system

Abstract

The system according to the embodiment aims to allow a user to easily communicate the design of clothing they desire and, based on that, provide clothing that matches their desires. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a searching unit, and a providing unit. The receiving unit receives text including clothing characteristics from a user. The generating unit analyzes the text received by the receiving unit and generates a clothing design. The searching unit searches for similar clothing based on the design generated by the generating unit. The providing unit provides the clothing searched for by the searching unit to the user.

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 specifically communicate the design of clothing they desire, making it difficult to find clothing that meets their needs.

[0005] The system according to the embodiment aims to allow a user to easily communicate the design of clothing they desire and, based on that, provide clothing that matches their desires. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a searching unit, and a providing unit. The receiving unit receives text including clothing characteristics from a user. The generating unit analyzes the text received by the receiving unit and generates a clothing design. The searching unit searches for similar clothing based on the design generated by the generating unit. The providing unit provides the clothing searched for by the searching unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily communicate the design of clothing they desire, and based on that, can provide clothing that matches their desires. [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) In an embodiment of the clothing design generation system of the present invention, a user inputs desired clothing characteristics via text, and a generation AI generates a clothing design based on that information. The clothing design generation system generates clothing designs based on the user's input of desired clothing characteristics. The user reviews the generated design, and if they like it, they can search for clothing similar to the design and purchase it. If they cannot find the desired design, they can create their own original clothing. For example, in a clothing design generation system, a user inputs text such as "I want clothing with a specific color, shape, and style." This information is input into the generation AI. The generation AI analyzes the text using natural language processing technology to extract the user's desired clothing characteristics. The generation AI then generates a design by combining elements such as color, shape, and style based on the extracted characteristics. The generated design is provided to the user, who reviews it. If they like it, they can search for clothing similar to the design and purchase it. The generation AI then searches an online shop database based on the generated design to suggest similar clothing. If they cannot find the desired design, they can create their own original clothing. The generative AI generates original clothing designs based on the user's preferences and provides information for creating the clothing based on those designs. This allows the clothing design generation system to easily find clothing that matches the user's preferences and also enables the user to create original clothing. This increases user satisfaction and broadens the enjoyment of fashion.

[0029] A clothing design generation system according to an embodiment includes a reception unit, a generation unit, a search unit, and a provision unit. The reception unit receives text from a user, including clothing characteristics. For example, the user may input text such as, "I want clothing with a specific color, shape, and style." This information is input to a generation AI. The generation unit uses the generation AI to analyze the text received by the reception unit and generate clothing designs. The generation AI analyzes the text using natural language processing technology and extracts the user's desired clothing characteristics. For example, the generation AI may use a text generation AI (e.g., LLM) to extract the user's desired clothing characteristics and generate a design by combining elements such as color, shape, and style. The generation unit may also generate clothing designs using a multimodal generation AI. For example, the generation AI may extract the user's desired clothing characteristics and generate a design by combining elements such as color, shape, and style. The search unit searches for similar clothing based on the design generated by the generation unit. For example, the search unit may search an online shop database based on the generated design to suggest similar clothing. The providing unit provides the user with the clothes searched for by the search unit. For example, the providing unit provides the user with information about the searched clothes. This allows the clothing design generation system according to the embodiment to generate a clothing design based on the user's preferences and search for and provide similar clothing. Some or all of the above-described processing by the generating unit may be performed using, for example, a generation AI. For example, the generating unit may generate a clothing design using a generation AI that extracts features of clothing desired by the user and combines elements such as color, shape, and style to generate a design. Some or all of the above-described processing by the searching unit may be performed using, for example, an AI. For example, the searching unit may search an online shop's database based on the generated design and search for similar clothing using an AI that suggests similar clothing. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI.For example, the providing unit can provide information about the searched clothes to the user using AI that provides information about the searched clothes to the user.

