System

Abstract

An object of a system according to an embodiment is to efficiently analyze a design request of a user and quickly generate a base design.SOLUTION: A system according to an embodiment includes a design request reception unit, a design analysis unit, and a base design generation unit. The design request reception unit receives a design request from a user. The design analysis unit analyzes the design request received by the design request reception unit. The base design generation unit generates a base design based on the content analyzed by the design analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to communicate specific design requests, and generating designs takes time and effort.

[0005] The system according to the embodiment aims to efficiently analyze a user's design request and quickly generate a base design. [Means for solving the problem]

[0006] The system according to the embodiment includes a design request receiving unit, a design analysis unit, and a base design generation unit. The design request receiving unit receives a design request from a user. The design analysis unit analyzes the design request received by the design request receiving unit. The base design generation unit generates a base design based on the content analyzed by the design analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently analyze a user's design request and quickly generate a base design. [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) The costume design system according to the embodiment of the present invention is a system in which a generation AI automatically creates basic costume designs based on user requests. This allows the costume design system to automatically generate designs based on user requests and efficiently design costumes.

[0029] A costume design system according to an embodiment includes a design request receiving unit, a design analysis unit, and a base design generation unit. The design request receiving unit receives design requests from users. For example, a user can input a specific request such as "lots of sequins on the chest area to create a flame-like image." The design analysis unit analyzes the design request received by the design request receiving unit. For example, the generation AI analyzes the request using natural language processing technology to understand the placement of sequins and the flame design. The base design generation unit generates a base design based on the content analyzed by the design analysis unit. For example, the generation AI generates a design based on the request using a pre-fine-tuned model. This allows designs to be automatically generated based on user requests.

[0030] The design request acceptance unit can reference the user's past design history, learn their preferences and tendencies, and automatically complete requests. For example, the design request acceptance unit collects design data created by the user in the past, and the generation AI uses that data to learn the user's preferences and tendencies. For example, if a user has used a lot of sequins in the past, the generation AI will automatically suggest sequins for the next request. The design request acceptance unit also references the user's past design history to automatically complete requests entered by the user. For example, if a user enters "more sequins on the chest," the AI ​​will suggest a completion such as "a flame motif" based on past designs. The design request acceptance unit also provides templates based on the user's past design history to simplify request input by the generation AI. For example, it can list design elements used in the past, allowing the user to complete the request simply by selecting them. This allows the AI ​​to automatically complete requests based on the user's preferences and tendencies, thereby providing more personalized designs.

[0031] The design request receiving unit can incorporate a multimodal interface that accepts requests not only via text but also via voice and gestures. The design request receiving unit, for example, allows a user to input a design request via voice. For example, if the user gives a voice instruction such as "Imagine a flame with lots of sequins on the chest," the generation AI analyzes the content and generates a design. The design request receiving unit also allows a user to input a design request via gesture. For example, if the user uses hand movements to indicate the placement of sequins or the shape of a flame, the generation AI analyzes the gesture and generates a design. The design request receiving unit also provides a multimodal interface that allows the user to input requests by selecting text, voice, or gesture. For example, the user can input requests in the way that is most convenient for them. This improves convenience by allowing users to input requests in a variety of ways.

[0032] The design request receiving unit provides request templates that incorporate design elements from different cultures and regions, allowing users to easily request a variety of designs. The design request receiving unit provides, for example, request templates that incorporate design elements from different cultures and regions. For example, templates including traditional Japanese patterns and African ethnic patterns are prepared. The design request receiving unit also incorporates elements from different cultures and regions into design templates that users can select. For example, templates incorporating Indian saris and American cowboy style are provided. The design request receiving unit also uses a generation AI that has learned design elements from different cultures and regions to propose a variety of designs in response to user requests. For example, if a user requests an "exotic design," the generation AI generates a design that incorporates elements from different cultures. This allows users to easily request a variety of designs, thereby expanding the range of designs available.

[0033] The design analysis unit can generate more personalized designs by taking into account the user's past design history and preferences. For example, the design analysis unit analyzes the user's past design history and generates personalized designs based on that data. For example, it proposes new designs by taking into account colors and materials used in the past. In addition, the design analysis unit uses a generation AI that has learned the user's preferences to generate designs that reflect those preferences when analyzing a request. For example, it creates a design based on the user's preferred style or theme. In addition, the design analysis unit considers the user's past design history and preferences, and the generation AI proposes the optimal design in response to the request. For example, it generates a base design that incorporates design elements that have been frequently used in the past. This makes it possible to provide more personalized designs based on the user's past design history and preferences.

[0034] The design analysis unit can generate multiple design options based on the request content and provide the user with choices. For example, the design analysis unit uses a generation AI to analyze the request content and generate multiple design options. For example, for a request such as "more sequins on the chest to create a flame-like image," the unit can suggest different sequin placements and flame designs. The design analysis unit also provides multiple design options to the user to expand their choices. For example, it can present multiple designs with different colors and materials, allowing the user to choose the one they like best. The design analysis unit also provides the user with multiple design options generated based on the request content and selects the optimal design based on feedback. For example, it can make further detailed adjustments based on the design selected by the user. In this way, providing the user with multiple design options expands their choices and allows them to select a design that will provide them with greater satisfaction.

[0035] The design analysis unit can generate new designs that incorporate design elements from different industries and fields when analyzing a request. For example, the design analysis unit generates new designs that incorporate design elements from different industries and fields when analyzing a request. For example, it may propose a costume design that incorporates trends in the fashion industry. The design analysis unit also generates new designs by combining design elements from different fields. For example, it may propose a costume design that incorporates elements of architectural design. The design analysis unit also analyzes the content of a request and generates new designs that incorporate design elements from different industries and fields. For example, it may propose a futuristic costume design that incorporates elements from the technology field. This makes it possible to provide a wider variety of designs by incorporating design elements from different industries and fields.

