System

Abstract

To provide a system according to an embodiment that efficiently collects the latest trends and skills of design from an on-line resource and makes a list of the trends and skills.SOLUTION: A system according to an embodiment comprises a collection unit, an extraction unit, and a listing unit, and a providing unit. The collection unit collects information on design from an on-line resource. The extraction unit analyzes the information collected by the collection unit, and extracts the trends and skills of design. The listing unit makes a list of the trends and skills extracted by the extraction unit. The providing unit provides a user with a result of the list made by the listing unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques are not sufficient to efficiently collect and catalog the latest design trends and skills from online resources, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and list the latest design trends and skills from online resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an extraction unit, a listing unit, and a provision unit. The collection unit collects information on designs from online resources. The extraction unit analyzes the information collected by the collection unit and extracts design trends and skills. The listing unit lists the trends and skills extracted by the extraction unit. The provision unit provides the listing result by the listing unit to a user. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect and catalog the latest design trends and skills from online resources. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A design information collection system according to an embodiment of the present invention is a system that collects information from online design communities and learning platforms and lists the latest design trends and technical skills. The system includes a collection unit, an extraction unit, a listing unit, and a provision unit. The collection unit collects information such as design-related articles, forum posts, and tutorial videos. For example, the collection unit collects information such as the latest design-related articles, forum posts, and tutorial videos. This information may include processing by a generation AI. Next, the extraction unit analyzes the collected information and extracts whether a particular design style is popular or not and what skills are in demand. For example, the extraction unit analyzes whether a particular design style is popular or not and extracts what skills are in demand. This analysis includes processing by a generation AI. The extracted trends and skills are listed by the listing unit. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by a generation AI. Finally, the provision unit provides the listing result to a user. For example, it is provided to a user in the form of a dashboard or report. It can also be provided using augmented reality technology or virtual reality technology. This provision may include processing by a generation AI. Furthermore, the system may include a reception unit that receives the selection of a trend or skill by the user, and a support unit that supports the acquisition of the selected trend or skill. For example, the system supports the user in selecting a specific trend or skill and acquiring the selected trend or skill. This support may include processing by a generation AI. This system allows the user to easily grasp and acquire the latest design trends and skills. For example, the system can collect information on the latest design-related articles, forum posts, and tutorial videos, analyze whether a specific design style is popular or not, and what skills are required, list the information in a dashboard or report format, and provide it to the user. The system can also support the user in selecting a specific trend or skill and acquiring the selected trend or skill. This allows the design information collection system to easily grasp and acquire the latest design trends and skills.

[0029] The design information collection system according to the embodiment includes a collection unit, an extraction unit, a listing unit, and a provision unit. The collection unit collects information on design from online resources. The collection unit collects information such as articles, forum posts, and tutorial videos on design. For example, the collection unit collects information on the latest articles, forum posts, and tutorial videos on design. This information may include processing by a generation AI. The extraction unit analyzes the collected information and extracts design trends and skills. The extraction unit extracts, for example, whether a particular design style is popular or not, and what skills are required. This analysis includes processing by a generation AI. The extracted trends and skills are listed by the listing unit. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by a generation AI. The provision unit provides the listing result to a user. The provision unit provides the listing result to a user in, for example, a dashboard or report format. It is also possible to provide the listing using an augmented reality technology or a virtual reality technology. This provision may include processing by a generation AI. As a result, the design information collection system according to the embodiment allows users to easily grasp and acquire the latest design trends and skills.

[0030] The collection unit collects information about design from online resources. Specifically, the collection unit uses a web crawler or an API to automatically collect information such as design-related articles, forum posts, and tutorial videos. The web crawler crawls designated websites and extracts content related to design. For example, the latest articles and posts are collected from design-specialized blogs, news sites, design community forums, and the like. In addition, the API can be used to collect design-related tutorial videos from video platforms such as YouTube (registered trademark) and Vimeo (registered trademark). These pieces of information are collected as text data, image data, and video data, and stored in a database. Furthermore, the collection unit performs a filtering process to evaluate the quality of the collected data and remove duplicate data and noise data. For example, if an article with the same content is posted on multiple sites, the most reliable source is selected and other duplicate data is deleted. In addition, the collection unit classifies the collected data into categories and organizes them by theme, such as design style, technology, and tools. This allows the collection unit to efficiently collect and organize design information required by users.

[0031] The extraction unit analyzes the collected information and extracts design trends and skills. Specifically, the extraction unit uses natural language processing (NLP) and image recognition technologies to analyze the collected text data and image data. For example, NLP technology is used to extract keywords related to specific design styles and technologies from articles and forum posts, and the frequency and relevance of these keywords are analyzed. Image recognition technology is also used to identify specific design elements and techniques from tutorial videos and images of design works. Furthermore, generative AI can be used to predict new design trends and skills from the collected data. For example, generative AI can learn from past design data and predict future trends based on current design trends. Generative AI can also identify demand for new design skills and technologies from the collected data and suggest resources for acquiring these skills. In this way, the extraction unit can provide information for users to grasp the latest design trends and skills and learn them efficiently.

[0032] The listing unit lists the extracted trends and skills. Specifically, the listing unit visually organizes the extracted information in a dashboard or report format to make it easy for the user to understand. For example, the dashboard displays a list of the latest design trends and skills, detailed information on each trend and skill, and links to related resources. In addition, the report format may organize the extracted information in chronological order and include graphs and charts showing trends and changes in skill demand. Furthermore, the generating AI may be used to provide a customized list based on the user's interests and learning history. For example, the generating AI may analyze design information viewed by the user in the past and skills learned by the user, and prioritize displaying trends and skills that are most suitable for the user. In addition, the generating AI may continuously update the contents of the list based on user feedback to provide the latest information. As a result, the listing unit can efficiently provide the design information required by the user and improve the efficiency of learning.

[0033] The providing unit provides the listing result to the user. Specifically, the providing unit displays the listed information to the user in a dashboard or report format. The dashboard can be accessed through a web browser or a mobile app, and the user can check the latest design information at any time. In addition, in the report format, the design information that has been regularly updated can be delivered to the user in PDF or email. Furthermore, the providing unit can provide interactive design information to the user using augmented reality (AR) technology or virtual reality (VR) technology. For example, the AR technology can be used to display design elements superimposed on the actual environment, allowing the user to visually check the effect of the design. In addition, the VR technology can be used to allow the user to experience and learn design trends and skills in a virtual space. Furthermore, the generating AI can be used to provide customized information based on the user's interests and learning history. For example, the generating AI can analyze design information that the user has viewed in the past and skills that the user has learned, and preferentially display trends and skills that are optimal for the user. In addition, the generating AI can continuously update the content provided based on user feedback to provide the latest information. This allows the providing unit to easily grasp and acquire the latest design trends and skills.

