system

Abstract

The system according to this embodiment aims to provide personalized design suggestions based on the user's past design tendencies and preferences. [Solution] The system according to the embodiment comprises a learning unit, an analysis unit, a proposal unit, and a feedback unit. The learning unit learns the user's past design tendencies and preferences. The analysis unit analyzes design trends based on the information learned by the learning unit. The proposal unit makes design proposals tailored to the user's preferences based on the analysis results obtained by the analysis unit. The feedback unit learns the user's feedback on the designs proposed by the proposal unit and refines the designs.

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, personalized design proposals based on the user's past design trends and preferences have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to make a personalized design proposal based on the user's past design trends and preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an analysis unit, a proposal unit, and a feedback unit. The learning unit learns the user's past design tendencies and preferences. The analysis unit analyzes design trends based on the information learned by the learning unit. The proposal unit makes design suggestions tailored to the user's preferences based on the analysis results obtained by the analysis unit. The feedback unit learns the user's feedback on the designs proposed by the proposal unit and refines the designs. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized design suggestions based on the user's past design tendencies and preferences. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 design support system according to an embodiment of the present invention is a system that utilizes AI to support designers in generating ideas and provides personalized design suggestions. This design support system learns the user's past design tendencies and preferences, analyzes design trends, provides design suggestions tailored to the user's preferences, and refines the design through interactive feedback. For example, the AI ​​in the design support system analyzes designs the user has created in the past and the characteristics of designs the user prefers. This allows the design support system to understand the user's design tendencies and preferences. Next, the AI ​​in the design support system analyzes design trends. The AI ​​analyzes the latest design trends and provides design suggestions tailored to the user's preferences. For example, it may suggest designs that incorporate currently popular design styles and color schemes. This allows the user to create designs that incorporate the latest trends. Furthermore, the design support system refines the design through interactive feedback. By providing feedback to the design suggested by the AI, the AI ​​learns from the feedback and improves the design. For example, if the user modifies part of the suggested design, the AI ​​learns from the modification and reflects it in the next suggestion. This results in design suggestions that are more suitable to the user's preferences. This mechanism improves the quality of the design. By having AI learn user preferences and provide suggestions incorporating the latest trends, users can create higher-quality designs. Furthermore, the design process time is shortened. The AI ​​automatically generates design suggestions and learns from user feedback, reducing design creation time. Additionally, user satisfaction is improved. By having AI provide suggestions tailored to user preferences and refine designs through interactive feedback, users can create more satisfying designs. This AI agent utilizes the latest machine learning technology to automatically generate and optimize designs.This will innovate the creative process and provide optimal support to designers who need new ideas in the design process and clients who seek personalized designs. The design support system will assist designers in generating ideas and provide personalized design proposals.

[0029] The design support system according to this embodiment comprises a learning unit, an analysis unit, a proposal unit, and a feedback unit. The learning unit learns the user's past design tendencies and preferences. For example, the learning unit learns designs the user has created in the past and the characteristics of designs the user likes. For example, the learning unit can learn the color schemes, layouts, and font selections of designs the user has created in the past. The learning unit can also learn the characteristics of designs the user likes. For example, the learning unit can learn the user's preferred color schemes and design styles. The analysis unit analyzes design trends based on the information learned by the learning unit. For example, the analysis unit analyzes the latest design trends. For example, the analysis unit can make proposals that incorporate currently popular design styles and color schemes. The analysis unit can also use AI to analyze design trends. For example, the analysis unit can use AI to analyze the latest design trends. The proposal unit makes design proposals tailored to the user's preferences based on the analysis results obtained by the analysis unit. For example, the proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make proposals that incorporate currently popular design styles and color schemes. Furthermore, the proposal unit can utilize AI to make design proposals. For instance, the proposal unit can use AI to provide design proposals tailored to the user's preferences. The feedback unit learns user feedback on the designs proposed by the proposal unit and refines the designs. For example, if the user modifies a part of a proposed design, the feedback unit can learn from the modifications and reflect them in future proposals. The feedback unit can also utilize AI to learn from user feedback. For instance, the feedback unit can use AI to learn from user feedback and improve the designs. As a result, the design support system according to this embodiment can learn the user's past design tendencies and preferences, analyze design trends, provide design proposals tailored to the user's preferences, and refine the designs by learning from feedback.

[0030] The learning unit learns the user's past design tendencies and preferences. Specifically, it collects data on designs the user has created in the past and extracts features such as color usage, layout, and font selection. These features are important elements that indicate the user's design tendencies, and the learning unit models the user's preferences based on this data. For example, it identifies and learns the color combinations, specific layout patterns, and font types that the user frequently uses. It also collects data on the user's evaluations of other designs and learns the characteristics of designs the user prefers. This includes the color usage, style, and placement of design elements in designs that the user highly rated. Using this data, the learning unit can gain a detailed understanding of the user's design preferences and reflect this in future design proposals. Furthermore, the learning unit also learns the user's design change history and modifications, tracking the evolution and changes in the user's designs. This allows for an understanding of how the user's design preferences change over time, enabling more appropriate design proposals. The learning unit centrally manages this data and can collaborate with other departments as needed to improve the overall performance of the design support system.

[0031] The Analysis Department analyzes design trends based on information learned by the Learning Department. Specifically, it analyzes the latest design trends and provides foundational data for design suggestions tailored to user preferences. The Analysis Department collects the latest design information from online design-related information sources, design contest results, and design magazines, and analyzes this information using AI. The AI ​​utilizes image recognition and natural language processing technologies to identify design trends and extract elements such as color schemes, layouts, and font selections. For example, it can identify currently popular design styles and color schemes and suggest them to users based on their preferences. The Analysis Department can also track changes in design trends and predict future trends based on past design data and user feedback. This allows the Analysis Department to provide users with the latest design information and support their design activities. Furthermore, the Analysis Department can efficiently process large amounts of design data by utilizing cloud computing technology to analyze design trends. This enables the Analysis Department to provide the latest design information in real time and support users' design activities quickly and effectively.

