System

Abstract

An object of a system according to an embodiment is to speed up a design process and efficiently generate various design proposals.SOLUTION: A system includes a collection unit, a generation unit, an incorporation unit, a proposal unit, a completion unit, a learning unit, and an efficiency improvement unit. The collection unit collects design data. The generation unit generates a design idea based on the data collected by the collection unit. The incorporating unit allows the designer to incorporate the design idea generated by the generating unit. The proposal unit proposes a material based on the design idea generated by the generation unit. The incorporating unit allows the designer to incorporate the material proposed by the proposing unit. The completion unit completes the design based on the design idea or the material incorporated by the incorporation unit. The learning unit allows the AI to learn through discussion with the designer based on the design completed by the completion unit. The streamlining unit streamlines the design process on the basis of the information learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that the design process takes time and it is difficult to efficiently generate a variety of design proposals.

[0005] The system according to the embodiment aims to speed up the design process and efficiently generate a variety of design proposals. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, a generation unit, an intake unit, a proposal unit, a completion unit, a learning unit, and an efficiency improvement unit. The collection unit collects design data. The generation unit generates design ideas based on the data collected by the collection unit. The intake unit allows a designer to incorporate the design ideas generated by the generation unit. The proposal unit suggests materials based on the design ideas generated by the generation unit. The intake unit allows a designer to incorporate the materials proposed by the proposal unit. The completion unit completes a design based on the design ideas or materials incorporated by the intake unit. The learning unit allows an AI to learn through discussions with the designer based on the design completed by the completion unit. The efficiency improvement unit improves the efficiency of the design process based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can speed up the design process and efficiently generate a variety of design ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A design support system according to an embodiment of the present invention is a system that performs a comprehensive process from collecting design data to generating designs, proposing materials, completing designs, learning, and improving efficiency. The design support system collects design data, and a generative AI generates design ideas, which the designer incorporates to complete the design. The generative AI also learns through discussions with the designer, streamlining the design process. For example, the design support system collects past design data and trend information, and the generative AI generates multiple design proposals based on the data. The designer selects the best design proposal from the generated proposals and adds their own ideas to complete the design. The generative AI then proposes material and color combinations suitable for the design, which the designer incorporates to complete the design. Furthermore, the generative AI revises the design ideas based on feedback from discussions with the designer, streamlining the design process. This allows the design support system to perform a comprehensive process from collecting design data to completing designs, learning, and improving efficiency. For example, a designer can quickly complete a design by incorporating design ideas and material proposals generated by the generative AI. The generative AI also provides better design direction through discussions with the designer, streamlining the design process.

[0029] A design support system according to an embodiment includes a collection unit, a generation unit, an integration unit, a proposal unit, a completion unit, a learning unit, and an efficiency improvement unit. The collection unit collects design data. The design data includes, but is not limited to, image data, text data, and 3D model data. The collection unit can collect, for example, past design data and trend information. The generation unit generates design ideas based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to analyze past design data and trend information and generate multiple design proposals. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes past design data to generate design ideas that match current trends. The integration unit allows a designer to incorporate the design ideas generated by the generation unit. For example, the integration unit selects the best design from the multiple design proposals generated by the generation AI, and the designer adds their own ideas to complete the design. The proposal unit suggests materials based on the design ideas generated by the generation unit. For example, the proposal unit uses the generation AI to suggest material and color combinations suitable for the design. The generation AI selects materials according to combinations based on color theory and the intended use. The completion unit completes the design based on the design ideas and materials incorporated by the incorporation unit. The completion unit, for example, uses the generation AI to make final adjustments to the design and complete the design. The learning unit allows the generation AI to learn through discussions with the designer based on the design completed by the completion unit. For example, the designer provides feedback to the generation AI in the learning unit, and the generation AI modifies the design idea based on that feedback. The efficiency unit improves the efficiency of the design process based on the information learned by the learning unit. For example, the efficiency unit improves the efficiency of the design process based on the learned information using the generation AI. As a result, the design support system according to the embodiment can consistently perform processes from collecting design data to completing the design, learning, and improving efficiency.

[0030] The collection unit can collect past design data or trend information. The collection unit, for example, collects past design data. Past design data includes data from a specific period or data from a specific project. The collection unit, for example, collects trend information. Trend information includes fashion trends and consumer preferences. By collecting past design data and trend information, a wider variety of design ideas can be generated. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input past design data and trend information into a generation AI and cause the generation AI to collect data.

[0031] The generation unit can generate multiple design proposals based on the collected data. The generation unit, for example, generates multiple design proposals based on the collected data. The generation unit can generate design proposals with different styles or different functions, for example. The generation unit, for example, uses a generation AI to analyze the collected data and generate multiple design proposals. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes past design data and generates design ideas that match current trends. This generates multiple design proposals, thereby providing designers with a variety of options. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected data into the generation AI and cause the generation AI to generate design proposals.

[0032] The suggestion unit can suggest combinations of materials and colors suitable for the design. The suggestion unit, for example, suggests combinations of materials and colors suitable for the design. The suggestion unit, for example, uses a generation AI to select materials based on color theory or according to the intended use. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and suggests combinations of materials and colors suitable for the design. In this way, suggesting combinations of materials and colors suitable for the design improves the quality of the design. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input combinations of materials and colors suitable for the design into the generation AI and cause the generation AI to suggest materials and colors.

[0033] The learning unit can revise the design idea based on feedback through discussions with the designer. The learning unit, for example, revises the design idea based on feedback through discussions with the designer. For example, the designer provides feedback to the generation AI, and the generation AI revises the design idea based on that feedback. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and revises the design idea based on feedback through discussions with the designer. In this way, the quality of the design is improved by revising the design idea based on feedback through discussions with the designer. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the designer's feedback into the generation AI and have the generation AI execute revisions to the design idea.

[0034] The efficiency unit can improve the efficiency of the design process based on the learned information. The efficiency unit, for example, improves the efficiency of the design process based on the learned information. The efficiency unit, for example, uses a generation AI to improve the efficiency of the design process based on the learned information. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and improves the efficiency of the design process based on the learned information. In this way, the efficiency of the design process is improved by improving the efficiency of the design based on the learned information. Some or all of the above-mentioned processing in the efficiency unit may be performed using an AI, for example, or may be performed without using an AI. For example, the efficiency unit may input the learned information to the generation AI and cause the generation AI to improve the efficiency of the design process.

[0035] The collection unit can prioritize collecting data related to a specific theme or style from among past design data. For example, the collection unit prioritizes collecting data related to a specific theme or style from among past design data. For example, if a user selects a specific theme (e.g., minimalism), the collection unit prioritizes collecting design data related to that theme. For example, if a user selects a specific style (e.g., vintage), the collection unit prioritizes collecting design data related to that style. For example, if a user selects a specific color tone (e.g., monochrome), the collection unit prioritizes collecting design data related to that color tone. In this way, by preferentially collecting data related to a specific theme or style, the direction of the design is clarified. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input design data related to a specific theme or style into a generation AI and cause the generation AI to collect data.

[0036] The collection unit can filter the design data by taking into account the designer's past works and evaluations when collecting the design data. For example, the collection unit filters the data by taking into account the designer's past works and evaluations when collecting the design data. For example, if the designer's past works were highly rated, the collection unit prioritizes collecting design data related to those works. For example, if the designer's past works are biased toward a particular style, the collection unit prioritizes collecting design data related to that style. For example, if the designer's past works are related to a particular theme, the collection unit prioritizes collecting design data related to that theme. In this way, by filtering the data by taking into account the designer's past works and evaluations, more relevant design data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the designer's past works and evaluations into a generation AI and cause the generation AI to filter the data.

[0037] The collection unit can customize the scope of collection based on the designer's current project or area of ​​interest when collecting design data. For example, the collection unit customizes the scope of collection based on the designer's current project or area of ​​interest when collecting design data. For example, the collection unit prioritizes collecting design data related to the project the designer is currently working on. For example, the collection unit prioritizes collecting design data related to the designer's area of ​​interest (e.g., eco-design). For example, if the designer is working for a specific client, the collection unit prioritizes collecting design data tailored to the client's needs. This allows for the collection of more relevant design data by customizing the scope of collection based on the designer's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the designer's current project or area of ​​interest into the generation AI and cause the generation AI to customize the scope of collection.

[0038] The collection unit can prioritize collecting highly relevant data by taking geographical trend information into consideration when collecting design data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical trend information into consideration when collecting design data. For example, the collection unit collects design styles that are popular in a specific region. For example, the collection unit collects colors and materials that are used in a specific region based on the geographical trend information. For example, the collection unit collects design themes that are popular in a specific region based on the geographical trend information. In this way, by prioritizing the collection of highly relevant data by taking geographical trend information into consideration, it is possible to provide design data that is specialized for a region. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical trend information to a generation AI and cause the generation AI to collect data.

[0039] The collection unit can analyze social media trends and collect related data when collecting design data. For example, the collection unit analyzes social media trends and collects related data when collecting design data. For example, the collection unit collects design styles that are popular on social media. For example, the collection unit collects design themes that are trending on social media. For example, the collection unit collects design materials shared on social media. By analyzing social media trends and collecting related data, it is possible to provide design data based on the latest trends. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input social media trend information to the generation AI and cause the generation AI to collect data.

[0040] The collection unit can customize the collection method by reflecting the designer's past feedback when collecting design data. For example, the collection unit customizes the collection method by reflecting the designer's past feedback when collecting design data. For example, the collection unit prioritizes collecting design data that the designer has previously given high ratings. For example, the collection unit adjusts the collection method based on design data for which the designer has previously provided feedback. For example, the collection unit analyzes the designer's past feedback and proposes an optimal collection method. In this way, by customizing the collection method by reflecting the designer's past feedback, it is possible to provide design data that meets the designer's needs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the designer's past feedback into a generation AI and causes the generation AI to customize the collection method.