[0034] The system includes a reception unit that receives a selection of a trend or skill by a user. The reception unit receives the selection of a trend or skill by the user. For example, the reception unit allows the user to select a specific trend or skill using a drop-down menu or a check box. This allows the user to select a specific trend or skill and assist in acquiring the trend or skill. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the reception unit inputs the user's selection history into the generation AI and allows the generation AI to suggest the optimal trend or skill selection.

[0035] The device includes a support unit that supports the user in acquiring the selected trend or skill. For example, the support unit provides a tutorial or presents practice questions. This allows the user to efficiently acquire the selected trend or skill. Some or all of the above-mentioned processing in the support unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the support unit may input the user's learning history into the generation AI and have the generation AI suggest an optimal learning method.

[0036] The collection unit can collect information on articles, forum posts, and tutorial videos related to the design. For example, the collection unit collects information on the latest articles, forum posts, and tutorial videos related to the design. This information may include processing by the generation AI. This allows the collection unit to collect various information related to the design. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input articles and forum posts related to the design into the generation AI to extract important information.

[0037] The extraction unit can analyze the collected information and extract whether a particular design style is popular or not. For example, the extraction unit can analyze the collected information and extract whether a particular design style is trendy or not. This analysis includes processing of the generation AI. This allows the extraction unit to identify a popular design style. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the collected information into the generation AI and extract a trend of design styles.

[0038] The extraction unit can analyze the collected information and extract what skills are required or not. For example, the extraction unit analyzes the collected information and extracts what skills are required. This analysis includes processing of the generation AI. This allows the extraction unit to identify the skills that are required. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the collected information into the generation AI and extract the required skills.

[0039] The providing unit can provide the extracted trends and skills to the user in a dashboard or report format. For example, the providing unit displays the extracted trends and skills in a dashboard format so that the user can visually grasp them. The providing unit also provides the extracted trends and skills in a report format so that the user can check detailed information. This provision may include processing by the generation AI. This allows the providing unit to allow the user to visually grasp the trends and skills. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the extracted trends and skills to the generation AI and have the generation AI suggest an optimal display method.

[0040] The collection unit can perform filtering based on the user's current field of interest when collecting information. For example, the collection unit collects related design articles and tutorials based on the user's field of interest. This filtering may include processing of the generation AI. This allows the collection unit to collect information based on the user's interests. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's field of interest into the generation AI to collect optimal information.

[0041] The providing unit can provide the listing result to the user using augmented reality technology or virtual reality technology. For example, the providing unit uses augmented reality technology to display the design information superimposed on the environment around the user. The providing unit also uses virtual reality technology to enable the user to experience the design information in a virtual space. This provision may include processing by the generating AI. As a result, the providing unit enables the user to experience information through augmented reality or virtual reality. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input augmented reality technology or virtual reality technology to the generating AI and have the generating AI suggest an optimal display method.

[0042] The providing unit can manage a user profile and provide the listing result to the user based on the user's history and skill level. For example, the providing unit provides optimal design information taking into account the user's past activity history and skill level. This provision may include processing by the generating AI. This allows the providing unit to provide information according to the user's history and skill level. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user profile to the generating AI and have the generating AI suggest optimal information.

[0043] The collection unit can analyze the user's past browsing history and select the optimal information collection method. For example, the collection unit preferentially collects information from design-related sites that the user frequently visited in the past. The collection unit can also analyze the contents of articles that the user viewed in the past and collect related new information. This analysis may include processing by the generation AI. This allows the collection unit to collect optimal information based on the user's past browsing history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's browsing history to the generation AI and have the generation AI suggest the optimal information collection method.

[0044] The collection unit may perform filtering based on the user's current project or field of interest when collecting information. For example, the collection unit may preferentially collect information related to a project that the user is currently working on. The collection unit may also collect related design articles and tutorials based on the user's field of interest. This filtering may include processing of the generation AI. This allows the collection unit to collect information based on the user's current project or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's project information into the generation AI to collect optimal information.

[0045] When collecting information, the collection unit can prioritize collection of highly relevant information based on the geographical location information of the user. For example, when the user is in a specific area, the collection unit collects information on design events and workshops held in that area. The collection unit can also collect information on nearby design studios and galleries based on the user's location information. This collection may include processing of the generation AI. This allows the collection unit to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI to collect optimal information.

[0046] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit collects the posted content of design influencers followed by the user. The collection unit can also collect posts from design-related groups and communities in which the user participates. This analysis may include processing of the generation AI. This allows the collection unit to collect information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activity into the generation AI to collect optimal information.

[0047] The extraction unit can adjust the level of extraction detail based on the importance of the design at the time of extraction. For example, the extraction unit can extract information on important design trends in detail and provide it to the user. The extraction unit can also extract general design information concisely and provide only the main points. This adjustment may include processing by the generation AI. This allows the extraction unit to extract information at a level of detail according to the importance of the design. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the importance of the design to the generation AI and have the generation AI suggest an optimal level of extraction detail.

[0048] The extraction unit can apply different extraction algorithms depending on the design category at the time of extraction. For example, the extraction unit can apply an extraction algorithm that emphasizes visual elements to information about graphic design. The extraction unit can also apply an extraction algorithm that emphasizes user experience to information about interaction design. This application may include processing by the generation AI. This allows the extraction unit to extract information using an optimal algorithm depending on the design category. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the design category to the generation AI and have the generation AI suggest an optimal extraction algorithm.

[0049] The extraction unit can determine the extraction priority based on the submission time of the design at the time of extraction. For example, the extraction unit can preferentially extract the latest design information and provide it to the user. The extraction unit can also extract past design information as necessary and provide it as reference information. This determination may include processing by the generation AI. This allows the extraction unit to extract information in a priority order according to the submission time of the design. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the submission time of the design to the generation AI and have the generation AI propose the optimal extraction priority order.

[0050] The extraction unit can adjust the order of extraction based on the relevance of the designs during extraction. For example, the extraction unit preferentially extracts information related to the user's current project. The extraction unit can also preferentially extract information related to the user's field of interest. This adjustment may include processing by the generation AI. This allows the extraction unit to extract information in an order according to the relevance of the designs. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the relevance of the designs to the generation AI and have the generation AI suggest an optimal extraction order.

[0051] The listing unit can adjust the level of detail of the listing based on the importance of the design when listing. For example, the listing unit can list information on important design trends in detail and provide it to the user. The listing unit can also list general design information concisely and provide only the main points. This adjustment may include processing by the generation AI. This allows the listing unit to list information with a level of detail according to the importance of the design. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the importance of the design to the generation AI and have the generation AI suggest an optimal level of listing detail.

[0052] The listing unit can apply different listing algorithms depending on the design category when listing. For example, the listing unit applies a listing algorithm that emphasizes visual elements to information related to graphic design. The listing unit can also apply a listing algorithm that emphasizes user experience to information related to interaction design. This application may include processing by the generation AI. This allows the listing unit to list information using an optimal algorithm depending on the design category. Some or all of the above-mentioned processing in the listing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the design category to the generation AI and have the generation AI suggest an optimal listing algorithm.