[0032] The proposal department provides design suggestions tailored to user preferences based on the analysis results obtained by the analysis department. Specifically, it generates optimal design suggestions by considering the user's past design tendencies and preferences, as well as the latest design trends. The proposal department uses AI to provide design suggestions tailored to user preferences. The AI ​​generates the optimal design for the user based on the user's past design data and feedback, as well as the latest design trends. For example, it may propose a design that incorporates the user's preferred color scheme and design style. Furthermore, the proposal department can provide optimal design suggestions according to the user's design purpose and context. For example, if the design created by the user is a website design, the proposal department will propose a design that incorporates the latest web design trends. In addition, the proposal department can improve its design suggestions based on user feedback. If the user makes revisions to a proposed design, the system learns from those revisions and incorporates them into future suggestions. In this way, the proposal department can continuously improve its design suggestions tailored to user preferences and support the user's design activities. To provide these design suggestions to users, the proposal department has an intuitive and easy-to-use interface, allowing users to easily review proposed designs and make revisions or provide feedback.

[0033] The Feedback Department learns from user feedback on designs proposed by the Proposal Department and refines the designs. Specifically, if a user modifies a part of a proposed design, the Feedback Department analyzes the modifications in detail and incorporates them into the next proposal. The Feedback Department uses AI to learn from user feedback and improve designs. The AI ​​analyzes the user's modifications and feedback patterns to gain a more detailed understanding of user preferences and design tendencies. For example, if a user prefers a particular color scheme or layout, this information is used to adjust the next design proposal. The Feedback Department can also collect user feedback in real time and quickly incorporate it into design proposals. This ensures that users always receive the latest design proposals and improves the quality of their designs. Furthermore, the Feedback Department can centrally manage user feedback and collaborate with other departments to improve the overall performance of the design support system. For example, the Feedback Department can collaborate with the Learning Department and the Analysis Department to share user preferences and design tendencies, improving the accuracy of design proposals across the entire system. This allows the Feedback Department to effectively utilize user feedback and improve the overall performance of the design support system.

[0034] The learning unit can learn the user's past designs and the design characteristics the user prefers. For example, the learning unit can learn the color schemes, layouts, and font choices of the user's past designs. For example, the learning unit can learn the user's preferred color schemes and design styles. For example, the learning unit can learn the color schemes, layouts, and font choices of the user's past designs. This allows the user's design tendencies and preferences to be understood. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data of designs the user has created in the past into a generating AI and have the generating AI perform the learning of design characteristics.

[0035] The analysis unit can analyze the latest design trends. For example, the analysis unit can analyze the latest design trends. For example, the analysis unit can make proposals that incorporate currently popular design styles and color schemes. For example, the analysis unit can analyze the latest design trends. This allows for design proposals tailored to the user's preferences. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the latest design trends into a generating AI and have the generating AI perform the design trend analysis.

[0036] The proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make proposals that incorporate currently popular design styles and color schemes. For example, the proposal unit can make design proposals tailored to the user's preferences. This can improve user satisfaction. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have a generation AI execute design proposals tailored to the user's preferences.

[0037] The feedback unit can learn from user feedback and incorporate it into future proposals. For example, if a user modifies part of a proposed design, the feedback unit can learn from that modification and incorporate it into future proposals. The feedback unit can learn from user feedback and incorporate it into future proposals. This can improve the quality of the design. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user feedback into a generating AI and have the generating AI learn from the feedback.

[0038] The learning unit can analyze a user's past design history over time and learn about the evolution and changes in their designs. For example, the learning unit can analyze designs created by a user in the past in chronological order to understand the changes in their designs. For example, the learning unit can extract the design features that a user preferred during a specific period and learn about the changes in those features. For example, the learning unit can analyze the changes in the design tools and techniques that a user has used in the past and learn about the evolution of their designs. In this way, by analyzing a user's past design history over time, the learning unit can learn about the evolution and changes in their designs. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input data on the user's past design history into a generating AI and have the generating AI perform the learning of the evolution and changes in their designs.

[0039] The learning unit can learn the user's operation history during design creation and understand patterns in the design process. For example, the learning unit can learn the tools and sequence of operations the user uses when creating a design. For example, the learning unit can analyze the frequency and timing of specific operations performed by the user to understand patterns in the design process. For example, the learning unit can learn the history of modifications and adjustments made by the user during design creation to understand the characteristics of the design process. In this way, by learning the user's operation history during design creation, patterns in the design process can be understood. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's operation history during design creation into a generating AI and have the generating AI perform the learning of patterns in the design process.

[0040] The learning unit can learn not only the user's design history but also their browsing and purchase history to gain a more detailed understanding of their design preferences. For example, the learning unit can learn about design-related websites and articles the user has previously viewed. For example, the learning unit can learn about design-related products and services the user has previously purchased. For example, the learning unit can learn about design-related events and workshops the user has previously attended. This allows for a more detailed understanding of the user's design preferences by learning about their browsing and purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data from the user's browsing and purchase history into a generating AI, allowing the generating AI to perform a detailed understanding of the user's design preferences.

[0041] The learning unit can compare a user's design history with that of other users and learn commonalities and differences. For example, the learning unit can compare a user's design history with that of other users and extract common design features. For example, the learning unit can compare a user's design history with that of other users and extract different design features. For example, the learning unit can compare a user's design history with that of other users and learn differences in design trends and preferences. In this way, by comparing a user's design history with that of other users, commonalities and differences can be learned. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data of the user's design history and other users' design histories into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0042] The analysis unit can predict current trends by referring to past trend data when analyzing design trends. For example, the analysis unit predicts current trends based on past design trend data. For example, the analysis unit can analyze past trend data in a time series to understand the changes in trends. For example, the analysis unit can identify factors that influence current trends by referring to past trend data. This allows the analysis unit to predict current trends by referring to past trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0043] The analysis unit can analyze design trends for each different design category. For example, it can analyze trends for each category, such as graphic design, interior design, and fashion design. The analysis unit can compare trend data for each category to identify similarities and differences. For example, it can analyze the trends for each category over time to understand how trends change. By analyzing trends for each different design category, it is possible to understand the trends for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trend data for each different design category into a generating AI and have the generating AI perform the trend analysis.