[0036] The design analysis unit can analyze the request content and generate a design that reflects related design trends and fashions. The design analysis unit, for example, analyzes the request content and generates a design that reflects related design trends and fashions. For example, it proposes a costume design that incorporates the latest fashion trends. The design analysis unit also analyzes design trends and fashions and generates a design that reflects the request content. For example, it proposes a design using currently popular colors and materials. The design analysis unit also analyzes the request content and generates a design that reflects related design trends and fashions. For example, it proposes a costume design that incorporates design elements that won awards in the latest design contest. This makes it possible to provide designs that are more in line with the times by reflecting design trends and fashions.

[0037] The base design generation unit can output the base design as a 3D model so that the user can visually check it. For example, the base design generation unit outputs the base design created by the generation AI as a 3D model so that the user can visually check it. For example, the design can be rotated 360 degrees using a 3D viewer. The base design generation unit can also output the base design using a 3D printer so that the user can actually hold it in their hands and check it. For example, a miniature model can be created to check the details of the design. The base design generation unit can also use AR (augmented reality) technology to display the 3D model in a real environment so that the user can check the design in its actual environment. For example, the design can be superimposed on the actual garment using a smartphone camera. This allows the user to visually check the design, deepening their understanding of the design.

[0038] The base design generation unit can provide an interactive interface that allows the user to fine-tune the design in real time when outputting the base design. The base design generation unit provides an interface that allows the user to fine-tune the design in real time when outputting the base design. For example, it allows the user to change the color or material or add decorations in real time. The base design generation unit also uses the interactive interface to allow the user to adjust each element of the design by drag and drop. For example, the placement of sequins or the shape of the flames can be freely changed. The base design generation unit also provides an interface that allows the user to fine-tune the design in real time, allowing the user to make adjustments while checking the details of the design. For example, a zoom-in / zoom-out function can be added. This allows the user to fine-tune the design in real time, resulting in a more satisfying design.

[0039] The base design generation unit can use AR technology to allow the user to check the base design in a real environment. The base design generation unit, for example, uses AR technology to allow the user to check the base design in a real environment. For example, the design may be superimposed on an actual outfit using a smartphone camera. The base design generation unit also uses AR technology to allow the user to try on the design in a real environment. For example, the design may be tried on using a smart mirror and checked in real time. The base design generation unit also uses AR technology to allow the user to check the details of the base design in a real environment. For example, each element of the design may be enlarged and checked. This allows the user to check the design in a real environment, improving the applicability of the design.

[0040] The base design generation unit can share the base design on a social networking site or a community platform and collect feedback from other users. For example, the base design generation unit shares the base design on a social networking site or a community platform and collects feedback from other users. For example, the base design generation unit posts a design and receives comments and ratings. The base design generation unit also collects opinions and advice from other users about the base design through the social networking site or community platform. For example, the base design generation unit asks users to suggest improvements to the design or new ideas. The base design generation unit also shares the base design and improves the design based on feedback from other users. For example, the design details are adjusted to reflect user opinions. This makes it possible to improve the design by collecting feedback from other users.

[0041] When accepting a revision request, the design request accepting unit can refer to the user's past revision history and make optimal revision suggestions. For example, the design request accepting unit analyzes the user's past revision history and makes optimal revision suggestions based on that data. For example, it makes new revision suggestions by referring to past revisions. In addition, when accepting a revision request, the design request accepting unit refers to the user's past revision history to perform auto-completion. For example, if you input "I want the sequins color changed from red to blue," the optimal suggestion will be made based on the past revision history. In addition, the design request accepting unit uses the generation AI to make optimal revision suggestions based on the user's past revision history. For example, it makes suggestions that incorporate revision elements that have been frequently used in the past. In this way, more appropriate revision suggestions can be made by referring to the user's past revision history.

[0042] The design request accepting unit can analyze the revision request and generate multiple revision options to provide to the user. For example, the design request accepting unit analyzes the revision request and generates multiple revision options. For example, in response to a request to "change the sequin color from red to blue," the design request accepting unit proposes revision options with different colors and arrangements. The design request accepting unit also provides the user with multiple revision options to expand their choices. For example, multiple revision options with different colors or materials may be presented, allowing the user to select the one they like best. The design request accepting unit also analyzes the revision request and generates multiple revision options to provide to the user. For example, revision options that combine different design elements may be proposed. In this way, by providing multiple revision options, the user can select the optimal revision.

[0043] When accepting a revision request, the design request accepting unit can suggest different design styles or themes to encourage revisions from a new perspective. For example, when accepting a revision request, the design request accepting unit suggests different design styles or themes. For example, it suggests styles such as classic, modern, and exotic to encourage revisions from a new perspective. The design request accepting unit also suggests different design themes to enable the user to make revisions from a new perspective. For example, it suggests themes such as nature, technology, and art. When accepting a revision request, the design request accepting unit also suggests different design styles or themes to enable the user to make revisions from a new perspective. For example, it makes suggestions that incorporate design elements from different cultures or regions. In this way, by proposing different design styles and themes, the user can make revisions from a new perspective.

[0044] When accepting a revision request, the design request accepting unit can make revision suggestions by referring to the revision history and feedback of other users. For example, when accepting a revision request, the design request accepting unit makes revision suggestions by referring to the revision history and feedback of other users. For example, it proposes revisions made by other users for similar requests. The design request accepting unit also analyzes the revision history of other users and makes revision suggestions based on that data. For example, it makes suggestions by referring to revisions made by many users in the past. Furthermore, when accepting a revision request, the design request accepting unit makes revision suggestions by referring to the feedback of other users. For example, it proposes revisions that other users have given high ratings to. In this way, by referring to the revision history and feedback of other users, more appropriate revision suggestions can be made.

[0045] The base design generation unit can provide detailed explanations and suggestions for each element of the design at the time of final confirmation, allowing the user to be convinced. The base design generation unit, for example, provides detailed explanations for each element of the design at the time of final confirmation. For example, it provides specific explanations for the placement of sequins and the design of flames. The base design generation unit also provides suggestions for each element of the design, allowing the user to be convinced. For example, it presents options for color and material, allowing the user to select the most suitable one. The base design generation unit also provides detailed explanations and suggestions for each element of the design at the time of final confirmation, allowing the user to be convinced. For example, it explains the background and concept of the design, allowing the user to understand. In this way, the user can receive detailed explanations and suggestions for each element of the design, allowing them to make the final confirmation with confidence.

