system

Abstract

The system according to the embodiment aims to centrally manage the design requirements of each medium and distribute them efficiently. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a management unit, and a distribution unit. The collection unit collects design requirements. The generation unit analyzes the design requirements collected by the collection unit and generates source code and materials. The management unit centrally manages the source code and materials generated by the generation unit. The distribution unit distributes the source code and materials centrally managed by the management unit to various media.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately managed the design requirements of each medium in a unified manner and distributed them efficiently, so there is room for improvement.

[0005] The system according to the embodiment aims to centrally manage the design requirements of each medium and distribute them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a management unit, and a distribution unit. The collection unit collects design requirements. The generation unit analyzes the design requirements collected by the collection unit and generates source code and materials. The management unit centrally manages the source code and materials generated by the generation unit. The distribution unit distributes the source code and materials centrally managed by the management unit to various media. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage design requirements for each medium and distribute them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A design management system according to an embodiment of the present invention is a system for improving design quality in cases where brand management is in place but no department manages the UI (design) of each medium. This design management system uses AI to analyze the design requirements for each medium and automatically generate source code and materials based on the analysis. For example, optimal designs are generated for different media, such as websites, mobile apps, and printed materials. The generated source code and materials are then centrally managed and distributed to each medium. Centralizing the distribution of materials reduces the amount of work required for each medium. For example, the design management system collects design requirements for each medium, such as design guidelines and user feedback. Next, AI analyzes the collected design requirements and automatically generates optimal source code and materials, such as HTML and CSS code and image files. The generated source code and materials are then centrally managed. For example, the generated source code and materials are centrally managed using cloud storage. The generated source code and materials are then distributed to each medium, for example, by introducing a mechanism for automatic downloading from cloud storage. This improves design quality while reducing work hours. For example, when changing the design of a website, using source code automatically generated by AI can prevent manual errors and provide high-quality designs quickly. Furthermore, by centralizing distributed materials, the amount of work required for each medium can be reduced and designs can be managed efficiently. This allows the design management system to improve design quality and reduce work hours.

[0029] A design management system according to an embodiment includes a collection unit, a generation unit, a management unit, and a distribution unit. The collection unit collects design requirements. Design requirements include, but are not limited to, color, layout, and font. The collection unit collects, for example, design guidelines and user feedback. Collection can be achieved, for example, through surveys, interviews, log data analysis, and other methods. The generation unit analyzes the design requirements collected by the collection unit and generates source code and materials. The generation unit generates, for example, HTML and CSS code, image files, and the like. For example, generation can be achieved using a template engine or an automatic generation tool. The management unit centrally manages the source code and materials generated by the generation unit. The management unit centrally manages the generated source code and materials, for example, using cloud storage. For example, cloud storage such as AWS (registered trademark) S3, Google (registered trademark) Drive, and Dropbox (registered trademark) can be used. The distribution unit distributes the source code and materials centrally managed by the management unit to various media. For example, the distribution unit implements a mechanism for automatic downloading from cloud storage. For example, an automatic download script or a manual download link can be provided. As a result, the design management system according to the embodiment can improve design quality and reduce work man-hours by centrally performing the collection, generation, management, and distribution of design requirements.

[0030] The collection unit can collect design guidelines or user feedback. The collection unit, for example, collects design guidelines. Design guidelines include, for example, brand guidelines and UI guidelines. The collection unit can also collect user feedback. User feedback includes, for example, surveys, reviews, and comments. By collecting design guidelines and user feedback, more appropriate design requirements can be obtained. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user feedback into AI, which analyzes the feedback and extracts design requirements.

[0031] The generation unit can generate HTML or CSS code and image files. The generation unit generates, for example, HTML code. The HTML code can be automatically generated, for example, using a template engine. The generation unit can also generate CSS code. The CSS code can be generated, for example, using a preprocessor. The generation unit can also generate image files. The image files can be generated, for example, using graphics software or an automatic generation tool. In this way, by generating HTML or CSS code, image files, etc., it is possible to provide designs suitable for each medium. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input design requirements into AI, which then generates optimal HTML or CSS code and image files.

[0032] The management unit can centrally manage source code or materials generated using cloud storage. The management unit centrally manages source code and materials generated using, for example, cloud storage. Cloud storage includes, for example, AWS S3, Google Drive, Dropbox, etc. This allows for efficient management of generated source code and materials by using cloud storage. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input generated source code and materials into AI, which then stores them in cloud storage and centrally manages them.