[0044] The analysis unit can perform design trend analysis while considering regional trends. For example, the analysis unit can collect design trend data for each region and analyze the trends for each region. For example, the analysis unit can compare regional trend data and identify commonalities and differences. For example, the analysis unit can analyze regional trends over time and understand the changes in trends. In this way, by performing analysis while considering regional trends, it is possible to understand regional trends. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional trend data into a generating AI and have the generating AI perform the analysis of regional trends.

[0045] The analysis department can perform design trend analysis by referring to trend data from related industries. For example, the analysis department can collect and analyze trend data from related industries such as the fashion industry, interior design industry, and graphic design industry. For example, the analysis department can compare trend data from each industry to identify commonalities and differences. For example, the analysis department can analyze trends in each industry over time to understand how trends change. By referring to trend data from related industries, the accuracy of design trend analysis can be improved. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input trend data from related industries into a generating AI and have the generating AI perform the trend analysis.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the design. For example, the proposal unit can provide detailed explanations and suggestions for highly important designs. For example, it can provide concise explanations and suggestions for less important designs. The proposal unit can adjust the level of detail of a proposal in stages according to its importance. This allows for detailed suggestions for important designs by adjusting the level of detail based on the importance of the design. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the importance of the designs into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the design category when making a proposal. For example, the proposal unit can apply different proposal algorithms for each category, such as graphic design, interior design, and fashion design. For example, the proposal unit can select the optimal proposal algorithm according to the characteristics of each category. For example, the proposal unit can adjust the proposal algorithm considering the design elements of each category. In this way, by applying different proposal algorithms according to the design category, it is possible to make the most suitable proposal for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input design category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the design submission deadlines. For example, the proposal department can prioritize proposals for designs with approaching deadlines. For example, the proposal department can provide detailed proposals for designs with ample time before the submission deadline. The proposal department can adjust the priority of proposals in stages according to the submission deadlines. This allows for prioritizing proposals based on the design submission deadlines, ensuring that designs with approaching deadlines receive priority. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input design submission deadline data into a generating AI and have the generating AI determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the designs during the proposal process. For example, the proposal unit can prioritize proposals for highly relevant designs. For example, the proposal unit can provide concise proposals for less relevant designs. For example, the proposal unit can adjust the order of proposals in stages according to their relevance. This allows for prioritizing proposals for highly relevant designs by adjusting the order of proposals based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the relevance of designs into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0050] The feedback unit can select the optimal method of reflection by referring to the user's past feedback history when providing feedback. For example, the feedback unit can analyze the user's past feedback and select the optimal method of reflection. For example, the feedback unit can extract specific patterns from the user's past feedback history and adjust the reflection method. For example, the feedback unit can optimize the feedback reflection method by referring to the user's past feedback history. This allows the optimal method of reflecting feedback to be selected by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data from the user's past feedback history into a generating AI and have the generating AI select the optimal reflection method.

[0051] The feedback unit can customize the next suggestion based on the design modifications made during the feedback process. For example, the feedback unit can learn the design modifications made by the user and reflect them in the next suggestion. For example, the feedback unit can analyze the characteristics of the design modified by the user and customize the next suggestion. For example, the feedback unit can optimize the next suggestion based on the user's modifications. This allows for suggestions that are better suited to the user's preferences by customizing the next suggestion based on the design modifications. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the design modifications into a generating AI and have the generating AI perform the customization of the next suggestion.

[0052] The feedback unit can reflect the user's design history when providing feedback. For example, the feedback unit can refer to the user's design history and reflect feedback based on past designs. For example, the feedback unit can analyze the user's design history and reflect optimal feedback. For example, the feedback unit can adjust the content of the feedback considering the user's design history. This allows for optimal feedback based on past designs by reflecting feedback while considering the user's design history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user design history data into a generating AI and have the generating AI perform the feedback reflection.

[0053] The feedback unit can optimize its feedback method by referring to other users' feedback when providing feedback. For example, the feedback unit can refer to feedback provided by other users and select the optimal method of reflection. For example, the feedback unit can analyze the feedback history of other users, extract common patterns, and adjust the reflection method. For example, the feedback unit can optimize the feedback reflection method by referring to other users' feedback. This allows the optimal method of reflecting feedback to be selected by referring to other users' feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on other users' feedback into a generating AI and have the generating AI perform the optimization of the reflection method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The design support system may further include an operation history learning unit that learns the user's operation history during design creation. The operation history learning unit can learn the tools and sequence of operations used by the user during design creation. For example, it can analyze the frequency and timing of specific operations performed by the user to understand patterns in the design process. Thus, by learning the user's operation history during design creation, patterns in the design process can be understood. Some or all of the above-described processes in the operation history learning unit may be performed using AI, for example, or without AI. For example, the operation history learning unit can input data on the user's operation history during design creation into a generating AI and cause the generating AI to learn patterns in the design process.

[0056] The design support system may further include a comparative learning unit that compares a user's design history with that of other users to learn commonalities and differences. The comparative learning unit can, for example, compare a user's design history with that of other users to extract common design features. It can also compare a user's design history with that of other users to extract different design features. In this way, by comparing a user's design history with that of other users, it is possible to learn commonalities and differences. Some or all of the above processing in the comparative learning unit may be performed using AI, for example, or without AI. For example, the comparative learning unit can input data of the user's design history and that of other users into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0057] The design support system may also include a history learning unit that learns the user's browsing and purchase history in addition to the user's design history. The history learning unit can, for example, learn design-related websites and articles that the user has previously viewed. It can also learn design-related products and services that the user has previously purchased. This allows for a more detailed understanding of the user's design preferences by learning their browsing and purchase history. Some or all of the above-described processes in the history learning unit may be performed using AI, for example, or without AI. For example, the history learning unit can input data from the user's browsing and purchase history into a generating AI, allowing the generating AI to perform a detailed understanding of the user's design preferences.