[0053] The listing unit can determine the priority of listing based on the submission time of the design when listing. For example, the listing unit prioritizes listing the latest design information and provides it to the user. The listing unit can also list past design information as necessary and provide it as reference information. This determination may include processing by the generation AI. This allows the listing unit to list information in priority order according to the submission time of the design. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the submission time of the design to the generation AI and have the generation AI suggest the optimal listing priority order.

[0054] The listing unit can adjust the order of listing based on the relevance of the designs when listing. For example, the listing unit prioritizes listing information related to the user's current project. The listing unit can also prioritize listing information related to the user's field of interest. This adjustment may include processing by the generation AI. This allows the listing unit to list information in an order according to the relevance of the designs. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the relevance of the designs to the generation AI and have the generation AI suggest an optimal listing order.

[0055] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also select the most efficient display method from the user's past operation history. This selection may include processing by the generation AI. As a result, the providing unit can provide information in the optimal display method based on the user's past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's operation history to the generation AI and have the generation AI suggest the optimal display method.

[0056] The providing unit can provide customized information based on the user's profile information at the time of providing. For example, the providing unit provides information of appropriate difficulty according to the user's skill level. The providing unit can also provide related information preferentially based on the user's field of interest. This provision may include processing of the generation AI. Thereby, the providing unit can provide customized information based on the user's profile information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's profile information to the generation AI and have the generation AI propose optimal customized information.

[0057] The providing unit can select the optimal display method based on the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. Also, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. This selection may include processing of the generation AI. Thereby, the providing unit can provide information in the optimal display method based on the user's device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's device information to the generation AI and have the generation AI suggest the optimal display method.

[0058] The providing unit can provide information using augmented reality technology or virtual reality technology when providing the information. For example, the providing unit can use augmented reality technology to display the design information superimposed on the environment around the user. The providing unit can also use virtual reality technology to enable the user to experience the design information in a virtual space. This provision may include processing by the generating AI. Thereby, the providing unit can provide information using augmented reality technology or virtual reality technology. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input augmented reality technology or virtual reality technology to the generating AI and have the generating AI suggest an optimal information providing method.

[0059] The reception unit can select the optimal reception method by referring to the user's past selection history at the time of reception. For example, the reception unit automatically displays trends and skills that the user has frequently selected in the past as candidates. The reception unit can also preferentially suggest reception methods (such as voice and text) that the user has used in the past. This selection may include processing by the generation AI. This allows the reception unit to perform reception using the optimal method based on the user's past selection history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's selection history into the generation AI and have the generation AI suggest the optimal reception method.

[0060] The reception unit can select the optimal reception method based on the device information of the user at the time of reception. For example, when the user is using a smartphone, the reception unit provides a reception method that matches the screen size. Also, when the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. This selection may include processing by the generation AI. This allows the reception unit to perform reception using the optimal method based on the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the device information of the user to the generation AI and have the generation AI suggest the optimal reception method.

[0061] When providing support, the support unit can select the optimal support method by referring to the user's past learning history. For example, the support unit suggests the next skill to be learned based on the skills the user has learned in the past. The support unit can also select the most effective support method from the user's past learning history. This selection may include processing by the generation AI. This allows the support unit to provide support in an optimal method based on the user's past learning history. Some or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's learning history into the generation AI and have the generation AI suggest the optimal support method.

[0062] The support unit can select the optimal support method based on the device information of the user when providing support. For example, when the user is using a smartphone, the support unit provides a support method that matches the screen size. Also, when the user is using a tablet, the support unit can provide a support method optimized for a large screen. This selection may include processing by the generation AI. This allows the support unit to provide support in an optimal method based on the device information of the user. A part or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the device information of the user to the generation AI and have the generation AI suggest the optimal support method.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can analyze the user's past search history and prioritize collection of related design information. For example, the collection unit collects the latest related design articles and forum posts based on keywords searched for by the user in the past. The collection unit can also analyze the contents of tutorial videos viewed by the user in the past and collect new related tutorials. Furthermore, the collection unit can input the user's search history to the generation AI and have the generation AI suggest an optimal information collection method. This allows the collection unit to collect optimal information based on the user's past search history.

[0065] When listing the extracted trends and skills, the listing unit can apply different listing algorithms depending on the design category. For example, the listing unit can apply a listing algorithm that emphasizes visual elements to information related to graphic design. Also, the listing unit can apply a listing algorithm that emphasizes user experience to information related to interaction design. Furthermore, the listing unit can input the design category to the generation AI and have the generation AI suggest the optimal listing algorithm. This allows the listing unit to list information using the optimal algorithm according to the design category.

[0066] When collecting information, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on design events and workshops held in that area. The collection unit can also collect information on nearby design studios and galleries based on the user's location information. Furthermore, the collection unit can input the user's geographical location information into the generation AI to collect optimal information. This allows the collection unit to collect information based on the user's geographical location information.

[0067] The extraction unit can determine the priority of extraction based on the time of submission of the design when analyzing the collected information and extracting whether a particular design style is popular or not. For example, the extraction unit can preferentially extract the latest design information and provide it to the user. Also, past design information can be extracted as necessary and provided as reference information. Furthermore, the extraction unit can input the time of submission of the design to the generation AI and have the generation AI propose the optimal extraction priority. This allows the extraction unit to extract information in a priority order according to the time of submission of the design.

[0068] When listing extracted trends and skills, the listing unit can select the optimal display method by referring to the user's past operation history. For example, the listing unit preferentially provides a display method that the user has used favorably in the past. The listing unit can also select the most efficient display method from the user's past operation history. Furthermore, the listing unit can input the user's operation history to the generation AI and have the generation AI suggest the optimal display method. This allows the listing unit to list information in the optimal display method based on the user's past operation history.

[0069] When providing the extracted trends and skills to the user, the providing unit can provide the information using augmented reality technology or virtual reality technology. For example, the providing unit can use augmented reality technology to display design information superimposed on the environment around the user. Also, the providing unit can use virtual reality technology to enable the user to experience design information in a virtual space. Furthermore, the providing unit can input the augmented reality technology or virtual reality technology to the generating AI and cause the generating AI to suggest an optimal information providing method. This allows the providing unit to provide information using augmented reality technology or virtual reality technology.

[0070] When assisting the user in acquiring a selected trend or skill, the support unit can select the optimal support method by referring to the user's past learning history. For example, the support unit can suggest the next skill to be learned based on the skills the user has learned in the past. The support unit can also select the most effective support method from the user's past learning history. Furthermore, the support unit can input the user's learning history into the generation AI and have the generation AI suggest the optimal support method. This allows the support unit to provide support in the optimal method based on the user's past learning history.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The collection unit collects information about the design from online resources. For example, the collection unit collects information such as articles, forum posts, and tutorial videos about the design. The collection unit collects information about the latest articles, forum posts, and tutorial videos about the design. This information may include the processing of the generation AI. Step 2: The extraction unit analyzes the collected information and extracts design trends and skills. The extraction unit extracts whether a particular design style is popular or not and what skills are in demand. This analysis includes the processing of generative AI. Step 3: The listing unit lists the extracted trends and skills. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by the generation AI. Step 4: The providing unit provides the listing result to the user. The providing unit provides the listing result to the user in the form of a dashboard or a report. The listing may also be provided using augmented reality technology or virtual reality technology. This provision may include processing by the generating AI.