[0041] The generation unit can adjust the level of detail of the generated ideas based on the importance of the design at the time of generation. The generation unit, for example, adjusts the level of detail of the generated ideas based on the importance of the design at the time of generation. For example, the generation unit generates detailed design ideas for an important design project. For example, the generation unit generates concise design ideas for a design project to be completed in a short period of time. The generation unit adjusts the level of detail of the generated ideas based on the importance of the design. In this way, by adjusting the level of detail of the generated ideas based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the importance of the design into the generation AI and cause the generation AI to adjust the level of detail of the ideas.

[0042] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit applies different generation algorithms depending on the design category during generation. For example, in the case of graphic design, the generation unit generates design ideas using a specific algorithm. For example, in the case of product design, the generation unit generates design ideas using a different algorithm. For example, in the case of web design, the generation unit generates design ideas using a different algorithm. In this way, by applying different generation algorithms depending on the design category, it is possible to provide design ideas specialized for the category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the design category into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] The generation unit can improve the accuracy of generation by referring to the designer's past design results during generation. The generation unit, for example, improves the accuracy of generation by referring to the designer's past design results during generation. The generation unit, for example, analyzes the designer's past design results and improves the accuracy of the design ideas to be generated. The generation unit, for example, generates optimal design ideas based on the designer's past design results. The generation unit, for example, improves the quality of the generated design ideas by referring to the designer's past design results. In this way, the accuracy of the generated design ideas is improved by referring to the designer's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the designer's past design results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0044] The generation unit can determine the priority of the ideas to be generated based on the time of design submission at the time of generation. The generation unit, for example, determines the priority of the ideas to be generated based on the time of design submission at the time of generation. For example, in the case of a design project with an approaching deadline, the generation unit prioritizes generating the most relevant design ideas. For example, in the case of a design project with a distant submission date, the generation unit generates a variety of design ideas. The generation unit, for example, adjusts the priority of the ideas to be generated based on the time of design submission. In this way, by determining the priority of the ideas to be generated based on the time of design submission, it is possible to provide design ideas according to the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the time of design submission to the generation AI and cause the generation AI to determine the priority of the ideas.

[0045] The generation unit can adjust the order of ideas to be generated based on the relevance of the designs at the time of generation. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of the designs at the time of generation. For example, the generation unit prioritizes generating ideas with high design relevance. For example, the generation unit postpones ideas with low design relevance. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of the designs. In this way, by adjusting the order of ideas to be generated based on the relevance of the designs, highly relevant design ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the relevance of the designs into the generation AI and cause the generation AI to adjust the order of the ideas.

[0046] The generation unit can adjust the use of technical terminology in the ideas to be generated according to the expertise level of the designer at the time of generation. For example, the generation unit adjusts the use of technical terminology in the ideas to be generated according to the expertise level of the designer at the time of generation. For example, if the expertise level of the designer is high, the generation unit generates design ideas that use a lot of technical terminology. For example, if the expertise level of the designer is low, the generation unit generates concise and easy-to-understand design ideas. For example, the generation unit adjusts the use of technical terminology in the ideas to be generated according to the expertise level of the designer. In this way, by adjusting the use of technical terminology in the ideas to be generated according to the expertise level of the designer, it is possible to provide design ideas that are suitable for the designer. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the expertise level of the designer into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The intake unit can adjust the level of detail of the ideas to be taken in based on the importance of the design when taking them in. The intake unit, for example, adjusts the level of detail of the ideas to be taken in based on the importance of the design when taking them in. For example, the intake unit proposes detailed design ideas for an important design project. For example, the intake unit proposes concise design ideas for a design project to be completed in a short period of time. The intake unit adjusts the level of detail of the proposed ideas based on the importance of the design. In this way, by adjusting the level of detail of the ideas to be taken in based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the importance of the design to a generation AI and cause the generation AI to adjust the level of detail of the ideas.

[0048] The intake unit can apply different intake algorithms depending on the design category when taking in the design. For example, the intake unit applies different intake algorithms depending on the design category when taking in the design. For example, in the case of graphic design, the intake unit uses a specific algorithm to take in design ideas. For example, in the case of product design, the intake unit uses a different algorithm to take in design ideas. For example, in the case of web design, the intake unit uses a different algorithm to take in design ideas. In this way, by applying different intake algorithms depending on the design category, it is possible to provide design ideas specialized for the category. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the design category into the generation AI and cause the generation AI to apply the intake algorithm.

[0049] The assimilation unit can improve the accuracy of the assimilation by referring to the designer's past assimilation results when assimilation occurs. The assimilation unit, for example, improves the accuracy of the assimilation by referring to the designer's past assimilation results when assimilation occurs. The assimilation unit, for example, analyzes the designer's past assimilation results and improves the accuracy of the design ideas to be incorporated. The assimilation unit, for example, incorporates the most appropriate design idea based on the designer's past assimilation results. The assimilation unit, for example, improves the quality of the design ideas to be incorporated by referring to the designer's past assimilation results. In this way, the accuracy of the design ideas to be incorporated is improved by referring to the designer's past assimilation results. Some or all of the above-mentioned processing in the assimilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the assimilation unit can input the designer's past assimilation results into the generation AI and cause the generation AI to improve the accuracy of the assimilation.

[0050] The intake unit can determine the priority of ideas to be incorporated based on the time of design submission when incorporating. For example, the intake unit determines the priority of ideas to be incorporated based on the time of design submission when incorporating. For example, in the case of a design project with an approaching deadline, the intake unit prioritizes proposing the most relevant design ideas. For example, in the case of a design project with a distant submission date, the intake unit proposes a variety of design ideas. For example, the intake unit adjusts the priority of proposed ideas based on the time of design submission. In this way, by determining the priority of ideas to be incorporated based on the time of design submission, it is possible to provide design ideas according to the submission time. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the time of design submission to the generation AI and have the generation AI determine the priority of ideas.

[0051] The intake unit can adjust the order of ideas to be taken in based on the relevance of the design when taking in ideas. The intake unit, for example, adjusts the order of ideas to be taken in based on the relevance of the design when taking in ideas. The intake unit, for example, prioritizes taking in ideas with high design relevance. The intake unit, for example, postpones ideas with low design relevance. The intake unit, for example, adjusts the order of ideas to be taken in based on the relevance of the design. In this way, by adjusting the order of ideas to be taken in based on the relevance of the design, highly relevant design ideas can be provided preferentially. Some or all of the above-mentioned processing in the intake unit may be performed using, for example, AI, or may be performed without using AI. For example, the intake unit can input the relevance of the design into a generation AI and have the generation AI adjust the order of the ideas.

[0052] The assimilation unit can adjust the use of technical terms in the ideas to be incorporated according to the expertise level of the designer when incorporating them. For example, the assimilation unit adjusts the use of technical terms in the ideas to be incorporated according to the expertise level of the designer when incorporating them. For example, if the expertise level of the designer is high, the assimilation unit proposes design ideas that use a lot of technical terms. For example, if the expertise level of the designer is low, the assimilation unit proposes concise and easy-to-understand design ideas. For example, the assimilation unit adjusts the use of technical terms in the proposed ideas according to the expertise level of the designer. In this way, by adjusting the use of technical terms in the ideas to be incorporated according to the expertise level of the designer, it is possible to provide design ideas that are suitable for the designer. Some or all of the above-mentioned processing in the assimilation unit may be performed using AI, for example, or may be performed without using AI. For example, the assimilation unit can input the expertise level of the designer into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The suggestion unit can adjust the level of detail of the proposed material based on the importance of the design when making a proposal. The suggestion unit, for example, adjusts the level of detail of the proposed material based on the importance of the design when making a proposal. For example, the suggestion unit proposes detailed material for an important design project. For example, the suggestion unit proposes concise material for a design project to be completed in a short period of time. The suggestion unit adjusts the level of detail of the proposed material based on the importance of the design. In this way, by adjusting the level of detail of the proposed material based on the importance of the design, it is possible to provide material according to the importance of the project. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the importance of the design to a generation AI and cause the generation AI to adjust the level of detail of the material.

[0054] The suggestion unit can apply different suggestion algorithms depending on the design category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the design category when making a suggestion. For example, in the case of graphic design, the suggestion unit suggests materials using a specific algorithm. For example, in the case of product design, the suggestion unit suggests materials using a different algorithm. For example, in the case of web design, the suggestion unit suggests materials using a different algorithm. In this way, by applying different suggestion algorithms depending on the design category, it is possible to provide materials specialized for the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the design category to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0055] The suggestion unit can improve the accuracy of the suggestion by referring to the designer's past proposal results when making a suggestion. The suggestion unit, for example, improves the accuracy of the suggestion by referring to the designer's past proposal results when making a suggestion. The suggestion unit, for example, analyzes the designer's past proposal results and improves the accuracy of the proposed material. The suggestion unit, for example, suggests the optimal material based on the designer's past proposal results. The suggestion unit, for example, improves the quality of the proposed material by referring to the designer's past proposal results. In this way, the accuracy of the proposed material is improved by referring to the designer's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the designer's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0056] The proposal unit can determine the priority of the proposed materials based on the time of design submission at the time of proposal. For example, the proposal unit determines the priority of the proposed materials based on the time of design submission at the time of proposal. For example, in the case of a design project with an approaching deadline, the proposal unit prioritizes the most relevant materials. For example, in the case of a design project with a distant submission date, the proposal unit proposes a variety of materials. For example, the proposal unit adjusts the priority of the proposed materials based on the time of design submission. In this way, by determining the priority of the proposed materials based on the time of design submission, materials appropriate for the submission time can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time of design submission to a generation AI and cause the generation AI to determine the priority of materials.