[0030] The generation unit can use a generation AI to extract the characteristics of the user's desired clothing and generate a design by combining elements of color, shape, and style. The generation unit, for example, uses a generation AI to extract the characteristics of the user's desired clothing. For example, the generation AI uses a text generation AI (e.g., LLM) to extract the characteristics of the user's desired clothing. The generation unit can also use the generation AI to generate a design by combining elements such as color, shape, and style. For example, the generation AI extracts the characteristics of the user's desired clothing and generates a design by combining elements such as color, shape, and style. The generation unit can also use the generation AI to generate a clothing design. For example, the generation AI extracts the characteristics of the user's desired clothing and generates a design by combining elements such as color, shape, and style. In this way, a clothing design based on the user's desires can be generated by using the generation AI. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a clothing design by extracting the characteristics of the user's desired clothing and generating a design by combining elements such as color, shape, and style.

[0031] The search unit can search the database of an online shop based on the generated design and suggest similar clothes. The search unit can, for example, search the database of an online shop based on the generated design. For example, the search unit can search the database of an online shop based on the generated design and suggest similar clothes. The search unit can also search the database of an online shop based on the generated design and suggest similar clothes. For example, the search unit can search the database of an online shop based on the generated design and suggest similar clothes. In this way, similar clothes can be suggested to the user by searching the database of the online shop. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can search the database of an online shop based on the generated design and search for similar clothes using AI that suggests similar clothes.

[0032] The providing unit can provide information about the searched clothes to the user. The providing unit, for example, provides information about the searched clothes to the user. For example, the providing unit provides information about the searched clothes to the user. The providing unit can also provide information about the searched clothes to the user. For example, the providing unit provides information about the searched clothes to the user. By providing information about the searched clothes to the user, the user can easily find clothes that they want. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information about the searched clothes to the user using AI that provides information about the searched clothes to the user.

[0033] The generation unit can generate an original clothing design based on the user's wishes and provide information for creating clothing based on that design. The generation unit can, for example, use a generation AI to generate an original clothing design based on the user's wishes. For example, the generation AI can generate an original clothing design based on the user's wishes. The generation unit can also use a generation AI to generate an original clothing design and provide information for creating clothing based on that design. For example, the generation AI can generate an original clothing design based on the user's wishes and provide information for creating clothing based on that design. This makes it possible to provide information for creating original clothing based on the user's wishes. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an original clothing design based on the user's wishes and generate an original clothing design using a generation AI that provides information for creating clothing based on that design.

[0034] The reception unit can analyze the user's past text input history and select an appropriate input method. The reception unit, for example, analyzes the user's past text input history and selects the optimal input method. For example, the reception unit automatically displays characteristics of clothing that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests characteristics of clothing to be worn during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the optimal input method using AI that analyzes the user's past text input history and selects the optimal input method.

[0035] The reception unit can filter text based on the user's current fashion trends or areas of interest when the text is input. The reception unit performs filtering based on the user's current fashion trends or areas of interest, for example. For example, the reception unit can suggest related features based on the style of clothes recently purchased by the user. The reception unit can also refer to the style of fashion influencers the user follows on social media. For example, the reception unit can suggest related features based on fashion items previously searched by the user. This allows for more appropriate suggestions to be made by filtering based on the user's fashion trends or areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using AI that performs filtering based on the user's current fashion trends or areas of interest.

[0036] The reception unit can select the optimal input means depending on the user's input method when inputting text. The reception unit selects the optimal input means depending on, for example, the user's input method (voice, text, image, etc.). For example, the reception unit can automatically set the characteristics of the clothing when the user simply inputs "red dress" by voice. The reception unit can also perform image analysis and extract the characteristics of the clothing when the user uploads an image. For example, when the user inputs detailed characteristics in text, the reception unit provides a completion function to assist the input. This can improve the convenience of input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select the optimal input means using AI that selects the optimal input means depending on the user's input method.