[0046] The base design generation unit can provide information about the feasibility and cost of the design at the time of final confirmation, allowing the user to make a realistic decision. The base design generation unit, for example, provides information about the feasibility of the design at the time of final confirmation. For example, it explains the availability of materials and technologies to be used, allowing the user to make a realistic decision. The base design generation unit also provides information about the cost of the design, allowing the user to make a realistic decision. For example, it presents the costs of materials and decorations to be used, allowing the user to select a design within their budget. The base design generation unit also provides information about the feasibility and cost of the design at the time of final confirmation, allowing the user to make a realistic decision. For example, it explains the time and effort required to create the design, allowing the user to be convinced. This allows the user to make a realistic decision by obtaining information about the feasibility and cost of the design.

[0047] The base design generation unit may actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, the base design generation unit may actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, a miniature model may be created to check the details of the design. The base design generation unit may also create a prototype of the design, allowing the user to try on the actual product. For example, a try-on sample may be created, allowing the user to actually wear it and check it. The base design generation unit may also actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, a 3D printer may be used to check the details of the design. This allows the user to check the actual product, making the final confirmation of the design more reliable.

[0048] The base design generation unit can collect feedback from other users about each element of the design during final confirmation and reflect it in the final adjustments. The base design generation unit, for example, collects feedback from other users about each element of the design during final confirmation. For example, the design is shared on a social networking site or community platform and comments and ratings are received. The base design generation unit also makes final adjustments to the design based on feedback from other users. For example, the details of the design are adjusted to reflect user opinions. The base design generation unit also collects feedback from other users about each element of the design during final confirmation and reflects it in the final adjustments. For example, the users are asked to suggest improvements to the design or new ideas. In this way, collecting feedback from other users enables more appropriate final adjustments.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The design request reception unit can reference the user's past design history, learn their preferences and trends, and automatically complete requests. For example, the generation AI can collect design data created by the user in the past and use that data to learn the user's preferences and trends. If many of the user's past designs have used a lot of sequins, it can automatically suggest sequins for the next request. It is also possible to automatically complete requests entered by the user by referencing their past design history. For example, if a user enters "more sequins on the chest," a completion suggestion such as "a flame motif" can be made based on past designs. Furthermore, the generation AI can provide templates based on the user's past design history to simplify request entry. For example, it can list previously used design elements so the user can complete the request simply by selecting them. This allows the AI ​​to automatically complete requests based on the user's preferences and trends, thereby providing more personalized designs.

[0051] The design request receiving unit can introduce a multimodal interface that accepts requests not only via text but also via voice and gestures. For example, a user can input a design request via voice. For example, if the user gives a voice instruction such as, "Imagine a flame with lots of sequins on the chest," the generation AI can analyze the instruction and generate a design. It is also possible to allow a user to input a design request via gesture. For example, if the user uses hand movements to indicate the placement of sequins or the shape of a flame, the generation AI can analyze the gesture and generate a design. Furthermore, a multimodal interface can be provided that allows users to input requests by selecting text, voice, or gesture. For example, a system can allow users to input requests in the way that is most convenient for them. This improves convenience by allowing users to input requests in a variety of ways.

[0052] The design request receiving unit can provide request templates that incorporate design elements from different cultures and regions, allowing users to easily request a variety of designs. For example, request templates that incorporate design elements from different cultures and regions can be provided. For example, templates including traditional Japanese patterns and African ethnic patterns can be prepared. It is also possible to incorporate elements from different cultures and regions into design templates that users can select. For example, templates incorporating Indian saris and American cowboy style can be provided. Furthermore, a generation AI that has learned design elements from different cultures and regions can propose a variety of designs in response to user requests. For example, if a user requests an "exotic design," the generation AI can generate a design that incorporates elements from different cultures. This allows users to easily request a variety of designs, thereby expanding the range of designs available.

[0053] The design analysis unit can generate more personalized designs by taking into account the user's past design history and preferences. For example, it can analyze the user's past design history and generate personalized designs based on that data. It can also propose new designs by taking into account colors and materials used in the past. The generation AI can also generate designs that reflect the user's preferences when analyzing a request by learning the user's preferences. For example, it can create a design based on the user's preferred style or theme. Furthermore, the generation AI can propose the optimal design for the request by taking into account the user's past design history and preferences. For example, it can generate a base design that incorporates design elements that have been frequently used in the past. This makes it possible to provide more personalized designs based on the user's past design history and preferences.

[0054] The design analysis unit can generate multiple design options based on the request content and provide the user with choices. For example, the generation AI can analyze the request content and generate multiple design options. For example, in response to a request such as "more sequins on the chest to create a flame-like effect," the AI ​​can suggest different sequin placements and flame designs. It is also possible to provide the user with multiple design options to expand their choices. For example, it is possible to present multiple designs with different colors and materials and allow the user to choose the one they like best. Furthermore, it is possible to provide the user with multiple design options generated based on the request content and select the optimal design based on feedback. For example, it is possible to make further detailed adjustments based on the design selected by the user. In this way, by providing the user with multiple design options, the AI ​​can expand their choices and select a design that will satisfy them.

[0055] The design analysis unit can generate new designs that incorporate design elements from different industries and fields when analyzing a request. For example, it can generate new designs that incorporate design elements from different industries and fields when analyzing a request. It can propose clothing designs that incorporate trends in the fashion industry. It can also generate new designs by combining design elements from different fields. For example, it can propose clothing designs that incorporate elements of architectural design. It can also analyze the content of a request and generate new designs that incorporate design elements from different industries and fields. For example, it can propose futuristic clothing designs that incorporate elements from the technology field. This makes it possible to provide a wider variety of designs by incorporating design elements from different industries and fields.

