system

Abstract

The system according to this embodiment aims to improve the accuracy of localization by recommending the most suitable foreign language fonts and colors for creative production aimed at overseas markets. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a recommendation unit, a proposal unit, and a learning unit. The reception unit receives creative information from the user. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. The proposal unit proposes a design based on the fonts and colors recommended by the recommendation unit. The learning unit learns the user's selections and feedback on the designs proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 select an optimal foreign language font and color for overseas creative production, and there is room for improving the accuracy of localization.

[0005] The system according to the embodiment aims to recommend an optimal foreign language font and color for overseas creative production and improve the accuracy of localization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a proposal unit, and a learning unit. The reception unit receives creative information from the user. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. The proposal unit proposes a design based on the fonts and colors recommended by the recommendation unit. The learning unit learns the user's selections and feedback on the designs proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable foreign language fonts and colors for creative production aimed at overseas markets, thereby improving the accuracy of localization. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F 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] [[ID=2l]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The design support system according to an embodiment of the present invention is a system that recommends optimal foreign language fonts and colors based on creative information input by the user and proposes designs that correspond to specific regions and cultures. In this design support system, the user inputs information about the creative, and an AI agent analyzes that information to recommend optimal foreign language fonts and colors. Furthermore, in order to realize the localization of the creative work, it proposes designs that reflect the sensibilities and emotions corresponding to specific regions and cultures. For example, if a user inputs information such as visuals, media, target region, and message for advertising production, the AI ​​agent analyzes that information and recommends fonts and colors that match Japanese culture and sensibilities if the advertisement is for Japan. If the advertisement is for China, it proposes designs that match Chinese culture and sensibilities. Furthermore, the AI ​​agent learns from the user's selections and feedback and improves the accuracy of its recommendations. For example, if the user selects a recommended font or color, the AI ​​agent learns from that selection and reflects it in the next recommendation. Also, if the user provides feedback, the AI ​​agent learns from that feedback and improves the accuracy of its recommendations. As a result, the user can receive personalized recommendations based on the project's purpose and content. For example, in advertising production, the system recommends the most suitable fonts and colors based on the advertisement's visuals, the media used, the target region, and the message to be conveyed. Furthermore, to achieve localization of the creative work, it proposes designs that reflect the sensibilities and emotions of specific regions and cultures. This allows users to select foreign language fonts suitable for their designs and gain inspiration for creating designs with a cross-cultural perspective. It also reduces the time and effort spent finding the best fonts and colors for a project, enabling the appropriate expression of cultural nuances and emotions in creative works. This allows the design support system to efficiently receive, analyze, recommend, suggest, and learn from the user's creative information.

[0029] The design support system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a proposal unit, and a learning unit. The reception unit receives creative information from the user. Creative information includes, but is not limited to, visuals, media, target region, and message. For example, the reception unit receives visual information entered by the user. The reception unit can also receive media information specified by the user. Furthermore, the reception unit can also receive target region and message information. For example, the reception unit receives visual information of an advertisement entered by the user. The reception unit can also receive advertising media information specified by the user. The reception unit can also receive target region and advertising message information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes visual information to propose a design that reflects the sensibilities and emotions corresponding to a specific region or culture. For example, the analysis unit analyzes visual information to propose a design that suits Japanese culture and sensibilities. The analysis unit can also analyze visual information to propose a design that suits Chinese culture and sensibilities. The analysis unit, for example, analyzes visual information and proposes designs that reflect the sensibilities and emotions corresponding to a specific region or culture. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. For example, if the advertisement is for Japan, the recommendation unit will recommend fonts and colors that match Japanese culture and sensibilities. For example, if the advertisement is for China, the recommendation unit will recommend fonts and colors that match Chinese culture and sensibilities. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. The proposal unit proposes designs corresponding to a specific region or culture based on the fonts and colors recommended by the recommendation unit. For example, if the advertisement is for Japan, the proposal unit will propose designs that match Japanese culture and sensibilities. For example, if the advertisement is for China, the proposal unit will propose designs that match Chinese culture and sensibilities. The proposal unit proposes designs corresponding to a specific region or culture based on the fonts and colors recommended by the recommendation unit. The learning unit learns from user selections and feedback on the designs proposed by the proposal unit and improves the accuracy of recommendations.The learning unit learns from, for example, when a user selects a recommended font or color, and reflects this in future recommendations. The learning unit also learns from, for example, feedback provided by a user, and improves the accuracy of recommendations. The learning unit also learns from, for example, the user's selections and feedback on designs proposed by the proposal unit, and improves the accuracy of recommendations. As a result, the design support system according to this embodiment can efficiently receive, analyze, recommend, propose, and learn from the user's creative information.