[0037] The reception unit can prioritize acquisition of highly relevant information based on the user's geographical location information when entering text. The reception unit, for example, prioritizes acquisition of highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit suggests fashion styles popular in that area. Furthermore, when the user is traveling, the reception unit can also make suggestions by taking into account fashion trends at the travel destination. For example, when the user is attending a specific event, the reception unit suggests characteristics of clothing suitable for the event. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire highly relevant information by using AI that prioritizes acquisition of highly relevant information by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and acquire related information when entering text. The reception unit, for example, analyzes the user's social media activity and acquires related information. For example, the reception unit can suggest related features based on fashion items that the user has "liked" on social media. The reception unit can also refer to the latest collections of brands the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related fashion styles. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire related information using AI that analyzes the user's social media activity and acquires related information.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting text. The reception unit, for example, customizes the input method by reflecting the user's past feedback. For example, the reception unit adjusts the input interface based on feedback provided by the user in the past. The reception unit can also preferentially suggest design features that the user has previously preferred. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method by using AI that customizes the input method by reflecting the user's past feedback.

[0040] The generation unit can adjust the level of detail of the design based on the importance of the clothes when generating the design. For example, the generation unit can adjust the level of detail of the design based on the importance of the clothes when generating the design. For example, the generation unit generates a detailed design for clothes to be used in formal occasions. The generation unit can also generate a simple design for clothes to be used in casual occasions. For example, the generation unit can generate a luxurious design for clothes to be used at special events. In this way, by adjusting the level of detail of the design based on the importance of the clothes, it is possible to provide a more appropriate design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail of the design using a generation AI that adjusts the level of detail of the design based on the importance of the clothes when generating the design.

[0041] The generation unit can apply different design algorithms depending on the clothing category when generating a design. For example, the generation unit can apply different design algorithms depending on the clothing category when generating a design. For example, the generation unit can apply an elegant design algorithm to a dress. The generation unit can also apply a design algorithm that emphasizes functionality to sportswear. For example, the generation unit can apply a simple and easy-to-use design algorithm to casual wear. In this way, by applying different design algorithms depending on the clothing category, more appropriate designs can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply a design algorithm using a generation AI that applies different design algorithms depending on the clothing category when generating a design.

[0042] The generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design. For example, the generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design. For example, the generation unit generates a new design based on design features that the user has previously preferred. The generation unit can also generate a new design by eliminating design features that the user has previously avoided. For example, the generation unit can analyze the user's past design results and propose an optimal design. In this way, the accuracy of the design can be improved by referring to the user's past design results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design.

[0043] The generation unit can determine the priority of designs based on the time of submission of the clothes when generating the designs. For example, the generation unit determines the priority of designs based on the time of submission of the clothes when generating the designs. For example, in the case of an urgent order, the generation unit generates designs with the highest priority. The generation unit can also generate designs with standard priority for normal orders. For example, the generation unit generates designs later for long-term projects. In this way, by determining the priority of designs based on the time of submission of the clothes, it is possible to generate designs in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority of designs using a generation AI that determines the priority of designs based on the time of submission of the clothes when generating the designs.

[0044] The generation unit can adjust the order of designs based on the relevance of the clothes when generating designs. The generation unit, for example, adjusts the order of designs based on the relevance of the clothes when generating designs. For example, the generation unit prioritizes designs of clothes in the same category. The generation unit can also prioritize designs of highly related clothes based on the user's past selection history. For example, the generation unit prioritizes designs of highly related clothes taking into account the user's current fashion trends. In this way, by adjusting the order of designs based on the relevance of the clothes, it is possible to provide more related designs. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order of designs using a generation AI that adjusts the order of designs based on the relevance of the clothes when generating designs.

[0045] The generation unit can adjust the use of design terminology according to the user's level of expertise when generating a design. For example, the generation unit can adjust the use of design terminology according to the user's level of expertise when generating a design. For example, the generation unit can provide a design description that uses a lot of technical terms to a user who is knowledgeable about fashion. The generation unit can also provide a design description in simple language to a user who is not knowledgeable about fashion. For example, the generation unit can adjust the use of optimal technical terms based on the user's past selection history. This allows for the provision of a more understandable design by adjusting the use of design terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can adjust the use of technical terms using a generation AI that adjusts the use of design terminology according to the user's level of expertise when generating a design.