[0058] The design support system may further include a feedback history unit that selects the optimal method of reflection by referring to the user's past feedback history. The feedback history unit, for example, analyzes the user's past feedback and selects the optimal method of reflection. It can extract specific patterns from the user's past feedback history and adjust the reflection method. This makes it possible to select the optimal method of reflection of feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback history unit may be performed using AI, for example, or without AI. For example, the feedback history unit can input data from the user's past feedback history into a generating AI and have the generating AI perform the selection of the optimal reflection method.

[0059] The design support system may further include a comparative learning unit that compares a user's design history with that of other users to learn commonalities and differences. The comparative learning unit can, for example, compare a user's design history with that of other users to extract common design features. It can also compare a user's design history with that of other users to extract different design features. In this way, by comparing a user's design history with that of other users, it is possible to learn commonalities and differences. Some or all of the above processing in the comparative learning unit may be performed using AI, for example, or without AI. For example, the comparative learning unit can input data of the user's design history and that of other users into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns the user's past design tendencies and preferences. For example, it learns the color schemes, layouts, and font choices of designs the user has created in the past. It can also learn the design characteristics the user prefers, such as color schemes and design styles. Step 2: The analysis unit analyzes design trends based on the information learned by the learning unit. For example, it analyzes the latest design trends and makes proposals incorporating currently popular design styles and color schemes. The analysis unit can also use AI to analyze design trends. Step 3: The proposal department creates design proposals tailored to the user's preferences based on the analysis results obtained by the analysis department. For example, they might propose designs that incorporate currently popular design styles and color schemes. The proposal department can also use AI to create design proposals tailored to the user's preferences. Step 4: The feedback unit learns from user feedback on the design proposed by the proposal unit and refines the design. For example, if a user modifies part of the proposed design, the feedback unit learns from that modification and incorporates it into the next proposal. The feedback unit can also use AI to learn from user feedback and improve the design.

[0062] (Example of form 2) The design support system according to an embodiment of the present invention is a system that utilizes AI to support designers in generating ideas and provides personalized design suggestions. This design support system learns the user's past design tendencies and preferences, analyzes design trends, provides design suggestions tailored to the user's preferences, and refines the design through interactive feedback. For example, the AI ​​in the design support system analyzes designs the user has created in the past and the characteristics of designs the user prefers. This allows the design support system to understand the user's design tendencies and preferences. Next, the AI ​​in the design support system analyzes design trends. The AI ​​analyzes the latest design trends and provides design suggestions tailored to the user's preferences. For example, it may suggest designs that incorporate currently popular design styles and color schemes. This allows the user to create designs that incorporate the latest trends. Furthermore, the design support system refines the design through interactive feedback. By providing feedback to the design suggested by the AI, the AI ​​learns from the feedback and improves the design. For example, if the user modifies part of the suggested design, the AI ​​learns from the modification and reflects it in the next suggestion. This results in design suggestions that are more suitable to the user's preferences. This mechanism improves the quality of the design. By having AI learn user preferences and provide suggestions incorporating the latest trends, users can create higher-quality designs. Furthermore, the design process time is shortened. The AI ​​automatically generates design suggestions and learns from user feedback, reducing design creation time. Additionally, user satisfaction is improved. By having AI provide suggestions tailored to user preferences and refine designs through interactive feedback, users can create more satisfying designs. This AI agent utilizes the latest machine learning technology to automatically generate and optimize designs.This will innovate the creative process and provide optimal support to designers who need new ideas in the design process and clients who seek personalized designs. The design support system will assist designers in generating ideas and provide personalized design proposals.

[0063] The design support system according to this embodiment comprises a learning unit, an analysis unit, a proposal unit, and a feedback unit. The learning unit learns the user's past design tendencies and preferences. For example, the learning unit learns designs the user has created in the past and the characteristics of designs the user likes. For example, the learning unit can learn the color schemes, layouts, and font selections of designs the user has created in the past. The learning unit can also learn the characteristics of designs the user likes. For example, the learning unit can learn the user's preferred color schemes and design styles. The analysis unit analyzes design trends based on the information learned by the learning unit. For example, the analysis unit analyzes the latest design trends. For example, the analysis unit can make proposals that incorporate currently popular design styles and color schemes. The analysis unit can also use AI to analyze design trends. For example, the analysis unit can use AI to analyze the latest design trends. The proposal unit makes design proposals tailored to the user's preferences based on the analysis results obtained by the analysis unit. For example, the proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make proposals that incorporate currently popular design styles and color schemes. Furthermore, the proposal unit can utilize AI to make design proposals. For instance, the proposal unit can use AI to provide design proposals tailored to the user's preferences. The feedback unit learns user feedback on the designs proposed by the proposal unit and refines the designs. For example, if the user modifies a part of a proposed design, the feedback unit can learn from the modifications and reflect them in future proposals. The feedback unit can also utilize AI to learn from user feedback. For instance, the feedback unit can use AI to learn from user feedback and improve the designs. As a result, the design support system according to this embodiment can learn the user's past design tendencies and preferences, analyze design trends, provide design proposals tailored to the user's preferences, and refine the designs by learning from feedback.

[0064] The learning unit learns the user's past design tendencies and preferences. Specifically, it collects data on designs the user has created in the past and extracts features such as color usage, layout, and font selection. These features are important elements that indicate the user's design tendencies, and the learning unit models the user's preferences based on this data. For example, it identifies and learns the color combinations, specific layout patterns, and font types that the user frequently uses. It also collects data on the user's evaluations of other designs and learns the characteristics of designs the user prefers. This includes the color usage, style, and placement of design elements in designs that the user highly rated. Using this data, the learning unit can gain a detailed understanding of the user's design preferences and reflect this in future design proposals. Furthermore, the learning unit also learns the user's design change history and modifications, tracking the evolution and changes in the user's designs. This allows for an understanding of how the user's design preferences change over time, enabling more appropriate design proposals. The learning unit centrally manages this data and can collaborate with other departments as needed to improve the overall performance of the design support system.