[0056] The design analysis unit can analyze the request content and generate a design that reflects related design trends and fashions. For example, it can analyze the request content and generate a design that reflects related design trends and fashions. It can propose a costume design that incorporates the latest fashion trends. It can also analyze design trends and fashions and generate a design that reflects the request content. For example, it can propose a design that uses currently popular colors and materials. It can also analyze the request content and generate a design that reflects related design trends and fashions. For example, it can propose a costume design that incorporates design elements that won awards in the latest design contest. This makes it possible to provide designs that are more in line with the times by reflecting design trends and fashions.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The design request receiving unit receives design requests from users. For example, a user can input a specific request such as "I want a lot of sequins on the chest area to create a flame-like image." Step 2: The design analysis unit analyzes the design request received by the design request reception unit. For example, the generation AI analyzes the request using natural language processing technology to understand the placement of sequins and the flame design. Step 3: The base design generation unit generates a base design based on the content analyzed by the design analysis unit. For example, the generation AI uses a pre-fine-tuned model to generate a design based on the request.

[0059] (Example 2) The costume design system according to the embodiment of the present invention is a system in which a generation AI automatically creates basic costume designs based on user requests. This allows the costume design system to automatically generate designs based on user requests and efficiently design costumes.

[0060] A costume design system according to an embodiment includes a design request receiving unit, a design analysis unit, and a base design generation unit. The design request receiving unit receives design requests from users. For example, a user can input a specific request such as "lots of sequins on the chest area to create a flame-like image." The design analysis unit analyzes the design request received by the design request receiving unit. For example, the generation AI analyzes the request using natural language processing technology to understand the placement of sequins and the flame design. The base design generation unit generates a base design based on the content analyzed by the design analysis unit. For example, the generation AI generates a design based on the request using a pre-fine-tuned model. This allows designs to be automatically generated based on user requests.

[0061] The design request acceptance unit can reference the user's past design history, learn their preferences and tendencies, and automatically complete requests. For example, the design request acceptance unit collects design data created by the user in the past, and the generation AI uses that data to learn the user's preferences and tendencies. For example, if a user has used a lot of sequins in the past, the generation AI will automatically suggest sequins for the next request. The design request acceptance unit also references the user's past design history to automatically complete requests entered by the user. For example, if a user enters "more sequins on the chest," the AI ​​will suggest a completion such as "a flame motif" based on past designs. The design request acceptance unit also provides templates based on the user's past design history to simplify request input by the generation AI. For example, it can list design elements used in the past, allowing the user to complete the request simply by selecting them. This allows the AI ​​to automatically complete requests based on the user's preferences and tendencies, thereby providing more personalized designs.

[0062] The design request receiving unit can analyze the user's tone of voice and facial expression and make design suggestions based on their emotions. For example, when a user inputs a request, the design request receiving unit uses a camera and microphone to analyze the user's tone of voice and facial expression to estimate their emotions. For example, if the user is excited, a bolder design is suggested. The design request receiving unit also analyzes the user's tone of voice and facial expression in real time when the request is input and makes design suggestions based on their emotions. For example, if the user speaks in a calm tone, a simple and elegant design is suggested. The design request receiving unit also analyzes the user's emotional state and suggests design styles and colors based on the results. For example, if the user inputs a request with a smile, bright colors and fun designs are suggested. This makes it possible to provide more appropriate designs by making design suggestions based on the user's emotions.

[0063] The design request receiving unit can use the emotion estimation function to estimate the emotion of the user when entering a request in real time and make suggestions to elicit positive emotions. For example, when the user enters a request, the design request receiving unit uses the emotion estimation function to analyze the emotion in real time and make suggestions to elicit positive emotions. For example, if the user is nervous, the design request receiving unit can make design suggestions that will help the user relax. The design request receiving unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user enters a request. For example, it can present encouraging messages and success stories. The design request receiving unit also provides feedback in real time based on the emotion estimation data when the user enters a request and offers advice to strengthen positive emotions. For example, it can display appropriate encouragement or praise based on the input content. This can elicit positive emotions from the user and provide a better design experience.

[0064] The design request receiving unit can incorporate a multimodal interface that accepts requests not only via text but also via voice and gestures. The design request receiving unit, for example, allows a user to input a design request via voice. For example, if the user gives a voice instruction such as "Imagine a flame with lots of sequins on the chest," the generation AI analyzes the content and generates a design. The design request receiving unit also allows a user to input a design request via gesture. For example, if the user uses hand movements to indicate the placement of sequins or the shape of a flame, the generation AI analyzes the gesture and generates a design. The design request receiving unit also provides a multimodal interface that allows the user to input requests by selecting text, voice, or gesture. For example, the user can input requests in the way that is most convenient for them. This improves convenience by allowing users to input requests in a variety of ways.

[0065] The design request receiving unit provides request templates that incorporate design elements from different cultures and regions, allowing users to easily request a variety of designs. The design request receiving unit provides, for example, request templates that incorporate design elements from different cultures and regions. For example, templates including traditional Japanese patterns and African ethnic patterns are prepared. The design request receiving unit also incorporates elements from different cultures and regions into design templates that users can select. For example, templates incorporating Indian saris and American cowboy style are provided. The design request receiving unit also uses a generation AI that has learned design elements from different cultures and regions to propose a variety of designs in response to user requests. For example, if a user requests an "exotic design," the generation AI generates a design that incorporates elements from different cultures. This allows users to easily request a variety of designs, thereby expanding the range of designs available.

[0066] The design request receiving unit is equipped with an emotion estimation function, which can estimate the user's emotion in real time when entering a request and make suggestions that elicit positive emotions. The design request receiving unit is equipped with a function that analyzes the user's facial expressions and voice when entering an idea and estimates the user's emotion in real time. For example, the design request receiving unit analyzes the user's emotion using a camera or microphone, and makes positive suggestions if negative emotions are detected. The design request receiving unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user enters an idea. For example, it presents encouraging messages and success stories. The design request receiving unit also provides feedback in real time based on the emotion estimation data when the user enters an idea, and offers advice that strengthens positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This elicits positive emotions from the user, thereby providing a better design experience.