[0033] The distribution unit can introduce a mechanism for downloading from cloud storage. The distribution unit, for example, introduces a mechanism for automatically downloading from cloud storage. The download mechanism includes, for example, an automatic download script or a manual download link. By introducing a mechanism for automatically downloading from cloud storage, the distribution work can be made more efficient. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can have AI control downloads from cloud storage to automatically download the necessary source code and materials.

[0034] The collection unit can analyze past design requirement collection history and select a collection method. For example, the collection unit can identify the collection method most frequently used by users from the past collection history and use that method preferentially. The collection unit can also analyze the past collection history and select a method with high collection efficiency. Furthermore, the collection unit can select a collection method that has received a good response from users based on the past collection history. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data into AI, which can select the optimal collection method.

[0035] When collecting design requirements, the collection unit can filter them based on the user's current project or area of ​​interest. For example, the collection unit can prioritize collection of design requirements related to the user's current project. The collection unit can also filter and collect highly relevant design requirements based on the user's area of ​​interest. Furthermore, the collection unit can collect necessary design requirements according to the progress of the user's project. This makes it possible to collect highly relevant design requirements by filtering based on the user's project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's project data and area of ​​interest data into AI, which can then perform filtering to collect design requirements.

[0036] When collecting design requirements, the collection unit can prioritize collecting highly relevant requirements based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting design requirements related to that area. The collection unit can also collect area-specific design requirements based on the user's location information. Furthermore, when the user is moving, the collection unit can collect design requirements that are most relevant to the user's current location. This allows area-specific design requirements to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can analyze the location information to collect highly relevant design requirements.

[0037] When collecting design requirements, the collection unit can collect relevant requirements based on the user's social media activities. For example, the collection unit can analyze the user's social media posts to collect design requirements of interest. The collection unit can also collect relevant design requirements based on posts from the user's followers and friends. Furthermore, the collection unit can identify trends from the user's social media activities and collect relevant design requirements. In this way, design requirements of interest can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can analyze the data to collect relevant design requirements.

[0038] The generation unit can adjust the level of detail of the generated source code based on the priority of the design requirement during generation. For example, the generation unit generates detailed source code for design requirements with high importance. The generation unit can also generate simplified source code for design requirements with low importance. Furthermore, the generation unit can adjust the level of detail of the material according to the importance of the design requirement. This allows source code and material to be generated efficiently by adjusting the level of detail of the generated source code based on the importance of the design requirement. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the design requirements into AI, which can adjust the level of detail of the source code and material based on the importance.

[0039] The generation unit can apply an appropriate generation algorithm depending on the design category during generation. For example, the generation unit can apply a specific generation algorithm to the design requirements of a website. The generation unit can also apply a different generation algorithm to the design requirements of a mobile app. Furthermore, the generation unit can apply a dedicated generation algorithm to the design requirements of printed materials. In this way, by applying different generation algorithms depending on the design category, it is possible to generate an optimal design for each medium. The generation algorithm includes, for example, a template engine, a machine learning algorithm, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design category data into AI, which then applies an appropriate generation algorithm depending on the category.

[0040] The generation unit can determine the order of generation based on the submission dates of the design requirements during generation. For example, the generation unit prioritizes the generation of design requirements that are submitted earlier. The generation unit can also postpone the generation of design requirements that are submitted later. Furthermore, the generation unit can adjust the generation schedule based on the submission dates. This allows for efficient design generation by determining the priority of generation based on the submission dates of the design requirements. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data of design requirements into AI, which can determine the order of generation based on the submission dates.

[0041] The generation unit can adjust the order of generation based on the relevance of the design requirements during generation. For example, the generation unit prioritizes the generation of highly relevant design requirements. The generation unit can also postpone the generation of less relevant design requirements. Furthermore, the generation unit can adjust the order of generation based on the relevance of the design requirements. In this way, by adjusting the order of generation based on the relevance of the design requirements, designs can be generated efficiently. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the design requirements into AI, and the AI ​​can adjust the order of generation based on the relevance.

[0042] During management, the management unit can apply a management algorithm by referring to past management data. For example, the management unit selects an optimal management algorithm based on the past management data. The management unit can also analyze the past management data and apply an efficient management algorithm. Furthermore, the management unit can improve the accuracy of management by referring to the past management data. In this way, by referring to the past management data, an optimal management algorithm can be applied. Examples of management algorithms include database management algorithms and cloud storage management algorithms. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input past management data into AI, which analyzes the data and applies an optimal management algorithm.