[0046] The search unit can improve search accuracy by taking into account the interrelationships between clothes during a search. The search unit can improve search accuracy by taking into account the interrelationships between clothes during a search. For example, the search unit can prioritize searching for clothes of the same brand. The search unit can also prioritize searching for clothes of the same style. For example, the search unit can prioritize searching for clothes of the same color. By taking into account the interrelationships between clothes, more accurate search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can improve search accuracy by using AI that takes into account the interrelationships between clothes during a search.

[0047] The search unit can perform a search taking into account attribute information of the person who submitted the clothing. For example, the search unit can perform a search taking into account attribute information of the person who submitted the clothing. For example, if the person who submitted the clothing is a professional designer, the search unit can prioritize searching for designs by that person. Furthermore, if the person who submitted the clothing is a popular brand, the search unit can also prioritize searching for designs by that person. For example, if the person who submitted the clothing matches the user's preferences, the search unit can prioritize searching for designs by that person. In this way, by taking into account the attribute information of the person who submitted the clothing, more relevant search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account attribute information of the person who submitted the clothing.

[0048] The search unit can weight the search based on the frequency of submission of clothing during a search. The search unit, for example, weights the search based on the frequency of submission of clothing during a search. For example, the search unit prioritizes searches for designs that are submitted more frequently. The search unit can also search for designs that are submitted less frequently later. For example, the search unit adjusts the display order of search results based on the submission frequency. By weighting the search based on the frequency of submission of clothing in this way, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can weight the search using AI that weights the search based on the frequency of submission of clothing during a search.

[0049] The search unit can perform a search taking into account the geographical distribution of clothes. For example, the search unit can perform a search taking into account the geographical distribution of clothes. For example, the search unit can prioritize searching for clothes in stores close to the user's current location. Furthermore, if the user is searching at a travel destination, the search unit can also prioritize searching for clothes in stores in that area. For example, if the user prefers a fashion style from a specific area, the search unit can prioritize searching for clothes from that area. In this way, by taking the geographical distribution of clothes into account, more relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account the geographical distribution of clothes.

[0050] The search unit can improve search accuracy by referring to literature related to clothing during a search. For example, the search unit can improve search accuracy by referring to literature related to clothing during a search. For example, the search unit can adjust search results based on articles in related fashion magazines. The search unit can also scrutinize search results by referring to related academic papers. For example, the search unit can adjust search results based on related online reviews. By referring to literature related to clothing, more accurate search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can improve search accuracy by using AI to improve search accuracy by referring to literature related to clothing during a search.

[0051] The search unit can perform a search taking into account the market value of the clothes. For example, the search unit can perform a search taking into account the market value of the clothes. For example, the search unit can prioritize searching for expensive clothes. The search unit can also prioritize searching for affordable clothes. For example, the search unit can adjust the display order of search results based on the market value. This makes it possible to provide more appropriate search results by taking into account the market value of the clothes. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account the market value of the clothes.

[0052] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit can provide the optimal display method based on display methods that the user has preferred in the past. The providing unit can also provide the optimal display method by eliminating display methods that the user has avoided in the past. For example, the providing unit can analyze the user's past operation history and suggest the optimal display method. In this way, by referring to the user's past operation history, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can select the display method by using AI that selects the optimal display method by referring to the user's past operation history when providing the display.

[0053] The providing unit can customize the display content according to the user's current task at the time of providing. The providing unit, for example, customizes the display content according to the user's current task at the time of providing. For example, when the user is shopping, the providing unit can prioritize displaying information about related products. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information about travel destinations. For example, when the user is at work, the providing unit can prioritize displaying information related to work. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using AI that customizes the display content according to the user's current task at the time of providing.

[0054] The providing unit can select the optimal display method by taking into account the user's device information at the time of providing. For example, the providing unit selects the optimal display method by taking into account the user's device information at the time of providing. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide a more appropriate display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method by using AI that selects the optimal display method by taking into account the user's device information at the time of providing.