[0030] The reception desk accepts creative information from users. This creative information includes, but is not limited to, visuals, media, target regions, and messages. For example, the reception desk accepts visual information entered by users. Specifically, it can accept image files uploaded by users, design sketches, color specifications, etc. The reception desk can also accept media information specified by users. Media information includes the platform on which the advertisement will be placed, and the type of media, such as print or digital signage. Furthermore, the reception desk can also accept target region and message information. Target region information includes geographical information such as the country, region, or city where the advertisement will be displayed, and message information includes the advertisement's tagline and the content of the message to be conveyed. For example, the reception desk accepts visual information for advertisements entered by users. It can accept image files uploaded by users, design sketches, color specifications, etc. The reception desk can also accept advertising media information specified by users. Media information includes the platform on which the advertisement will be placed, and the type of media, such as print or digital signage. The reception department can also receive target region and advertising message information. Target region information includes geographical information such as the country, region, and city where the advertisement will be displayed, while message information includes the advertisement's catchphrase and the content of the message to be conveyed. This allows the reception department to efficiently receive diverse creative information from users and smoothly provide the data to the analysis department.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Specifically, it uses image analysis technology to analyze the colors, composition, and themes of visual information and extract design elements suitable for the region or culture. For example, when proposing a design that suits Japanese culture and sensibilities, it incorporates traditional colors, patterns, and symbolic motifs. The analysis unit can also analyze visual information to propose designs that suit Chinese culture and sensibilities. Design elements suitable for Chinese culture include the use of red and gold colors, and traditional symbols such as dragons and phoenixes. The analysis unit analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Furthermore, the analysis unit can use AI to learn from users' past preferences and trend data to provide more personalized design suggestions. The AI ​​uses image recognition technology to analyze camera footage and identify flooded areas and obstacles on roads. It also analyzes pedestrian location information to calculate the optimal evacuation route. Furthermore, the system analyzes water level information from household sensors to predict the rate and extent of flooding. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past flooding data, it can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The recommendation department recommends the most suitable foreign language fonts and colors based on information analyzed by the analysis department. Specifically, it selects fonts and colors appropriate to the target region and culture based on design elements extracted by the analysis department. For example, for an advertisement targeting Japan, it would recommend fonts and colors that match Japanese culture and sensibilities. Common fonts suitable for Japanese advertisements include Mincho and Gothic typefaces, and for colors, traditional Japanese colors and colors that reflect the seasons are preferred. For example, for an advertisement targeting China, the recommendation department would recommend fonts and colors that match Chinese culture and sensibilities. Common fonts suitable for Chinese advertisements include Kaisho and Gyosho typefaces, and for colors, red and gold are symbolic. The recommendation department recommends the most suitable foreign language fonts and colors based on information analyzed by the analysis department. Furthermore, the recommendation department can use AI to learn from users' past preferences and trend data to provide more personalized font and color recommendations. The AI ​​analyzes past design choices and user feedback to understand user preferences and trends. This allows the recommendation department to provide the most suitable fonts and colors that meet user needs. Furthermore, the recommendation system can continuously improve its recommendations based on real-time updated data, adapting to the latest trends and user preferences. This allows the recommendation system to provide users with highly accurate font and color recommendations, thereby improving the quality of the design.

[0033] The proposal team proposes designs tailored to specific regions and cultures, based on fonts and colors recommended by the recommendation team. Specifically, they create designs suitable for the target region and culture using the recommended fonts and colors. For example, for an advertisement targeting Japan, they propose a design that matches Japanese culture and sensibilities. Japanese advertising designs often incorporate traditional Japanese elements and modern minimalism. For example, for an advertisement targeting China, the proposal team proposes a design that matches Chinese culture and sensibilities. Chinese advertising designs often incorporate traditional symbols and colors and elements of modern pop culture. The proposal team proposes designs tailored to specific regions and cultures, based on fonts and colors recommended by the recommendation team. Furthermore, the proposal team can use AI to learn from users' past preferences and trend data, enabling them to provide more personalized design suggestions. The AI ​​analyzes past design choices and user feedback to understand user preferences and trends. This allows the proposal team to provide the optimal design that meets user needs. In addition, the proposal team can continuously improve its suggestions based on real-time updated data, adapting to the latest trends and user preferences. This allows the proposal department to provide users with highly accurate design proposals and improve the quality of the designs.

[0034] The learning unit learns from user selections and feedback on designs proposed by the suggestion unit to improve the accuracy of recommendations. Specifically, if a user selects a recommended font or color, the learning unit learns from that selection and reflects it in future recommendations. For example, if a user frequently selects a particular font or color, the learning unit learns this trend and reflects it in future recommendations. Furthermore, if a user provides feedback, the learning unit learns from that feedback to improve the accuracy of recommendations. User feedback includes design preferences, areas for improvement, and user experience. The learning unit analyzes this feedback to improve the recommendation algorithm. In addition, the learning unit can use AI to learn from users' past preferences and trend data to provide more personalized recommendations. The AI ​​analyzes past design selections and user feedback to understand user preferences and trends. This allows the learning unit to provide optimal recommendations that meet user needs. The learning unit can also continuously improve recommendations based on real-time updated data, adapting to the latest trends and user preferences. This enables the learning unit to provide highly accurate recommendations to users and improve the quality of designs.

[0035] The reception unit can accept creative information such as visuals, media, target regions, and messages. For example, the reception unit can accept visual information entered by the user. The reception unit can also accept media information specified by the user. The reception unit can also accept target regions and message information. For example, the reception unit can accept visual information of an advertisement entered by the user. The reception unit can also accept advertising media information specified by the user. The reception unit can also accept target regions and advertising message information. This enables detailed input of creative information. Creative information includes, but is not limited to, visuals, media, target regions, and messages. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input visual information entered by the user into a generating AI and have the generating AI perform analysis of the visual information.

[0036] The analysis unit can analyze the information received by the reception unit and propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. For example, the analysis unit can analyze visual information and propose designs that suit Japanese culture and sensibilities. The analysis unit can also analyze visual information and propose designs that suit Chinese culture and sensibilities. For example, the analysis unit can analyze visual information and propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. This makes it possible to propose designs that are appropriate for a region or culture. 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 the visual information received by the reception unit into a generation AI and have the generation AI perform the analysis of the visual information.

[0037] The recommendation unit can recommend the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. For example, for an advertisement aimed at Japan, the recommendation unit will recommend fonts and colors that suit Japanese culture and sensibilities. For example, for an advertisement aimed at China, the recommendation unit will recommend fonts and colors that suit Chinese culture and sensibilities. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. This makes it possible to recommend the most suitable foreign language fonts and colors. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI perform the recommendation of the most suitable foreign language fonts and colors.

[0038] The proposal department can propose designs that are appropriate for a specific region or culture, based on the fonts and colors recommended by the recommendation department. For example, if the advertisement is for Japan, the proposal department will propose a design that suits Japanese culture and sensibilities. For example, if the advertisement is for China, the proposal department will propose a design that suits Chinese culture and sensibilities. The proposal department proposes designs that are appropriate for a specific region or culture, based on the fonts and colors recommended by the recommendation department. This makes it possible to propose designs that are appropriate for a region or culture. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the fonts and colors recommended by the recommendation department into a generation AI and have the generation AI execute a proposal for a design appropriate for a specific region or culture.