[0043] During management, the management unit can apply an appropriate management method for each category of design requirements. For example, the management unit can apply a specific management method to website design requirements. The management unit can also apply a different management method to mobile app design requirements. Furthermore, the management unit can apply a dedicated management method to printed material design requirements. This allows for efficient management by applying different management methods to each category of design requirements. Management methods include, for example, database management and cloud storage management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input category data of design requirements into AI, which can then apply an appropriate management method according to the category.

[0044] During management, the management unit can determine the priority of management data based on the submission date of the design requirements. For example, the management unit prioritizes management of design requirements that are submitted earlier. The management unit can also postpone design requirements that are submitted later. Furthermore, the management unit can weight the management data based on the submission date. This allows for efficient management by weighting the management data based on the submission date of the design requirements. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input submission date data of design requirements into AI, and the AI ​​can weight the management data based on the submission date.

[0045] The management unit can manage design requirements by referring to market data on the design requirements. For example, the management unit manages the design requirements based on related market data. The management unit can also determine the priority of the design requirements by referring to the market data. Furthermore, the management unit can adjust the management method for the design requirements based on the market data. In this way, by referring to the related market data, the management of design requirements can be performed efficiently. Market data includes, for example, market research data, sales data, etc. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input market data into AI, which analyzes the data and performs management.

[0046] The distribution unit can apply a distribution algorithm by referring to past distribution data during distribution. For example, the distribution unit selects an optimal distribution algorithm based on the past distribution data. The distribution unit can also analyze the past distribution data and apply an efficient distribution algorithm. Furthermore, the distribution unit can improve distribution accuracy by referring to the past distribution data. This allows the optimal distribution algorithm to be applied by referring to the past distribution data. Examples of distribution algorithms include email distribution algorithms and download link generation algorithms. Some or all of the above-mentioned processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input past distribution data into AI, which analyzes the data and applies the optimal distribution algorithm.

[0047] The distribution unit can apply an appropriate distribution method for each category of design requirements during distribution. For example, the distribution unit can apply a specific distribution method to website design requirements. The distribution unit can also apply a different distribution method to mobile app design requirements. Furthermore, the distribution unit can apply a dedicated distribution method to print design requirements. This allows for efficient distribution by applying different distribution methods to each category of design requirements. Distribution methods include, for example, email distribution and providing a download link. Some or all of the above-mentioned processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input category data of design requirements into AI, which can then apply an appropriate distribution method according to the category.

[0048] At the time of distribution, the distribution unit can determine the priority of distribution data based on the submission time of the design requirements. For example, the distribution unit prioritizes the distribution of design requirements that are submitted earlier. The distribution unit can also postpone the distribution of design requirements that are submitted later. Furthermore, the distribution unit can weight the distribution data based on the submission time. By weighting the distribution data based on the submission time of the design requirements, efficient distribution is possible. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input submission time data of design requirements into AI, and the AI ​​can weight the distribution data based on the submission time.

[0049] The distribution unit can distribute design requirements by referring to market data on the design requirements. The distribution unit distributes design requirements based on, for example, related market data. The distribution unit can also determine the priority of design requirements by referring to the market data. The distribution unit can also adjust the distribution method for design requirements based on the market data. This allows for efficient distribution of design requirements by referring to related market data. Market data includes, for example, market research data, sales data, etc. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input market data into AI, which analyzes the data and performs distribution.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can collect design requirements based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize collection of design requirements related to that area. It can also collect area-specific design requirements based on the user's location information. Furthermore, if the user is moving, it can collect design requirements that are most relevant to the user's current location. In this way, it is possible to prioritize collection of area-specific design requirements by taking the user's geographical location information into consideration.

[0052] The generation unit can determine the order of generation of source code and materials to be generated based on the submission dates of design requirements. For example, it can prioritize the generation of design requirements that are due early. It can also postpone the generation of design requirements that are due later. It can also adjust the generation schedule based on the submission dates. This allows for efficient design generation by determining the priority of generation based on the submission dates of design requirements.

[0053] The management department can apply the appropriate management method for each category of design requirements. For example, a specific management method can be applied to the design requirements for websites. A different management method can also be applied to the design requirements for mobile apps. Furthermore, a dedicated management method can be applied to the design requirements for printed materials. This allows for efficient management by applying different management methods to each category of design requirements.

[0054] The distribution unit can distribute design requirements by referring to market data of the design requirements. For example, the distribution unit can distribute design requirements based on related market data. The distribution unit can also determine the priority of design requirements by referring to the market data. Furthermore, the distribution method of design requirements can be adjusted based on the market data. In this way, the distribution of design requirements can be performed efficiently by referring to the related market data.