[0067] The design analysis unit can generate more personalized designs by taking into account the user's past design history and preferences. For example, the design analysis unit analyzes the user's past design history and generates personalized designs based on that data. For example, it proposes new designs by taking into account colors and materials used in the past. In addition, the design analysis unit uses a generation AI that has learned the user's preferences to generate designs that reflect those preferences when analyzing a request. For example, it creates a design based on the user's preferred style or theme. In addition, the design analysis unit considers the user's past design history and preferences, and the generation AI proposes the optimal design in response to the request. For example, it generates a base design that incorporates design elements that have been frequently used in the past. This makes it possible to provide more personalized designs based on the user's past design history and preferences.

[0068] The design analysis unit can generate multiple design options based on the request content and provide the user with choices. For example, the design analysis unit uses a generation AI to analyze the request content and generate multiple design options. For example, for a request such as "more sequins on the chest to create a flame-like image," the unit can suggest different sequin placements and flame designs. The design analysis unit also provides multiple design options to the user to expand their choices. For example, it can present multiple designs with different colors and materials, allowing the user to choose the one they like best. The design analysis unit also provides the user with multiple design options generated based on the request content and selects the optimal design based on feedback. For example, it can make further detailed adjustments based on the design selected by the user. In this way, providing the user with multiple design options expands their choices and allows them to select a design that will provide them with greater satisfaction.

[0069] The design analysis unit can use the emotion estimation function to analyze the user's emotions regarding the request content and generate a design based on the emotions. For example, the design analysis unit analyzes the user's emotions regarding the request content and generates a design based on the results. For example, if the user is excited, a bolder and more eye-catching design is suggested. The design analysis unit also uses the emotion estimation function to analyze the user's emotions regarding the request content in real time and generate a design based on the emotions. For example, if the user enters the request in a calm tone, a simple and elegant design is suggested. The design analysis unit also analyzes the user's emotional state and suggests a design style and color based on the results. For example, if the user enters the request with a smile, a bright color or a fun design is suggested. In this way, a more appropriate design can be provided by generating a design based on the user's emotions.

[0070] The design analysis unit can generate new designs that incorporate design elements from different industries and fields when analyzing a request. For example, the design analysis unit generates new designs that incorporate design elements from different industries and fields when analyzing a request. For example, it may propose a costume design that incorporates trends in the fashion industry. The design analysis unit also generates new designs by combining design elements from different fields. For example, it may propose a costume design that incorporates elements of architectural design. The design analysis unit also analyzes the content of a request and generates new designs that incorporate design elements from different industries and fields. For example, it may propose a futuristic costume design that incorporates elements from the technology field. This makes it possible to provide a wider variety of designs by incorporating design elements from different industries and fields.

[0071] The design analysis unit can analyze the request content and generate a design that reflects related design trends and fashions. The design analysis unit, for example, analyzes the request content and generates a design that reflects related design trends and fashions. For example, it proposes a costume design that incorporates the latest fashion trends. The design analysis unit also analyzes design trends and fashions and generates a design that reflects the request content. For example, it proposes a design using currently popular colors and materials. The design analysis unit also analyzes the request content and generates a design that reflects related design trends and fashions. For example, it proposes a costume design that incorporates design elements that won awards in the latest design contest. This makes it possible to provide designs that are more in line with the times by reflecting design trends and fashions.

[0072] The design analysis unit can use the emotion estimation function to analyze the user's emotions regarding the request content and generate a design based on the emotions. For example, the design analysis unit analyzes the user's emotions regarding the request content and generates a design based on the results. For example, if the user is excited, a bolder and more eye-catching design is suggested. The design analysis unit also uses the emotion estimation function to analyze the user's emotions regarding the request content in real time and generate a design based on the emotions. For example, if the user enters the request in a calm tone, a simple and elegant design is suggested. The design analysis unit also analyzes the user's emotional state and suggests a design style and color based on the results. For example, if the user enters the request with a smile, a bright color or a fun design is suggested. In this way, a more appropriate design can be provided by generating a design based on the user's emotions.

[0073] The base design generation unit can output the base design as a 3D model so that the user can visually check it. For example, the base design generation unit outputs the base design created by the generation AI as a 3D model so that the user can visually check it. For example, the design can be rotated 360 degrees using a 3D viewer. The base design generation unit can also output the base design using a 3D printer so that the user can actually hold it in their hands and check it. For example, a miniature model can be created to check the details of the design. The base design generation unit can also use AR (augmented reality) technology to display the 3D model in a real environment so that the user can check the design in its actual environment. For example, the design can be superimposed on the actual garment using a smartphone camera. This allows the user to visually check the design, deepening their understanding of the design.

[0074] The base design generation unit can provide an interactive interface that allows the user to fine-tune the design in real time when outputting the base design. The base design generation unit provides an interface that allows the user to fine-tune the design in real time when outputting the base design. For example, it allows the user to change the color or material or add decorations in real time. The base design generation unit also uses the interactive interface to allow the user to adjust each element of the design by drag and drop. For example, the placement of sequins or the shape of the flames can be freely changed. The base design generation unit also provides an interface that allows the user to fine-tune the design in real time, allowing the user to make adjustments while checking the details of the design. For example, a zoom-in / zoom-out function can be added. This allows the user to fine-tune the design in real time, resulting in a more satisfying design.

[0075] The base design generation unit can use the emotion estimation function to monitor the user's emotions toward the base design in real time and provide feedback based on the emotions. The base design generation unit uses the emotion estimation function to monitor the user's emotions in real time when outputting the base design. For example, it analyzes the user's facial expressions and tone of voice while looking at the design. The base design generation unit also monitors the user's emotional state in real time and provides feedback based on the results. For example, if the user is satisfied, it proceeds as is, but if the user is dissatisfied, it makes suggestions for improvement. The base design generation unit also analyzes the user's emotions toward the design based on the emotion estimation data and provides feedback based on the emotions. For example, if the user is excited, it suggests a bolder design. This allows for more appropriate design adjustments by providing feedback based on the user's emotions.