[0065] The Analysis Department analyzes design trends based on information learned by the Learning Department. Specifically, it analyzes the latest design trends and provides foundational data for design suggestions tailored to user preferences. The Analysis Department collects the latest design information from online design-related information sources, design contest results, and design magazines, and analyzes this information using AI. The AI ​​utilizes image recognition and natural language processing technologies to identify design trends and extract elements such as color schemes, layouts, and font selections. For example, it can identify currently popular design styles and color schemes and suggest them to users based on their preferences. The Analysis Department can also track changes in design trends and predict future trends based on past design data and user feedback. This allows the Analysis Department to provide users with the latest design information and support their design activities. Furthermore, the Analysis Department can efficiently process large amounts of design data by utilizing cloud computing technology to analyze design trends. This enables the Analysis Department to provide the latest design information in real time and support users' design activities quickly and effectively.

[0066] The proposal department provides design suggestions tailored to user preferences based on the analysis results obtained by the analysis department. Specifically, it generates optimal design suggestions by considering the user's past design tendencies and preferences, as well as the latest design trends. The proposal department uses AI to provide design suggestions tailored to user preferences. The AI ​​generates the optimal design for the user based on the user's past design data and feedback, as well as the latest design trends. For example, it may propose a design that incorporates the user's preferred color scheme and design style. Furthermore, the proposal department can provide optimal design suggestions according to the user's design purpose and context. For example, if the design created by the user is a website design, the proposal department will propose a design that incorporates the latest web design trends. In addition, the proposal department can improve its design suggestions based on user feedback. If the user makes revisions to a proposed design, the system learns from those revisions and incorporates them into future suggestions. In this way, the proposal department can continuously improve its design suggestions tailored to user preferences and support the user's design activities. To provide these design suggestions to users, the proposal department has an intuitive and easy-to-use interface, allowing users to easily review proposed designs and make revisions or provide feedback.

[0067] The Feedback Department learns from user feedback on designs proposed by the Proposal Department and refines the designs. Specifically, if a user modifies a part of a proposed design, the Feedback Department analyzes the modifications in detail and incorporates them into the next proposal. The Feedback Department uses AI to learn from user feedback and improve designs. The AI ​​analyzes the user's modifications and feedback patterns to gain a more detailed understanding of user preferences and design tendencies. For example, if a user prefers a particular color scheme or layout, this information is used to adjust the next design proposal. The Feedback Department can also collect user feedback in real time and quickly incorporate it into design proposals. This ensures that users always receive the latest design proposals and improves the quality of their designs. Furthermore, the Feedback Department can centrally manage user feedback and collaborate with other departments to improve the overall performance of the design support system. For example, the Feedback Department can collaborate with the Learning Department and the Analysis Department to share user preferences and design tendencies, improving the accuracy of design proposals across the entire system. This allows the Feedback Department to effectively utilize user feedback and improve the overall performance of the design support system.

[0068] The learning unit can learn the user's past designs and the design characteristics the user prefers. For example, the learning unit can learn the color schemes, layouts, and font choices of the user's past designs. For example, the learning unit can learn the user's preferred color schemes and design styles. For example, the learning unit can learn the color schemes, layouts, and font choices of the user's past designs. This allows the user's design tendencies and preferences to be understood. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data of designs the user has created in the past into a generating AI and have the generating AI perform the learning of design characteristics.

[0069] The analysis unit can analyze the latest design trends. For example, the analysis unit can analyze the latest design trends. For example, the analysis unit can make proposals that incorporate currently popular design styles and color schemes. For example, the analysis unit can analyze the latest design trends. This allows for design proposals tailored to the user's preferences. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the latest design trends into a generating AI and have the generating AI perform the design trend analysis.

[0070] The proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make design proposals tailored to the user's preferences. For example, the proposal unit can make proposals that incorporate currently popular design styles and color schemes. For example, the proposal unit can make design proposals tailored to the user's preferences. This can improve user satisfaction. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have a generation AI execute design proposals tailored to the user's preferences.

[0071] The feedback unit can learn from user feedback and incorporate it into future proposals. For example, if a user modifies part of a proposed design, the feedback unit can learn from that modification and incorporate it into future proposals. The feedback unit can learn from user feedback and incorporate it into future proposals. This can improve the quality of the design. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user feedback into a generating AI and have the generating AI learn from the feedback.

[0072] The learning unit can estimate the user's emotions and select design features to learn based on the estimated user emotions. For example, if the user is stressed, the learning unit may prioritize relaxing colors and simple designs. If the user is excited, the learning unit may prioritize vibrant colors and bold designs. If the user is calm, the learning unit may prioritize balanced designs and harmonious colors. This allows the system to learn designs that are appropriate for 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 learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI select design features based on emotions.

[0073] The learning unit can analyze a user's past design history over time and learn about the evolution and changes in their designs. For example, the learning unit can analyze designs created by a user in the past in chronological order to understand the changes in their designs. For example, the learning unit can extract the design features that a user preferred during a specific period and learn about the changes in those features. For example, the learning unit can analyze the changes in the design tools and techniques that a user has used in the past and learn about the evolution of their designs. In this way, by analyzing a user's past design history over time, the learning unit can learn about the evolution and changes in their designs. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input data on the user's past design history into a generating AI and have the generating AI perform the learning of the evolution and changes in their designs.

[0074] The learning unit can learn the user's operation history during design creation and understand patterns in the design process. For example, the learning unit can learn the tools and sequence of operations the user uses when creating a design. For example, the learning unit can analyze the frequency and timing of specific operations performed by the user to understand patterns in the design process. For example, the learning unit can learn the history of modifications and adjustments made by the user during design creation to understand the characteristics of the design process. In this way, by learning the user's operation history during design creation, patterns in the design process can be understood. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's operation history during design creation into a generating AI and have the generating AI perform the learning of patterns in the design process.