[0055] The distribution unit can apply a distribution algorithm by referring to past distribution data at the time of distribution. For example, it can select an optimal distribution algorithm based on past distribution data. It can also analyze past distribution data and apply an efficient distribution algorithm. Furthermore, it can also improve the accuracy of distribution by referring to past distribution data. In this way, it is possible to apply an optimal distribution algorithm by referring to past distribution data.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects design requirements. Design requirements include, but are not limited to, color, layout, and font. The collection unit collects design guidelines and user feedback. For example, collection can be done through methods such as questionnaires, interviews, and log data analysis. Step 2: The generator analyzes the design requirements collected by the collector and generates source code and materials. For example, the generator generates HTML and CSS code, image files, etc. This can be done using a template engine or an automatic generation tool. Step 3: The management unit centrally manages the source code and materials generated by the generation unit. The management unit centrally manages the generated source code and materials, for example, by using cloud storage. Cloud storage such as AWS S3, Google Drive, and Dropbox can be used. Step 4: The distribution department distributes the source code and materials centrally managed by the management department to each medium. The distribution department may, for example, implement a mechanism for automatic downloads from cloud storage. They may provide an automatic download script or a manual download link.

[0058] (Example 2) A design management system according to an embodiment of the present invention is a system for improving design quality in cases where brand management is in place but no department manages the UI (design) of each medium. This design management system uses AI to analyze the design requirements for each medium and automatically generate source code and materials based on the analysis. For example, optimal designs are generated for different media, such as websites, mobile apps, and printed materials. The generated source code and materials are then centrally managed and distributed to each medium. Centralizing the distribution of materials reduces the amount of work required for each medium. For example, the design management system collects design requirements for each medium, such as design guidelines and user feedback. Next, AI analyzes the collected design requirements and automatically generates optimal source code and materials, such as HTML and CSS code and image files. The generated source code and materials are then centrally managed. For example, the generated source code and materials are centrally managed using cloud storage. The generated source code and materials are then distributed to each medium, for example, by introducing a mechanism for automatic downloading from cloud storage. This improves design quality while reducing work hours. For example, when changing the design of a website, using source code automatically generated by AI can prevent manual errors and provide high-quality designs quickly. Furthermore, by centralizing distributed materials, the amount of work required for each medium can be reduced and designs can be managed efficiently. This allows the design management system to improve design quality and reduce work hours.

[0059] A design management system according to an embodiment includes a collection unit, a generation unit, a management unit, and a distribution unit. The collection unit collects design requirements. Design requirements include, but are not limited to, color, layout, and font. The collection unit collects, for example, design guidelines and user feedback. Collection can be achieved, for example, through surveys, interviews, log data analysis, and other methods. The generation unit analyzes the design requirements collected by the collection unit and generates source code and materials. The generation unit generates, for example, HTML and CSS code, image files, and the like. For example, generation can be achieved using a template engine or an automatic generation tool. The management unit centrally manages the source code and materials generated by the generation unit. The management unit centrally manages the generated source code and materials, for example, using cloud storage. For example, cloud storage such as AWS S3, Google Drive, and Dropbox can be used. The distribution unit distributes the source code and materials centrally managed by the management unit to various media. The distribution unit, for example, implements a mechanism for automatic downloads from cloud storage. For example, the distribution unit can provide an automatic download script or a manual download link. As a result, the design management system according to the embodiment can improve design quality and reduce work man-hours by centrally performing the collection, generation, management, and distribution of design requirements.

[0060] The collection unit can collect design guidelines or user feedback. The collection unit, for example, collects design guidelines. Design guidelines include, for example, brand guidelines and UI guidelines. The collection unit can also collect user feedback. User feedback includes, for example, surveys, reviews, and comments. By collecting design guidelines and user feedback, more appropriate design requirements can be obtained. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user feedback into AI, which analyzes the feedback and extracts design requirements.

[0061] The generation unit can generate HTML or CSS code and image files. The generation unit generates, for example, HTML code. The HTML code can be automatically generated, for example, using a template engine. The generation unit can also generate CSS code. The CSS code can be generated, for example, using a preprocessor. The generation unit can also generate image files. The image files can be generated, for example, using graphics software or an automatic generation tool. In this way, by generating HTML or CSS code, image files, etc., it is possible to provide designs suitable for each medium. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input design requirements into AI, which then generates optimal HTML or CSS code and image files.

[0062] The management unit can centrally manage source code or materials generated using cloud storage. The management unit centrally manages source code and materials generated using, for example, cloud storage. Cloud storage includes, for example, AWS S3, Google Drive, Dropbox, etc. This allows for efficient management of generated source code and materials by using cloud storage. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input generated source code and materials into AI, which then stores them in cloud storage and centrally manages them.

