system

Abstract

The system according to this embodiment aims to recommend the optimal design for a specific region or culture. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects information related to creative work. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends the optimal design based on the information analyzed by the analysis unit. The learning unit learns from user selections and feedback to improve the accuracy of recommendations.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to find an optimal design corresponding to a specific region or culture in creative production, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an optimal design corresponding to a specific region or culture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects information related to creative work. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends the optimal design based on the information analyzed by the analysis unit. The learning unit learns from user selections and feedback to improve the accuracy of recommendations. [Effects of the Invention]

[0007] The system according to this embodiment can recommend an optimal design that is appropriate for a specific region or culture. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The design recommendation system according to an embodiment of the present invention is an AI-based design recommendation tool that assists in the production of creative works for overseas markets. This design recommendation system proposes designs that reflect the sensibilities and emotions of specific regions and cultures, based on user input of creative information (visuals, media, target region, message, etc.). For example, it recommends optimal foreign language fonts and colors to localize the creative work. It also recommends personalized designs based on the project's objectives and content. Furthermore, the AI ​​learns from user selections and feedback to improve the accuracy of its recommendations. For example, when an advertiser creates a design for a new campaign, they input information such as the target region, message, and media to be used. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​identifies optimal design elements from the perspective of the target region and culture. For example, it recommends fonts and colors popular in a particular region. This ensures the design is appropriate for that region and culture. Furthermore, the AI ​​personalizes the design based on the project's objectives and content. For example, when a brand manager promotes a new product, it proposes a design that matches the product's characteristics and message. This ensures the design aligns with the project's objectives. Finally, the AI ​​learns from user selections and feedback to improve the accuracy of its recommendations. For example, if a user's chosen font and color combination is successful, the AI ​​learns from that information and incorporates it into future recommendations. This makes the AI's recommendations more accurate. This system allows designers and advertisers to efficiently create designs that appropriately express cultural nuances and emotions. It also saves time and effort, allowing them to focus on more creative work. For instance, a graphic designer can use this tool to quickly find the optimal fonts and colors when creating a design that incorporates a multicultural perspective. In this way, the design recommendation system can efficiently support users' creative work and provide designs that appropriately express cultural nuances and emotions.

[0029] The design recommendation system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects information related to creative work. This information includes, but is not limited to, visuals, media, target region, and message. For example, the collection unit collects visual information entered by the user. The collection unit can also collect media information specified by the user. Furthermore, the collection unit can also collect information related to the target region. For example, the collection unit collects relevant design elements based on target region information entered by the user. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit identifies optimal design elements from the perspective of the target region and culture. For example, the analysis unit identifies fonts and colors that are popular in a particular region. The analysis unit can also personalize designs based on the project's objectives and content. For example, when a brand manager is promoting a new product, the analysis unit identifies a design that matches the product's characteristics and message. The recommendation unit recommends the optimal design based on the information analyzed by the analysis unit. For example, the recommendation unit recommends fonts and colors that are popular in a particular region. Furthermore, the recommendation unit can personalize designs based on the project's objectives and content. For example, if a brand manager is promoting a new product, the recommendation unit will recommend a design that matches the product's characteristics and message. The learning unit learns from user selections and feedback to improve the accuracy of recommendations. For instance, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. As a result, the design recommendation system according to this embodiment can efficiently support users' creative work and provide designs that appropriately express cultural nuances and emotions.

[0030] The data collection unit collects information related to creative work. This information includes, but is not limited to, visuals, media, target regions, and messages. For example, the unit collects visual information entered by users. Specifically, it collects visual elements such as images, graphics, and logos uploaded by users, and simultaneously obtains their metadata. This allows for the understanding of detailed information such as the resolution, color, and format of visual elements. The data collection unit can also collect media information specified by users. For example, it collects media information such as magazines, websites, and social media platforms where advertisements are planned to be published, and understands the characteristics and format requirements of each medium. Furthermore, the data collection unit can also collect information related to target regions. For example, based on target region information entered by users, the unit collects relevant design elements. It collects information such as the culture, language, and trends of the target region and considers their impact on the design. This allows the data collection unit to efficiently collect diverse information tailored to user needs and utilize it as foundational data for design. In addition, the data collection unit can also obtain the latest design trends and market research data using external databases and APIs. This allows the data collection unit to always provide design elements based on the latest information and support users' creative production.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit identifies optimal design elements from the perspective of target region and culture. Specifically, it analyzes the collected visual information using image analysis technology to extract elements such as color, shape, and layout. Furthermore, it evaluates how these elements will be received based on the culture and trends of the target region. For example, to identify fonts and colors popular in a particular region, it refers to past design data and market research data and performs statistical analysis. The analysis unit can also personalize designs based on the project's objectives and content. For example, when a brand manager is promoting a new product, the analysis unit identifies a design that matches the product's characteristics and message. It selects the optimal design elements considering the product's target audience and brand image. Furthermore, the analysis unit can optimize combinations of design elements using AI. For example, it uses generative AI to generate multiple design options based on the collected data and evaluates them. By reflecting users' past choices and feedback in the evaluation, more accurate analysis becomes possible. In this way, the analysis unit can analyze the collected data from multiple angles and identify the design elements that best suit the user's needs.