[0075] The learning unit can estimate the user's emotions and determine the design priorities to learn based on the estimated user emotions. For example, if the user is stressed, the learning unit may prioritize relaxing designs. For example, if the user is excited, the learning unit may prioritize stimulating designs. For example, if the user is calm, the learning unit may prioritize balanced designs. In this way, by determining the design priorities to learn based on the user's emotions, designs that are appropriate for the user's emotions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using 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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the determination of design priorities based on emotions.

[0076] The learning unit can learn not only the user's design history but also their browsing and purchase history to gain a more detailed understanding of their design preferences. For example, the learning unit can learn about design-related websites and articles the user has previously viewed. For example, the learning unit can learn about design-related products and services the user has previously purchased. For example, the learning unit can learn about design-related events and workshops the user has previously attended. This allows for a more detailed understanding of the user's design preferences by learning about their browsing and purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data from the user's browsing and purchase history into a generating AI, allowing the generating AI to perform a detailed understanding of the user's design preferences.

[0077] The learning unit can compare a user's design history with that of other users and learn commonalities and differences. For example, the learning unit can compare a user's design history with that of other users and extract common design features. For example, the learning unit can compare a user's design history with that of other users and extract different design features. For example, the learning unit can compare a user's design history with that of other users and learn differences in design trends and preferences. In this way, by comparing a user's design history with that of other users, commonalities and differences can be learned. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data of the user's design history and other users' design histories into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0078] The analysis unit can estimate the user's emotions and adjust the design trend analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will perform a trend analysis at a leisurely pace. For example, if the user is in a hurry, the analysis unit can adjust the analysis method to quickly grasp the trends. For example, if the user is excited, the analysis unit can highlight visually stimulating trends in the analysis. In this way, by adjusting the design trend analysis method based on the user's emotions, a trend analysis that is appropriate for the user's emotions can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the design trend analysis method based on emotions.

[0079] The analysis unit can predict current trends by referring to past trend data when analyzing design trends. For example, the analysis unit predicts current trends based on past design trend data. For example, the analysis unit can analyze past trend data in a time series to understand the changes in trends. For example, the analysis unit can identify factors that influence current trends by referring to past trend data. This allows the analysis unit to predict current trends by referring to past trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0080] The analysis unit can analyze design trends for each different design category. For example, it can analyze trends for each category, such as graphic design, interior design, and fashion design. The analysis unit can compare trend data for each category to identify similarities and differences. For example, it can analyze the trends for each category over time to understand how trends change. By analyzing trends for each different design category, it is possible to understand the trends for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trend data for each different design category into a generating AI and have the generating AI perform the trend analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the importance of trends based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can analyze even low-importance trends. For example, if the user is in a hurry, the analysis unit can focus on high-importance trends. For example, if the user is excited, the analysis unit can highlight and analyze visually stimulating trends. This allows for trend analysis tailored to the user's emotions by adjusting the importance of trends based on their 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of trend importance based on emotions.

[0082] The analysis unit can perform design trend analysis while considering regional trends. For example, the analysis unit can collect design trend data for each region and analyze the trends for each region. For example, the analysis unit can compare regional trend data and identify commonalities and differences. For example, the analysis unit can analyze regional trends over time and understand the changes in trends. In this way, by performing analysis while considering regional trends, it is possible to understand regional trends. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional trend data into a generating AI and have the generating AI perform the analysis of regional trends.

[0083] The analysis department can perform design trend analysis by referring to trend data from related industries. For example, the analysis department can collect and analyze trend data from related industries such as the fashion industry, interior design industry, and graphic design industry. For example, the analysis department can compare trend data from each industry to identify commonalities and differences. For example, the analysis department can analyze trends in each industry over time to understand how trends change. By referring to trend data from related industries, the accuracy of design trend analysis can be improved. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input trend data from related industries into a generating AI and have the generating AI perform the trend analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, suggestions appropriate to the user's emotions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented based on those emotions.

[0085] The proposal unit can adjust the level of detail of a proposal based on the importance of the design. For example, the proposal unit can provide detailed explanations and suggestions for highly important designs. For example, it can provide concise explanations and suggestions for less important designs. The proposal unit can adjust the level of detail of a proposal in stages according to its importance. This allows for detailed suggestions for important designs by adjusting the level of detail based on the importance of the design. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the importance of the designs into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the design category when making a proposal. For example, the proposal unit can apply different proposal algorithms for each category, such as graphic design, interior design, and fashion design. For example, the proposal unit can select the optimal proposal algorithm according to the characteristics of each category. For example, the proposal unit can adjust the proposal algorithm considering the design elements of each category. In this way, by applying different proposal algorithms according to the design category, it is possible to make the most suitable proposal for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input design category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions based on emotions.

[0088] The proposal department can determine the priority of proposals based on the design submission deadlines. For example, the proposal department can prioritize proposals for designs with approaching deadlines. For example, the proposal department can provide detailed proposals for designs with ample time before the submission deadline. The proposal department can adjust the priority of proposals in stages according to the submission deadlines. This allows for prioritizing proposals based on the design submission deadlines, ensuring that designs with approaching deadlines receive priority. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input design submission deadline data into a generating AI and have the generating AI determine the priority of proposals.

[0089] The proposal unit can adjust the order of proposals based on the relevance of the designs during the proposal process. For example, the proposal unit can prioritize proposals for highly relevant designs. For example, the proposal unit can provide concise proposals for less relevant designs. For example, the proposal unit can adjust the order of proposals in stages according to their relevance. This allows for prioritizing proposals for highly relevant designs by adjusting the order of proposals based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the relevance of designs into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0090] The feedback unit can estimate the user's emotions and adjust how feedback is delivered based on the estimated emotions. For example, if the user is relaxed, the feedback unit may deliver detailed feedback. If the user is in a hurry, the feedback unit may deliver concise feedback. If the user is excited, the feedback unit may deliver visually stimulating feedback. By adjusting how feedback is delivered based on the user's emotions, feedback appropriate to the user's emotions can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust how feedback is delivered based on emotions.