[0057] The suggestion unit can adjust the order of the proposed materials based on the relevance of the design when making a suggestion. The suggestion unit, for example, adjusts the order of the proposed materials based on the relevance of the design when making a suggestion. The suggestion unit, for example, prioritizes suggesting materials with high design relevance. The suggestion unit, for example, postpones materials with low design relevance. The suggestion unit, for example, adjusts the order of the proposed materials based on the relevance of the design. In this way, by adjusting the order of the proposed materials based on the relevance of the design, highly relevant materials can be provided preferentially. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the design to a generation AI and cause the generation AI to adjust the order of the materials.

[0058] The suggestion unit can adjust the use of technical terms in the proposed material according to the expertise level of the designer when making a suggestion. For example, the suggestion unit adjusts the use of technical terms in the proposed material according to the expertise level of the designer when making a suggestion. For example, if the expertise level of the designer is high, the suggestion unit suggests material that uses a lot of technical terms. For example, if the expertise level of the designer is low, the suggestion unit suggests concise and easy-to-understand material. For example, the suggestion unit adjusts the use of technical terms in the proposed material according to the expertise level of the designer. In this way, by adjusting the use of technical terms in the proposed material according to the expertise level of the designer, it is possible to provide material that is suitable for the designer. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the expertise level of the designer to the generation AI and cause the generation AI to adjust the use of technical terms.

[0059] The completion unit can adjust the level of detail of the completed design based on the importance of the design upon completion. The completion unit, for example, adjusts the level of detail of the completed design based on the importance of the design upon completion. For example, in the case of an important design project, the completion unit completes a detailed design. For example, in the case of a design project to be completed in a short period of time, the completion unit completes a concise design. For example, the completion unit adjusts the level of detail of the completed design based on the importance of the design. In this way, by adjusting the level of detail of the completed design based on the importance of the design, it is possible to provide a design that corresponds to the importance of the project. Some or all of the above-mentioned processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit may input the importance of the design to the generation AI and cause the generation AI to adjust the level of detail of the design.

[0060] The completion unit can apply different completion algorithms depending on the category of the design during completion. For example, the completion unit applies different completion algorithms depending on the category of the design during completion. For example, in the case of graphic design, the completion unit completes the design using a specific algorithm. For example, in the case of product design, the completion unit completes the design using a different algorithm. For example, in the case of web design, the completion unit completes the design using a different algorithm. In this way, by applying different completion algorithms depending on the category of the design, it is possible to provide designs specialized for each category. Some or all of the above-mentioned processing in the completion unit may be performed using, for example, AI, or may be performed without using AI. For example, the completion unit can input the category of the design to the generation AI and cause the generation AI to apply the completion algorithm.

[0061] The completion unit can improve the accuracy of the completion at the time of completion by referring to the designer's past completion results. The completion unit, for example, improves the accuracy of the completion at the time of completion by referring to the designer's past completion results. The completion unit, for example, analyzes the designer's past completion results and improves the accuracy of the completed design. The completion unit, for example, completes an optimal design based on the designer's past completion results. The completion unit, for example, improves the quality of the completed design by referring to the designer's past completion results. In this way, the accuracy of the completed design is improved by referring to the designer's past completion results. Some or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the designer's past completion results into the generation AI and cause the generation AI to improve the accuracy of the completion.

[0062] The completion unit, upon completion, can determine the priority of the designs to be completed based on the submission date of the designs. For example, upon completion, the completion unit determines the priority of the designs to be completed based on the submission date of the designs. For example, in the case of a design project with an approaching deadline, the completion unit prioritizes completing the most relevant designs. For example, in the case of a design project with a distant submission date, the completion unit completes a variety of designs. For example, the completion unit adjusts the priority of the designs to be completed based on the submission date of the designs. In this way, by determining the priority of the designs to be completed based on the submission date of the designs, it is possible to provide designs according to the submission date. Some or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit may input the submission date of the designs to the generation AI and have the generation AI determine the priority of the designs.

[0063] The completion unit can adjust the order of designs to be completed based on the relevance of the designs upon completion. The completion unit, for example, adjusts the order of designs to be completed based on the relevance of the designs upon completion. The completion unit, for example, prioritizes completing designs with high relevance. The completion unit, for example, postpones designs with low relevance. The completion unit, for example, adjusts the order of designs to be completed based on the relevance of the designs. In this way, by adjusting the order of designs to be completed based on the relevance of the designs, highly related designs can be provided preferentially. Some or all of the above-described processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit can input the relevance of the designs to a generation AI and cause the generation AI to adjust the order of the designs.

[0064] The completion unit can adjust the use of technical terms in the completed design according to the designer's level of expertise upon completion. For example, the completion unit adjusts the use of technical terms in the completed design according to the designer's level of expertise upon completion. For example, if the designer's level of expertise is high, the completion unit completes a design that makes heavy use of technical terms. For example, if the designer's level of expertise is low, the completion unit completes a concise and easy-to-understand design. For example, the completion unit adjusts the use of technical terms in the completed design according to the designer's level of expertise. This allows for providing a design that is suitable for the designer by adjusting the use of technical terms in the completed design according to the designer's level of expertise. Some or all of the above-described processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit can input the designer's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms.

[0065] The learning unit can improve the efficiency of the learning algorithm by referring to past learning data during learning. The learning unit, for example, improves the efficiency of the learning algorithm by referring to past learning data during learning. The learning unit, for example, analyzes past learning data and selects an optimal learning algorithm. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit, for example, improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning accuracy is improved by improving the efficiency of the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the efficiency of the learning algorithm.

[0066] The learning unit can update the learning data during learning by reflecting the designer's feedback. For example, the learning unit updates the learning data during learning by reflecting the designer's feedback. For example, the learning unit updates the learning data based on the designer's feedback. For example, the learning unit improves the accuracy of the learning data by reflecting the designer's feedback. For example, the learning unit makes the learning data more efficient by referring to the designer's feedback. In this way, the learning data is updated to reflect the designer's feedback, thereby improving the accuracy of learning. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the designer's feedback into the generation AI and cause the generation AI to update the learning data.

[0067] The learning unit can weight the learning data based on the time of design submission during learning. For example, the learning unit weights the learning data based on the time of design submission during learning. For example, the learning unit weights the learning data higher for a design project with an approaching deadline. For example, the learning unit weights the learning data lower for a design project with a distant submission date. The learning unit adjusts the weighting of the learning data according to the time of design submission, for example. By weighting the learning data based on the time of design submission, learning according to the time of submission can be provided. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the time of design submission to the generation AI and cause the generation AI to weight the learning data.

[0068] The learning unit can integrate information from different data sources to expand the training data during learning. For example, the learning unit integrates information from different data sources to expand the training data during learning. For example, the learning unit integrates information from different data sources to expand the training data. For example, the learning unit improves the accuracy of the training data based on information from different data sources. For example, the learning unit makes the training data more efficient by referring to information from different data sources. In this way, the accuracy of learning is improved by expanding the training data by integrating information from different data sources. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input information from different data sources into a generation AI and cause the generation AI to integrate the information.

[0069] The efficiency improvement unit can improve the efficiency of the efficiency algorithm by referring to past efficiency data when improving efficiency. For example, the efficiency improvement unit improves the efficiency of the efficiency algorithm by referring to past efficiency data when improving efficiency. For example, the efficiency improvement unit analyzes past efficiency data and selects an optimal efficiency algorithm. For example, the efficiency improvement unit adjusts the efficiency algorithm based on past efficiency data. For example, the efficiency improvement unit improves the accuracy of the efficiency algorithm by referring to past efficiency data. In this way, the efficiency improvement algorithm is improved by improving the efficiency of the efficiency algorithm by referring to past efficiency data. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency improvement unit can input past efficiency data to the generation AI and cause the generation AI to improve the efficiency of the efficiency algorithm.

[0070] The efficiency unit can improve the efficiency improvement method by reflecting the designer's feedback during efficiency improvement. For example, the efficiency unit improves the efficiency improvement method by reflecting the designer's feedback during efficiency improvement. For example, the efficiency unit improves the efficiency improvement method based on the designer's feedback. For example, the efficiency unit improves the accuracy of efficiency improvement by reflecting the designer's feedback. For example, the efficiency unit improves the efficiency improvement method by referring to the designer's feedback. In this way, the accuracy of efficiency improvement is improved by improving the efficiency improvement method by reflecting the designer's feedback. Some or all of the above-mentioned processing in the efficiency unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency unit can input the designer's feedback into the generation AI and cause the generation AI to improve the efficiency improvement method.

[0071] The efficiency unit can weight the efficiency based on the time of design submission when improving efficiency. For example, the efficiency unit weights the efficiency based on the time of design submission when improving efficiency. For example, the efficiency unit weights the efficiency higher for a design project with an approaching deadline. For example, the efficiency unit weights the efficiency lower for a design project with a distant submission date. The efficiency unit adjusts the efficiency weight according to the time of design submission, for example. By weighting the efficiency based on the time of design submission, it is possible to provide efficiency according to the time of submission. Some or all of the above-mentioned processing in the efficiency unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency unit can input the time of design submission to the generation AI and cause the generation AI to perform the efficiency weighting.