[0076] The base design generation unit can use AR technology to allow the user to check the base design in a real environment. The base design generation unit, for example, uses AR technology to allow the user to check the base design in a real environment. For example, the design may be superimposed on an actual outfit using a smartphone camera. The base design generation unit also uses AR technology to allow the user to try on the design in a real environment. For example, the design may be tried on using a smart mirror and checked in real time. The base design generation unit also uses AR technology to allow the user to check the details of the base design in a real environment. For example, each element of the design may be enlarged and checked. This allows the user to check the design in a real environment, improving the applicability of the design.

[0077] The base design generation unit can share the base design on a social networking site or a community platform and collect feedback from other users. For example, the base design generation unit shares the base design on a social networking site or a community platform and collects feedback from other users. For example, the base design generation unit posts a design and receives comments and ratings. The base design generation unit also collects opinions and advice from other users about the base design through the social networking site or community platform. For example, the base design generation unit asks users to suggest improvements to the design or new ideas. The base design generation unit also shares the base design and improves the design based on feedback from other users. For example, the design details are adjusted to reflect user opinions. This makes it possible to improve the design by collecting feedback from other users.

[0078] The base design generation unit can use the emotion estimation function to monitor the user's emotions toward the base design in real time and provide feedback based on the emotions. The base design generation unit uses the emotion estimation function to monitor the user's emotions in real time when outputting the base design. For example, it analyzes the user's facial expressions and tone of voice while looking at the design. The base design generation unit also monitors the user's emotional state in real time and provides feedback based on the results. For example, if the user is satisfied, it proceeds as is, but if the user is dissatisfied, it makes suggestions for improvement. The base design generation unit also analyzes the user's emotions toward the design based on the emotion estimation data and provides feedback based on the emotions. For example, if the user is excited, it suggests a bolder design. This allows for more appropriate design adjustments by providing feedback based on the user's emotions.

[0079] When accepting a revision request, the design request accepting unit can refer to the user's past revision history and make optimal revision suggestions. For example, the design request accepting unit analyzes the user's past revision history and makes optimal revision suggestions based on that data. For example, it makes new revision suggestions by referring to past revisions. In addition, when accepting a revision request, the design request accepting unit refers to the user's past revision history to perform auto-completion. For example, if you input "I want the sequins color changed from red to blue," the optimal suggestion will be made based on the past revision history. In addition, the design request accepting unit uses the generation AI to make optimal revision suggestions based on the user's past revision history. For example, it makes suggestions that incorporate revision elements that have been frequently used in the past. In this way, more appropriate revision suggestions can be made by referring to the user's past revision history.

[0080] The design request accepting unit can analyze the revision request and generate multiple revision options to provide to the user. For example, the design request accepting unit analyzes the revision request and generates multiple revision options. For example, in response to a request to "change the sequin color from red to blue," the design request accepting unit proposes revision options with different colors and arrangements. The design request accepting unit also provides the user with multiple revision options to expand their choices. For example, multiple revision options with different colors or materials may be presented, allowing the user to select the one they like best. The design request accepting unit also analyzes the revision request and generates multiple revision options to provide to the user. For example, revision options that combine different design elements may be proposed. In this way, by providing multiple revision options, the user can select the optimal revision.

[0081] The design request receiving unit can use the emotion estimation function to analyze the user's emotion regarding the revision request and propose revisions based on the emotion. For example, the design request receiving unit analyzes the user's emotion regarding the revision request and proposes revisions based on the result. For example, if the user is dissatisfied, the design request receiving unit proposes revisions that will result in greater satisfaction. The design request receiving unit also uses the emotion estimation function to analyze the user's emotion regarding the revision request in real time and proposes revisions based on the emotion. For example, if the user is excited, the design request receiving unit proposes bolder revisions. The design request receiving unit also analyzes the user's emotional state and proposes the style and content of the revisions based on the result. For example, if the user enters the request in a calm tone, the design request receiving unit proposes simple and elegant revisions. This allows for more appropriate revisions by proposing revisions based on the user's emotion.

[0082] When accepting a revision request, the design request accepting unit can suggest different design styles or themes to encourage revisions from a new perspective. For example, when accepting a revision request, the design request accepting unit suggests different design styles or themes. For example, it suggests styles such as classic, modern, and exotic to encourage revisions from a new perspective. The design request accepting unit also suggests different design themes to enable the user to make revisions from a new perspective. For example, it suggests themes such as nature, technology, and art. When accepting a revision request, the design request accepting unit also suggests different design styles or themes to enable the user to make revisions from a new perspective. For example, it makes suggestions that incorporate design elements from different cultures or regions. In this way, by proposing different design styles and themes, the user can make revisions from a new perspective.

[0083] When accepting a revision request, the design request accepting unit can make revision suggestions by referring to the revision history and feedback of other users. For example, when accepting a revision request, the design request accepting unit makes revision suggestions by referring to the revision history and feedback of other users. For example, it proposes revisions made by other users for similar requests. The design request accepting unit also analyzes the revision history of other users and makes revision suggestions based on that data. For example, it makes suggestions by referring to revisions made by many users in the past. Furthermore, when accepting a revision request, the design request accepting unit makes revision suggestions by referring to the feedback of other users. For example, it proposes revisions that other users have given high ratings to. In this way, by referring to the revision history and feedback of other users, more appropriate revision suggestions can be made.

[0084] The design request receiving unit can use the emotion estimation function to analyze the user's emotion regarding the revision request and propose revisions based on the emotion. For example, the design request receiving unit analyzes the user's emotion regarding the revision request and proposes revisions based on the result. For example, if the user is dissatisfied, the design request receiving unit proposes revisions that will result in greater satisfaction. The design request receiving unit also uses the emotion estimation function to analyze the user's emotion regarding the revision request in real time and proposes revisions based on the emotion. For example, if the user is excited, the design request receiving unit proposes bolder revisions. The design request receiving unit also analyzes the user's emotional state and proposes the style and content of the revisions based on the result. For example, if the user enters the request in a calm tone, the design request receiving unit proposes simple and elegant revisions. This allows for more appropriate revisions by proposing revisions based on the user's emotion.