[0032] The recommendation department recommends the optimal design based on information analyzed by the analysis department. For example, the recommendation department might recommend fonts or colors popular in a specific region. Specifically, it generates and recommends the most suitable design proposals for the user's project based on the design elements identified by the analysis department. For example, if a user is promoting a new product, it will recommend a design that matches the product's characteristics and message. The recommendation department uses AI to learn from the user's past choices and feedback, improving the accuracy of its recommendations. For example, it incorporates data on design elements previously selected by the user and successful projects into future recommendations. Furthermore, the recommendation department can receive real-time feedback from users and immediately modify its recommendations. For example, if a user provides feedback on a recommended design proposal, it will generate a new design proposal based on that feedback and recommend it again. This allows the recommendation department to respond quickly and flexibly to user needs and provide the optimal design. In addition, the recommendation department can maintain the user's creative freedom by presenting multiple design proposals and allowing the user to choose. This enables the recommendation department to efficiently support the user's creative production and provide designs that appropriately express cultural nuances and emotions.

[0033] The learning unit learns from user choices and feedback to improve the accuracy of recommendations. For example, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. Specifically, it collects user choice history and feedback data and analyzes it using machine learning algorithms. This allows it to understand user preferences and trends and utilize them in future recommendations. For example, if a user frequently chooses certain colors or fonts, the learning unit learns this trend and incorporates it into future recommendations. The learning unit can also adjust the parameters of the recommendation algorithm based on user feedback to improve accuracy. Furthermore, the learning unit can learn the latest design trends and user preferences by utilizing external data sources and market research data. This allows the learning unit to always provide highly accurate recommendations based on the latest information. In addition, the learning unit can instantly modify recommendations by incorporating user feedback in real time. For example, if a user provides feedback on a recommended design, it generates a new design based on that feedback and recommends it again. This allows the learning unit to respond quickly and flexibly to user needs and provide the optimal design. As a result, the design recommendation system according to this embodiment can efficiently support the user's creative production and provide designs that appropriately express cultural nuances and emotions.

[0034] The data collection unit can collect creative information such as visuals, media, target regions, and messages. For example, the data collection unit can collect visual information entered by the user. It can also collect media information specified by the user. Furthermore, the data collection unit can collect information about the target region. For example, the data collection unit can collect relevant design elements based on the target region information entered by the user. This allows for the comprehensive collection of creative information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input visual information entered by the user into AI, which can then analyze and collect the visual information.

[0035] The analysis unit can identify optimal design elements from the perspective of target regions and cultures. For example, it can identify fonts and colors that are popular in a particular region. The analysis unit can also personalize designs based on project objectives and content. For instance, if a brand manager is promoting a new product, the analysis unit can identify designs that match the product's characteristics and messaging. This allows for the identification of design elements suitable for target regions and cultures. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input target region and cultural information into an AI, which can then identify optimal design elements.

[0036] The recommendation system can recommend fonts and colors that are popular in a particular region. For example, the recommendation system can recommend fonts that are popular in a particular region. It can also recommend colors that are popular in a particular region. This allows for the recommendation of fonts and colors that are appropriate for the region. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input information about fonts and colors that are popular in a particular region into an AI, and the AI ​​can recommend the most suitable fonts and colors.

[0037] The recommendation system can personalize designs based on the project's objectives and content. For example, if a brand manager is promoting a new product, the recommendation system can recommend a design that matches the product's characteristics and message. The recommendation system can also personalize designs for advertising creators for new campaigns based on information such as target regions, messaging, and media to be used. This ensures that designs align with the project's objectives. Some or all of the processes described above in the recommendation system may be performed using AI, or not. For example, the recommendation system can input project objectives and content information into an AI, which can then personalize and recommend the optimal design.

[0038] The learning unit can improve the accuracy of recommendations based on user selections and feedback. For example, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. The learning unit can also improve the accuracy of recommendations based on user feedback. For example, if a user provides feedback on a recommended design, the learning unit learns that feedback and incorporates it into future recommendations. This improves the accuracy of recommendations by reflecting user feedback. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user selection and feedback information into the AI, which can then improve the accuracy of recommendations.