[0073] (Example 2) A design information collection system according to an embodiment of the present invention is a system that collects information from online design communities and learning platforms and lists the latest design trends and technical skills. The system includes a collection unit, an extraction unit, a listing unit, and a provision unit. The collection unit collects information such as design-related articles, forum posts, and tutorial videos. For example, the collection unit collects information such as the latest design-related articles, forum posts, and tutorial videos. This information may include processing by a generation AI. Next, the extraction unit analyzes the collected information and extracts whether a particular design style is popular or not and what skills are in demand. For example, the extraction unit analyzes whether a particular design style is popular or not and extracts what skills are in demand. This analysis includes processing by a generation AI. The extracted trends and skills are listed by the listing unit. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by a generation AI. Finally, the provision unit provides the listing result to a user. For example, it is provided to a user in the form of a dashboard or report. It can also be provided using augmented reality technology or virtual reality technology. This provision may include processing by a generation AI. Furthermore, the system may include a reception unit that receives the selection of a trend or skill by the user, and a support unit that supports the acquisition of the selected trend or skill. For example, the system supports the user in selecting a specific trend or skill and acquiring the selected trend or skill. This support may include processing by a generation AI. This system allows the user to easily grasp and acquire the latest design trends and skills. For example, the system can collect information on the latest design-related articles, forum posts, and tutorial videos, analyze whether a specific design style is popular or not, and what skills are required, list the information in a dashboard or report format, and provide it to the user. The system can also support the user in selecting a specific trend or skill and acquiring the selected trend or skill. This allows the design information collection system to easily grasp and acquire the latest design trends and skills.

[0074] The design information collection system according to the embodiment includes a collection unit, an extraction unit, a listing unit, and a provision unit. The collection unit collects information on design from online resources. The collection unit collects information such as articles, forum posts, and tutorial videos on design. For example, the collection unit collects information on the latest articles, forum posts, and tutorial videos on design. This information may include processing by a generation AI. The extraction unit analyzes the collected information and extracts design trends and skills. The extraction unit extracts, for example, whether a particular design style is popular or not, and what skills are required. This analysis includes processing by a generation AI. The extracted trends and skills are listed by the listing unit. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by a generation AI. The provision unit provides the listing result to a user. The provision unit provides the listing result to a user in, for example, a dashboard or report format. It is also possible to provide the listing using an augmented reality technology or a virtual reality technology. This provision may include processing by a generation AI. As a result, the design information collection system according to the embodiment allows users to easily grasp and acquire the latest design trends and skills.

[0075] The collection unit collects information about design from online resources. Specifically, the collection unit uses a web crawler or API to automatically collect information such as design-related articles, forum posts, and tutorial videos. The web crawler crawls designated websites and extracts content related to design. For example, the latest articles and posts are collected from design-specialized blogs, news sites, and design community forums. APIs can also be used to collect design-related tutorial videos from video platforms such as YouTube and Vimeo. These pieces of information are collected as text data, image data, and video data and stored in a database. Furthermore, the collection unit performs a filtering process to evaluate the quality of the collected data and remove duplicate data and noise data. For example, if an article with the same content is posted on multiple sites, the most reliable source is selected and other duplicate data is deleted. The collection unit also classifies the collected data into categories and organizes them by theme, such as design style, technology, and tools. This allows the collection unit to efficiently collect and organize design information required by users.

[0076] The extraction unit analyzes the collected information and extracts design trends and skills. Specifically, the extraction unit uses natural language processing (NLP) and image recognition technologies to analyze the collected text data and image data. For example, NLP technology is used to extract keywords related to specific design styles and technologies from articles and forum posts, and the frequency and relevance of these keywords are analyzed. Image recognition technology is also used to identify specific design elements and techniques from tutorial videos and images of design works. Furthermore, generative AI can be used to predict new design trends and skills from the collected data. For example, generative AI can learn from past design data and predict future trends based on current design trends. Generative AI can also identify demand for new design skills and technologies from the collected data and suggest resources for acquiring these skills. In this way, the extraction unit can provide information for users to grasp the latest design trends and skills and learn them efficiently.

[0077] The listing unit lists the extracted trends and skills. Specifically, the listing unit visually organizes the extracted information in a dashboard or report format to make it easy for the user to understand. For example, the dashboard displays a list of the latest design trends and skills, detailed information on each trend and skill, and links to related resources. In addition, the report format may organize the extracted information in chronological order and include graphs and charts showing trends and changes in skill demand. Furthermore, the generating AI may be used to provide a customized list based on the user's interests and learning history. For example, the generating AI may analyze design information viewed by the user in the past and skills learned by the user, and prioritize displaying trends and skills that are most suitable for the user. In addition, the generating AI may continuously update the contents of the list based on user feedback to provide the latest information. As a result, the listing unit can efficiently provide the design information required by the user and improve the efficiency of learning.

[0078] The providing unit provides the listing result to the user. Specifically, the providing unit displays the listed information to the user in a dashboard or report format. The dashboard can be accessed through a web browser or a mobile app, and the user can check the latest design information at any time. In addition, in the report format, the design information that has been regularly updated can be delivered to the user in PDF or email. Furthermore, the providing unit can provide interactive design information to the user using augmented reality (AR) technology or virtual reality (VR) technology. For example, the AR technology can be used to display design elements superimposed on the actual environment, allowing the user to visually check the effect of the design. In addition, the VR technology can be used to allow the user to experience and learn design trends and skills in a virtual space. Furthermore, the generating AI can be used to provide customized information based on the user's interests and learning history. For example, the generating AI can analyze design information that the user has viewed in the past and skills that the user has learned, and preferentially display trends and skills that are optimal for the user. In addition, the generating AI can continuously update the content provided based on user feedback to provide the latest information. This allows the providing unit to easily grasp and acquire the latest design trends and skills.

[0079] The system includes a reception unit that receives a selection of a trend or skill by a user. The reception unit receives the selection of a trend or skill by the user. For example, the reception unit allows the user to select a specific trend or skill using a drop-down menu or a check box. This allows the user to select a specific trend or skill and assist in acquiring the trend or skill. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the reception unit inputs the user's selection history into the generation AI and allows the generation AI to suggest the optimal trend or skill selection.

[0080] The device includes a support unit that supports the user in acquiring the selected trend or skill. For example, the support unit provides a tutorial or presents practice questions. This allows the user to efficiently acquire the selected trend or skill. Some or all of the above-mentioned processing in the support unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the support unit may input the user's learning history into the generation AI and have the generation AI suggest an optimal learning method.