[0085] The base design generation unit can provide detailed explanations and suggestions for each element of the design at the time of final confirmation, allowing the user to be convinced. The base design generation unit, for example, provides detailed explanations for each element of the design at the time of final confirmation. For example, it provides specific explanations for the placement of sequins and the design of flames. The base design generation unit also provides suggestions for each element of the design, allowing the user to be convinced. For example, it presents options for color and material, allowing the user to select the most suitable one. The base design generation unit also provides detailed explanations and suggestions for each element of the design at the time of final confirmation, allowing the user to be convinced. For example, it explains the background and concept of the design, allowing the user to understand. In this way, the user can receive detailed explanations and suggestions for each element of the design, allowing them to make the final confirmation with confidence.

[0086] The base design generation unit can provide information about the feasibility and cost of the design at the time of final confirmation, allowing the user to make a realistic decision. The base design generation unit, for example, provides information about the feasibility of the design at the time of final confirmation. For example, it explains the availability of materials and technologies to be used, allowing the user to make a realistic decision. The base design generation unit also provides information about the cost of the design, allowing the user to make a realistic decision. For example, it presents the costs of materials and decorations to be used, allowing the user to select a design within their budget. The base design generation unit also provides information about the feasibility and cost of the design at the time of final confirmation, allowing the user to make a realistic decision. For example, it explains the time and effort required to create the design, allowing the user to be convinced. This allows the user to make a realistic decision by obtaining information about the feasibility and cost of the design.

[0087] The base design generation unit can use the emotion estimation function to analyze the user's emotions at the time of final confirmation and make final adjustments based on the emotions. For example, the base design generation unit uses the emotion estimation function to analyze the user's emotions at the time of final confirmation and makes final adjustments based on the results. For example, if the user is satisfied, the unit proceeds as is, but if the user is dissatisfied, the unit makes suggestions for improvements. The base design generation unit also monitors the user's emotional state in real time and makes final adjustments based on the results. For example, if the user is excited, the unit proposes a bolder design. The base design generation unit also analyzes the user's emotions toward the design based on the emotion estimation data and makes final adjustments based on the emotions. For example, if the user enters a request in a calm tone, the unit proposes a simple and elegant design. In this way, by making final adjustments based on the user's emotions, it is possible to provide a design that provides greater satisfaction.

[0088] The base design generation unit may actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, the base design generation unit may actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, a miniature model may be created to check the details of the design. The base design generation unit may also create a prototype of the design, allowing the user to try on the actual product. For example, a try-on sample may be created, allowing the user to actually wear it and check it. The base design generation unit may also actually create a prototype of the design at the time of final confirmation, allowing the user to check the actual product. For example, a 3D printer may be used to check the details of the design. This allows the user to check the actual product, making the final confirmation of the design more reliable.

[0089] The base design generation unit can collect feedback from other users about each element of the design during final confirmation and reflect it in the final adjustments. The base design generation unit, for example, collects feedback from other users about each element of the design during final confirmation. For example, the design is shared on a social networking site or community platform and comments and ratings are received. The base design generation unit also makes final adjustments to the design based on feedback from other users. For example, the details of the design are adjusted to reflect user opinions. The base design generation unit also collects feedback from other users about each element of the design during final confirmation and reflects it in the final adjustments. For example, the users are asked to suggest improvements to the design or new ideas. In this way, collecting feedback from other users enables more appropriate final adjustments.

[0090] The base design generation unit can use the emotion estimation function to analyze the user's emotions at the time of final confirmation and make final adjustments based on the emotions. For example, the base design generation unit uses the emotion estimation function to analyze the user's emotions at the time of final confirmation and makes final adjustments based on the results. For example, if the user is satisfied, the unit proceeds as is, but if the user is dissatisfied, the unit makes suggestions for improvements. The base design generation unit also monitors the user's emotional state in real time and makes final adjustments based on the results. For example, if the user is excited, the unit proposes a bolder design. The base design generation unit also analyzes the user's emotions toward the design based on the emotion estimation data and makes final adjustments based on the emotions. For example, if the user enters a request in a calm tone, the unit proposes a simple and elegant design. In this way, by making final adjustments based on the user's emotions, it is possible to provide a design that provides greater satisfaction.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The design request receiving unit can analyze the user's tone of voice and facial expression to make design suggestions based on their emotions. For example, when a user inputs a request, the camera and microphone can be used to analyze the user's tone of voice and facial expression to estimate their emotions. If the user is excited, a bolder design can be suggested. Furthermore, the unit can analyze the user's tone of voice and facial expression in real time when inputting a request to make design suggestions based on their emotions. For example, if the user speaks in a calm tone, a simple and elegant design can be suggested. Furthermore, the unit can analyze the user's emotional state and suggest design styles and colors based on the results. For example, if the user inputs a request with a smile, bright colors and fun designs can be suggested. This allows for design suggestions based on the user's emotions, providing more appropriate designs.

[0093] The design request reception unit can reference the user's past design history, learn their preferences and trends, and automatically complete requests. For example, the generation AI can collect design data created by the user in the past and use that data to learn the user's preferences and trends. If many of the user's past designs have used a lot of sequins, it can automatically suggest sequins for the next request. It is also possible to automatically complete requests entered by the user by referencing their past design history. For example, if a user enters "more sequins on the chest," a completion suggestion such as "a flame motif" can be made based on past designs. Furthermore, the generation AI can provide templates based on the user's past design history to simplify request entry. For example, it can list previously used design elements so the user can complete the request simply by selecting them. This allows the AI ​​to automatically complete requests based on the user's preferences and trends, thereby providing more personalized designs.

[0094] The design request receiving unit can use the emotion estimation function to estimate the emotion of the user when entering a request in real time and make suggestions to elicit positive emotions. For example, when the user enters a request, the emotion estimation function can be used to analyze the emotion in real time and make suggestions to elicit positive emotions. If the user is nervous, design suggestions can be made to help the user relax. The emotion estimation function can also be used to provide an interface that elicits positive emotions when the user enters a request. For example, encouraging messages and success stories can be presented. Furthermore, when the user enters a request, feedback can be provided in real time based on the emotion estimation data, and advice can be provided to strengthen positive emotions. For example, appropriate encouragement or praise can be displayed according to the input content. This can elicit positive emotions from the user and provide a better design experience.