[0039] The data collection unit can analyze the user's past creative information collection history and select the optimal collection method. For example, the data collection unit can prioritize suggesting collection methods that the user has frequently used in the past. The data collection unit can also suggest the optimal collection method for a specific time period based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and select the most efficient collection method. This allows for the selection of the optimal collection method based on past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history into AI, which can then select the optimal collection method.

[0040] The data collection unit can filter creative information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the user's current projects. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, the data collection unit can collect necessary information in a timely manner according to the progress of the user's projects. This allows for the collection of highly relevant information based on projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information about the user's projects and areas of interest into an AI, which can then filter and collect highly relevant information.

[0041] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when gathering creative information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. The data collection unit can also collect information on nearby events and trends based on the user's location. Furthermore, if the user is traveling, the data collection unit can collect information on the culture and design of the destination. This allows for the collection of highly relevant information based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting creative information. For example, the data collection unit can collect relevant information based on content shared by the user on social media. It can also collect information based on topics of interest to the user's followers and friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect information that might interest them. This allows for the collection of highly relevant information based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, which can then collect relevant information.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of the creative information during the analysis. For example, the analysis unit can perform a detailed analysis on important creative information. It can also perform a concise analysis on general creative information. Furthermore, the analysis unit can perform an in-depth analysis on important information related to a specific project. This allows the level of detail of the analysis to be adjusted according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the creative information into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of creative information during analysis. For example, the analysis unit can apply an image analysis algorithm to information related to visual design. It can also apply a natural language processing algorithm to information related to text content. Furthermore, it can apply a video analysis algorithm to information related to video content. This allows the optimal analysis algorithm to be applied according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of creative information into the AI, which can then apply the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of creative information during the analysis process. For example, the analysis unit may prioritize the analysis of information from projects with approaching deadlines. The analysis unit can also determine the priority of analysis based on deadlines specified by the user. Furthermore, the analysis unit can prioritize the analysis of information with earlier submission dates. This enables efficient analysis by prioritizing analysis based on submission dates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission dates of creative information into AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the creative information during the analysis process. For example, the analysis unit may prioritize the analysis of information most relevant to the project. It can also prioritize the analysis of information related to the user's areas of interest. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the creative information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the creative information into the AI, which can then adjust the order of analysis.

[0047] The recommendation system can adjust the level of detail of its recommendations based on the importance of the design elements. For example, it can provide detailed recommendations for important design elements, and concise recommendations for general design elements. Furthermore, it can provide in-depth recommendations for important design elements relevant to a specific project. This allows for adjusting the level of detail of recommendations according to the importance of the design elements. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the importance of the design elements into the AI, which can then adjust the level of detail of the recommendations.

[0048] The recommendation system can apply different recommendation algorithms depending on the category of the design element during the recommendation process. For example, the recommendation system can apply an image recommendation algorithm to elements related to visual design. It can also apply a natural language processing algorithm to elements related to text content. Furthermore, it can apply a video recommendation algorithm to elements related to video content. This allows the system to apply the most suitable recommendation algorithm depending on the category of the design element. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the category of the design element into the AI, which can then apply the most suitable recommendation algorithm.

[0049] The recommendation system can prioritize recommendations based on the submission timing of design elements. For example, it might prioritize recommending design elements from projects with approaching deadlines. It can also prioritize recommendations based on deadlines specified by the user. Furthermore, it can prioritize recommending design elements with earlier submission dates. This allows for efficient recommendations by prioritizing recommendations based on submission timing. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system could input the submission timing of design elements into an AI, which could then determine the recommendation priority.

[0050] The recommendation system can adjust the order of recommendations based on the relevance of design elements. For example, the recommendation system may prioritize recommending design elements that are most relevant to the project. It can also prioritize recommending design elements that are relevant to the user's areas of interest. Furthermore, the recommendation system can adjust the order of recommendations based on the relevance of design elements. This allows for efficient recommendations by adjusting the order of recommendations based on the relevance of design elements. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the relevance of design elements into an AI, which can then adjust the order of recommendations.

[0051] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm based on past successes. It can also improve the learning algorithm based on past failures. Furthermore, the learning unit can analyze past learning data and select the optimal learning algorithm. This enables efficient learning by optimizing the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0052] The learning unit can weight the training data based on the submission timing of creative information during the learning process. For example, the learning unit can prioritize learning information on projects with approaching deadlines. It can also weight the training data based on deadlines specified by the user. Furthermore, it can prioritize learning information with earlier submission dates. This allows for efficient learning by weighting the training data based on submission timing. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input the submission timing of creative information into the AI, which can then weight the training data.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The design recommendation system can further analyze the user's past design selection history and recommend design elements based on the user's preferences. For example, it can analyze the trends in fonts and colors the user has previously chosen and prioritize recommending similar design elements. It can also suggest similar designs based on design elements the user has previously given high ratings to. Furthermore, it can recommend design elements suitable for a specific project based on the user's past design selection history. This enables personalized design recommendations based on the user's preferences. These processes may or may not be performed using AI.