[0055] The providing unit can make the display content multilingual according to the user's language setting at the time of providing. The providing unit, for example, can make the display content multilingual according to the user's language setting at the time of providing. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual according to the user's language setting at the time of providing.

[0056] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide related information at the time of providing the information using AI that analyzes the user's social media activity and provides related information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past purchase history and understand the user's design preferences. For example, the reception unit can suggest related designs based on the color and style of clothes the user has previously purchased. If the user prefers a particular brand, the reception unit can also preferentially suggest new designs from that brand. Furthermore, the reception unit can suggest new designs by eliminating design features that the user has avoided in the past. This makes it possible to make more personalized design suggestions by utilizing the user's past purchase history.

[0059] The search unit can prioritize searching for clothes that can be purchased at nearby stores based on the user's current location information. For example, if the user is in a specific area, the search unit can prioritize suggesting clothes that are available at stores in that area. Also, if the user is traveling, the search unit can suggest clothes that can be purchased at stores in the user's travel destination. Furthermore, if the user is attending a specific event, the search unit can suggest clothes that are suitable for that event. This makes it possible to provide more relevant search results by utilizing the user's location information.

[0060] The generation unit can improve the accuracy of the design based on the user's past design selection history. For example, the generation unit generates a new design based on the features of designs that the user has previously preferred. The generation unit can also generate a new design by eliminating the features of designs that the user has previously avoided. Furthermore, the generation unit can analyze the user's past design selection history and propose an optimal design. In this way, the user's past design selection history can be utilized to improve the accuracy of the design.

[0061] The search unit can analyze the user's past search history and provide optimal search results. For example, the search unit can prioritize relevant search results based on the characteristics of clothing that the user has previously searched for. The search unit can also provide new search results by eliminating search results that the user has avoided in the past. Furthermore, the search unit can analyze the user's past search history and apply an optimal search algorithm. This makes it possible to provide more accurate search results by utilizing the user's past search history.

[0062] The generation unit can adjust the design by taking into account the user's current fashion trends when generating the design. For example, the generation unit can generate a related design based on the style of clothes recently purchased by the user. The generation unit can also refer to the style of a fashion influencer the user follows on social media. Furthermore, the generation unit can generate a related design based on fashion items the user has searched for in the past. This makes it possible to provide a more appropriate design by taking into account the user's current fashion trends.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives text from the user that includes the characteristics of the clothing. For example, the user might enter text such as, "I want clothing with a specific color, shape, and style." This information is then input into the generation AI. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate a clothing design. The generation AI analyzes the text using natural language processing technology and extracts the characteristics of the clothing desired by the user. For example, the generation AI uses a text generation AI (e.g., LLM) to extract the characteristics of the clothing desired by the user and generate a design by combining elements such as color, shape, and style. The generation unit can also use a multimodal generation AI to generate clothing designs. Step 3: The search unit searches for similar clothes based on the design generated by the generation unit. For example, the search unit searches a database of an online shop based on the generated design and suggests similar clothes. Step 4: The providing unit provides the clothes searched for by the searching unit to the user. For example, the providing unit provides information about the searched clothes to the user.

[0065] (Example 2) In an embodiment of the clothing design generation system of the present invention, a user inputs desired clothing characteristics via text, and a generation AI generates a clothing design based on that information. The clothing design generation system generates clothing designs based on the user's input of desired clothing characteristics. The user reviews the generated design, and if they like it, they can search for clothing similar to the design and purchase it. If they cannot find the desired design, they can create their own original clothing. For example, in a clothing design generation system, a user inputs text such as "I want clothing with a specific color, shape, and style." This information is input into the generation AI. The generation AI analyzes the text using natural language processing technology to extract the user's desired clothing characteristics. The generation AI then generates a design by combining elements such as color, shape, and style based on the extracted characteristics. The generated design is provided to the user, who reviews it. If they like it, they can search for clothing similar to the design and purchase it. The generation AI then searches an online shop database based on the generated design to suggest similar clothing. If they cannot find the desired design, they can create their own original clothing. The generative AI generates original clothing designs based on the user's preferences and provides information for creating the clothing based on those designs. This allows the clothing design generation system to easily find clothing that matches the user's preferences and also enables the user to create original clothing. This increases user satisfaction and broadens the enjoyment of fashion.