[0063] The distribution unit can introduce a mechanism for downloading from cloud storage. The distribution unit, for example, introduces a mechanism for automatically downloading from cloud storage. The download mechanism includes, for example, an automatic download script or a manual download link. By introducing a mechanism for automatically downloading from cloud storage, the distribution work can be made more efficient. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can have AI control downloads from cloud storage to automatically download the necessary source code and materials.

[0064] The collection unit can estimate the user's emotions and adjust the timing of collecting design requirements based on the user's emotions. For example, if the user is stressed, the collection unit can delay the collection timing and collect design requirements when the user is relaxed. Furthermore, if the user is excited, the collection unit can immediately collect design requirements and quickly reflect the user's opinions. Furthermore, if the user is tired, the collection unit can adjust the collection timing and collect design requirements after the user has rested. This allows design requirements to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can analyze the emotion and adjust the collection timing.

[0065] The collection unit can analyze past design requirement collection history and select a collection method. For example, the collection unit can identify the collection method most frequently used by users from the past collection history and use that method preferentially. The collection unit can also analyze the past collection history and select a method with high collection efficiency. Furthermore, the collection unit can select a collection method that has received a good response from users based on the past collection history. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data into AI, which can select the optimal collection method.

[0066] When collecting design requirements, the collection unit can filter them based on the user's current project or area of ​​interest. For example, the collection unit can prioritize collection of design requirements related to the user's current project. The collection unit can also filter and collect highly relevant design requirements based on the user's area of ​​interest. Furthermore, the collection unit can collect necessary design requirements according to the progress of the user's project. This makes it possible to collect highly relevant design requirements by filtering based on the user's project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's project data and area of ​​interest data into AI, which can then perform filtering to collect design requirements.

[0067] The collection unit can estimate the user's emotions and determine the priority of design requirements to be collected based on the user's emotions. For example, if the user is feeling stressed, the collection unit can postpone collecting less important design requirements. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting more important design requirements. Furthermore, if the user is excited, the collection unit can quickly collect important design requirements. This allows the design requirements to be prioritized according to the user's emotions, thereby allowing important requirements to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which then analyzes the emotions and determines the priority of the design requirements.

[0068] When collecting design requirements, the collection unit can prioritize collecting highly relevant requirements based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting design requirements related to that area. The collection unit can also collect area-specific design requirements based on the user's location information. Furthermore, when the user is moving, the collection unit can collect design requirements that are most relevant to the user's current location. This allows area-specific design requirements to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can analyze the location information to collect highly relevant design requirements.

[0069] When collecting design requirements, the collection unit can collect relevant requirements based on the user's social media activities. For example, the collection unit can analyze the user's social media posts to collect design requirements of interest. The collection unit can also collect relevant design requirements based on posts from the user's followers and friends. Furthermore, the collection unit can identify trends from the user's social media activities and collect relevant design requirements. In this way, design requirements of interest can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can analyze the data to collect relevant design requirements.

[0070] The generation unit can estimate the user's emotions and adjust the expression method of the generated source code and materials based on the user's emotions. For example, if the user is relaxed, the generation unit can generate materials with soft colors. If the user is in a hurry, the generation unit can also generate simple and intuitive source code. Furthermore, if the user is excited, the generation unit can generate visually stimulating materials. This allows for the generation of more appropriate source code and materials by adjusting the expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotional data into an AI, which can analyze the emotion and adjust the expression method of the source code and materials.

[0071] The generation unit can adjust the level of detail of the generated source code based on the priority of the design requirement during generation. For example, the generation unit generates detailed source code for design requirements with high importance. The generation unit can also generate simplified source code for design requirements with low importance. Furthermore, the generation unit can adjust the level of detail of the material according to the importance of the design requirement. This allows source code and material to be generated efficiently by adjusting the level of detail of the generated source code based on the importance of the design requirement. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the design requirements into AI, which can adjust the level of detail of the source code and material based on the importance.

[0072] The generation unit can apply an appropriate generation algorithm depending on the design category during generation. For example, the generation unit can apply a specific generation algorithm to the design requirements of a website. The generation unit can also apply a different generation algorithm to the design requirements of a mobile app. Furthermore, the generation unit can apply a dedicated generation algorithm to the design requirements of printed materials. In this way, by applying different generation algorithms depending on the design category, it is possible to generate an optimal design for each medium. The generation algorithm includes, for example, a template engine, a machine learning algorithm, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design category data into AI, which then applies an appropriate generation algorithm depending on the category.