[0039] The learning unit can learn from user selections and feedback on designs proposed by the proposal unit and improve the accuracy of recommendations. For example, if a user selects a recommended font or color, the learning unit learns from that selection and reflects it in future recommendations. For example, if a user provides feedback, the learning unit learns from that feedback and improves the accuracy of recommendations. The learning unit learns from user selections and feedback on designs proposed by the proposal unit and improves the accuracy of recommendations. In this way, the accuracy of recommendations improves by learning from user selections and feedback. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user selections and feedback on designs proposed by the proposal unit into a generating AI and have the generating AI perform the improvement of recommendation accuracy.

[0040] The reception desk can analyze the user's past creative information input history and suggest the optimal input method. For example, the reception desk can automatically display visuals and messages that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest creative information to be used at specific times based on the user's past input history. This makes it possible to suggest the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past creative information input history into a generating AI and have the generating AI suggest the optimal input method.

[0041] The reception unit can customize input fields based on the user's current projects and areas of interest when receiving creative information. For example, the reception unit can prioritize displaying input fields related to the user's current project. The reception unit can also suggest relevant creative information input fields based on the user's areas of interest. The reception unit can also dynamically adjust the necessary input fields according to the progress of the user's project. This allows for customization of input fields according to projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0042] The reception unit can prioritize receiving highly relevant information when receiving creative information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving creative information related to that region. The reception unit can also suggest region-specific fonts and colors based on the user's location. If the user is on the move, the reception unit can also suggest the most suitable creative information based on their current location. This enables the reception of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant information.

[0043] The reception unit can analyze the user's social media activity and accept relevant information when receiving creative information. For example, the reception unit can analyze the user's social media posts and suggest relevant creative information. The reception unit can also accept relevant creative information by referring to the activities of the user's followers and friends. The reception unit can also analyze the user's social media trends and suggest the most suitable creative information. This makes it possible to receive relevant information based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI perform the reception of relevant information.

[0044] The analysis unit can adjust the level of detail of the 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 to provide deeper insights. For less important creative information, the analysis unit can perform a concise analysis to provide only basic information. The analysis unit can also dynamically allocate analysis resources according to the importance of the creative information. This makes it possible to adjust the level of detail of the analysis according to the importance of the creative 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the creative information during analysis. For example, the analysis unit can apply an advertising-specific analysis algorithm to creative information in the advertising category. The analysis unit can also apply a social media-specific analysis algorithm to creative information in the social media category. The analysis unit can also apply an entertainment-specific analysis algorithm to creative information in the entertainment category. This makes it possible to apply an analysis algorithm according to the category of the creative information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the creative information during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted creative information. The analysis unit may also postpone the analysis of older creative information. The analysis unit can also dynamically allocate analysis resources based on the submission date. This enables the determination of analysis priorities based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date of the creative information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0047] 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 highly relevant creative information. The analysis unit may also postpone the analysis of less relevant creative information. The analysis unit can also dynamically allocate analysis resources based on relevance. This enables adjustment of the analysis order based on relevance. 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 relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The recommendation unit can adjust the level of detail of recommendations based on the importance of the creative information during the recommendation process. For example, the recommendation unit can provide detailed recommendations for important creative information. It can also provide concise recommendations for less important creative information. The recommendation unit can also dynamically allocate recommendation resources according to the importance of the creative information. This allows for adjustment of the level of detail of recommendations according to the importance of the creative information. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the importance of the creative information into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0049] The recommendation unit can apply different recommendation algorithms depending on the category of the creative information during the recommendation process. For example, the recommendation unit can apply an advertising-specific recommendation algorithm to creative information in the advertising category. The recommendation unit can also apply a social media-specific recommendation algorithm to creative information in the social media category. The recommendation unit can also apply an entertainment-specific recommendation algorithm to creative information in the entertainment category. This makes it possible to apply recommendation algorithms according to the category of the creative information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0050] The recommendation department can determine the priority of recommendations based on the submission date of the creative information. For example, the recommendation department may prioritize recommending recently submitted creative information. The recommendation department may also postpone recommending older creative information. The recommendation department can also dynamically allocate recommendation resources based on the submission date. This enables the determination of recommendation priorities based on the submission date. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the submission date of the creative information into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0051] The recommendation unit can adjust the order of recommendations based on the relevance of the creative information during the recommendation process. For example, the recommendation unit may prioritize recommending highly relevant creative information. The recommendation unit may also recommend less relevant creative information later. The recommendation unit can also dynamically allocate recommendation resources based on relevance. This enables adjustment of the recommendation order based on relevance. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0052] The proposal unit can adjust the level of detail of a proposal based on the importance of the creative information during the proposal process. For example, the proposal unit can provide detailed proposal information for important creative information. It can also provide concise proposal information for less important creative information. The proposal unit can also dynamically allocate proposal resources according to the importance of the creative information. This makes it possible to adjust the level of detail of a proposal according to the importance of the creative information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the creative information into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0053] The proposal unit can apply different proposal algorithms depending on the category of the creative information during the proposal process. For example, the proposal unit can apply an advertising-specific proposal algorithm to creative information in the advertising category. The proposal unit can also apply a social media-specific proposal algorithm to creative information in the social media category. The proposal unit can also apply an entertainment-specific proposal algorithm to creative information in the entertainment category. This makes it possible to apply a proposal algorithm according to the category of the creative information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0054] The proposal department can prioritize proposals based on the submission timing of the creative information. For example, the proposal department may prioritize recently submitted creative information. The proposal department may also postpone the proposal of older creative information. The proposal department can also dynamically allocate resources to proposals based on submission timing. This enables the prioritization of proposals based on submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the creative information into a generating AI and have the generating AI perform the determination of proposal prioritization.