[0072] The efficiency improvement unit can integrate information from different data sources to enhance the efficiency improvement method during efficiency improvement. For example, the efficiency improvement unit integrates information from different data sources to enhance the efficiency improvement method during efficiency improvement. For example, the efficiency improvement unit integrates information from different data sources to enhance the efficiency improvement method. For example, the efficiency improvement unit improves the accuracy of the efficiency improvement based on information from different data sources. For example, the efficiency improvement unit improves the efficiency of the efficiency improvement method by referring to information from different data sources. In this way, the accuracy of the efficiency improvement is improved by integrating information from different data sources to enhance the efficiency improvement method. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency improvement unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The design support system can further include an importance adjustment unit that adjusts the level of detail of the ideas generated based on the importance of the design when generating the design. For example, for an important design project, the generation unit can generate detailed design ideas. For a design project that can be completed in a short period of time, the generation unit can generate concise design ideas. In this way, by adjusting the level of detail of the ideas generated based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project.

[0075] The design support system may further include a category adjustment unit that applies different generation algorithms depending on the category of the design when generating the design. For example, for graphic design, a specific algorithm may be used to generate design ideas. For product design, a different algorithm may be used to generate design ideas. For web design, a different algorithm may be used to generate design ideas. In this way, by applying different generation algorithms depending on the design category, it is possible to provide design ideas specialized for each category.

[0076] The design support system can further include a past reference unit that, when generating a design, refers to the designer's past design results to improve the accuracy of the generation. For example, the designer's past design results can be analyzed to improve the accuracy of the generated design ideas. Also, optimal design ideas can be generated based on the designer's past design results. In this way, by referring to the designer's past design results, the accuracy of the generated design ideas can be improved.

[0077] The design support system can further include a submission time adjustment unit that, when generating a design, determines the priority of ideas to be generated based on the time of submission of the design. For example, in a design project with an approaching deadline, the generation unit can prioritize generating the most relevant design ideas. In addition, in a design project with a distant submission time, the generation unit can generate a variety of design ideas. In this way, by determining the priority of ideas to be generated based on the time of submission of the design, it is possible to provide design ideas according to the submission time.

[0078] The design support system can further include a relevance adjustment unit that adjusts the order of ideas to be generated based on the relevance of the designs when generating designs. For example, ideas with high design relevance can be generated with priority. Also, ideas with low design relevance can be postponed. In this way, by adjusting the order of ideas to be generated based on the relevance of the designs, highly relevant design ideas can be provided with priority.

[0079] The design support system can further include a terminology adjustment unit that adjusts the use of terminology in the ideas generated according to the designer's level of expertise when generating a design. For example, if the designer has a high level of expertise, it can generate design ideas that use a lot of terminology. On the other hand, if the designer has a low level of expertise, it can generate design ideas that are concise and easy to understand. In this way, by adjusting the use of terminology in the ideas generated according to the designer's level of expertise, it is possible to provide design ideas that are suitable for the designer.

[0080] The design support system can further include a submission time adjustment unit that, when generating a design, determines the priority of ideas to be generated based on the time of submission of the design. For example, in a design project with an approaching deadline, the generation unit can prioritize generating the most relevant design ideas. In addition, in a design project with a distant submission time, the generation unit can generate a variety of design ideas. In this way, by determining the priority of ideas to be generated based on the time of submission of the design, it is possible to provide design ideas according to the submission time.

[0081] The processing flow of the first embodiment will be briefly explained below.

[0082] Step 1: The collection unit collects design data. The design data includes image data, text data, 3D model data, etc. The collection unit can collect past design data and trend information. Step 2: The generation unit generates design ideas based on the data collected by the collection unit. The generation unit uses a generation AI to analyze past design data and trend information and generate multiple design proposals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes past design data to generate design ideas that match current trends. Step 3: In the integration section, a designer incorporates the design ideas generated by the generation section. In the integration section, the designer selects the best design from the multiple design proposals generated by the generation AI, and then adds their own ideas to complete the design. Step 4: The proposal unit proposes materials based on the design ideas generated by the generation unit. Using the generation AI, the proposal unit proposes material and color combinations suitable for the design. The generation AI selects materials based on combinations based on color theory and the intended use. Step 5: The Finalization Department completes the design based on the design ideas and materials incorporated by the Intake Department. The Finalization Department uses generative AI to make final adjustments to the design and complete the design. Step 6: In the learning section, the generative AI learns through discussions with the designer based on the design completed by the completion section. In the learning section, the designer provides feedback to the generative AI, and the generative AI modifies the design idea based on that feedback. Step 7: The efficiency improvement unit improves the efficiency of the design process based on the information learned by the learning unit. The efficiency improvement unit improves the efficiency of the design process based on the information learned by the generative AI.

[0083] (Example 2) A design support system according to an embodiment of the present invention is a system that performs a comprehensive process from collecting design data to generating designs, proposing materials, completing designs, learning, and improving efficiency. The design support system collects design data, and a generative AI generates design ideas, which the designer incorporates to complete the design. The generative AI also learns through discussions with the designer, streamlining the design process. For example, the design support system collects past design data and trend information, and the generative AI generates multiple design proposals based on the data. The designer selects the best design proposal from the generated proposals and adds their own ideas to complete the design. The generative AI then proposes material and color combinations suitable for the design, which the designer incorporates to complete the design. Furthermore, the generative AI revises the design ideas based on feedback from discussions with the designer, streamlining the design process. This allows the design support system to perform a comprehensive process from collecting design data to completing designs, learning, and improving efficiency. For example, a designer can quickly complete a design by incorporating design ideas and material proposals generated by the generative AI. The generative AI also provides better design direction through discussions with the designer, streamlining the design process.

[0084] A design support system according to an embodiment includes a collection unit, a generation unit, an integration unit, a proposal unit, a completion unit, a learning unit, and an efficiency improvement unit. The collection unit collects design data. The design data includes, but is not limited to, image data, text data, and 3D model data. The collection unit can collect, for example, past design data and trend information. The generation unit generates design ideas based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to analyze past design data and trend information and generate multiple design proposals. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes past design data to generate design ideas that match current trends. The integration unit allows a designer to incorporate the design ideas generated by the generation unit. For example, the integration unit selects the best design from the multiple design proposals generated by the generation AI, and the designer adds their own ideas to complete the design. The proposal unit suggests materials based on the design ideas generated by the generation unit. For example, the proposal unit uses the generation AI to suggest material and color combinations suitable for the design. The generation AI selects materials according to combinations based on color theory and the intended use. The completion unit completes the design based on the design ideas and materials incorporated by the incorporation unit. The completion unit, for example, uses the generation AI to make final adjustments to the design and complete the design. The learning unit allows the generation AI to learn through discussions with the designer based on the design completed by the completion unit. For example, the designer provides feedback to the generation AI in the learning unit, and the generation AI modifies the design idea based on that feedback. The efficiency unit improves the efficiency of the design process based on the information learned by the learning unit. For example, the efficiency unit improves the efficiency of the design process based on the learned information using the generation AI. As a result, the design support system according to the embodiment can consistently perform processes from collecting design data to completing the design, learning, and improving efficiency.

[0085] The collection unit can collect past design data or trend information. The collection unit, for example, collects past design data. Past design data includes data from a specific period or data from a specific project. The collection unit, for example, collects trend information. Trend information includes fashion trends and consumer preferences. By collecting past design data and trend information, a wider variety of design ideas can be generated. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input past design data and trend information into a generation AI and cause the generation AI to collect data.

[0086] The generation unit can generate multiple design proposals based on the collected data. The generation unit, for example, generates multiple design proposals based on the collected data. The generation unit can generate design proposals with different styles or different functions, for example. The generation unit, for example, uses a generation AI to analyze the collected data and generate multiple design proposals. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes past design data and generates design ideas that match current trends. This generates multiple design proposals, thereby providing designers with a variety of options. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected data into the generation AI and cause the generation AI to generate design proposals.

[0087] The suggestion unit can suggest combinations of materials and colors suitable for the design. The suggestion unit, for example, suggests combinations of materials and colors suitable for the design. The suggestion unit, for example, uses a generation AI to select materials based on color theory or according to the intended use. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and suggests combinations of materials and colors suitable for the design. In this way, suggesting combinations of materials and colors suitable for the design improves the quality of the design. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input combinations of materials and colors suitable for the design into the generation AI and cause the generation AI to suggest materials and colors.

[0088] The learning unit can revise the design idea based on feedback through discussions with the designer. The learning unit, for example, revises the design idea based on feedback through discussions with the designer. For example, the designer provides feedback to the generation AI, and the generation AI revises the design idea based on that feedback. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and revises the design idea based on feedback through discussions with the designer. In this way, the quality of the design is improved by revising the design idea based on feedback through discussions with the designer. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the designer's feedback into the generation AI and have the generation AI execute revisions to the design idea.

[0089] The efficiency unit can improve the efficiency of the design process based on the learned information. The efficiency unit, for example, improves the efficiency of the design process based on the learned information. The efficiency unit, for example, uses a generation AI to improve the efficiency of the design process based on the learned information. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and improves the efficiency of the design process based on the learned information. In this way, the efficiency of the design process is improved by improving the efficiency of the design based on the learned information. Some or all of the above-mentioned processing in the efficiency unit may be performed using an AI, for example, or may be performed without using an AI. For example, the efficiency unit may input the learned information to the generation AI and cause the generation AI to improve the efficiency of the design process.

[0090] The collection unit can estimate the user's emotions and adjust the timing of design data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of design data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit temporarily stops collecting design data and waits until the user relaxes. For example, when the user is concentrating, the collection unit quickly collects design data to improve the user's work efficiency. For example, when the user is tired, the collection unit slowly collects design data to reduce the user's burden. This reduces the user's burden by adjusting the timing of design data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the collection timing based on the emotion.