[0073] The generation unit can estimate the user's emotions and adjust the length of the generated source code or material based on the user's emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point source code. The generation unit can also generate longer source code with detailed explanations if the user is relaxed. Furthermore, the generation unit can generate visually stimulating material if the user is excited. This allows for adjusting the length of the source code or material according to the user's emotions, thereby providing a more appropriate design. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotional data into an AI, which can then analyze the emotion and adjust the length of the source code or material.

[0074] The generation unit can determine the order of generation based on the submission dates of the design requirements during generation. For example, the generation unit prioritizes the generation of design requirements that are submitted earlier. The generation unit can also postpone the generation of design requirements that are submitted later. Furthermore, the generation unit can adjust the generation schedule based on the submission dates. This allows for efficient design generation by determining the priority of generation based on the submission dates of the design requirements. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data of design requirements into AI, which can determine the order of generation based on the submission dates.

[0075] The generation unit can adjust the order of generation based on the relevance of the design requirements during generation. For example, the generation unit prioritizes the generation of highly relevant design requirements. The generation unit can also postpone the generation of less relevant design requirements. Furthermore, the generation unit can adjust the order of generation based on the relevance of the design requirements. In this way, by adjusting the order of generation based on the relevance of the design requirements, designs can be generated efficiently. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the design requirements into AI, and the AI ​​can adjust the order of generation based on the relevance.

[0076] The management unit can estimate the user's emotions and adjust the classification method of the source code and materials it manages based on the user's emotions. For example, the management unit can provide a detailed classification method when the user is relaxed. The management unit can also provide a simplified classification method when the user is in a hurry. Furthermore, the management unit can provide a visually stimulating classification method when the user is excited. This allows for more appropriate management of source code and materials by adjusting the classification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into an AI, which then analyzes the emotion and adjusts the classification method.

[0077] During management, the management unit can apply a management algorithm by referring to past management data. For example, the management unit selects an optimal management algorithm based on the past management data. The management unit can also analyze the past management data and apply an efficient management algorithm. Furthermore, the management unit can improve the accuracy of management by referring to the past management data. In this way, by referring to the past management data, an optimal management algorithm can be applied. Examples of management algorithms include database management algorithms and cloud storage management algorithms. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input past management data into AI, which analyzes the data and applies an optimal management algorithm.

[0078] During management, the management unit can apply an appropriate management method for each category of design requirements. For example, the management unit can apply a specific management method to website design requirements. The management unit can also apply a different management method to mobile app design requirements. Furthermore, the management unit can apply a dedicated management method to printed material design requirements. This allows for efficient management by applying different management methods to each category of design requirements. Management methods include, for example, database management and cloud storage management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input category data of design requirements into AI, which can then apply an appropriate management method according to the category.

[0079] The management unit can estimate the user's emotions and determine the priority of source code and materials to be managed based on the user's emotions. For example, if the user is relaxed, the management unit can prioritize managing source code and materials of high importance. Furthermore, if the user is in a hurry, the management unit can also postpone managing source code and materials of low importance. Furthermore, if the user is excited, the management unit can prioritize managing visually stimulating source code and materials. Thus, by determining priorities based on the user's emotions, important source code and materials can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into an AI, which then analyzes the emotions and determines the priority.

[0080] During management, the management unit can determine the priority of management data based on the submission date of the design requirements. For example, the management unit prioritizes management of design requirements that are submitted earlier. The management unit can also postpone design requirements that are submitted later. Furthermore, the management unit can weight the management data based on the submission date. This allows for efficient management by weighting the management data based on the submission date of the design requirements. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input submission date data of design requirements into AI, and the AI ​​can weight the management data based on the submission date.

[0081] The management unit can manage design requirements by referring to market data on the design requirements. For example, the management unit manages the design requirements based on related market data. The management unit can also determine the priority of the design requirements by referring to the market data. Furthermore, the management unit can adjust the management method for the design requirements based on the market data. In this way, by referring to the related market data, the management of design requirements can be performed efficiently. Market data includes, for example, market research data, sales data, etc. Some or all of the above-mentioned processing in the management unit may be performed using, or without, AI. For example, the management unit can input market data into AI, which analyzes the data and performs management.

[0082] The distribution unit can estimate the user's emotions and adjust the display method of the distributed source code and materials based on the user's emotions. For example, the distribution unit can provide a detailed display method when the user is relaxed. The distribution unit can also provide a simplified display method when the user is in a hurry. Furthermore, the distribution unit can provide a visually stimulating display method when the user is excited. This allows the source code and materials to be distributed more appropriately by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the distribution unit can be performed using, for example, an AI, or without an AI. For example, the distribution unit can input the user's emotion data into an AI, which can analyze the emotion and adjust the display method.