[0066] A clothing design generation system according to an embodiment includes a reception unit, a generation unit, a search unit, and a provision unit. The reception unit receives text from a user, including clothing characteristics. For example, the user may input text such as, "I want clothing with a specific color, shape, and style." This information is input to a generation AI. The generation unit uses the generation AI to analyze the text received by the reception unit and generate clothing designs. The generation AI analyzes the text using natural language processing technology and extracts the user's desired clothing characteristics. For example, the generation AI may use a text generation AI (e.g., LLM) to extract the user's desired clothing characteristics and generate a design by combining elements such as color, shape, and style. The generation unit may also generate clothing designs using a multimodal generation AI. For example, the generation AI may extract the user's desired clothing characteristics and generate a design by combining elements such as color, shape, and style. The search unit searches for similar clothing based on the design generated by the generation unit. For example, the search unit may search an online shop database based on the generated design to suggest similar clothing. The providing unit provides the user with the clothes searched for by the search unit. For example, the providing unit provides the user with information about the searched clothes. This allows the clothing design generation system according to the embodiment to generate a clothing design based on the user's preferences and search for and provide similar clothing. Some or all of the above-described processing by the generating unit may be performed using, for example, a generation AI. For example, the generating unit may generate a clothing design using a generation AI that extracts features of clothing desired by the user and combines elements such as color, shape, and style to generate a design. Some or all of the above-described processing by the searching unit may be performed using, for example, an AI. For example, the searching unit may search an online shop's database based on the generated design and search for similar clothing using an AI that suggests similar clothing. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI.For example, the providing unit can provide information about the searched clothes to the user using AI that provides information about the searched clothes to the user.

[0067] The generation unit can use a generation AI to extract the characteristics of the user's desired clothing and generate a design by combining elements of color, shape, and style. The generation unit, for example, uses a generation AI to extract the characteristics of the user's desired clothing. For example, the generation AI uses a text generation AI (e.g., LLM) to extract the characteristics of the user's desired clothing. The generation unit can also use the generation AI to generate a design by combining elements such as color, shape, and style. For example, the generation AI extracts the characteristics of the user's desired clothing and generates a design by combining elements such as color, shape, and style. The generation unit can also use the generation AI to generate a clothing design. For example, the generation AI extracts the characteristics of the user's desired clothing and generates a design by combining elements such as color, shape, and style. In this way, a clothing design based on the user's desires can be generated by using the generation AI. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a clothing design by extracting the characteristics of the user's desired clothing and generating a design by combining elements such as color, shape, and style.

[0068] The search unit can search the database of an online shop based on the generated design and suggest similar clothes. The search unit can, for example, search the database of an online shop based on the generated design. For example, the search unit can search the database of an online shop based on the generated design and suggest similar clothes. The search unit can also search the database of an online shop based on the generated design and suggest similar clothes. For example, the search unit can search the database of an online shop based on the generated design and suggest similar clothes. In this way, similar clothes can be suggested to the user by searching the database of the online shop. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can search the database of an online shop based on the generated design and search for similar clothes using AI that suggests similar clothes.

[0069] The providing unit can provide information about the searched clothes to the user. The providing unit, for example, provides information about the searched clothes to the user. For example, the providing unit provides information about the searched clothes to the user. The providing unit can also provide information about the searched clothes to the user. For example, the providing unit provides information about the searched clothes to the user. By providing information about the searched clothes to the user, the user can easily find clothes that they want. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information about the searched clothes to the user using AI that provides information about the searched clothes to the user.