[0055] The proposal unit can adjust the order of proposals based on the relevance of the creative information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant creative information. The proposal unit may also postpone proposing less relevant creative information. The proposal unit can also dynamically allocate resources for proposals based on relevance. This enables adjustment of the order of proposals based on relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0056] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. This makes it possible to optimize 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 using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0057] The learning unit can weight the training data based on the submission date of the creative information during training. For example, the learning unit can assign a higher weight to recently submitted creative information. The learning unit can also assign a lower weight to older creative information. The learning unit can also dynamically adjust the weighting of the training data based on the submission date. This enables weighting of training data based on the submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission date of the creative information into a generating AI and have the generating AI perform the weighting of the training data.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The design support system can analyze a user's past design selection history and propose the most suitable design. For example, it can suggest similar designs based on fonts and colors the user has previously selected. It can also prioritize suggesting design elements the user has used in the past. Furthermore, it can predict and suggest design elements that the user will use at specific times based on their past selection history. This enables the suggestion of the most suitable design based on past selection history. The analysis unit inputs the user's past design selection history into the generation AI, which then generates the optimal design proposal.

[0060] The design support system can suggest region-specific design elements by considering the user's geographical location. For example, if the user is in a specific region, it can suggest fonts and colors related to that region. It can also suggest region-specific design elements based on the user's location. Furthermore, if the user is on the move, it can suggest the most suitable design elements based on their current location. This enables the suggestion of design elements based on geographical location information. The analysis unit inputs the user's geographical location information into the generation AI, allowing the generation AI to execute the suggestion of region-specific design elements.

[0061] The design support system can analyze a user's social media activity and suggest relevant design elements. For example, it can analyze a user's social media posts and suggest relevant design elements. It can also suggest relevant design elements by referring to the activities of the user's followers and friends. Furthermore, it can analyze the user's social media trends and suggest the most suitable design elements. This makes it possible to suggest design elements based on social media activity. The analysis unit inputs the user's social media activity into the generation AI, which then generates suggestions for relevant design elements.

[0062] The design support system can analyze a user's past design selection history and propose the most suitable design. For example, it can suggest similar designs based on fonts and colors the user has previously selected. It can also prioritize suggesting design elements the user has used in the past. Furthermore, it can predict and suggest design elements that the user will use at specific times based on their past selection history. This enables the suggestion of the most suitable design based on past selection history. The analysis unit inputs the user's past design selection history into the generation AI, which then generates the optimal design proposal.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives creative information from users. This creative information includes visuals, media, target region, and message. For example, it receives visual information, specified media information, target region, and message information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Step 3: The recommendation team recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis team. For example, for an advertisement targeting Japan, they would recommend fonts and colors that are appropriate for Japanese culture and sensibilities. Step 4: The proposal team proposes designs tailored to specific regions and cultures, based on the fonts and colors recommended by the recommendation team. For example, for an advertisement targeting Japan, they would propose a design that suits Japanese culture and sensibilities. Step 5: The learning unit learns from user selections and feedback on designs proposed by the proposal unit, and improves the accuracy of recommendations. For example, if a user selects a recommended font or color, the unit learns from that selection and incorporates it into future recommendations.

[0065] (Example of form 2) The design support system according to an embodiment of the present invention is a system that recommends optimal foreign language fonts and colors based on creative information input by the user and proposes designs that correspond to specific regions and cultures. In this design support system, the user inputs information about the creative, and an AI agent analyzes that information to recommend optimal foreign language fonts and colors. Furthermore, in order to realize the localization of the creative work, it proposes designs that reflect the sensibilities and emotions corresponding to specific regions and cultures. For example, if a user inputs information such as visuals, media, target region, and message for advertising production, the AI ​​agent analyzes that information and recommends fonts and colors that match Japanese culture and sensibilities if the advertisement is for Japan. If the advertisement is for China, it proposes designs that match Chinese culture and sensibilities. Furthermore, the AI ​​agent learns from the user's selections and feedback and improves the accuracy of its recommendations. For example, if the user selects a recommended font or color, the AI ​​agent learns from that selection and reflects it in the next recommendation. Also, if the user provides feedback, the AI ​​agent learns from that feedback and improves the accuracy of its recommendations. As a result, the user can receive personalized recommendations based on the project's purpose and content. For example, in advertising production, the system recommends the most suitable fonts and colors based on the advertisement's visuals, the media used, the target region, and the message to be conveyed. Furthermore, to achieve localization of the creative work, it proposes designs that reflect the sensibilities and emotions of specific regions and cultures. This allows users to select foreign language fonts suitable for their designs and gain inspiration for creating designs with a cross-cultural perspective. It also reduces the time and effort spent finding the best fonts and colors for a project, enabling the appropriate expression of cultural nuances and emotions in creative works. This allows the design support system to efficiently receive, analyze, recommend, suggest, and learn from the user's creative information.

[0066] The design support system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a proposal unit, and a learning unit. The reception unit receives creative information from the user. Creative information includes, but is not limited to, visuals, media, target region, and message. For example, the reception unit receives visual information entered by the user. The reception unit can also receive media information specified by the user. Furthermore, the reception unit can also receive target region and message information. For example, the reception unit receives visual information of an advertisement entered by the user. The reception unit can also receive advertising media information specified by the user. The reception unit can also receive target region and advertising message information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes visual information to propose a design that reflects the sensibilities and emotions corresponding to a specific region or culture. For example, the analysis unit analyzes visual information to propose a design that suits Japanese culture and sensibilities. The analysis unit can also analyze visual information to propose a design that suits Chinese culture and sensibilities. The analysis unit, for example, analyzes visual information and proposes designs that reflect the sensibilities and emotions corresponding to a specific region or culture. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. For example, if the advertisement is for Japan, the recommendation unit will recommend fonts and colors that match Japanese culture and sensibilities. For example, if the advertisement is for China, the recommendation unit will recommend fonts and colors that match Chinese culture and sensibilities. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. The proposal unit proposes designs corresponding to a specific region or culture based on the fonts and colors recommended by the recommendation unit. For example, if the advertisement is for Japan, the proposal unit will propose designs that match Japanese culture and sensibilities. For example, if the advertisement is for China, the proposal unit will propose designs that match Chinese culture and sensibilities. The proposal unit proposes designs corresponding to a specific region or culture based on the fonts and colors recommended by the recommendation unit. The learning unit learns from user selections and feedback on the designs proposed by the proposal unit and improves the accuracy of recommendations.The learning unit learns from, for example, when a user selects a recommended font or color, and reflects this in future recommendations. The learning unit also learns from, for example, feedback provided by a user, and improves the accuracy of recommendations. The learning unit also learns from, for example, the user's selections and feedback on designs proposed by the proposal unit, and improves the accuracy of recommendations. As a result, the design support system according to this embodiment can efficiently receive, analyze, recommend, propose, and learn from the user's creative information.