[0081] The collection unit can collect information on articles, forum posts, and tutorial videos related to the design. For example, the collection unit collects information on the latest articles, forum posts, and tutorial videos related to the design. This information may include processing by the generation AI. This allows the collection unit to collect various information related to the design. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input articles and forum posts related to the design into the generation AI to extract important information.

[0082] The extraction unit can analyze the collected information and extract whether a particular design style is popular or not. For example, the extraction unit can analyze the collected information and extract whether a particular design style is trendy or not. This analysis includes processing of the generation AI. This allows the extraction unit to identify a popular design style. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the collected information into the generation AI and extract a trend of design styles.

[0083] The extraction unit can analyze the collected information and extract what skills are required or not. For example, the extraction unit analyzes the collected information and extracts what skills are required. This analysis includes processing of the generation AI. This allows the extraction unit to identify the skills that are required. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the collected information into the generation AI and extract the required skills.

[0084] The providing unit can provide the extracted trends and skills to the user in a dashboard or report format. For example, the providing unit displays the extracted trends and skills in a dashboard format so that the user can visually grasp them. The providing unit also provides the extracted trends and skills in a report format so that the user can check detailed information. This provision may include processing by the generation AI. This allows the providing unit to allow the user to visually grasp the trends and skills. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the extracted trends and skills to the generation AI and have the generation AI suggest an optimal display method.

[0085] The collection unit can perform filtering based on the user's current field of interest when collecting information. For example, the collection unit collects related design articles and tutorials based on the user's field of interest. This filtering may include processing of the generation AI. This allows the collection unit to collect information based on the user's interests. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's field of interest into the generation AI to collect optimal information.

[0086] The providing unit can provide the listing result to the user using augmented reality technology or virtual reality technology. For example, the providing unit uses augmented reality technology to display the design information superimposed on the environment around the user. The providing unit also uses virtual reality technology to enable the user to experience the design information in a virtual space. This provision may include processing by the generating AI. As a result, the providing unit enables the user to experience information through augmented reality or virtual reality. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input augmented reality technology or virtual reality technology to the generating AI and have the generating AI suggest an optimal display method.

[0087] The providing unit can manage a user profile and provide the listing result to the user based on the user's history and skill level. For example, the providing unit provides optimal design information taking into account the user's past activity history and skill level. This provision may include processing by the generating AI. This allows the providing unit to provide information according to the user's history and skill level. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user profile to the generating AI and have the generating AI suggest optimal information.

[0088] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects information at a timing when the user is relaxed. In addition, when the user is concentrating, the collection unit can also increase the frequency of information collection and provide the latest information in real time. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the collection unit to collect information at a timing according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI suggest the optimal information collection timing.

[0089] The collection unit can analyze the user's past browsing history and select the optimal information collection method. For example, the collection unit preferentially collects information from design-related sites that the user frequently visited in the past. The collection unit can also analyze the contents of articles that the user viewed in the past and collect related new information. This analysis may include processing by the generation AI. This allows the collection unit to collect optimal information based on the user's past browsing history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's browsing history to the generation AI and have the generation AI suggest the optimal information collection method.

[0090] The collection unit may perform filtering based on the user's current project or field of interest when collecting information. For example, the collection unit may preferentially collect information related to a project that the user is currently working on. The collection unit may also collect related design articles and tutorials based on the user's field of interest. This filtering may include processing of the generation AI. This allows the collection unit to collect information based on the user's current project or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's project information into the generation AI to collect optimal information.

[0091] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects information about designs that can relax. Also, when the user is excited, the collection unit can preferentially collect information about the latest trends and stimulating designs. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the collection unit to collect information in a priority order according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI propose an optimal information collection priority order.

[0092] When collecting information, the collection unit can prioritize collection of highly relevant information based on the geographical location information of the user. For example, when the user is in a specific area, the collection unit collects information on design events and workshops held in that area. The collection unit can also collect information on nearby design studios and galleries based on the user's location information. This collection may include processing of the generation AI. This allows the collection unit to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI to collect optimal information.

[0093] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit collects the posted content of design influencers followed by the user. The collection unit can also collect posts from design-related groups and communities in which the user participates. This analysis may include processing of the generation AI. This allows the collection unit to collect information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activity into the generation AI to collect optimal information.

[0094] The extraction unit can estimate the user's emotion and adjust the expression method of the extraction based on the estimated user's emotion. For example, the extraction unit displays the extraction result in soft colors and a simple design when the user is relaxed. The extraction unit can also display the extraction result using vivid colors and dynamic effects when the user is excited. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the extraction unit to provide the extraction result in an expression method according to the user's emotion. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal expression method.

[0095] The extraction unit can adjust the level of extraction detail based on the importance of the design at the time of extraction. For example, the extraction unit can extract information on important design trends in detail and provide it to the user. The extraction unit can also extract general design information concisely and provide only the main points. This adjustment may include processing by the generation AI. This allows the extraction unit to extract information at a level of detail according to the importance of the design. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the importance of the design to the generation AI and have the generation AI suggest an optimal level of extraction detail.

[0096] The extraction unit can apply different extraction algorithms depending on the design category at the time of extraction. For example, the extraction unit can apply an extraction algorithm that emphasizes visual elements to information about graphic design. The extraction unit can also apply an extraction algorithm that emphasizes user experience to information about interaction design. This application may include processing by the generation AI. This allows the extraction unit to extract information using an optimal algorithm depending on the design category. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the design category to the generation AI and have the generation AI suggest an optimal extraction algorithm.

[0097] The extraction unit can estimate the user's emotion and adjust the length of the extraction based on the estimated user's emotion. For example, the extraction unit provides a short and to the point extraction result when the user is in a hurry. The extraction unit can also provide a longer extraction result including detailed explanations when the user is relaxed. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the extraction unit to extract information at a length according to the user's emotion. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the user's emotion data to the generation AI and have the generation AI suggest the optimal extraction length.

[0098] The extraction unit can determine the extraction priority based on the submission time of the design at the time of extraction. For example, the extraction unit can preferentially extract the latest design information and provide it to the user. The extraction unit can also extract past design information as necessary and provide it as reference information. This determination may include processing by the generation AI. This allows the extraction unit to extract information in a priority order according to the submission time of the design. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the submission time of the design to the generation AI and have the generation AI propose the optimal extraction priority order.

[0099] The extraction unit can adjust the order of extraction based on the relevance of the designs during extraction. For example, the extraction unit preferentially extracts information related to the user's current project. The extraction unit can also preferentially extract information related to the user's field of interest. This adjustment may include processing by the generation AI. This allows the extraction unit to extract information in an order according to the relevance of the designs. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the extraction unit can input the relevance of the designs to the generation AI and have the generation AI suggest an optimal extraction order.