[0070] The generation unit can generate an original clothing design based on the user's wishes and provide information for creating clothing based on that design. The generation unit can, for example, use a generation AI to generate an original clothing design based on the user's wishes. For example, the generation AI can generate an original clothing design based on the user's wishes. The generation unit can also use a generation AI to generate an original clothing design and provide information for creating clothing based on that design. For example, the generation AI can generate an original clothing design based on the user's wishes and provide information for creating clothing based on that design. This makes it possible to provide information for creating original clothing based on the user's wishes. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an original clothing design based on the user's wishes and generate an original clothing design using a generation AI that provides information for creating clothing based on that design.

[0071] The reception unit can analyze the user's emotions and adjust the timing of text input based on the analyzed user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of text input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input clothing characteristics. This allows for adjusting the timing of text input according to the user's emotions, thereby providing a more appropriate input environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can adjust the timing of text input using AI that estimates the user's emotions and adjusts the timing of text input based on the estimated user emotions.

[0072] The reception unit can analyze the user's past text input history and select an appropriate input method. The reception unit, for example, analyzes the user's past text input history and selects the optimal input method. For example, the reception unit automatically displays characteristics of clothing that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests characteristics of clothing to be worn during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the optimal input method using AI that analyzes the user's past text input history and selects the optimal input method.

[0073] The reception unit can filter text based on the user's current fashion trends or areas of interest when the text is input. The reception unit performs filtering based on the user's current fashion trends or areas of interest, for example. For example, the reception unit can suggest related features based on the style of clothes recently purchased by the user. The reception unit can also refer to the style of fashion influencers the user follows on social media. For example, the reception unit can suggest related features based on fashion items previously searched by the user. This allows for more appropriate suggestions to be made by filtering based on the user's fashion trends or areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using AI that performs filtering based on the user's current fashion trends or areas of interest.

[0074] The reception unit can select the optimal input means depending on the user's input method when inputting text. The reception unit selects the optimal input means depending on, for example, the user's input method (voice, text, image, etc.). For example, the reception unit can automatically set the characteristics of the clothing when the user simply inputs "red dress" by voice. The reception unit can also perform image analysis and extract the characteristics of the clothing when the user uploads an image. For example, when the user inputs detailed characteristics in text, the reception unit provides a completion function to assist the input. This can improve the convenience of input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select the optimal input means using AI that selects the optimal input means depending on the user's input method.

[0075] The reception unit can analyze the user's emotions and prioritize the input text based on the analyzed user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input text based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize processing the input text and quickly generate a design. Furthermore, if the user is relaxed, the reception unit can prioritize processing detailed input and quickly generate a highly accurate design. For example, if the user is stressed, the reception unit can prioritize processing simple input and quickly provide results. Thus, by prioritizing the input text based on the user's emotions, processing can be performed in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority of text using AI that estimates the user's emotions and determines the priority of input text based on the estimated user emotions.

[0076] The reception unit can prioritize acquisition of highly relevant information based on the user's geographical location information when entering text. The reception unit, for example, prioritizes acquisition of highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit suggests fashion styles popular in that area. Furthermore, when the user is traveling, the reception unit can also make suggestions by taking into account fashion trends at the travel destination. For example, when the user is attending a specific event, the reception unit suggests characteristics of clothing suitable for the event. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire highly relevant information by using AI that prioritizes acquisition of highly relevant information by taking into account the user's geographical location information.

[0077] The reception unit can analyze the user's social media activity and acquire related information when entering text. The reception unit, for example, analyzes the user's social media activity and acquires related information. For example, the reception unit can suggest related features based on fashion items that the user has "liked" on social media. The reception unit can also refer to the latest collections of brands the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related fashion styles. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire related information using AI that analyzes the user's social media activity and acquires related information.

[0078] The reception unit can customize the input method by reflecting the user's past feedback when inputting text. The reception unit, for example, customizes the input method by reflecting the user's past feedback. For example, the reception unit adjusts the input interface based on feedback provided by the user in the past. The reception unit can also preferentially suggest design features that the user has previously preferred. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method by using AI that customizes the input method by reflecting the user's past feedback.