[0091] The collection unit can prioritize collecting data related to a specific theme or style from among past design data. For example, the collection unit prioritizes collecting data related to a specific theme or style from among past design data. For example, if a user selects a specific theme (e.g., minimalism), the collection unit prioritizes collecting design data related to that theme. For example, if a user selects a specific style (e.g., vintage), the collection unit prioritizes collecting design data related to that style. For example, if a user selects a specific color tone (e.g., monochrome), the collection unit prioritizes collecting design data related to that color tone. In this way, by preferentially collecting data related to a specific theme or style, the direction of the design is clarified. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input design data related to a specific theme or style into a generation AI and cause the generation AI to collect data.

[0092] The collection unit can filter the design data by taking into account the designer's past works and evaluations when collecting the design data. For example, the collection unit filters the data by taking into account the designer's past works and evaluations when collecting the design data. For example, if the designer's past works were highly rated, the collection unit prioritizes collecting design data related to those works. For example, if the designer's past works are biased toward a particular style, the collection unit prioritizes collecting design data related to that style. For example, if the designer's past works are related to a particular theme, the collection unit prioritizes collecting design data related to that theme. In this way, by filtering the data by taking into account the designer's past works and evaluations, more relevant design data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the designer's past works and evaluations into a generation AI and cause the generation AI to filter the data.

[0093] The collection unit can customize the scope of collection based on the designer's current project or area of ​​interest when collecting design data. For example, the collection unit customizes the scope of collection based on the designer's current project or area of ​​interest when collecting design data. For example, the collection unit prioritizes collecting design data related to the project the designer is currently working on. For example, the collection unit prioritizes collecting design data related to the designer's area of ​​interest (e.g., eco-design). For example, if the designer is working for a specific client, the collection unit prioritizes collecting design data tailored to the client's needs. This allows for the collection of more relevant design data by customizing the scope of collection based on the designer's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the designer's current project or area of ​​interest into the generation AI and cause the generation AI to customize the scope of collection.

[0094] The collection unit can estimate the user's emotions and determine the priority of the design data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of the design data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects a variety of design data to provide the user with many options. For example, when the user is in a hurry, the collection unit prioritizes collecting the most relevant design data. For example, when the user is excited, the collection unit prioritizes collecting visually stimulating design data. This allows the design data to be provided according to the user's needs by determining the priority of the design data to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to determine the priority of design data based on emotion.

[0095] The collection unit can prioritize collecting highly relevant data by taking geographical trend information into consideration when collecting design data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical trend information into consideration when collecting design data. For example, the collection unit collects design styles that are popular in a specific region. For example, the collection unit collects colors and materials that are used in a specific region based on the geographical trend information. For example, the collection unit collects design themes that are popular in a specific region based on the geographical trend information. In this way, by prioritizing the collection of highly relevant data by taking geographical trend information into consideration, it is possible to provide design data that is specialized for a region. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical trend information to a generation AI and cause the generation AI to collect data.

[0096] The collection unit can analyze social media trends and collect related data when collecting design data. For example, the collection unit analyzes social media trends and collects related data when collecting design data. For example, the collection unit collects design styles that are popular on social media. For example, the collection unit collects design themes that are trending on social media. For example, the collection unit collects design materials shared on social media. By analyzing social media trends and collecting related data, it is possible to provide design data based on the latest trends. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input social media trend information to the generation AI and cause the generation AI to collect data.

[0097] The collection unit can customize the collection method by reflecting the designer's past feedback when collecting design data. For example, the collection unit customizes the collection method by reflecting the designer's past feedback when collecting design data. For example, the collection unit prioritizes collecting design data that the designer has previously given high ratings. For example, the collection unit adjusts the collection method based on design data for which the designer has previously provided feedback. For example, the collection unit analyzes the designer's past feedback and proposes an optimal collection method. In this way, by customizing the collection method by reflecting the designer's past feedback, it is possible to provide design data that meets the designer's needs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the designer's past feedback into a generation AI and causes the generation AI to customize the collection method.

[0098] The generation unit can estimate the user's emotions and adjust the design idea generation method based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the design idea generation method based on the estimated user emotions. For example, when the user is relaxed, the generation unit generates a variety of design ideas. For example, when the user is in a hurry, the generation unit generates the most relevant design ideas. For example, when the user is excited, the generation unit generates visually stimulating design ideas. This allows design ideas that meet the user's needs to be provided by adjusting the design idea generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the design idea generation method based on the emotion.

[0099] The generation unit can adjust the level of detail of the generated ideas based on the importance of the design at the time of generation. The generation unit, for example, adjusts the level of detail of the generated ideas based on the importance of the design at the time of generation. For example, the generation unit generates detailed design ideas for an important design project. For example, the generation unit generates concise design ideas for a design project to be completed in a short period of time. The generation unit adjusts the level of detail of the generated ideas based on the importance of the design. In this way, by adjusting the level of detail of the generated ideas based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the importance of the design into the generation AI and cause the generation AI to adjust the level of detail of the ideas.

[0100] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit applies different generation algorithms depending on the design category during generation. For example, in the case of graphic design, the generation unit generates design ideas using a specific algorithm. For example, in the case of product design, the generation unit generates design ideas using a different algorithm. For example, in the case of web design, the generation unit generates design ideas using a different algorithm. In this way, by applying different generation algorithms depending on the design category, it is possible to provide design ideas specialized for the category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the design category into the generation AI and cause the generation AI to apply the generation algorithm.

[0101] The generation unit can improve the accuracy of generation by referring to the designer's past design results during generation. The generation unit, for example, improves the accuracy of generation by referring to the designer's past design results during generation. The generation unit, for example, analyzes the designer's past design results and improves the accuracy of the design ideas to be generated. The generation unit, for example, generates optimal design ideas based on the designer's past design results. The generation unit, for example, improves the quality of the generated design ideas by referring to the designer's past design results. In this way, the accuracy of the generated design ideas is improved by referring to the designer's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the designer's past design results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0102] The generation unit can estimate the user's emotions and adjust the length of the generated design ideas based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the length of the generated design ideas based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point design ideas. For example, if the user is relaxed, the generation unit generates longer design ideas with detailed explanations. For example, if the user is excited, the generation unit generates design ideas with visually stimulating effects. This allows the length of the generated design ideas to be adjusted according to the user's emotions, thereby providing design ideas that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the design ideas based on the emotion.

[0103] The generation unit can determine the priority of the ideas to be generated based on the time of design submission at the time of generation. The generation unit, for example, determines the priority of the ideas to be generated based on the time of design submission at the time of generation. For example, in the case of a design project with an approaching deadline, the generation unit prioritizes generating the most relevant design ideas. For example, in the case of a design project with a distant submission date, the generation unit generates a variety of design ideas. The generation unit, for example, adjusts the priority of the ideas to be generated based on the time of design submission. In this way, by determining the priority of the ideas to be generated based on the time of design submission, it is possible to provide design ideas according to the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the time of design submission to the generation AI and cause the generation AI to determine the priority of the ideas.

[0104] The generation unit can adjust the order of ideas to be generated based on the relevance of the designs at the time of generation. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of the designs at the time of generation. For example, the generation unit prioritizes generating ideas with high design relevance. For example, the generation unit postpones ideas with low design relevance. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of the designs. In this way, by adjusting the order of ideas to be generated based on the relevance of the designs, highly relevant design ideas can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the relevance of the designs into the generation AI and cause the generation AI to adjust the order of the ideas.

[0105] The generation unit can adjust the use of technical terminology in the ideas to be generated according to the expertise level of the designer at the time of generation. For example, the generation unit adjusts the use of technical terminology in the ideas to be generated according to the expertise level of the designer at the time of generation. For example, if the expertise level of the designer is high, the generation unit generates design ideas that use a lot of technical terminology. For example, if the expertise level of the designer is low, the generation unit generates concise and easy-to-understand design ideas. For example, the generation unit adjusts the use of technical terminology in the ideas to be generated according to the expertise level of the designer. In this way, by adjusting the use of technical terminology in the ideas to be generated according to the expertise level of the designer, it is possible to provide design ideas that are suitable for the designer. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the expertise level of the designer into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0106] The incorporation unit can estimate the user's emotions and adjust the method of incorporating design ideas based on the estimated user emotions. For example, the incorporation unit estimates the user's emotions and adjusts the method of incorporating design ideas based on the estimated user emotions. For example, when the user is relaxed, the incorporation unit proposes a variety of design ideas to provide the user with options. For example, when the user is in a hurry, the incorporation unit prioritizes proposing the most relevant design ideas. For example, when the user is excited, the incorporation unit proposes visually stimulating design ideas. This allows design ideas that meet the user's needs to be provided by adjusting the method of incorporating design ideas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the incorporation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the incorporation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of incorporating design ideas based on the emotion.

[0107] The intake unit can adjust the level of detail of the ideas to be taken in based on the importance of the design when taking them in. The intake unit, for example, adjusts the level of detail of the ideas to be taken in based on the importance of the design when taking them in. For example, the intake unit proposes detailed design ideas for an important design project. For example, the intake unit proposes concise design ideas for a design project to be completed in a short period of time. The intake unit adjusts the level of detail of the proposed ideas based on the importance of the design. In this way, by adjusting the level of detail of the ideas to be taken in based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the importance of the design to a generation AI and cause the generation AI to adjust the level of detail of the ideas.

[0108] The intake unit can apply different intake algorithms depending on the design category when taking in the design. For example, the intake unit applies different intake algorithms depending on the design category when taking in the design. For example, in the case of graphic design, the intake unit uses a specific algorithm to take in design ideas. For example, in the case of product design, the intake unit uses a different algorithm to take in design ideas. For example, in the case of web design, the intake unit uses a different algorithm to take in design ideas. In this way, by applying different intake algorithms depending on the design category, it is possible to provide design ideas specialized for the category. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the design category into the generation AI and cause the generation AI to apply the intake algorithm.