[0083] The distribution unit can apply a distribution algorithm by referring to past distribution data during distribution. For example, the distribution unit selects an optimal distribution algorithm based on the past distribution data. The distribution unit can also analyze the past distribution data and apply an efficient distribution algorithm. Furthermore, the distribution unit can improve distribution accuracy by referring to the past distribution data. This allows the optimal distribution algorithm to be applied by referring to the past distribution data. Examples of distribution algorithms include email distribution algorithms and download link generation algorithms. Some or all of the above-mentioned processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input past distribution data into AI, which analyzes the data and applies the optimal distribution algorithm.

[0084] The distribution unit can apply an appropriate distribution method for each category of design requirements during distribution. For example, the distribution unit can apply a specific distribution method to website design requirements. The distribution unit can also apply a different distribution method to mobile app design requirements. Furthermore, the distribution unit can apply a dedicated distribution method to print design requirements. This allows for efficient distribution by applying different distribution methods to each category of design requirements. Distribution methods include, for example, email distribution and providing a download link. Some or all of the above-mentioned processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input category data of design requirements into AI, which can then apply an appropriate distribution method according to the category.

[0085] The distribution unit can estimate the user's emotions and determine the priority of source code and materials to be distributed based on the user's emotions. For example, if the user is relaxed, the distribution unit can prioritize distributing source code and materials of high importance. Furthermore, if the user is in a hurry, the distribution unit can also postpone distributing source code and materials of low importance. Furthermore, if the user is excited, the distribution unit can prioritize distributing visually stimulating source code and materials. Thus, by determining priorities according to the user's emotions, important source code and materials can be distributed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the distribution unit can be performed using, for example, an AI, or without an AI. For example, the distribution unit can input the user's emotion data into an AI, which then analyzes the emotions and determines the priority.

[0086] At the time of distribution, the distribution unit can determine the priority of distribution data based on the submission time of the design requirements. For example, the distribution unit prioritizes the distribution of design requirements that are submitted earlier. The distribution unit can also postpone the distribution of design requirements that are submitted later. Furthermore, the distribution unit can weight the distribution data based on the submission time. By weighting the distribution data based on the submission time of the design requirements, efficient distribution is possible. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input submission time data of design requirements into AI, and the AI ​​can weight the distribution data based on the submission time.

[0087] The distribution unit can distribute design requirements by referring to market data on the design requirements. The distribution unit distributes design requirements based on, for example, related market data. The distribution unit can also determine the priority of design requirements by referring to the market data. The distribution unit can also adjust the distribution method for design requirements based on the market data. This allows for efficient distribution of design requirements by referring to related market data. Market data includes, for example, market research data, sales data, etc. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit can input market data into AI, which analyzes the data and performs distribution. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, management unit, and distribution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects design guidelines and user feedback. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected design requirements and generates HTML and CSS code, image files, etc. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and centrally manages the generated source code and materials using cloud storage. The distribution unit is realized, for example, by the control unit 46A of the smart device 14 and provides a mechanism for automatic downloading from cloud storage. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, management unit, and distribution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects design guidelines and user feedback. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected design requirements and generates HTML and CSS code, image files, etc. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and centrally manages the generated source code and materials using cloud storage. The distribution unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a mechanism for automatic downloading from cloud storage. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, management unit, and distribution unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects design guidelines and user feedback. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected design requirements and generates HTML and CSS code, image files, etc. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and centrally manages the generated source code and materials using cloud storage. The distribution unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a mechanism for automatic downloading from cloud storage. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, management unit, and distribution unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects design guidelines and user feedback. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected design requirements and generates HTML and CSS code, image files, etc. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and centrally manages the generated source code and materials using cloud storage. The distribution unit is realized, for example, by the control unit 46A of the robot 414 and provides a mechanism for automatic downloading from cloud storage.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can estimate the user's emotions and adjust the design requirement collection method based on the user's emotions. For example, if the user is feeling stressed, the collection unit can collect design requirements in a simple multiple-choice format rather than a questionnaire format. Alternatively, if the user is relaxed, the collection unit can collect design requirements in a detailed interview format. Furthermore, if the user is excited, the collection unit can request real-time feedback. This allows the collection method to be adjusted according to the user's emotions, thereby enabling more appropriate design requirements to be collected.

[0090] The generation unit can customize the source code and materials to be generated based on the user's emotions. For example, if the user is relaxed, the generation unit can generate a design with soft colors. If the user is in a hurry, the generation unit can generate a simple and intuitive design. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design. In this way, by customizing the design according to the user's emotions, it is possible to provide a more appropriate design.