[0055] The design recommendation system can further collect region-specific design elements based on the user's geographical location. For example, if the user is in a specific region, it can collect design elements based on the culture and trends of that region. If the user is traveling, it can also collect design elements related to their destination. Furthermore, it can collect information on nearby events and trends based on the user's location. This allows for the collection of highly relevant design elements based on geographical location. These processes may or may not be performed using AI.

[0056] The design recommendation system can further analyze the user's social media activity and collect relevant design elements. For example, it can collect relevant design elements based on content the user has shared on social media. It can also collect design elements based on topics of interest to the user's followers and friends. Furthermore, it can analyze the user's social media activity history and collect design elements that are likely to interest them. This allows for the collection of highly relevant design elements based on social media activity. These processes may or may not be performed using AI.

[0057] The design recommendation system can further analyze the user's past creative information gathering history and select the optimal gathering method. For example, it can prioritize suggesting gathering methods that the user has frequently used in the past. It can also suggest the optimal gathering method for a specific time period based on the user's past gathering history. Furthermore, it can analyze the user's past gathering history and select the most efficient gathering method. This allows for the selection of the optimal gathering method based on past history. These processes may or may not be performed using AI.

[0058] The design recommendation system can further filter creative information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current project. It can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, it can collect necessary information in a timely manner according to the progress of the user's project. This allows for the collection of highly relevant information based on projects and areas of interest. These processes may or may not be performed using AI.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The collection unit collects information about the creative. This information includes, for example, visuals, media, target region, and message. The collection unit collects visual information entered by the user, specified media information, and information about the target region. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit identifies the optimal design elements from the perspective of the target region and culture, and personalizes the design based on the project's objectives and content. Step 3: The recommendation team recommends the optimal design based on the information analyzed by the analysis team. The recommendation team recommends fonts and colors popular in specific regions and personalizes the design based on the project's objectives and content. Step 4: The learning unit learns from user choices and feedback to improve the accuracy of recommendations. If the font and color combination chosen by the user is successful, the learning unit learns that information and incorporates it into future recommendations.

[0061] (Example of form 2) The design recommendation system according to an embodiment of the present invention is an AI-based design recommendation tool that assists in the production of creative works for overseas markets. This design recommendation system proposes designs that reflect the sensibilities and emotions of specific regions and cultures, based on user input of creative information (visuals, media, target region, message, etc.). For example, it recommends optimal foreign language fonts and colors to localize the creative work. It also recommends personalized designs based on the project's objectives and content. Furthermore, the AI ​​learns from user selections and feedback to improve the accuracy of its recommendations. For example, when an advertiser creates a design for a new campaign, they input information such as the target region, message, and media to be used. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​identifies optimal design elements from the perspective of the target region and culture. For example, it recommends fonts and colors popular in a particular region. This ensures the design is appropriate for that region and culture. Furthermore, the AI ​​personalizes the design based on the project's objectives and content. For example, when a brand manager promotes a new product, it proposes a design that matches the product's characteristics and message. This ensures the design aligns with the project's objectives. Finally, the AI ​​learns from user selections and feedback to improve the accuracy of its recommendations. For example, if a user's chosen font and color combination is successful, the AI ​​learns from that information and incorporates it into future recommendations. This makes the AI's recommendations more accurate. This system allows designers and advertisers to efficiently create designs that appropriately express cultural nuances and emotions. It also saves time and effort, allowing them to focus on more creative work. For instance, a graphic designer can use this tool to quickly find the optimal fonts and colors when creating a design that incorporates a multicultural perspective. In this way, the design recommendation system can efficiently support users' creative work and provide designs that appropriately express cultural nuances and emotions.

[0062] The design recommendation system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects information related to creative work. This information includes, but is not limited to, visuals, media, target region, and message. For example, the collection unit collects visual information entered by the user. The collection unit can also collect media information specified by the user. Furthermore, the collection unit can also collect information related to the target region. For example, the collection unit collects relevant design elements based on target region information entered by the user. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit identifies optimal design elements from the perspective of the target region and culture. For example, the analysis unit identifies fonts and colors that are popular in a particular region. The analysis unit can also personalize designs based on the project's objectives and content. For example, when a brand manager is promoting a new product, the analysis unit identifies a design that matches the product's characteristics and message. The recommendation unit recommends the optimal design based on the information analyzed by the analysis unit. For example, the recommendation unit recommends fonts and colors that are popular in a particular region. Furthermore, the recommendation unit can personalize designs based on the project's objectives and content. For example, if a brand manager is promoting a new product, the recommendation unit will recommend a design that matches the product's characteristics and message. The learning unit learns from user selections and feedback to improve the accuracy of recommendations. For instance, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. As a result, the design recommendation system according to this embodiment can efficiently support users' creative work and provide designs that appropriately express cultural nuances and emotions.