[0109] The assimilation unit can improve the accuracy of the assimilation by referring to the designer's past assimilation results when assimilation occurs. The assimilation unit, for example, improves the accuracy of the assimilation by referring to the designer's past assimilation results when assimilation occurs. The assimilation unit, for example, analyzes the designer's past assimilation results and improves the accuracy of the design ideas to be incorporated. The assimilation unit, for example, incorporates the most appropriate design idea based on the designer's past assimilation results. The assimilation unit, for example, improves the quality of the design ideas to be incorporated by referring to the designer's past assimilation results. In this way, the accuracy of the design ideas to be incorporated is improved by referring to the designer's past assimilation results. Some or all of the above-mentioned processing in the assimilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the assimilation unit can input the designer's past assimilation results into the generation AI and cause the generation AI to improve the accuracy of the assimilation.

[0110] The adoption unit can estimate the user's emotions and determine the priority of design ideas to be adopted based on the estimated user emotions. For example, the adoption unit estimates the user's emotions and determines the priority of design ideas to be adopted based on the estimated user emotions. For example, when the user is relaxed, the adoption unit proposes a variety of design ideas to provide the user with options. For example, when the user is in a hurry, the adoption unit prioritizes the most relevant design ideas. For example, when the user is excited, the adoption unit proposes visually stimulating design ideas. This allows the system to provide design ideas that meet the user's needs by determining the priority of design ideas to be adopted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adoption unit may be performed using, for example, an AI, or without an AI. For example, the intake unit can input user emotional data into the generation AI and have the generation AI determine the priorities of design ideas based on emotions.

[0111] The intake unit can determine the priority of ideas to be incorporated based on the time of design submission when incorporating. For example, the intake unit determines the priority of ideas to be incorporated based on the time of design submission when incorporating. For example, in the case of a design project with an approaching deadline, the intake unit prioritizes proposing the most relevant design ideas. For example, in the case of a design project with a distant submission date, the intake unit proposes a variety of design ideas. For example, the intake unit adjusts the priority of proposed ideas based on the time of design submission. In this way, by determining the priority of ideas to be incorporated based on the time of design submission, it is possible to provide design ideas according to the submission time. Some or all of the above-mentioned processing in the intake unit may be performed using AI, for example, or may be performed without using AI. For example, the intake unit can input the time of design submission to the generation AI and have the generation AI determine the priority of ideas.

[0112] The intake unit can adjust the order of ideas to be taken in based on the relevance of the design when taking in ideas. The intake unit, for example, adjusts the order of ideas to be taken in based on the relevance of the design when taking in ideas. The intake unit, for example, prioritizes taking in ideas with high design relevance. The intake unit, for example, postpones ideas with low design relevance. The intake unit, for example, adjusts the order of ideas to be taken in based on the relevance of the design. In this way, by adjusting the order of ideas to be taken in based on the relevance of the design, highly relevant design ideas can be provided preferentially. Some or all of the above-mentioned processing in the intake unit may be performed using, for example, AI, or may be performed without using AI. For example, the intake unit can input the relevance of the design into a generation AI and have the generation AI adjust the order of the ideas.

[0113] The assimilation unit can adjust the use of technical terms in the ideas to be incorporated according to the expertise level of the designer when incorporating them. For example, the assimilation unit adjusts the use of technical terms in the ideas to be incorporated according to the expertise level of the designer when incorporating them. For example, if the expertise level of the designer is high, the assimilation unit proposes design ideas that use a lot of technical terms. For example, if the expertise level of the designer is low, the assimilation unit proposes concise and easy-to-understand design ideas. For example, the assimilation unit adjusts the use of technical terms in the proposed ideas according to the expertise level of the designer. In this way, by adjusting the use of technical terms in the ideas to be incorporated according to the expertise level of the designer, it is possible to provide design ideas that are suitable for the designer. Some or all of the above-mentioned processing in the assimilation unit may be performed using AI, for example, or may be performed without using AI. For example, the assimilation unit can input the expertise level of the designer into the generation AI and cause the generation AI to adjust the use of technical terms.

[0114] The suggestion unit can estimate the user's emotions and adjust the material suggestion method based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the material suggestion method based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit proposes a variety of materials and provides the user with options. For example, when the user is in a hurry, the suggestion unit prioritizes suggesting the most relevant materials. For example, when the user is excited, the suggestion unit proposes visually stimulating materials. This allows the material suggestion method to be adjusted according to the user's emotions, thereby providing materials that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input user emotion data into the generation AI and cause the generation AI to adjust the material suggestion method based on the emotion.

[0115] The suggestion unit can adjust the level of detail of the proposed material based on the importance of the design when making a proposal. The suggestion unit, for example, adjusts the level of detail of the proposed material based on the importance of the design when making a proposal. For example, the suggestion unit proposes detailed material for an important design project. For example, the suggestion unit proposes concise material for a design project to be completed in a short period of time. The suggestion unit adjusts the level of detail of the proposed material based on the importance of the design. In this way, by adjusting the level of detail of the proposed material based on the importance of the design, it is possible to provide material according to the importance of the project. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the importance of the design to a generation AI and cause the generation AI to adjust the level of detail of the material.

[0116] The suggestion unit can apply different suggestion algorithms depending on the design category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the design category when making a suggestion. For example, in the case of graphic design, the suggestion unit suggests materials using a specific algorithm. For example, in the case of product design, the suggestion unit suggests materials using a different algorithm. For example, in the case of web design, the suggestion unit suggests materials using a different algorithm. In this way, by applying different suggestion algorithms depending on the design category, it is possible to provide materials specialized for the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the design category to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0117] The suggestion unit can improve the accuracy of the suggestion by referring to the designer's past proposal results when making a suggestion. The suggestion unit, for example, improves the accuracy of the suggestion by referring to the designer's past proposal results when making a suggestion. The suggestion unit, for example, analyzes the designer's past proposal results and improves the accuracy of the proposed material. The suggestion unit, for example, suggests the optimal material based on the designer's past proposal results. The suggestion unit, for example, improves the quality of the proposed material by referring to the designer's past proposal results. In this way, the accuracy of the proposed material is improved by referring to the designer's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the designer's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0118] The suggestion unit can estimate the user's emotions and prioritize the materials to be suggested based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and prioritizes the materials to be suggested based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit suggests a variety of materials and provides the user with options. For example, when the user is in a hurry, the suggestion unit prioritizes the most relevant materials. For example, when the user is excited, the suggestion unit suggests visually stimulating materials. This allows materials to be provided that meet the user's needs by prioritizing the materials to be suggested based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit may input user emotion data into the generation AI and cause the generation AI to prioritize materials based on emotions.

[0119] The proposal unit can determine the priority of the proposed materials based on the time of design submission at the time of proposal. For example, the proposal unit determines the priority of the proposed materials based on the time of design submission at the time of proposal. For example, in the case of a design project with an approaching deadline, the proposal unit prioritizes the most relevant materials. For example, in the case of a design project with a distant submission date, the proposal unit proposes a variety of materials. For example, the proposal unit adjusts the priority of the proposed materials based on the time of design submission. In this way, by determining the priority of the proposed materials based on the time of design submission, materials appropriate for the submission time can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time of design submission to a generation AI and cause the generation AI to determine the priority of materials.

[0120] The suggestion unit can adjust the order of the proposed materials based on the relevance of the design when making a suggestion. The suggestion unit, for example, adjusts the order of the proposed materials based on the relevance of the design when making a suggestion. The suggestion unit, for example, prioritizes suggesting materials with high design relevance. The suggestion unit, for example, postpones materials with low design relevance. The suggestion unit, for example, adjusts the order of the proposed materials based on the relevance of the design. In this way, by adjusting the order of the proposed materials based on the relevance of the design, highly relevant materials can be provided preferentially. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the design to a generation AI and cause the generation AI to adjust the order of the materials.

[0121] The suggestion unit can adjust the use of technical terms in the proposed material according to the expertise level of the designer when making a suggestion. For example, the suggestion unit adjusts the use of technical terms in the proposed material according to the expertise level of the designer when making a suggestion. For example, if the expertise level of the designer is high, the suggestion unit suggests material that uses a lot of technical terms. For example, if the expertise level of the designer is low, the suggestion unit suggests concise and easy-to-understand material. For example, the suggestion unit adjusts the use of technical terms in the proposed material according to the expertise level of the designer. In this way, by adjusting the use of technical terms in the proposed material according to the expertise level of the designer, it is possible to provide material that is suitable for the designer. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the expertise level of the designer to the generation AI and cause the generation AI to adjust the use of technical terms.

[0122] The completion unit can estimate the user's emotions and adjust the design completion method based on the estimated user emotions. For example, the completion unit estimates the user's emotions and adjusts the design completion method based on the estimated user emotions. For example, if the user is relaxed, the completion unit proposes various design ideas and provides the user with options. For example, if the user is in a hurry, the completion unit prioritizes proposing the most relevant design ideas. For example, if the user is excited, the completion unit proposes visually stimulating design ideas. This allows the design completion method to be adjusted according to the user's emotions, thereby providing a design that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the completion unit may be performed using an AI, for example, or without an AI. For example, the completion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the design completion method based on the emotion.

[0123] The completion unit can adjust the level of detail of the completed design based on the importance of the design upon completion. The completion unit, for example, adjusts the level of detail of the completed design based on the importance of the design upon completion. For example, in the case of an important design project, the completion unit completes a detailed design. For example, in the case of a design project to be completed in a short period of time, the completion unit completes a concise design. For example, the completion unit adjusts the level of detail of the completed design based on the importance of the design. In this way, by adjusting the level of detail of the completed design based on the importance of the design, it is possible to provide a design that corresponds to the importance of the project. Some or all of the above-mentioned processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit may input the importance of the design to the generation AI and cause the generation AI to adjust the level of detail of the design.