[0067] The reception desk accepts creative information from users. This creative information includes, but is not limited to, visuals, media, target regions, and messages. For example, the reception desk accepts visual information entered by users. Specifically, it can accept image files uploaded by users, design sketches, color specifications, etc. The reception desk can also accept media information specified by users. Media information includes the platform on which the advertisement will be placed, and the type of media, such as print or digital signage. Furthermore, the reception desk can also accept target region and message information. Target region information includes geographical information such as the country, region, or city where the advertisement will be displayed, and message information includes the advertisement's tagline and the content of the message to be conveyed. For example, the reception desk accepts visual information for advertisements entered by users. It can accept image files uploaded by users, design sketches, color specifications, etc. The reception desk can also accept advertising media information specified by users. Media information includes the platform on which the advertisement will be placed, and the type of media, such as print or digital signage. The reception department can also receive target region and advertising message information. Target region information includes geographical information such as the country, region, and city where the advertisement will be displayed, while message information includes the advertisement's catchphrase and the content of the message to be conveyed. This allows the reception department to efficiently receive diverse creative information from users and smoothly provide the data to the analysis department.

[0068] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Specifically, it uses image analysis technology to analyze the colors, composition, and themes of visual information and extract design elements suitable for the region or culture. For example, when proposing a design that suits Japanese culture and sensibilities, it incorporates traditional colors, patterns, and symbolic motifs. The analysis unit can also analyze visual information to propose designs that suit Chinese culture and sensibilities. Design elements suitable for Chinese culture include the use of red and gold colors, and traditional symbols such as dragons and phoenixes. The analysis unit analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Furthermore, the analysis unit can use AI to learn from users' past preferences and trend data to provide more personalized design suggestions. The AI ​​uses image recognition technology to analyze camera footage and identify flooded areas and obstacles on roads. It also analyzes pedestrian location information to calculate the optimal evacuation route. Furthermore, the system analyzes water level information from household sensors to predict the rate and extent of flooding. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past flooding data, it can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The recommendation department recommends the most suitable foreign language fonts and colors based on information analyzed by the analysis department. Specifically, it selects fonts and colors appropriate to the target region and culture based on design elements extracted by the analysis department. For example, for an advertisement targeting Japan, it would recommend fonts and colors that match Japanese culture and sensibilities. Common fonts suitable for Japanese advertisements include Mincho and Gothic typefaces, and for colors, traditional Japanese colors and colors that reflect the seasons are preferred. For example, for an advertisement targeting China, the recommendation department would recommend fonts and colors that match Chinese culture and sensibilities. Common fonts suitable for Chinese advertisements include Kaisho and Gyosho typefaces, and for colors, red and gold are symbolic. The recommendation department recommends the most suitable foreign language fonts and colors based on information analyzed by the analysis department. Furthermore, the recommendation department can use AI to learn from users' past preferences and trend data to provide more personalized font and color recommendations. The AI ​​analyzes past design choices and user feedback to understand user preferences and trends. This allows the recommendation department to provide the most suitable fonts and colors that meet user needs. Furthermore, the recommendation system can continuously improve its recommendations based on real-time updated data, adapting to the latest trends and user preferences. This allows the recommendation system to provide users with highly accurate font and color recommendations, thereby improving the quality of the design.

[0070] The proposal team proposes designs tailored to specific regions and cultures, based on fonts and colors recommended by the recommendation team. Specifically, they create designs suitable for the target region and culture using the recommended fonts and colors. For example, for an advertisement targeting Japan, they propose a design that matches Japanese culture and sensibilities. Japanese advertising designs often incorporate traditional Japanese elements and modern minimalism. For example, for an advertisement targeting China, the proposal team proposes a design that matches Chinese culture and sensibilities. Chinese advertising designs often incorporate traditional symbols and colors and elements of modern pop culture. The proposal team proposes designs tailored to specific regions and cultures, based on fonts and colors recommended by the recommendation team. Furthermore, the proposal team can use AI to learn from users' past preferences and trend data, enabling them to provide more personalized design suggestions. The AI ​​analyzes past design choices and user feedback to understand user preferences and trends. This allows the proposal team to provide the optimal design that meets user needs. In addition, the proposal team can continuously improve its suggestions based on real-time updated data, adapting to the latest trends and user preferences. This allows the proposal department to provide users with highly accurate design proposals and improve the quality of the designs.

[0071] The learning unit learns from user selections and feedback on designs proposed by the suggestion unit to improve the accuracy of recommendations. Specifically, if a user selects a recommended font or color, the learning unit learns from that selection and reflects it in future recommendations. For example, if a user frequently selects a particular font or color, the learning unit learns this trend and reflects it in future recommendations. Furthermore, if a user provides feedback, the learning unit learns from that feedback to improve the accuracy of recommendations. User feedback includes design preferences, areas for improvement, and user experience. The learning unit analyzes this feedback to improve the recommendation algorithm. In addition, the learning unit can use AI to learn from users' past preferences and trend data to provide more personalized recommendations. The AI ​​analyzes past design selections and user feedback to understand user preferences and trends. This allows the learning unit to provide optimal recommendations that meet user needs. The learning unit can also continuously improve recommendations based on real-time updated data, adapting to the latest trends and user preferences. This enables the learning unit to provide highly accurate recommendations to users and improve the quality of designs.