[0063] The data collection unit collects information related to creative work. This information includes, but is not limited to, visuals, media, target regions, and messages. For example, the unit collects visual information entered by users. Specifically, it collects visual elements such as images, graphics, and logos uploaded by users, and simultaneously obtains their metadata. This allows for the understanding of detailed information such as the resolution, color, and format of visual elements. The data collection unit can also collect media information specified by users. For example, it collects media information such as magazines, websites, and social media platforms where advertisements are planned to be published, and understands the characteristics and format requirements of each medium. Furthermore, the data collection unit can also collect information related to target regions. For example, based on target region information entered by users, the unit collects relevant design elements. It collects information such as the culture, language, and trends of the target region and considers their impact on the design. This allows the data collection unit to efficiently collect diverse information tailored to user needs and utilize it as foundational data for design. In addition, the data collection unit can also obtain the latest design trends and market research data using external databases and APIs. This allows the data collection unit to always provide design elements based on the latest information and support users' creative production.

[0064] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit identifies optimal design elements from the perspective of target region and culture. Specifically, it analyzes the collected visual information using image analysis technology to extract elements such as color, shape, and layout. Furthermore, it evaluates how these elements will be received based on the culture and trends of the target region. For example, to identify fonts and colors popular in a particular region, it refers to past design data and market research data and performs statistical analysis. The analysis unit can also personalize designs based on the project's objectives and content. For example, when a brand manager is promoting a new product, the analysis unit identifies a design that matches the product's characteristics and message. It selects the optimal design elements considering the product's target audience and brand image. Furthermore, the analysis unit can optimize combinations of design elements using AI. For example, it uses generative AI to generate multiple design options based on the collected data and evaluates them. By reflecting users' past choices and feedback in the evaluation, more accurate analysis becomes possible. In this way, the analysis unit can analyze the collected data from multiple angles and identify the design elements that best suit the user's needs.

[0065] The recommendation department recommends the optimal design based on information analyzed by the analysis department. For example, the recommendation department might recommend fonts or colors popular in a specific region. Specifically, it generates and recommends the most suitable design proposals for the user's project based on the design elements identified by the analysis department. For example, if a user is promoting a new product, it will recommend a design that matches the product's characteristics and message. The recommendation department uses AI to learn from the user's past choices and feedback, improving the accuracy of its recommendations. For example, it incorporates data on design elements previously selected by the user and successful projects into future recommendations. Furthermore, the recommendation department can receive real-time feedback from users and immediately modify its recommendations. For example, if a user provides feedback on a recommended design proposal, it will generate a new design proposal based on that feedback and recommend it again. This allows the recommendation department to respond quickly and flexibly to user needs and provide the optimal design. In addition, the recommendation department can maintain the user's creative freedom by presenting multiple design proposals and allowing the user to choose. This enables the recommendation department to efficiently support the user's creative production and provide designs that appropriately express cultural nuances and emotions.

[0066] The learning unit learns from user choices and feedback to improve the accuracy of recommendations. For example, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. Specifically, it collects user choice history and feedback data and analyzes it using machine learning algorithms. This allows it to understand user preferences and trends and utilize them in future recommendations. For example, if a user frequently chooses certain colors or fonts, the learning unit learns this trend and incorporates it into future recommendations. The learning unit can also adjust the parameters of the recommendation algorithm based on user feedback to improve accuracy. Furthermore, the learning unit can learn the latest design trends and user preferences by utilizing external data sources and market research data. This allows the learning unit to always provide highly accurate recommendations based on the latest information. In addition, the learning unit can instantly modify recommendations by incorporating user feedback in real time. For example, if a user provides feedback on a recommended design, it generates a new design based on that feedback and recommends it again. This allows the learning unit to respond quickly and flexibly to user needs and provide the optimal design. As a result, the design recommendation system according to this embodiment can efficiently support the user's creative production and provide designs that appropriately express cultural nuances and emotions.

[0067] The data collection unit can collect creative information such as visuals, media, target regions, and messages. For example, the data collection unit can collect visual information entered by the user. It can also collect media information specified by the user. Furthermore, the data collection unit can collect information about the target region. For example, the data collection unit can collect relevant design elements based on the target region information entered by the user. This allows for the comprehensive collection of creative information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input visual information entered by the user into AI, which can then analyze and collect the visual information.

[0068] The analysis unit can identify optimal design elements from the perspective of target regions and cultures. For example, it can identify fonts and colors that are popular in a particular region. The analysis unit can also personalize designs based on project objectives and content. For instance, if a brand manager is promoting a new product, the analysis unit can identify designs that match the product's characteristics and messaging. This allows for the identification of design elements suitable for target regions and cultures. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input target region and cultural information into an AI, which can then identify optimal design elements.