[0091] The management unit can estimate the user's emotions and adjust the display method of the source code and materials being managed based on the user's emotions. For example, if the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a simplified display method can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. In this way, by adjusting the display method according to the user's emotions, the source code and materials can be managed more appropriately.

[0092] The distribution unit can estimate the user's emotions and determine the priority of source code and materials to be distributed based on the user's emotions. For example, if the user is relaxed, source code and materials of high importance can be distributed with priority. Also, if the user is in a hurry, source code and materials of low importance can be postponed. Furthermore, if the user is excited, source code and materials that are visually stimulating can be distributed with priority. In this way, by determining the priority according to the user's emotions, important source code and materials can be distributed with priority.

[0093] The collection unit can estimate the user's emotions and determine the priority of design requirements to be collected based on the user's emotions. For example, if the user is feeling stressed, it is possible to postpone the collection of less important design requirements. Also, if the user is relaxed, it is possible to prioritize the collection of more important design requirements. Furthermore, if the user is excited, it is possible to immediately collect important design requirements. In this way, by determining the priority of design requirements according to the user's emotions, it is possible to prioritize the collection of important requirements.

[0094] The collection unit can collect design requirements based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize collection of design requirements related to that area. It can also collect area-specific design requirements based on the user's location information. Furthermore, if the user is moving, it can collect design requirements that are most relevant to the user's current location. In this way, it is possible to prioritize collection of area-specific design requirements by taking the user's geographical location information into consideration.

[0095] The generation unit can determine the order of generation of source code and materials to be generated based on the submission dates of design requirements. For example, it can prioritize the generation of design requirements that are due early. It can also postpone the generation of design requirements that are due later. It can also adjust the generation schedule based on the submission dates. This allows for efficient design generation by determining the priority of generation based on the submission dates of design requirements.

[0096] The management department can apply the appropriate management method for each category of design requirements. For example, a specific management method can be applied to the design requirements for websites. A different management method can also be applied to the design requirements for mobile apps. Furthermore, a dedicated management method can be applied to the design requirements for printed materials. This allows for efficient management by applying different management methods to each category of design requirements.

[0097] The distribution unit can distribute design requirements by referring to market data of the design requirements. For example, the distribution unit can distribute design requirements based on related market data. The distribution unit can also determine the priority of design requirements by referring to the market data. Furthermore, the distribution method of design requirements can be adjusted based on the market data. In this way, the distribution of design requirements can be performed efficiently by referring to the related market data.

[0098] The distribution unit can apply a distribution algorithm by referring to past distribution data at the time of distribution. For example, it can select an optimal distribution algorithm based on past distribution data. It can also analyze past distribution data and apply an efficient distribution algorithm. Furthermore, it can also improve the accuracy of distribution by referring to past distribution data. In this way, it is possible to apply an optimal distribution algorithm by referring to past distribution data.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects design requirements. Design requirements include, but are not limited to, color, layout, and font. The collection unit collects design guidelines and user feedback. For example, collection can be done through methods such as questionnaires, interviews, and log data analysis. Step 2: The generator analyzes the design requirements collected by the collector and generates source code and materials. For example, the generator generates HTML and CSS code, image files, etc. This can be done using a template engine or an automatic generation tool. Step 3: The management unit centrally manages the source code and materials generated by the generation unit. The management unit centrally manages the generated source code and materials, for example, by using cloud storage. Cloud storage such as AWS S3, Google Drive, and Dropbox can be used. Step 4: The distribution department distributes the source code and materials centrally managed by the management department to each medium. The distribution department may, for example, implement a mechanism for automatic downloads from cloud storage. They may provide an automatic download script or a manual download link.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[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 audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 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 design requirements; a generation unit that analyzes the design requirements collected by the collection unit and generates source code and materials; a management unit that centrally manages the source code and materials generated by the generation unit; a distribution unit that distributes the source code and materials that are centrally managed by the management unit to each medium. A system characterized by:

2. The collecting unit Gather design guidelines or user feedback 2. The system of claim 1.

3. The generation unit Generate HTML or CSS code and image files 2. The system of claim 1.

4. The management unit Use cloud storage to centralize the source code or materials generated 2. The system of claim 1.

5. The distribution unit Introducing a system for downloading from cloud storage 2. The system of claim 1.

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

7. The collecting unit Analyze past design requirements collection history and select collection methods 2. The system of claim 1.

8. The collecting unit When gathering design requirements, filter based on your current project or area of ​​interest 2. The system of claim 1.

Images