[0100] The listing unit can estimate the user's emotion and adjust the display method of the listing based on the estimated user's emotion. For example, when the user is relaxed, the listing unit displays the listing result in soft colors and a simple design. Also, when the user is excited, the listing unit can display the listing result using vivid colors and dynamic effects. This estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Thereby, the listing unit can provide the listing result in a display method according to the user's emotion. A part or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal display method.

[0101] The listing unit can adjust the level of detail of the listing based on the importance of the design when listing. For example, the listing unit can list information on important design trends in detail and provide it to the user. The listing unit can also list general design information concisely and provide only the main points. This adjustment may include processing by the generation AI. This allows the listing unit to list information with a level of detail according to the importance of the design. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the importance of the design to the generation AI and have the generation AI suggest an optimal level of listing detail.

[0102] The listing unit can apply different listing algorithms depending on the design category when listing. For example, the listing unit applies a listing algorithm that emphasizes visual elements to information related to graphic design. The listing unit can also apply a listing algorithm that emphasizes user experience to information related to interaction design. This application may include processing by the generation AI. This allows the listing unit to list information using an optimal algorithm depending on the design category. Some or all of the above-mentioned processing in the listing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the design category to the generation AI and have the generation AI suggest an optimal listing algorithm.

[0103] The listing unit can estimate the user's emotion and adjust the length of the listing based on the estimated user's emotion. For example, when the user is in a hurry, the listing unit provides a short listing result that is to the point. Also, when the user is relaxed, the listing unit can provide a longer listing result that includes detailed explanations. This estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the listing unit to list information with a length according to the user's emotion. A part or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal listing length.

[0104] The listing unit can determine the priority of listing based on the submission time of the design when listing. For example, the listing unit prioritizes listing the latest design information and provides it to the user. The listing unit can also list past design information as necessary and provide it as reference information. This determination may include processing by the generation AI. This allows the listing unit to list information in priority order according to the submission time of the design. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the submission time of the design to the generation AI and have the generation AI suggest the optimal listing priority order.

[0105] The listing unit can adjust the order of listing based on the relevance of the designs when listing. For example, the listing unit prioritizes listing information related to the user's current project. The listing unit can also prioritize listing information related to the user's field of interest. This adjustment may include processing by the generation AI. This allows the listing unit to list information in an order according to the relevance of the designs. Some or all of the above-mentioned processing in the listing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the listing unit can input the relevance of the designs to the generation AI and have the generation AI suggest an optimal listing order.

[0106] The providing unit can estimate the user's emotion and adjust the display method of the provided result based on the estimated user's emotion. For example, when the user is relaxed, the providing unit displays the provided result in soft colors and a simple design. Also, when the user is excited, the providing unit can display the provided result using vivid colors and dynamic effects. This estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the providing unit to provide the provided result in a display method according to the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal display method.

[0107] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also select the most efficient display method from the user's past operation history. This selection may include processing by the generation AI. As a result, the providing unit can provide information in the optimal display method based on the user's past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's operation history to the generation AI and have the generation AI suggest the optimal display method.

[0108] The providing unit can provide customized information based on the user's profile information at the time of providing. For example, the providing unit provides information of appropriate difficulty according to the user's skill level. The providing unit can also provide related information preferentially based on the user's field of interest. This provision may include processing of the generation AI. Thereby, the providing unit can provide customized information based on the user's profile information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's profile information to the generation AI and have the generation AI propose optimal customized information.

[0109] The providing unit can estimate the user's emotion and determine the priority of provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide information that can relax the user with priority. In addition, when the user is excited, the providing unit can also provide the latest trend or stimulating information with priority. This estimation of emotion is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the providing unit to provide information in a priority order according to the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI propose an optimal provision priority order.

[0110] The providing unit can select the optimal display method based on the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. Also, when the user is using a tablet, the providing unit can provide a display method optimized for a large screen. This selection may include processing of the generation AI. Thereby, the providing unit can provide information in the optimal display method based on the user's device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's device information to the generation AI and have the generation AI suggest the optimal display method.

[0111] The providing unit can provide information using augmented reality technology or virtual reality technology when providing the information. For example, the providing unit can use augmented reality technology to display the design information superimposed on the environment around the user. The providing unit can also use virtual reality technology to enable the user to experience the design information in a virtual space. This provision may include processing by the generating AI. Thereby, the providing unit can provide information using augmented reality technology or virtual reality technology. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input augmented reality technology or virtual reality technology to the generating AI and have the generating AI suggest an optimal information providing method.

[0112] The reception unit can estimate the user's emotion and adjust the reception method based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the reception unit to perform reception in a manner according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal reception method.

[0113] The reception unit can select the optimal reception method by referring to the user's past selection history at the time of reception. For example, the reception unit automatically displays trends and skills that the user has frequently selected in the past as candidates. The reception unit can also preferentially suggest reception methods (such as voice and text) that the user has used in the past. This selection may include processing by the generation AI. This allows the reception unit to perform reception using the optimal method based on the user's past selection history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's selection history into the generation AI and have the generation AI suggest the optimal reception method.

[0114] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the reception unit can provide an interface with a bright color scheme to make the input work enjoyable. This emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the reception unit to provide an input interface with a design according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI propose an optimal interface design.

[0115] The reception unit can select the optimal reception method based on the device information of the user at the time of reception. For example, when the user is using a smartphone, the reception unit provides a reception method that matches the screen size. Also, when the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. This selection may include processing by the generation AI. This allows the reception unit to perform reception using the optimal method based on the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the device information of the user to the generation AI and have the generation AI suggest the optimal reception method.

[0116] The support unit can estimate the user's emotion and adjust the method of support based on the estimated user's emotion. For example, the support unit provides a simple and intuitive support method when the user is stressed. The support unit can also provide a support method including detailed explanations when the user is relaxed. This estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the support unit to provide support in a manner according to the user's emotion. A part or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's emotion data to the generation AI and have the generation AI suggest an optimal support method.

[0117] When providing support, the support unit can select the optimal support method by referring to the user's past learning history. For example, the support unit suggests the next skill to be learned based on the skills the user has learned in the past. The support unit can also select the most effective support method from the user's past learning history. This selection may include processing by the generation AI. This allows the support unit to provide support in an optimal method based on the user's past learning history. Some or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's learning history into the generation AI and have the generation AI suggest the optimal support method.

[0118] The support unit can estimate the user's emotion and determine the priority of the support based on the estimated user's emotion. For example, when the user is stressed, the support unit can provide a support method that allows the user to relax with priority. Also, when the user is excited, the support unit can provide a support method related to the latest trends or exciting skills with priority. This estimation of emotion is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the support unit to provide support with a priority according to the user's emotion. A part or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's emotion data to the generation AI and have the generation AI propose an optimal support priority order.