[0072] The reception unit can accept creative information such as visuals, media, target regions, and messages. For example, the reception unit can accept visual information entered by the user. The reception unit can also accept media information specified by the user. The reception unit can also accept target regions and message information. For example, the reception unit can accept visual information of an advertisement entered by the user. The reception unit can also accept advertising media information specified by the user. The reception unit can also accept target regions and advertising message information. This enables detailed input of creative information. Creative information includes, but is not limited to, visuals, media, target regions, and messages. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input visual information entered by the user into a generating AI and have the generating AI perform analysis of the visual information.

[0073] The analysis unit can analyze the information received by the reception unit and propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. For example, the analysis unit can analyze visual information and propose designs that suit Japanese culture and sensibilities. The analysis unit can also analyze visual information and propose designs that suit Chinese culture and sensibilities. For example, the analysis unit can analyze visual information and propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. This makes it possible to propose designs that are appropriate for a region or culture. 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 the visual information received by the reception unit into a generation AI and have the generation AI perform the analysis of the visual information.

[0074] The recommendation unit can recommend the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. For example, for an advertisement aimed at Japan, the recommendation unit will recommend fonts and colors that suit Japanese culture and sensibilities. For example, for an advertisement aimed at China, the recommendation unit will recommend fonts and colors that suit Chinese culture and sensibilities. The recommendation unit recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis unit. This makes it possible to recommend the most suitable foreign language fonts and colors. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI perform the recommendation of the most suitable foreign language fonts and colors.

[0075] The proposal department can propose designs that are appropriate for a specific region or culture, based on the fonts and colors recommended by the recommendation department. For example, if the advertisement is for Japan, the proposal department will propose a design that suits Japanese culture and sensibilities. For example, if the advertisement is for China, the proposal department will propose a design that suits Chinese culture and sensibilities. The proposal department proposes designs that are appropriate for a specific region or culture, based on the fonts and colors recommended by the recommendation department. This makes it possible to propose designs that are appropriate for a region or culture. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the fonts and colors recommended by the recommendation department into a generation AI and have the generation AI execute a proposal for a design appropriate for a specific region or culture.

[0076] The learning unit can learn from user selections and feedback on designs proposed by the proposal unit and improve the accuracy of recommendations. For example, if a user selects a recommended font or color, the learning unit learns from that selection and reflects it in future recommendations. For example, if a user provides feedback, the learning unit learns from that feedback and improves the accuracy of recommendations. The learning unit learns from user selections and feedback on designs proposed by the proposal unit and improves the accuracy of recommendations. In this way, the accuracy of recommendations improves by learning from user selections and feedback. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user selections and feedback on designs proposed by the proposal unit into a generating AI and have the generating AI perform the improvement of recommendation accuracy.

[0077] The reception desk can estimate the user's emotions and adjust the creative information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of creative information. This enables the input interface to be adjusted 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can analyze the user's past creative information input history and suggest the optimal input method. For example, the reception desk can automatically display visuals and messages that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest creative information to be used at specific times based on the user's past input history. This makes it possible to suggest the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past creative information input history into a generating AI and have the generating AI suggest the optimal input method.

[0079] The reception unit can customize input fields based on the user's current projects and areas of interest when receiving creative information. For example, the reception unit can prioritize displaying input fields related to the user's current project. The reception unit can also suggest relevant creative information input fields based on the user's areas of interest. The reception unit can also dynamically adjust the necessary input fields according to the progress of the user's project. This allows for customization of input fields according to projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0080] The reception desk can estimate the user's emotions and prioritize the creative information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk may also display all input items equally, allowing the user to choose freely. If the user is in a hurry, the reception desk may also display only the most important input items, allowing for quick input. This enables the prioritization of input information 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception unit can prioritize receiving highly relevant information when receiving creative information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving creative information related to that region. The reception unit can also suggest region-specific fonts and colors based on the user's location. If the user is on the move, the reception unit can also suggest the most suitable creative information based on their current location. This enables the reception of highly relevant information based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant information.

[0082] The reception unit can analyze the user's social media activity and accept relevant information when receiving creative information. For example, the reception unit can analyze the user's social media posts and suggest relevant creative information. The reception unit can also accept relevant creative information by referring to the activities of the user's followers and friends. The reception unit can also analyze the user's social media trends and suggest the most suitable creative information. This makes it possible to receive relevant information based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI perform the reception of relevant information.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide rich information. If the user is in a hurry, the analysis unit can also perform a concise analysis and provide only the essentials. If the user is stressed, the analysis unit can also display the analysis results in a visually easy-to-understand manner. This allows for adjustment of the analysis algorithm 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis algorithm.