[0069] The recommendation system can recommend fonts and colors that are popular in a particular region. For example, the recommendation system can recommend fonts that are popular in a particular region. It can also recommend colors that are popular in a particular region. This allows for the recommendation of fonts and colors that are appropriate for the region. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input information about fonts and colors that are popular in a particular region into an AI, and the AI ​​can recommend the most suitable fonts and colors.

[0070] The recommendation system can personalize designs based on the project's objectives and content. For example, if a brand manager is promoting a new product, the recommendation system can recommend a design that matches the product's characteristics and message. The recommendation system can also personalize designs for advertising creators for new campaigns based on information such as target regions, messaging, and media to be used. This ensures that designs align with the project's objectives. Some or all of the processes described above in the recommendation system may be performed using AI, or not. For example, the recommendation system can input project objectives and content information into an AI, which can then personalize and recommend the optimal design.

[0071] The learning unit can improve the accuracy of recommendations based on user selections and feedback. For example, if a user's chosen font or color combination is successful, the learning unit learns that information and incorporates it into future recommendations. The learning unit can also improve the accuracy of recommendations based on user feedback. For example, if a user provides feedback on a recommended design, the learning unit learns that feedback and incorporates it into future recommendations. This improves the accuracy of recommendations by reflecting user feedback. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user selection and feedback information into the AI, which can then improve the accuracy of recommendations.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of creative information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect information when the user is relaxed. Conversely, if the user is focused, the data collection unit can collect information immediately to proceed efficiently. Furthermore, if the user is tired, the data collection unit can adjust the collection timing to collect information after a break. This allows for efficient information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the collection timing.

[0073] The data collection unit can analyze the user's past creative information collection history and select the optimal collection method. For example, the data collection unit can prioritize suggesting collection methods that the user has frequently used in the past. The data collection unit can also suggest the optimal collection method for a specific time period based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and select the most efficient collection method. This allows for the selection of the optimal collection method based on past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history into AI, which can then select the optimal collection method.

[0074] The data collection unit can filter creative information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the user's current projects. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, the data collection unit can collect necessary information in a timely manner according to the progress of the user's projects. This allows for the collection of highly relevant information based on projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information about the user's projects and areas of interest into an AI, which can then filter and collect highly relevant information.

[0075] The data collection unit can estimate the user's emotions and determine the priority of creative information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. It can also prioritize collecting important information if the user is in a hurry. Furthermore, if the user is excited, the data collection unit may prioritize collecting visually stimulating information. This enables efficient information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into an AI to determine the priority of information the AI ​​should collect.

[0076] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when gathering creative information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. The data collection unit can also collect information on nearby events and trends based on the user's location. Furthermore, if the user is traveling, the data collection unit can collect information on the culture and design of the destination. This allows for the collection of highly relevant information based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant information.

[0077] The data collection unit can analyze the user's social media activity and collect relevant information when collecting creative information. For example, the data collection unit can collect relevant information based on content shared by the user on social media. It can also collect information based on topics of interest to the user's followers and friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect information that might interest them. This allows for the collection of highly relevant information based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, which can then collect relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of its analysis based on the importance of the creative information during the analysis. For example, the analysis unit can perform a detailed analysis on important creative information. It can also perform a concise analysis on general creative information. Furthermore, the analysis unit can perform an in-depth analysis on important information related to a specific project. This allows the level of detail of the analysis to be adjusted according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the creative information into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of creative information during analysis. For example, the analysis unit can apply an image analysis algorithm to information related to visual design. It can also apply a natural language processing algorithm to information related to text content. Furthermore, it can apply a video analysis algorithm to information related to video content. This allows the optimal analysis algorithm to be applied according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of creative information into the AI, which can then apply the optimal analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the submission timing of creative information during the analysis process. For example, the analysis unit may prioritize the analysis of information from projects with approaching deadlines. The analysis unit can also determine the priority of analysis based on deadlines specified by the user. Furthermore, the analysis unit can prioritize the analysis of information with earlier submission dates. This enables efficient analysis by prioritizing analysis based on submission dates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission dates of creative information into AI, which can then determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the creative information during the analysis process. For example, the analysis unit may prioritize the analysis of information most relevant to the project. It can also prioritize the analysis of information related to the user's areas of interest. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the creative information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the creative information into the AI, which can then adjust the order of analysis.

[0084] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations that get straight to the point. Furthermore, if the user is excited, it can provide visually stimulating recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into an AI, which can then adjust the way recommendations are presented.

