides advice in a friendly language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, feedback unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for uploading digital illustrations drawn by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the digital illustration using a generation AI. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and points out areas for improvement in the illustration based on the analysis results. The provision unit is implemented, for example, by the speaker 240 of the headset terminal 314 and includes a character display unit that provides advice in friendly language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, feedback unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for uploading digital illustrations drawn by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the digital illustration using a generation AI. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and points out areas for improvement in the illustration based on the analysis results. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and includes a character display unit that provides advice in a friendly language. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception desk that accepts digital illustrations from users, An analysis unit that analyzes the digital illustrations received by the reception unit, Based on the results of the analysis performed by the aforementioned analysis unit, the suggestion unit points out areas for improvement in the illustration, The system includes a provisioning unit that provides the user with the improvements identified by the aforementioned pointing unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Evaluate illustrations from perspectives such as coloring and composition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned point is, I pointed out specific areas for improvement, such as the balance of colors and the unnaturalness of the composition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features a character display that provides advice to the user in user-friendly language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides users with specific advice to help them improve their illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of illustration requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past illustration submission history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving illustrations, the system filters them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of illustrations to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When accepting illustrations, the system prioritizes accepting illustrations that are highly relevant to the user's geographical location, taking into account the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting illustrations, the system analyzes the user's social media activity and accepts illustrations that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the illustration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the illustrations were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned point is, It estimates the user's emotions and adjusts the way criticisms are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned point is, When providing feedback, adjust the level of detail based on the importance of the illustration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned point is, When providing feedback, different feedback algorithms are applied depending on the category of the illustration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned point is, It estimates the user's emotions and adjusts the length of the comment based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned point is, When providing feedback, the priority of the feedback will be determined based on when the illustrations were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned point is, When providing feedback, adjust the order of feedback based on the relevance of the illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing advice, the system selects the most appropriate advice by referring to the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity to provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned character display unit is The system estimates the user's emotions and adjusts the character's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned character display unit is When displaying a character, the system selects the most appropriate character representation by referring to the user's past reactions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned character display unit is It estimates the user's emotions and adjusts the display order of characters based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned character display unit is When displaying characters, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts digital illustrations from users, An analysis unit that analyzes the digital illustrations received by the reception unit, Based on the results of the analysis performed by the aforementioned analysis unit, the suggestion unit points out areas for improvement in the illustration, The system includes a provisioning unit that provides the user with the improvements identified by the aforementioned pointing unit. A system characterized by the following features.

2. The aforementioned analysis unit is Evaluate illustrations from perspectives such as coloring and composition. The system according to feature 1.

3. The aforementioned point is, I pointed out specific areas for improvement, such as the balance of colors and the unnaturalness of the composition. The system according to feature 1.

4. The aforementioned supply unit is, It features a character display that provides advice to the user in user-friendly language. The system according to feature 1.

5. The aforementioned supply unit is, Provides users with specific advice to help them improve their illustrations. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of illustration requests based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past illustration submission history and select the most suitable submission method. The system according to feature 1.

8. The aforementioned reception unit is When receiving illustrations, the system filters them based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of illustrations to accept based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When accepting illustrations, the system prioritizes accepting illustrations that are highly relevant to the user's geographical location, taking into account the user's location information. The system according to feature 1.

Images