[0124] The completion unit can apply different completion algorithms depending on the category of the design during completion. For example, the completion unit applies different completion algorithms depending on the category of the design during completion. For example, in the case of graphic design, the completion unit completes the design using a specific algorithm. For example, in the case of product design, the completion unit completes the design using a different algorithm. For example, in the case of web design, the completion unit completes the design using a different algorithm. In this way, by applying different completion algorithms depending on the category of the design, it is possible to provide designs specialized for each category. Some or all of the above-mentioned processing in the completion unit may be performed using, for example, AI, or may be performed without using AI. For example, the completion unit can input the category of the design to the generation AI and cause the generation AI to apply the completion algorithm.

[0125] The completion unit can improve the accuracy of the completion at the time of completion by referring to the designer's past completion results. The completion unit, for example, improves the accuracy of the completion at the time of completion by referring to the designer's past completion results. The completion unit, for example, analyzes the designer's past completion results and improves the accuracy of the completed design. The completion unit, for example, completes an optimal design based on the designer's past completion results. The completion unit, for example, improves the quality of the completed design by referring to the designer's past completion results. In this way, the accuracy of the completed design is improved by referring to the designer's past completion results. Some or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the designer's past completion results into the generation AI and cause the generation AI to improve the accuracy of the completion.

[0126] The completion unit can estimate the user's emotions and determine the priority of the designs to be completed based on the estimated user emotions. For example, the completion unit estimates the user's emotions and determines the priority of the designs to be completed based on the estimated user emotions. For example, when the user is relaxed, the completion unit proposes various design ideas and provides the user with options. For example, when the user is in a hurry, the completion unit prioritizes the most relevant design ideas. For example, when the user is excited, the completion unit proposes visually stimulating design ideas. This allows designs to be provided that meet the user's needs by determining the priority of the designs to be completed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the completion unit can be performed using, for example, an AI, or without an AI. For example, the completion unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of designs based on emotions.

[0127] The completion unit, upon completion, can determine the priority of the designs to be completed based on the submission date of the designs. For example, upon completion, the completion unit determines the priority of the designs to be completed based on the submission date of the designs. For example, in the case of a design project with an approaching deadline, the completion unit prioritizes completing the most relevant designs. For example, in the case of a design project with a distant submission date, the completion unit completes a variety of designs. For example, the completion unit adjusts the priority of the designs to be completed based on the submission date of the designs. In this way, by determining the priority of the designs to be completed based on the submission date of the designs, it is possible to provide designs according to the submission date. Some or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit may input the submission date of the designs to the generation AI and have the generation AI determine the priority of the designs.

[0128] The completion unit can adjust the order of designs to be completed based on the relevance of the designs upon completion. The completion unit, for example, adjusts the order of designs to be completed based on the relevance of the designs upon completion. The completion unit, for example, prioritizes completing designs with high relevance. The completion unit, for example, postpones designs with low relevance. The completion unit, for example, adjusts the order of designs to be completed based on the relevance of the designs. In this way, by adjusting the order of designs to be completed based on the relevance of the designs, highly related designs can be provided preferentially. Some or all of the above-described processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit can input the relevance of the designs to a generation AI and cause the generation AI to adjust the order of the designs.

[0129] The completion unit can adjust the use of technical terms in the completed design according to the designer's level of expertise upon completion. For example, the completion unit adjusts the use of technical terms in the completed design according to the designer's level of expertise upon completion. For example, if the designer's level of expertise is high, the completion unit completes a design that makes heavy use of technical terms. For example, if the designer's level of expertise is low, the completion unit completes a concise and easy-to-understand design. For example, the completion unit adjusts the use of technical terms in the completed design according to the designer's level of expertise. This allows for providing a design that is suitable for the designer by adjusting the use of technical terms in the completed design according to the designer's level of expertise. Some or all of the above-described processing in the completion unit may be performed using AI, for example, or may be performed without using AI. For example, the completion unit can input the designer's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms.

[0130] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit estimates the user's emotions and selects training data based on the estimated user emotions. For example, when the user is relaxed, the learning unit selects diverse training data. For example, when the user is in a hurry, the learning unit prioritizes selecting the most relevant training data. For example, when the user is excited, the learning unit selects visually stimulating training data. This allows the system to provide training data tailored to the user's needs by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit may input the user's emotion data into the generation AI and cause the generation AI to select training data based on the emotion.

[0131] The learning unit can improve the efficiency of the learning algorithm by referring to past learning data during learning. The learning unit, for example, improves the efficiency of the learning algorithm by referring to past learning data during learning. The learning unit, for example, analyzes past learning data and selects an optimal learning algorithm. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit, for example, improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning accuracy is improved by improving the efficiency of the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the efficiency of the learning algorithm.

[0132] The learning unit can update the learning data during learning by reflecting the designer's feedback. For example, the learning unit updates the learning data during learning by reflecting the designer's feedback. For example, the learning unit updates the learning data based on the designer's feedback. For example, the learning unit improves the accuracy of the learning data by reflecting the designer's feedback. For example, the learning unit makes the learning data more efficient by referring to the designer's feedback. In this way, the learning data is updated to reflect the designer's feedback, thereby improving the accuracy of learning. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the designer's feedback into the generation AI and cause the generation AI to update the learning data.

[0133] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, the learning unit increases the frequency of learning when the user is relaxed. For example, the learning unit decreases the frequency of learning when the user is in a hurry. For example, the learning unit adjusts the frequency of learning when the user is excited. This allows learning to be provided according to the user's needs by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit may input user emotion data into the generation AI and cause the generation AI to adjust the frequency of learning based on the emotion.

[0134] The learning unit can weight the learning data based on the time of design submission during learning. For example, the learning unit weights the learning data based on the time of design submission during learning. For example, the learning unit weights the learning data higher for a design project with an approaching deadline. For example, the learning unit weights the learning data lower for a design project with a distant submission date. The learning unit adjusts the weighting of the learning data according to the time of design submission, for example. By weighting the learning data based on the time of design submission, learning according to the time of submission can be provided. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the time of design submission to the generation AI and cause the generation AI to weight the learning data.

[0135] The learning unit can integrate information from different data sources to expand the training data during learning. For example, the learning unit integrates information from different data sources to expand the training data during learning. For example, the learning unit integrates information from different data sources to expand the training data. For example, the learning unit improves the accuracy of the training data based on information from different data sources. For example, the learning unit makes the training data more efficient by referring to information from different data sources. In this way, the accuracy of learning is improved by expanding the training data by integrating information from different data sources. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input information from different data sources into a generation AI and cause the generation AI to integrate the information.

[0136] The efficiency improvement unit can estimate the user's emotions and adjust the efficiency improvement method based on the estimated user's emotions. For example, the efficiency improvement unit estimates the user's emotions and adjusts the efficiency improvement method based on the estimated user's emotions. For example, when the user is relaxed, the efficiency improvement unit suggests various efficiency improvement methods. For example, when the user is in a hurry, the efficiency improvement unit preferentially suggests the most relevant efficiency improvement method. For example, when the user is excited, the efficiency improvement unit suggests a visually stimulating efficiency improvement method. This allows the efficiency improvement method to be adjusted according to the user's emotions, thereby providing efficiency that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using an AI, for example, or without an AI. For example, the efficiency improvement unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the efficiency improvement method based on the emotion.

[0137] The efficiency improvement unit can improve the efficiency of the efficiency algorithm by referring to past efficiency data when improving efficiency. For example, the efficiency improvement unit improves the efficiency of the efficiency algorithm by referring to past efficiency data when improving efficiency. For example, the efficiency improvement unit analyzes past efficiency data and selects an optimal efficiency algorithm. For example, the efficiency improvement unit adjusts the efficiency algorithm based on past efficiency data. For example, the efficiency improvement unit improves the accuracy of the efficiency algorithm by referring to past efficiency data. In this way, the efficiency improvement algorithm is improved by improving the efficiency of the efficiency algorithm by referring to past efficiency data. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency improvement unit can input past efficiency data to the generation AI and cause the generation AI to improve the efficiency of the efficiency algorithm.

[0138] The efficiency unit can improve the efficiency improvement method by reflecting the designer's feedback during efficiency improvement. For example, the efficiency unit improves the efficiency improvement method by reflecting the designer's feedback during efficiency improvement. For example, the efficiency unit improves the efficiency improvement method based on the designer's feedback. For example, the efficiency unit improves the accuracy of efficiency improvement by reflecting the designer's feedback. For example, the efficiency unit improves the efficiency improvement method by referring to the designer's feedback. In this way, the accuracy of efficiency improvement is improved by improving the efficiency improvement method by reflecting the designer's feedback. Some or all of the above-mentioned processing in the efficiency unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency unit can input the designer's feedback into the generation AI and cause the generation AI to improve the efficiency improvement method.

[0139] The efficiency improvement unit can estimate the user's emotions and determine the priority of efficiency improvements based on the estimated user emotions. For example, the efficiency improvement unit estimates the user's emotions and determines the priority of efficiency improvements based on the estimated user emotions. For example, when the user is relaxed, the efficiency improvement unit suggests various efficiency improvement methods. For example, when the user is in a hurry, the efficiency improvement unit preferentially suggests the most relevant efficiency improvement method. For example, when the user is excited, the efficiency improvement unit suggests a visually stimulating efficiency improvement method. In this way, by determining the priority of efficiency improvements according to the user's emotions, efficiency improvements that meet the user's needs can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using an AI, for example, or without an AI. For example, the efficiency improvement unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of efficiency improvements based on emotions.