[0085] The recommendation system can adjust the level of detail of its recommendations based on the importance of the design elements. For example, it can provide detailed recommendations for important design elements, and concise recommendations for general design elements. Furthermore, it can provide in-depth recommendations for important design elements relevant to a specific project. This allows for adjusting the level of detail of recommendations according to the importance of the design elements. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the importance of the design elements into the AI, which can then adjust the level of detail of the recommendations.

[0086] The recommendation system can apply different recommendation algorithms depending on the category of the design element during the recommendation process. For example, the recommendation system can apply an image recommendation algorithm to elements related to visual design. It can also apply a natural language processing algorithm to elements related to text content. Furthermore, it can apply a video recommendation algorithm to elements related to video content. This allows the system to apply the most suitable recommendation algorithm depending on the category of the design element. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the category of the design element into the AI, which can then apply the most suitable recommendation algorithm.

[0087] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation section can provide short, concise recommendations. If the user is relaxed, it can provide detailed recommendations. Furthermore, if the user is excited, it can provide visually stimulating recommendations. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into an AI, which can then adjust the length of recommendations.

[0088] The recommendation system can prioritize recommendations based on the submission timing of design elements. For example, it might prioritize recommending design elements from projects with approaching deadlines. It can also prioritize recommendations based on deadlines specified by the user. Furthermore, it can prioritize recommending design elements with earlier submission dates. This allows for efficient recommendations by prioritizing recommendations based on submission timing. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system could input the submission timing of design elements into an AI, which could then determine the recommendation priority.

[0089] The recommendation system can adjust the order of recommendations based on the relevance of design elements. For example, the recommendation system may prioritize recommending design elements that are most relevant to the project. It can also prioritize recommending design elements that are relevant to the user's areas of interest. Furthermore, the recommendation system can adjust the order of recommendations based on the relevance of design elements. This allows for efficient recommendations by adjusting the order of recommendations based on the relevance of design elements. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the relevance of design elements into an AI, which can then adjust the order of recommendations.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data that gets straight to the point. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into an AI, and the AI ​​can select the training data.

[0091] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm based on past successes. It can also improve the learning algorithm based on past failures. Furthermore, the learning unit can analyze past learning data and select the optimal learning algorithm. This enables efficient learning by optimizing the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0092] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will learn more frequently. If the user is in a hurry, the learning unit can reduce the learning frequency to work more efficiently. Furthermore, if the user is tired, the learning unit can adjust the learning frequency to allow for breaks before learning. This allows for efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into an AI, which can then adjust the learning frequency.

[0093] The learning unit can weight the training data based on the submission timing of creative information during the learning process. For example, the learning unit can prioritize learning information on projects with approaching deadlines. It can also weight the training data based on deadlines specified by the user. Furthermore, it can prioritize learning information with earlier submission dates. This allows for efficient learning by weighting the training data based on submission timing. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input the submission timing of creative information into the AI, which can then weight the training data.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The design recommendation system can further analyze the user's past design selection history and recommend design elements based on the user's preferences. For example, it can analyze the trends in fonts and colors the user has previously chosen and prioritize recommending similar design elements. It can also suggest similar designs based on design elements the user has previously given high ratings to. Furthermore, it can recommend design elements suitable for a specific project based on the user's past design selection history. This enables personalized design recommendations based on the user's preferences. These processes may or may not be performed using AI.

[0096] The data collection unit can estimate the user's emotions and adjust the type of creative information collected based on the estimated emotions. For example, if the user is relaxed, detailed visual information can be collected. If the user is in a hurry, concise message information can be prioritized. Furthermore, if the user is excited, visually stimulating information can also be collected. This allows for efficient information collection by adjusting the type of information collected according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0097] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be prioritized. Furthermore, if the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the timing of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0098] The recommendation section can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is relaxed, it can provide detailed recommendations. If the user is in a hurry, it can prioritize concise recommendations. Furthermore, if the user is excited, it can provide visually stimulating recommendations. By adjusting the timing of recommendations according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the recommendation section may be performed using AI or not.

[0099] The learning unit can estimate the user's emotions and adjust the timing of learning based on the estimated emotions. For example, if the user is relaxed, it can perform detailed learning. If the user is in a hurry, it can prioritize concise learning. Furthermore, if the user is excited, it can provide visually stimulating learning results. By adjusting the timing of learning according to the user's emotions, more appropriate learning results can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the learning unit may be performed using AI or not.

[0100] The design recommendation system can further collect region-specific design elements based on the user's geographical location. For example, if the user is in a specific region, it can collect design elements based on the culture and trends of that region. If the user is traveling, it can also collect design elements related to their destination. Furthermore, it can collect information on nearby events and trends based on the user's location. This allows for the collection of highly relevant design elements based on geographical location. These processes may or may not be performed using AI.