[0119] The support unit can select the optimal support method based on the device information of the user when providing support. For example, when the user is using a smartphone, the support unit provides a support method that matches the screen size. Also, when the user is using a tablet, the support unit can provide a support method optimized for a large screen. This selection may include processing by the generation AI. This allows the support unit to provide support in an optimal method based on the device information of the user. A part or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the device information of the user to the generation AI and have the generation AI suggest the optimal support method.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The collection unit can analyze the user's past search history and prioritize collection of related design information. For example, the collection unit collects the latest related design articles and forum posts based on keywords searched for by the user in the past. The collection unit can also analyze the contents of tutorial videos viewed by the user in the past and collect new related tutorials. Furthermore, the collection unit can input the user's search history to the generation AI and have the generation AI suggest an optimal information collection method. This allows the collection unit to collect optimal information based on the user's past search history.

[0122] The extraction unit can estimate the user's emotions when analyzing the collected information and extracting design trends and skills, and adjust the extraction priority based on the estimated emotions. For example, when the user is feeling stressed, the extraction unit can preferentially extract information about relaxing designs. Also, when the user is excited, the extraction unit can preferentially extract information about the latest trends and stimulating designs. Furthermore, the extraction unit can input the user's emotion data to the generation AI and have the generation AI suggest optimal extraction priorities. This allows the extraction unit to extract information in a priority order according to the user's emotions.

[0123] When listing the extracted trends and skills, the listing unit can apply different listing algorithms depending on the design category. For example, the listing unit can apply a listing algorithm that emphasizes visual elements to information related to graphic design. Also, the listing unit can apply a listing algorithm that emphasizes user experience to information related to interaction design. Furthermore, the listing unit can input the design category to the generation AI and have the generation AI suggest the optimal listing algorithm. This allows the listing unit to list information using the optimal algorithm according to the design category.

[0124] When providing the extracted trends and skills to the user, the providing unit can estimate the user's emotions and adjust the display method of the provided results based on the estimated emotions. For example, when the user is relaxed, the providing unit can display the provided results in soft colors and a simple design. Also, when the user is excited, the providing unit can display the provided results using vivid colors and dynamic effects. Furthermore, the providing unit can input the user's emotion data to the generating AI and have the generating AI suggest the optimal display method. This allows the providing unit to provide the provided results in a display method according to the user's emotions.

[0125] When assisting the user in acquiring the selected trend or skill, the support unit can estimate the user's emotions and adjust the method of assistance based on the estimated emotions. For example, the support unit can provide a simple and intuitive assistance method when the user is stressed. Also, the support unit can provide a assistance method including detailed explanations when the user is relaxed. Furthermore, the support unit can input the user's emotion data to the generation AI and have the generation AI suggest the optimal assistance method. This allows the support unit to provide assistance in a manner according to the user's emotions.

[0126] When collecting information, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on design events and workshops held in that area. The collection unit can also collect information on nearby design studios and galleries based on the user's location information. Furthermore, the collection unit can input the user's geographical location information into the generation AI to collect optimal information. This allows the collection unit to collect information based on the user's geographical location information.

[0127] The extraction unit can determine the priority of extraction based on the time of submission of the design when analyzing the collected information and extracting whether a particular design style is popular or not. For example, the extraction unit can preferentially extract the latest design information and provide it to the user. Also, past design information can be extracted as necessary and provided as reference information. Furthermore, the extraction unit can input the time of submission of the design to the generation AI and have the generation AI propose the optimal extraction priority. This allows the extraction unit to extract information in a priority order according to the time of submission of the design.

[0128] When listing extracted trends and skills, the listing unit can select the optimal display method by referring to the user's past operation history. For example, the listing unit preferentially provides a display method that the user has used favorably in the past. The listing unit can also select the most efficient display method from the user's past operation history. Furthermore, the listing unit can input the user's operation history to the generation AI and have the generation AI suggest the optimal display method. This allows the listing unit to list information in the optimal display method based on the user's past operation history.

[0129] When providing the extracted trends and skills to the user, the providing unit can provide the information using augmented reality technology or virtual reality technology. For example, the providing unit can use augmented reality technology to display design information superimposed on the environment around the user. Also, the providing unit can use virtual reality technology to enable the user to experience design information in a virtual space. Furthermore, the providing unit can input the augmented reality technology or virtual reality technology to the generating AI and cause the generating AI to suggest an optimal information providing method. This allows the providing unit to provide information using augmented reality technology or virtual reality technology.

[0130] When assisting the user in acquiring a selected trend or skill, the support unit can select the optimal support method by referring to the user's past learning history. For example, the support unit can suggest the next skill to be learned based on the skills the user has learned in the past. The support unit can also select the most effective support method from the user's past learning history. Furthermore, the support unit can input the user's learning history into the generation AI and have the generation AI suggest the optimal support method. This allows the support unit to provide support in the optimal method based on the user's past learning history.

[0131] The process flow of the second embodiment will be briefly described below.

[0132] Step 1: The collection unit collects information about the design from online resources. For example, the collection unit collects information such as articles, forum posts, and tutorial videos about the design. The collection unit collects information about the latest articles, forum posts, and tutorial videos about the design. This information may include the processing of the generation AI. Step 2: The extraction unit analyzes the collected information and extracts design trends and skills. The extraction unit extracts whether a particular design style is popular or not and what skills are in demand. This analysis includes the processing of generative AI. Step 3: The listing unit lists the extracted trends and skills. For example, the extracted trends and skills are listed in a dashboard or report format. This listing may include processing by the generation AI. Step 4: The providing unit provides the listing result to the user. The providing unit provides the listing result to the user in the form of a dashboard or a report. The listing may also be provided using augmented reality technology or virtual reality technology. This provision may include processing by the generating AI.