[0140] The efficiency unit can weight the efficiency based on the time of design submission when improving efficiency. For example, the efficiency unit weights the efficiency based on the time of design submission when improving efficiency. For example, the efficiency unit weights the efficiency higher for a design project with an approaching deadline. For example, the efficiency unit weights the efficiency lower for a design project with a distant submission date. The efficiency unit adjusts the efficiency weight according to the time of design submission, for example. By weighting the efficiency based on the time of design submission, it is possible to provide efficiency according to the time of submission. Some or all of the above-mentioned processing in the efficiency unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency unit can input the time of design submission to the generation AI and cause the generation AI to perform the efficiency weighting.

[0141] The efficiency improvement unit can integrate information from different data sources to enhance the efficiency improvement method during efficiency improvement. For example, the efficiency improvement unit integrates information from different data sources to enhance the efficiency improvement method during efficiency improvement. For example, the efficiency improvement unit integrates information from different data sources to enhance the efficiency improvement method. For example, the efficiency improvement unit improves the accuracy of the efficiency improvement based on information from different data sources. For example, the efficiency improvement unit improves the efficiency of the efficiency improvement method by referring to information from different data sources. In this way, the accuracy of the efficiency improvement is improved by integrating information from different data sources to enhance the efficiency improvement method. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the efficiency improvement unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, incorporation unit, proposal unit, completion unit, learning unit, and efficiency unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect design data using the camera 42 or microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design ideas based on the collected data. The incorporation unit is realized by the control unit 46A of the smart device 14 and allows the designer to incorporate the generated design ideas. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and suggests material and color combinations suitable for the design. The completion unit is realized by the control unit 46A of the smart device 14 and completes the design. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and allows the generation AI to learn through discussions with the designer. The efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the efficiency of the design process based on the learned information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, incorporation unit, proposal unit, completion unit, learning unit, and efficiency unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect design data using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design ideas based on the collected data. The incorporation unit is realized by the control unit 46A of the smart glasses 214 and the designer incorporates the generated design ideas. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes material and color combinations suitable for the design. The completion unit is realized by the control unit 46A of the smart glasses 214 and completes the design. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and the generation AI learns through discussions with the designer. The efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the efficiency of the design process based on the learned information. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, incorporation unit, proposal unit, completion unit, learning unit, and efficiency unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect design data using the camera 42 and microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design ideas based on the collected data. The incorporation unit is realized by the control unit 46A of the headset-type terminal 314 and the designer incorporates the generated design ideas. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes material and color combinations suitable for the design. The completion unit is realized by the control unit 46A of the headset-type terminal 314 and completes the design. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and the generation AI learns through discussions with the designer. The efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the efficiency of the design process based on the learned information. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, incorporation unit, proposal unit, completion unit, learning unit, and efficiency unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect design data using the camera 42 and microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates design ideas based on the collected data. The incorporation unit is realized by the control unit 46A of the robot 414 and the designer incorporates the generated design ideas. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes material and color combinations suitable for the design. The completion unit is realized by the control unit 46A of the robot 414 and completes the design. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and the generation AI learns through discussions with the designer. The efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the efficiency of the design process based on the learned information.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The design support system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the design generation method based on the estimated emotion. For example, if the user is relaxed, the generation unit may generate a variety of design ideas to provide the user with options. If the user is in a hurry, the generation unit may prioritize generating the most relevant design ideas. If the user is excited, the generation unit may generate visually stimulating design ideas. In this way, by adjusting the design generation method according to the user's emotion, it is possible to provide design ideas that meet the user's needs.

[0144] The design support system can further include an importance adjustment unit that adjusts the level of detail of the ideas generated based on the importance of the design when generating the design. For example, for an important design project, the generation unit can generate detailed design ideas. For a design project that can be completed in a short period of time, the generation unit can generate concise design ideas. In this way, by adjusting the level of detail of the ideas generated based on the importance of the design, it is possible to provide design ideas that correspond to the importance of the project.

[0145] The design support system may further include a category adjustment unit that applies different generation algorithms depending on the category of the design when generating the design. For example, for graphic design, a specific algorithm may be used to generate design ideas. For product design, a different algorithm may be used to generate design ideas. For web design, a different algorithm may be used to generate design ideas. In this way, by applying different generation algorithms depending on the design category, it is possible to provide design ideas specialized for each category.

[0146] The design support system can further include a past reference unit that, when generating a design, refers to the designer's past design results to improve the accuracy of the generation. For example, the designer's past design results can be analyzed to improve the accuracy of the generated design ideas. Also, optimal design ideas can be generated based on the designer's past design results. In this way, by referring to the designer's past design results, the accuracy of the generated design ideas can be improved.

[0147] The design support system can further include an emotion length adjustment unit that estimates the user's emotion during design generation and adjusts the length of the generated design idea based on the estimated emotion. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point design idea. If the user is relaxed, the generation unit can generate a longer design idea that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a design idea that adds visually stimulating effects. In this way, by adjusting the length of the generated design idea according to the user's emotion, it is possible to provide design ideas that meet the user's needs.

[0148] The design support system can further include a submission time adjustment unit that, when generating a design, determines the priority of ideas to be generated based on the time of submission of the design. For example, in a design project with an approaching deadline, the generation unit can prioritize generating the most relevant design ideas. In addition, in a design project with a distant submission time, the generation unit can generate a variety of design ideas. In this way, by determining the priority of ideas to be generated based on the time of submission of the design, it is possible to provide design ideas according to the submission time.

[0149] The design support system can further include a relevance adjustment unit that adjusts the order of ideas to be generated based on the relevance of the designs when generating designs. For example, ideas with high design relevance can be generated with priority. Also, ideas with low design relevance can be postponed. In this way, by adjusting the order of ideas to be generated based on the relevance of the designs, highly relevant design ideas can be provided with priority.

[0150] The design support system can further include a terminology adjustment unit that adjusts the use of terminology in the ideas generated according to the designer's level of expertise when generating a design. For example, if the designer has a high level of expertise, it can generate design ideas that use a lot of terminology. On the other hand, if the designer has a low level of expertise, it can generate design ideas that are concise and easy to understand. In this way, by adjusting the use of terminology in the ideas generated according to the designer's level of expertise, it is possible to provide design ideas that are suitable for the designer.

[0151] The design support system can further include an emotion priority adjustment unit that estimates the user's emotion during design generation and prioritizes the design ideas to be generated based on the estimated emotion. For example, if the user is relaxed, a variety of design ideas can be generated to provide the user with options. If the user is in a hurry, the most relevant design ideas can be generated with priority. If the user is excited, visually stimulating design ideas can be generated. In this way, by prioritizing the design ideas to be generated according to the user's emotion, design ideas that meet the user's needs can be provided.

[0152] The design support system can further include a submission time adjustment unit that, when generating a design, determines the priority of ideas to be generated based on the time of submission of the design. For example, in a design project with an approaching deadline, the generation unit can prioritize generating the most relevant design ideas. In addition, in a design project with a distant submission time, the generation unit can generate a variety of design ideas. In this way, by determining the priority of ideas to be generated based on the time of submission of the design, it is possible to provide design ideas according to the submission time.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The collection unit collects design data. The design data includes image data, text data, 3D model data, etc. The collection unit can collect past design data and trend information. Step 2: The generation unit generates design ideas based on the data collected by the collection unit. The generation unit uses a generation AI to analyze past design data and trend information and generate multiple design proposals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes past design data to generate design ideas that match current trends. Step 3: In the integration section, a designer incorporates the design ideas generated by the generation section. In the integration section, the designer selects the best design from the multiple design proposals generated by the generation AI, and then adds their own ideas to complete the design. Step 4: The proposal unit proposes materials based on the design ideas generated by the generation unit. Using the generation AI, the proposal unit proposes material and color combinations suitable for the design. The generation AI selects materials based on combinations based on color theory and the intended use. Step 5: The Finalization Department completes the design based on the design ideas and materials incorporated by the Intake Department. The Finalization Department uses generative AI to make final adjustments to the design and complete the design. Step 6: In the learning section, the generative AI learns through discussions with the designer based on the design completed by the completion section. In the learning section, the designer provides feedback to the generative AI, and the generative AI modifies the design idea based on that feedback. Step 7: The efficiency improvement unit improves the efficiency of the design process based on the information learned by the learning unit. The efficiency improvement unit improves the efficiency of the design process based on the information learned by the generative AI.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 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.

[0193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0195] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0198] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0199] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0200] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0203] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0210] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0211] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0212] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0216] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0217] 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.

[0218] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0219] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0220] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0221] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0223] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0224] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0225] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects design data; a generation unit that generates design ideas based on the data collected by the collection unit; an incorporating unit in which a designer incorporates the design ideas generated by the generating unit; a proposal unit that proposes materials based on the design ideas generated by the generation unit; an intake unit in which a designer takes in the materials suggested by the suggestion unit; a completion unit that completes a design based on the design idea or material taken in by the taking unit; A learning unit in which AI learns through discussions with designers based on the design completed by the completion unit; an efficiency improvement unit that improves the efficiency of the design process based on the information learned by the learning unit. A system characterized by:

2. The collecting unit Collect historical design data or trend information 2. The system of claim 1.

3. The generation unit Generate multiple design alternatives based on collected data 2. The system of claim 1.

4. The proposal unit Proposing suitable materials and color combinations for your design 2. The system of claim 1.

5. The learning unit Discuss with designers and revise design ideas based on feedback 2. The system of claim 1.

6. The efficiency improvement unit Streamline the design process based on learned information 2. The system of claim 1.

7. The collecting unit Infer user emotions and adjust the timing of design data collection based on the inferred user emotions 2. The system of claim 1.

8. The collecting unit Prioritize the collection of past design data related to a specific theme or style 2. The system of claim 1.

Images