[0101] The design recommendation system can further analyze the user's social media activity and collect relevant design elements. For example, it can collect relevant design elements based on content the user has shared on social media. It can also collect design elements based on topics of interest to the user's followers and friends. Furthermore, it can analyze the user's social media activity history and collect design elements that are likely to interest them. This allows for the collection of highly relevant design elements based on social media activity. These processes may or may not be performed using AI.

[0102] The design recommendation system can further analyze the user's past creative information gathering history and select the optimal gathering method. For example, it can prioritize suggesting gathering methods that the user has frequently used in the past. It can also suggest the optimal gathering method for a specific time period based on the user's past gathering history. Furthermore, it can analyze the user's past gathering history and select the most efficient gathering method. This allows for the selection of the optimal gathering method based on past history. These processes may or may not be performed using AI.

[0103] The design recommendation system can further filter creative information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current project. It can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, it can collect necessary information in a timely manner according to the progress of the user's project. This allows for the collection of highly relevant information based on projects and areas of interest. These processes may or may not be performed using AI.

[0104] The design recommendation system can further estimate the user's emotions and prioritize the creative information to collect based on those emotions. For example, if the user is relaxed, detailed information can be prioritized. If the user is in a hurry, important information can be prioritized. Furthermore, if the user is excited, visually stimulating information can be prioritized. This enables efficient information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects information about the creative. This information includes, for example, visuals, media, target region, and message. The collection unit collects visual information entered by the user, specified media information, and information about the target region. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit identifies the optimal design elements from the perspective of the target region and culture, and personalizes the design based on the project's objectives and content. Step 3: The recommendation team recommends the optimal design based on the information analyzed by the analysis team. The recommendation team recommends fonts and colors popular in specific regions and personalizes the design based on the project's objectives and content. Step 4: The learning unit learns from user choices and feedback to improve the accuracy of recommendations. If the font and color combination chosen by the user is successful, the learning unit learns that information and incorporates it into future recommendations.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects visual and media information input by the user via the control unit 46A of the smart device 14. The analysis unit analyzes the collected information via the specific processing unit 290 of the data processing unit 12 to identify optimal design elements from the perspective of target region and culture. The recommendation unit recommends the optimal design based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The learning unit learns user selections and feedback via the specific processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects visual and media information input by the user via the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information via the specific processing unit 290 of the data processing unit 12 to identify optimal design elements from the perspective of the target region and culture. The recommendation unit recommends the optimal design based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The learning unit learns user selections and feedback via the specific processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects visual and media information input by the user via the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information via the specific processing unit 290 of the data processing unit 12 to identify optimal design elements from the perspective of the target region and culture. The recommendation unit recommends the optimal design based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The learning unit learns user selections and feedback via the specific processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and learning unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects visual and media information input by the user via the control unit 46A of the robot 414. The analysis unit analyzes the collected information via the specific processing unit 290 of the data processing unit 12 to identify optimal design elements from the perspective of the target region and culture. The recommendation unit recommends the optimal design based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The learning unit learns user selections and feedback via the specific processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A collection department that gathers information related to creative work, An analysis unit analyzes the information collected by the aforementioned collection unit, A recommendation unit recommends the optimal design based on the information analyzed by the aforementioned analysis unit, It includes a learning unit that learns from user choices and feedback to improve the accuracy of recommendations. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information about creative aspects such as visuals, media, target region, and message. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Identify the optimal design elements from the perspective of the target region and culture. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, Recommend fonts and colors that are popular in a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Personalize the design based on the project's objectives and content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, We improve the accuracy of recommendations based on user choices and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of creative information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past creative information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting creative information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes the creative information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting creative information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting creative information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the creative information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of creative information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the creative information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the creative information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the design elements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the category of the design element. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making a recommendation, we will prioritize the recommendations based on the submission timing of the design elements. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the design elements. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when the creative information was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that gathers information related to creative work, An analysis unit analyzes the information collected by the aforementioned collection unit, A recommendation unit recommends the optimal design based on the information analyzed by the aforementioned analysis unit, It includes a learning unit that learns from user choices and feedback to improve the accuracy of recommendations. A system characterized by the following features.

2. The aforementioned collection unit is We collect information about creative aspects such as visuals, media, target region, and message. The system according to feature 1.

3. The aforementioned analysis unit, Identify the optimal design elements from the perspective of the target region and culture. The system according to feature 1.

4. The aforementioned recommendation department, Recommend fonts and colors that are popular in a specific region. The system according to feature 1.

5. The aforementioned recommendation department, Personalize the design based on the project's objectives and content. The system according to feature 1.

6. The aforementioned learning unit, We improve the accuracy of recommendations based on user choices and feedback. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of creative information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past creative information gathering history and select the optimal collection method. The system according to feature 1.

Images