[0084] The analysis unit can adjust the level of detail of the 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 to provide deeper insights. For less important creative information, the analysis unit can perform a concise analysis to provide only basic information. The analysis unit can also dynamically allocate analysis resources according to the importance of the creative information. This makes it possible to adjust the level of detail of the analysis according to the importance of the creative 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the creative information during analysis. For example, the analysis unit can apply an advertising-specific analysis algorithm to creative information in the advertising category. The analysis unit can also apply a social media-specific analysis algorithm to creative information in the social media category. The analysis unit can also apply an entertainment-specific analysis algorithm to creative information in the entertainment category. This makes it possible to apply an analysis algorithm according to the category of the creative information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0087] The analysis unit can determine the priority of analysis based on the submission date of the creative information during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted creative information. The analysis unit may also postpone the analysis of older creative information. The analysis unit can also dynamically allocate analysis resources based on the submission date. This enables the determination of analysis priorities based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date of the creative information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0088] 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 highly relevant creative information. The analysis unit may also postpone the analysis of less relevant creative information. The analysis unit can also dynamically allocate analysis resources based on relevance. This enables adjustment of the analysis order based on relevance. 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 relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] 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, the recommendation system can provide concise recommendations. If the user is stressed, the recommendation system can provide visually easy-to-understand recommendations. This allows for the adjustment of recommendation presentation 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0090] 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, the recommendation system can provide concise recommendations. If the user is stressed, the recommendation system can provide visually easy-to-understand recommendations. This allows for the adjustment of recommendation presentation 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0091] The recommendation unit can adjust the level of detail of recommendations based on the importance of the creative information during the recommendation process. For example, the recommendation unit can provide detailed recommendations for important creative information. It can also provide concise recommendations for less important creative information. The recommendation unit can also dynamically allocate recommendation resources according to the importance of the creative information. This allows for adjustment of the level of detail of recommendations according to the importance of the creative information. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the importance of the creative information into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0092] The recommendation unit can apply different recommendation algorithms depending on the category of the creative information during the recommendation process. For example, the recommendation unit can apply an advertising-specific recommendation algorithm to creative information in the advertising category. The recommendation unit can also apply a social media-specific recommendation algorithm to creative information in the social media category. The recommendation unit can also apply an entertainment-specific recommendation algorithm to creative information in the entertainment category. This makes it possible to apply recommendation algorithms according to the category of the creative information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0093] 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 will provide short, concise recommendations. If the user is relaxed, the recommendation section may provide longer recommendations with detailed explanations. If the user is stressed, the recommendation section may provide short, visually easy-to-understand recommendations. This allows for adjustment of recommendation length 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 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 recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0094] The recommendation department can determine the priority of recommendations based on the submission date of the creative information. For example, the recommendation department may prioritize recommending recently submitted creative information. The recommendation department may also postpone recommending older creative information. The recommendation department can also dynamically allocate recommendation resources based on the submission date. This enables the determination of recommendation priorities based on the submission date. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the submission date of the creative information into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0095] The recommendation unit can adjust the order of recommendations based on the relevance of the creative information during the recommendation process. For example, the recommendation unit may prioritize recommending highly relevant creative information. The recommendation unit may also recommend less relevant creative information later. The recommendation unit can also dynamically allocate recommendation resources based on relevance. This enables adjustment of the recommendation order based on relevance. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestion information. If the user is in a hurry, the suggestion unit can provide concise suggestion information. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestion information. This allows for adjustment of the suggestion presentation 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0097] The proposal unit can adjust the level of detail of a proposal based on the importance of the creative information during the proposal process. For example, the proposal unit can provide detailed proposal information for important creative information. It can also provide concise proposal information for less important creative information. The proposal unit can also dynamically allocate proposal resources according to the importance of the creative information. This makes it possible to adjust the level of detail of a proposal according to the importance of the creative information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the creative information into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0098] The proposal unit can apply different proposal algorithms depending on the category of the creative information during the proposal process. For example, the proposal unit can apply an advertising-specific proposal algorithm to creative information in the advertising category. The proposal unit can also apply a social media-specific proposal algorithm to creative information in the social media category. The proposal unit can also apply an entertainment-specific proposal algorithm to creative information in the entertainment category. This makes it possible to apply a proposal algorithm according to the category of the creative information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the creative information into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is stressed, the suggestion unit can provide short, visually easy-to-understand suggestions. This allows for adjustment of suggestion length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0100] The proposal department can prioritize proposals based on the submission timing of the creative information. For example, the proposal department may prioritize recently submitted creative information. The proposal department may also postpone the proposal of older creative information. The proposal department can also dynamically allocate resources to proposals based on submission timing. This enables the prioritization of proposals based on submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the creative information into a generating AI and have the generating AI perform the determination of proposal prioritization.

[0101] The proposal unit can adjust the order of proposals based on the relevance of the creative information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant creative information. The proposal unit may also postpone proposing less relevant creative information. The proposal unit can also dynamically allocate resources for proposals based on relevance. This enables adjustment of the order of proposals based on relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the creative information into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0102] 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 training data based on detailed feedback. If the user is in a hurry, the learning unit can also select training data based on concise feedback. If the user is stressed, the learning unit can also select training data based on visually easy-to-understand feedback. This makes it possible to select 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, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0103] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. This makes it possible to optimize 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 using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0104] 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 can learn more frequently to improve accuracy. If the user is in a hurry, the learning unit can also reduce the learning frequency to provide results quickly. If the user is stressed, the learning unit can adjust the learning frequency to reduce the user's burden. This makes it possible to adjust the learning frequency 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, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0105] The learning unit can weight the training data based on the submission date of the creative information during training. For example, the learning unit can assign a higher weight to recently submitted creative information. The learning unit can also assign a lower weight to older creative information. The learning unit can also dynamically adjust the weighting of the training data based on the submission date. This enables weighting of training data based on the submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission date of the creative information into a generating AI and have the generating AI perform the weighting of the training data.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The design support system can estimate the user's emotions and adjust design suggestions based on those emotions. For example, if the user is stressed, it can suggest a simple and intuitive design; if the user is relaxed, it can suggest a detailed and complex design. It can also suggest a design that is quickly understood if the user is in a hurry. This enables design suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. The analysis unit inputs the user's emotion data into the generative AI, which then generates design suggestions.

[0108] The design support system can analyze a user's past design selection history and propose the most suitable design. For example, it can suggest similar designs based on fonts and colors the user has previously selected. It can also prioritize suggesting design elements the user has used in the past. Furthermore, it can predict and suggest design elements that the user will use at specific times based on their past selection history. This enables the suggestion of the most suitable design based on past selection history. The analysis unit inputs the user's past design selection history into the generation AI, which then generates the optimal design proposal.

[0109] The design support system can suggest region-specific design elements by considering the user's geographical location. For example, if the user is in a specific region, it can suggest fonts and colors related to that region. It can also suggest region-specific design elements based on the user's location. Furthermore, if the user is on the move, it can suggest the most suitable design elements based on their current location. This enables the suggestion of design elements based on geographical location information. The analysis unit inputs the user's geographical location information into the generation AI, allowing the generation AI to execute the suggestion of region-specific design elements.

[0110] The design support system can analyze a user's social media activity and suggest relevant design elements. For example, it can analyze a user's social media posts and suggest relevant design elements. It can also suggest relevant design elements by referring to the activities of the user's followers and friends. Furthermore, it can analyze the user's social media trends and suggest the most suitable design elements. This makes it possible to suggest design elements based on social media activity. The analysis unit inputs the user's social media activity into the generation AI, which then generates suggestions for relevant design elements.