[0095] The design request receiving unit can introduce a multimodal interface that accepts requests not only via text but also via voice and gestures. For example, a user can input a design request via voice. For example, if the user gives a voice instruction such as, "Imagine a flame with lots of sequins on the chest," the generation AI can analyze the instruction and generate a design. It is also possible to allow a user to input a design request via gesture. For example, if the user uses hand movements to indicate the placement of sequins or the shape of a flame, the generation AI can analyze the gesture and generate a design. Furthermore, a multimodal interface can be provided that allows users to input requests by selecting text, voice, or gesture. For example, a system can allow users to input requests in the way that is most convenient for them. This improves convenience by allowing users to input requests in a variety of ways.

[0096] The design request receiving unit can provide request templates that incorporate design elements from different cultures and regions, allowing users to easily request a variety of designs. For example, request templates that incorporate design elements from different cultures and regions can be provided. For example, templates including traditional Japanese patterns and African ethnic patterns can be prepared. It is also possible to incorporate elements from different cultures and regions into design templates that users can select. For example, templates incorporating Indian saris and American cowboy style can be provided. Furthermore, a generation AI that has learned design elements from different cultures and regions can propose a variety of designs in response to user requests. For example, if a user requests an "exotic design," the generation AI can generate a design that incorporates elements from different cultures. This allows users to easily request a variety of designs, thereby expanding the range of designs available.

[0097] The design analysis unit can generate more personalized designs by taking into account the user's past design history and preferences. For example, it can analyze the user's past design history and generate personalized designs based on that data. It can also propose new designs by taking into account colors and materials used in the past. The generation AI can also generate designs that reflect the user's preferences when analyzing a request by learning the user's preferences. For example, it can create a design based on the user's preferred style or theme. Furthermore, the generation AI can propose the optimal design for the request by taking into account the user's past design history and preferences. For example, it can generate a base design that incorporates design elements that have been frequently used in the past. This makes it possible to provide more personalized designs based on the user's past design history and preferences.

[0098] The design analysis unit can generate multiple design options based on the request content and provide the user with choices. For example, the generation AI can analyze the request content and generate multiple design options. For example, in response to a request such as "more sequins on the chest to create a flame-like effect," the AI ​​can suggest different sequin placements and flame designs. It is also possible to provide the user with multiple design options to expand their choices. For example, it is possible to present multiple designs with different colors and materials and allow the user to choose the one they like best. Furthermore, it is possible to provide the user with multiple design options generated based on the request content and select the optimal design based on feedback. For example, it is possible to make further detailed adjustments based on the design selected by the user. In this way, by providing the user with multiple design options, the AI ​​can expand their choices and select a design that will satisfy them.

[0099] The design analysis unit can use the emotion estimation function to analyze the user's emotions regarding the request content and generate a design based on the emotions. For example, the design analysis unit can analyze the user's emotions regarding the request content and generate a design based on the analysis result. If the user is excited, a bolder and more eye-catching design can be suggested. The emotion estimation function can also be used to analyze the user's emotions regarding the request content in real time and generate a design based on the emotions. For example, if the user enters the request in a calm tone, a simple and elegant design can be suggested. Furthermore, the design analysis unit can analyze the user's emotional state and suggest a design style or color based on the analysis result. For example, if the user enters the request with a smile, a bright color or a fun design can be suggested. This allows the system to provide a more appropriate design by generating a design based on the user's emotions.

[0100] The design analysis unit can generate new designs that incorporate design elements from different industries and fields when analyzing a request. For example, it can generate new designs that incorporate design elements from different industries and fields when analyzing a request. It can propose clothing designs that incorporate trends in the fashion industry. It can also generate new designs by combining design elements from different fields. For example, it can propose clothing designs that incorporate elements of architectural design. It can also analyze the content of a request and generate new designs that incorporate design elements from different industries and fields. For example, it can propose futuristic clothing designs that incorporate elements from the technology field. This makes it possible to provide a wider variety of designs by incorporating design elements from different industries and fields.

[0101] The design analysis unit can analyze the request content and generate a design that reflects related design trends and fashions. For example, it can analyze the request content and generate a design that reflects related design trends and fashions. It can propose a costume design that incorporates the latest fashion trends. It can also analyze design trends and fashions and generate a design that reflects the request content. For example, it can propose a design that uses currently popular colors and materials. It can also analyze the request content and generate a design that reflects related design trends and fashions. For example, it can propose a costume design that incorporates design elements that won awards in the latest design contest. This makes it possible to provide designs that are more in line with the times by reflecting design trends and fashions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The design request receiving unit receives design requests from users. For example, a user can input a specific request such as "I want a lot of sequins on the chest area to create a flame-like image." Step 2: The design analysis unit analyzes the design request received by the design request reception unit. For example, the generation AI analyzes the request using natural language processing technology to understand the placement of sequins and the flame design. Step 3: The base design generation unit generates a base design based on the content analyzed by the design analysis unit. For example, the generation AI uses a pre-fine-tuned model to generate a design based on the request.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

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

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[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 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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. [Explanation of symbols]

[0171] 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 design request receiving unit that receives a design request from a user; a design analysis unit that analyzes the design request accepted by the design request acceptance unit; a base design generation unit that generates a base design based on the content analyzed by the design analysis unit; A system characterized by:

2. The design request receiving unit Refer to the user's past design history, learn their preferences and tendencies, and automatically complete requests.

2. The system of claim 1.

3. The design request receiving unit Analyze the user's tone of voice and facial expressions to make design suggestions based on their emotions 2. The system of claim 1.

4. The design request receiving unit The system estimates the user's emotions in real time when they input their requests and makes suggestions to elicit positive emotions.

2. The system of claim 1.

5. The design request receiving unit Introduce a multimodal interface that accepts requests not only by text but also by voice and gestures.

2. The system of claim 1.

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