[0091] The feedback unit can select the optimal method of reflection by referring to the user's past feedback history when providing feedback. For example, the feedback unit can analyze the user's past feedback and select the optimal method of reflection. For example, the feedback unit can extract specific patterns from the user's past feedback history and adjust the reflection method. For example, the feedback unit can optimize the feedback reflection method by referring to the user's past feedback history. This allows the optimal method of reflecting feedback to be selected by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data from the user's past feedback history into a generating AI and have the generating AI select the optimal reflection method.

[0092] The feedback unit can customize the next suggestion based on the design modifications made during the feedback process. For example, the feedback unit can learn the design modifications made by the user and reflect them in the next suggestion. For example, the feedback unit can analyze the characteristics of the design modified by the user and customize the next suggestion. For example, the feedback unit can optimize the next suggestion based on the user's modifications. This allows for suggestions that are better suited to the user's preferences by customizing the next suggestion based on the design modifications. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the design modifications into a generating AI and have the generating AI perform the customization of the next suggestion.

[0093] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit may prioritize detailed feedback. For example, if the user is in a hurry, the feedback unit may prioritize concise feedback. For example, if the user is excited, the feedback unit may prioritize visually stimulating feedback. In this way, by determining the priority of feedback based on the user's emotions, it is possible to prioritize and reflect feedback that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based feedback priorities.

[0094] The feedback unit can reflect the user's design history when providing feedback. For example, the feedback unit can refer to the user's design history and reflect feedback based on past designs. For example, the feedback unit can analyze the user's design history and reflect optimal feedback. For example, the feedback unit can adjust the content of the feedback considering the user's design history. This allows for optimal feedback based on past designs by reflecting feedback while considering the user's design history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user design history data into a generating AI and have the generating AI perform the feedback reflection.

[0095] The feedback unit can optimize its feedback method by referring to other users' feedback when providing feedback. For example, the feedback unit can refer to feedback provided by other users and select the optimal method of reflection. For example, the feedback unit can analyze the feedback history of other users, extract common patterns, and adjust the reflection method. For example, the feedback unit can optimize the feedback reflection method by referring to other users' feedback. This allows the optimal method of reflecting feedback to be selected by referring to other users' feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on other users' feedback into a generating AI and have the generating AI perform the optimization of the reflection method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The design support system may further include an operation history learning unit that learns the user's operation history during design creation. The operation history learning unit can learn the tools and sequence of operations used by the user during design creation. For example, it can analyze the frequency and timing of specific operations performed by the user to understand patterns in the design process. Thus, by learning the user's operation history during design creation, patterns in the design process can be understood. Some or all of the above-described processes in the operation history learning unit may be performed using AI, for example, or without AI. For example, the operation history learning unit can input data on the user's operation history during design creation into a generating AI and cause the generating AI to learn patterns in the design process.

[0098] The design support system may further include an emotion-prioritizing unit that estimates the user's emotions and determines design priorities based on the estimated user emotions. For example, if the user is stressed, the emotion-prioritizing unit may prioritize and suggest relaxing designs. If the user is excited, it may prioritize and suggest stimulating designs. If the user is calm, it may prioritize and suggest balanced designs. In this way, by determining design priorities based on the user's emotions, designs that are appropriate to the user's emotions can be prioritized and suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion-prioritizing unit may be performed using AI, for example, or not using AI. For example, the emotion-prioritizing unit may input user emotion data into the generative AI and have the generative AI perform the determination of design priorities based on emotions.

[0099] The design support system may further include a comparative learning unit that compares a user's design history with that of other users to learn commonalities and differences. The comparative learning unit can, for example, compare a user's design history with that of other users to extract common design features. It can also compare a user's design history with that of other users to extract different design features. In this way, by comparing a user's design history with that of other users, it is possible to learn commonalities and differences. Some or all of the above processing in the comparative learning unit may be performed using AI, for example, or without AI. For example, the comparative learning unit can input data of the user's design history and that of other users into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0100] The design support system may further include an emotion expression unit that estimates the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the emotion expression unit can provide suggestions that include detailed explanations. If the user is in a hurry, it can provide concise and to-the-point suggestions. If the user is excited, it can provide visually stimulating suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, suggestions that are appropriate to the user's emotions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion expression unit may be performed using AI, for example, or not using AI. For example, the emotion expression unit can input the user's emotion data into the generative AI and have the generative AI perform adjustments to the way suggestions are presented based on emotions.

[0101] The design support system may also include a history learning unit that learns the user's browsing and purchase history in addition to the user's design history. The history learning unit can, for example, learn design-related websites and articles that the user has previously viewed. It can also learn design-related products and services that the user has previously purchased. This allows for a more detailed understanding of the user's design preferences by learning their browsing and purchase history. Some or all of the above-described processes in the history learning unit may be performed using AI, for example, or without AI. For example, the history learning unit can input data from the user's browsing and purchase history into a generating AI, allowing the generating AI to perform a detailed understanding of the user's design preferences.

[0102] The design support system may further include an emotion feedback unit that estimates the user's emotions and adjusts how feedback is reflected based on the estimated user emotions. For example, the emotion feedback unit may reflect detailed feedback when the user is relaxed, concise feedback when the user is in a hurry, and visually stimulating feedback when the user is excited. This allows the system to reflect feedback appropriate to the user's emotions by adjusting how feedback is reflected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion feedback unit may be performed using AI or not using AI. For example, the emotion feedback unit may input user emotion data into the generative AI and have the generative AI perform adjustments to how feedback is reflected based on emotions.

[0103] The design support system may further include a feedback history unit that selects the optimal method of reflection by referring to the user's past feedback history. The feedback history unit, for example, analyzes the user's past feedback and selects the optimal method of reflection. It can extract specific patterns from the user's past feedback history and adjust the reflection method. This makes it possible to select the optimal method of reflection of feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback history unit may be performed using AI, for example, or without AI. For example, the feedback history unit can input data from the user's past feedback history into a generating AI and have the generating AI perform the selection of the optimal reflection method.