[0111] The design support system can estimate the user's emotions and determine design priorities based on those emotions. For example, if the user is stressed, it can prioritize displaying important design elements and delay other elements. If the user is relaxed, it can display all design elements equally, allowing the user to choose freely. If the user is in a hurry, it can display only the most important design elements, allowing for quick selection. This makes it possible to determine the priority of design elements according to the user's emotions. The analysis unit inputs the user's emotion data into the generation AI, which then performs the task of determining the priority of design elements.

[0112] The design support system can estimate the user's emotions and adjust the design's display method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. This makes it possible to adjust the design's display method according to the user's emotions. The analysis unit inputs the user's emotion data into the generating AI, which then performs the adjustment of the design's display method.

[0113] The design support system can estimate the user's emotions and adjust the design length based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point design. If the user is relaxed, it can provide a longer design with detailed explanations. If the user is stressed, it can provide a short, visually easy-to-understand design. This allows for adjustment of the design length according to the user's emotions. The analysis unit inputs the user's emotion data into the generation AI, which then performs the design length adjustment.

[0114] The design support system can estimate the user's emotions and adjust the design's presentation based on those emotions. For example, if the user is relaxed, it can provide detailed design information. If the user is in a hurry, it can provide concise design information. Furthermore, if the user is stressed, it can provide visually easy-to-understand design information. This allows for adjustments to the design's presentation according to the user's emotions. The analysis unit inputs the user's emotion data into the generating AI, which then performs the adjustments to the design's presentation.

[0115] The design support system can estimate the user's emotions and select design training data based on those emotions. For example, if the user is relaxed, it can select training data based on detailed feedback. If the user is in a hurry, it can select training data based on concise feedback. If the user is stressed, it can select training data based on visually easy-to-understand feedback. This makes it possible to select training data according to the user's emotions. The analysis unit inputs the user's emotion data into the generation AI, and the generation AI can then perform the selection of training data.

[0116] The design support system can analyze a user's past design selection history and propose the most suitable design. For example, it can suggest similar designs based on fonts and colors the user has previously selected. It can also prioritize suggesting design elements the user has used in the past. Furthermore, it can predict and suggest design elements that the user will use at specific times based on their past selection history. This enables the suggestion of the most suitable design based on past selection history. The analysis unit inputs the user's past design selection history into the generation AI, which then generates the optimal design proposal.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk receives creative information from users. This creative information includes visuals, media, target region, and message. For example, it receives visual information, specified media information, target region, and message information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes visual information to propose designs that reflect the sensibilities and emotions corresponding to a specific region or culture. Step 3: The recommendation team recommends the most suitable foreign language fonts and colors based on the information analyzed by the analysis team. For example, for an advertisement targeting Japan, they would recommend fonts and colors that are appropriate for Japanese culture and sensibilities. Step 4: The proposal team proposes designs tailored to specific regions and cultures, based on the fonts and colors recommended by the recommendation team. For example, for an advertisement targeting Japan, they would propose a design that suits Japanese culture and sensibilities. Step 5: The learning unit learns from user selections and feedback on designs proposed by the proposal unit, and improves the accuracy of recommendations. For example, if a user selects a recommended font or color, the unit learns from that selection and incorporates it into future recommendations.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives creative information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable foreign language font and color based on the analyzed information. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes a design based on the recommended font and color. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's selections and feedback 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 changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives creative information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable foreign language font and color based on the analyzed information. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes a design based on the recommended font and color. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's selections and feedback 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 changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives creative information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable foreign language font and color based on the analyzed information. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes a design based on the recommended font and color. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's selections and feedback 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 changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives creative information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable foreign language font and color based on the analyzed information. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes a design based on the recommended font and color. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's selections and feedback 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 changed in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A reception desk that receives creative information from users, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a recommendation unit recommends the most suitable foreign language font and color. The proposal department proposes designs based on the fonts and colors recommended by the aforementioned recommendation department, The system includes a learning unit that learns user selections and feedback on the design proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept creative information 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, The information received by the aforementioned reception department is analyzed, and designs are proposed that reflect the sensibilities and emotions corresponding to specific regions and cultures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, Based on the information analyzed by the aforementioned analysis unit, the optimal foreign language font and color are recommended. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the fonts and colors recommended by the aforementioned recommendation committee, we propose designs that are appropriate for specific regions and cultures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system learns user selections and feedback on the designs proposed by the aforementioned proposal unit, and improves the accuracy of recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the creative information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past creative information input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving creative information, the input fields are customized 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 reception unit is It estimates the user's emotions and determines the priority of the creative information to be input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving creative information, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving creative information, the system analyzes the user's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user 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 how the analysis results are displayed based on the estimated 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, 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 21) The aforementioned recommendation department, When making recommendations, 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 22) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the category of creative information. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the creative information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the creative information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, 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 28) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of creative information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of the submission of creative information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the creative information. The system described in Appendix 1, characterized by the features described herein. (Note 32) 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 33) 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 34) 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 35) 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]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives creative information from users, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a recommendation unit recommends the most suitable foreign language font and color. The proposal department proposes designs based on the fonts and colors recommended by the aforementioned recommendation department, The system includes a learning unit that learns user selections and feedback on the design proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept creative information such as visuals, media, target region, and message. The system according to feature 1.

3. The aforementioned analysis unit, The information received by the aforementioned reception department is analyzed, and designs are proposed that reflect the sensibilities and emotions corresponding to specific regions and cultures. The system according to feature 1.

4. The aforementioned recommendation department, Based on the information analyzed by the aforementioned analysis unit, the optimal foreign language font and color are recommended. The system according to feature 1.

5. The aforementioned proposal section is, Based on the fonts and colors recommended by the aforementioned recommendation committee, we propose designs that are appropriate for specific regions and cultures. The system according to feature 1.

6. The aforementioned learning unit, The system learns user selections and feedback on the designs proposed by the aforementioned proposal unit, and improves the accuracy of recommendations. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the creative information input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past creative information input history and suggest the optimal input method. The system according to feature 1.

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