[0133] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0135] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] For example, the collection unit can collect information on design-related articles, forum posts, and tutorial videos using the camera 42 and communication I / F 44 of the smart device 14. The extraction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to extract whether a specific design style is popular or not and what skills are required. The extracted trends and skills are listed in the listing unit by the specific processing unit 290 of the data processing device 12. The provision unit provides the listing result to the user using the display 40A and speaker 40B of the smart device 14. The acceptance unit accepts the selection of a trend or skill by the user using the touch panel 38A and microphone 38B of the smart device 14. The support unit supports the acquisition of the trend or skill selected by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] For example, the collection unit can collect information on design-related articles, forum posts, and tutorial videos using the camera 42 and communication I / F 44 of the smart glasses 214. The extraction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to extract whether a specific design style is popular or not and what skills are required. The extracted trends and skills are listed in the listing unit by the specific processing unit 290 of the data processing device 12. The provision unit provides the listing result to the user using the speaker 240 of the smart glasses 214. The reception unit accepts the selection of a trend or skill by the user using the microphone 238 of the smart glasses 214. The support unit supports the acquisition of the trend or skill selected by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] For example, the collection unit can collect information on design-related articles, forum posts, and tutorial videos using the camera 42 and communication I / F 44 of the headset terminal 314. The extraction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to extract whether a specific design style is popular or not and what skills are required. The extracted trends and skills are listed in the listing unit by the specific processing unit 290 of the data processing device 12. The provision unit provides the listing result to the user using the display 343 and speaker 240 of the headset terminal 314. The acceptance unit accepts the selection of a trend or skill by the user using the microphone 238 of the headset terminal 314. The support unit supports the acquisition of the trend or skill selected by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] For example, the collection unit can collect information on design-related articles, forum posts, and tutorial videos using the camera 42 and communication I / F 44 of the robot 414. The extraction unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to extract whether a specific design style is popular or not and what skills are required. The extracted trends and skills are listed in the listing unit by the specific processing unit 290 of the data processing device 12. The provision unit provides the listing result to the user using the speaker 240 of the robot 414. The reception unit accepts the selection of a trend or skill by the user using the microphone 238 of the robot 414. The support unit supports the acquisition of the trend or skill selected by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0187] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0191] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] In addition, 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 upon request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0198] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0202] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0204] (Appendix 1) a collection unit for collecting information about designs from online resources; an extracting unit that analyzes the information collected by the collecting unit and extracts design trends and skills; a listing unit that lists the trends and skills extracted by the extraction unit; a providing unit that provides a list result generated by the listing unit to a user. A system characterized by: (Appendix 2) A reception unit is provided for receiving a selection of the trend or the skill by a user. 2. The system of claim 1, (Appendix 3) a support unit that supports the user in acquiring the trend or the skill selected by the reception unit; 3. The system of claim 2, (Appendix 4) The collecting unit includes: Collect design articles, forum posts, and tutorial videos 2. The system of claim 1, (Appendix 5) The extraction unit is Analyzing the information collected by the collection unit to extract whether a particular design style is popular or not. 2. The system of claim 1, (Appendix 6) The extraction unit is Analyzing the information collected by the collection unit and extracting what skills are required or not. 2. The system of claim 1, (Appendix 7) The providing unit is The trends and skills extracted by the extracting unit are provided to a user in the form of a dashboard or report. 2. The system of claim 1, (Appendix 8) The collecting unit includes: As information is collected, it is filtered based on the user's current areas of interest 2. The system of claim 1, (Appendix 9) The providing unit is Using an augmented reality or virtual reality technique, the listing result by the listing unit is provided to the user. 2. The system of claim 1, (Appendix 10) The providing unit is Manage user profiles and provide the user with a list of results generated by the list generating unit based on the user's history and skill level. 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze users' past browsing history and select the most appropriate method of collecting information 2. The system of claim 1, (Appendix 13) The collecting unit includes: As information is collected, it can be filtered based on the user's current project or area of ​​interest. 2. The system of claim 1, (Appendix 14) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting information, prioritize collection of information that is highly relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 17) The extraction unit is Infer user emotion and adjust the representation of the extraction based on the estimated user emotion 2. The system of claim 1, (Appendix 18) The extraction unit is During extraction, adjust the extraction detail based on the importance of the design 2. The system of claim 1, (Appendix 19) The extraction unit is During extraction, apply different extraction algorithms depending on the design category 2. The system of claim 1, (Appendix 20) The extraction unit is Estimating a user's emotion and adjusting the length of the extraction based on the estimated user's emotion 2. The system of claim 1, (Appendix 21) The extraction unit is During extraction, prioritize extraction based on when designs were submitted 2. The system of claim 1, (Appendix 22) The extraction unit is During extraction, adjust the order of extraction based on design relevance 2. The system of claim 1, (Appendix 23) The listing unit includes: Infer user emotions and adjust the display method of the list based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The listing unit includes: When listing, adjust the level of detail based on the importance of the design 2. The system of claim 1, (Appendix 25) The listing unit includes: During listing, apply different listing algorithms depending on the design category 2. The system of claim 1, (Appendix 26) The listing unit includes: Infer user sentiment and adjust the length of the listing based on the inferred user sentiment 2. The system of claim 1, (Appendix 27) The listing unit includes: During listing, we will prioritize listings based on when designs were submitted 2. The system of claim 1, (Appendix 28) The listing unit includes: When listing, adjust the order of listing based on design relevance 2. The system of claim 1, (Appendix 29) The providing unit is Inferring a user's emotion and adjusting how the offering is presented based on the inferred user emotion 2. The system of claim 1, (Appendix 30) The providing unit is When providing the information, the optimal display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 31) The providing unit is When provided, provide you with customized information based on your profile information 2. The system of claim 1, (Appendix 32) The providing unit is Estimate user sentiment and determine priority of offerings based on the estimated user sentiment 2. The system of claim 1, (Appendix 33) The providing unit is When providing the content, the optimal display method is selected based on the user's device information. 2. The system of claim 1, (Appendix 34) The providing unit is When provided, provide information using augmented or virtual reality technology 2. The system of claim 1, (Appendix 35) The reception unit is Estimate the user's emotions and adjust the reception method based on the estimated user's emotions 2. The system of claim 1, (Appendix 36) The reception unit is At the time of reception, the optimal reception method is selected by referring to the user's past selection history. 2. The system of claim 1, (Appendix 37) The reception unit is Estimating user emotions and adjusting the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 38) The reception unit is At the time of reception, the optimal reception method is selected based on the user's device information. 2. The system of claim 1, (Appendix 39) The support unit includes: Estimating a user's emotions and adjusting the method of assistance based on the estimated user's emotions 2. The system of claim 1, (Appendix 40) The support unit includes: When providing assistance, the system selects the most appropriate assistance method by referring to the user's past learning history. 2. The system of claim 1, (Appendix 41) The support unit includes: Estimating a user's emotions and determining the priority of assistance based on the estimated user's emotions 2. The system of claim 1, (Appendix 42) The support unit includes: When providing assistance, the optimal assistance method is selected based on the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting information about designs from online resources; an extracting unit that analyzes the information collected by the collecting unit and extracts design trends and skills; a listing unit that lists the trends and skills extracted by the extraction unit; a providing unit that provides a list result generated by the listing unit to a user. A system characterized by:

2. A reception unit is provided for receiving a selection of the trend or the skill by a user.

2. The system of claim 1 .

3. a support unit that supports the user in acquiring the trend or the skill selected by the reception unit; 3. The system of claim 2.

4. The collecting unit includes: Collect design articles, forum posts, and tutorial videos 2. The system of claim 1 .

5. The extraction unit is Analyzing the information collected by the collection unit to extract whether a particular design style is popular or not.

2. The system of claim 1 .

6. The extraction unit is Analyzing the information collected by the collection unit and extracting what skills are required or not.

2. The system of claim 1 .

7. The providing unit is The trends and skills extracted by the extracting unit are provided to a user in the form of a dashboard or report.

2. The system of claim 1 .

8. The collecting unit includes: As information is collected, it is filtered based on the user's current areas of interest 2. The system of claim 1 .

9. The providing unit is Using an augmented reality or virtual reality technique, the listing result by the listing unit is provided to the user.

2. The system of claim 1 .

10. The providing unit is Manage user profiles and provide the user with a list of results generated by the list generating unit based on the user's history and skill level.

2. The system of claim 1 .

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