[0104] The design support system may further include an emotion trend unit that estimates the user's emotions and adjusts the importance of trends based on the estimated user emotions. For example, if the user is relaxed, the emotion trend unit may analyze even low-importance trends. If the user is in a hurry, it may focus the analysis on high-importance trends. If the user is excited, it may highlight and analyze visually stimulating trends. This allows for trend analysis tailored to the user's emotions by adjusting the importance of trends based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 emotion trend unit may be performed using AI or not. For example, the emotion trend unit may input user emotion data into the generative AI and have the generative AI perform the adjustment of trend importance based on emotions.

[0105] The design support system may further include a comparative learning unit that compares a user's design history with that of other users to learn commonalities and differences. The comparative learning unit can, for example, compare a user's design history with that of other users to extract common design features. It can also compare a user's design history with that of other users to extract different design features. In this way, by comparing a user's design history with that of other users, it is possible to learn commonalities and differences. Some or all of the above processing in the comparative learning unit may be performed using AI, for example, or without AI. For example, the comparative learning unit can input data of the user's design history and that of other users into a generating AI and have the generating AI perform the learning of commonalities and differences.

[0106] The design support system may further include an emotion suggestion unit that estimates the user's emotions and adjusts the length of suggestions based on the estimated emotions. For example, the emotion suggestion unit can provide detailed suggestions when the user is relaxed, concise suggestions when the user is in a hurry, and visually stimulating suggestions when the user is excited. By adjusting the length of suggestions based on the user's emotions, the system can provide suggestions that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion suggestion unit may be performed using AI or not. For example, the emotion suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the length of suggestions based on the emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The learning unit learns the user's past design tendencies and preferences. For example, it learns the color schemes, layouts, and font choices of designs the user has created in the past. It can also learn the design characteristics the user prefers, such as color schemes and design styles. Step 2: The analysis unit analyzes design trends based on the information learned by the learning unit. For example, it analyzes the latest design trends and makes proposals incorporating currently popular design styles and color schemes. The analysis unit can also use AI to analyze design trends. Step 3: The proposal department creates design proposals tailored to the user's preferences based on the analysis results obtained by the analysis department. For example, they might propose designs that incorporate currently popular design styles and color schemes. The proposal department can also use AI to create design proposals tailored to the user's preferences. Step 4: The feedback unit learns from user feedback on the design proposed by the proposal unit and refines the design. For example, if a user modifies part of the proposed design, the feedback unit learns from that modification and incorporates it into the next proposal. The feedback unit can also use AI to learn from user feedback and improve the design.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the learning unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's past design tendencies and preferences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes design trends. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes design suggestions tailored to the user's preferences. The feedback unit is implemented by the control unit 46A of the smart device 14 and learns user feedback to refine the design. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the learning unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's past design tendencies and preferences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes design trends. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes design suggestions tailored to the user's preferences. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and learns user feedback to refine the design. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the learning unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's past design tendencies and preferences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes design trends. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes design suggestions tailored to the user's preferences. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and learns user feedback to refine the design. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the learning unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the user's past design tendencies and preferences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes design trends. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes design suggestions tailored to the user's preferences. The feedback unit is implemented by the control unit 46A of the robot 414 and learns user feedback to refine the design. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A learning unit that learns the user's past design trends and preferences, An analysis unit analyzes design trends based on the information learned by the aforementioned learning unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes design suggestions tailored to the user's preferences. The system includes a feedback unit that learns user feedback on the design proposed by the proposal unit and refines the design. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learns the user's past designs and the characteristics of designs the user prefers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyzing the latest design trends The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer design suggestions tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Learn from user feedback and incorporate it into future suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system estimates user emotions and selects design features that learn based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Analyze the user's past design history over time to learn about the evolution and changes in their designs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, Learn the user's design creation history and understand patterns in the design process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, We estimate user emotions and prioritize design elements that learn based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, In addition to the user's design history, the system learns from their browsing and purchase history to gain a more detailed understanding of their design preferences. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, The system compares a user's design history with that of other users to learn from similarities and differences. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate user sentiment and adjust our design trend analysis methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing design trends, we refer to past trend data to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing design trends, analyze trends for each different design category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates user sentiment and adjusts the importance of trends based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing design trends, consider regional trends in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing design trends, we refer to trend data from relevant industries. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, different proposal algorithms are applied depending on the design category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline for the designs. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the designs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is reflected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When receiving feedback, the system will refer to the user's past feedback history to select the most appropriate method for incorporating the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When you provide feedback, we will customize the next proposal based on the design revisions you made. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we take the user's design history into consideration and incorporate that feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we will refer to other users' feedback to optimize how it is incorporated. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 learning unit that learns the user's past design trends and preferences, An analysis unit analyzes design trends based on the information learned by the aforementioned learning unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes design suggestions tailored to the user's preferences. The system includes a feedback unit that learns user feedback on the design proposed by the proposal unit and refines the design. A system characterized by the following features.

2. The aforementioned learning unit, Learns the user's past designs and the characteristics of designs the user prefers. The system according to feature 1.

3. The aforementioned analysis unit is Analyzing the latest design trends The system according to feature 1.

4. The aforementioned proposal section is, We offer design suggestions tailored to the user's preferences. The system according to feature 1.

5. The aforementioned feedback unit is Learn from user feedback and incorporate it into future suggestions. The system according to feature 1.

6. The aforementioned learning unit, The system estimates user emotions and selects design features that learn based on those estimated emotions. The system according to feature 1.

7. The aforementioned learning unit, Analyze the user's past design history over time to learn about the evolution and changes in their designs. The system according to feature 1.

8. The aforementioned learning unit, Learn the user's design creation history and understand patterns in the design process. The system according to feature 1.

9. The aforementioned learning unit, We estimate user emotions and prioritize design elements that learn based on those estimated emotions. The system according to feature 1.

10. The aforementioned learning unit, In addition to the user's design history, the system learns from their browsing and purchase history to gain a more detailed understanding of their design preferences. The system according to feature 1.

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