[0079] The generation unit can analyze the user's emotions and adjust the design presentation method based on the analyzed user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the design presentation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a soft color scheme and a simple design. Alternatively, if the user is excited, the generation unit can generate a bright color scheme and a bold design. For example, if the user is stressed, the generation unit generates a muted color scheme and a simple design. This allows for adjusting the design presentation method based on the user's emotions to provide a more appropriate design. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can adjust the design presentation method using a generation AI that estimates the user's emotions and adjusts the design presentation method based on the estimated user's emotions.

[0080] The generation unit can adjust the level of detail of the design based on the importance of the clothes when generating the design. For example, the generation unit can adjust the level of detail of the design based on the importance of the clothes when generating the design. For example, the generation unit generates a detailed design for clothes to be used in formal occasions. The generation unit can also generate a simple design for clothes to be used in casual occasions. For example, the generation unit can generate a luxurious design for clothes to be used at special events. In this way, by adjusting the level of detail of the design based on the importance of the clothes, it is possible to provide a more appropriate design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail of the design using a generation AI that adjusts the level of detail of the design based on the importance of the clothes when generating the design.

[0081] The generation unit can apply different design algorithms depending on the clothing category when generating a design. For example, the generation unit can apply different design algorithms depending on the clothing category when generating a design. For example, the generation unit can apply an elegant design algorithm to a dress. The generation unit can also apply a design algorithm that emphasizes functionality to sportswear. For example, the generation unit can apply a simple and easy-to-use design algorithm to casual wear. In this way, by applying different design algorithms depending on the clothing category, more appropriate designs can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply a design algorithm using a generation AI that applies different design algorithms depending on the clothing category when generating a design.

[0082] The generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design. For example, the generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design. For example, the generation unit generates a new design based on design features that the user has previously preferred. The generation unit can also generate a new design by eliminating design features that the user has previously avoided. For example, the generation unit can analyze the user's past design results and propose an optimal design. In this way, the accuracy of the design can be improved by referring to the user's past design results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can improve the accuracy of the design by referring to the user's past design results when generating a design.

[0083] The generation unit can analyze the user's emotions and adjust the length of the design based on the analyzed user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the design based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a longer design including detailed explanations. Also, if the user is in a hurry, the generation unit can generate a short, to-the-point design. For example, if the user is excited, the generation unit generates a design with a visually stimulating effect. This allows for adjusting the length of the design based on the user's emotions to provide a more appropriate design. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can adjust the length of the design using a generation AI that estimates the user's emotions and adjusts the length of the design based on the estimated user's emotions.

[0084] The generation unit can determine the priority of designs based on the time of submission of the clothes when generating the designs. For example, the generation unit determines the priority of designs based on the time of submission of the clothes when generating the designs. For example, in the case of an urgent order, the generation unit generates designs with the highest priority. The generation unit can also generate designs with standard priority for normal orders. For example, the generation unit generates designs later for long-term projects. In this way, by determining the priority of designs based on the time of submission of the clothes, it is possible to generate designs in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority of designs using a generation AI that determines the priority of designs based on the time of submission of the clothes when generating the designs.

[0085] The generation unit can adjust the order of designs based on the relevance of the clothes when generating designs. The generation unit, for example, adjusts the order of designs based on the relevance of the clothes when generating designs. For example, the generation unit prioritizes designs of clothes in the same category. The generation unit can also prioritize designs of highly related clothes based on the user's past selection history. For example, the generation unit prioritizes designs of highly related clothes taking into account the user's current fashion trends. In this way, by adjusting the order of designs based on the relevance of the clothes, it is possible to provide more related designs. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order of designs using a generation AI that adjusts the order of designs based on the relevance of the clothes when generating designs.

[0086] The generation unit can adjust the use of design terminology according to the user's level of expertise when generating a design. For example, the generation unit can adjust the use of design terminology according to the user's level of expertise when generating a design. For example, the generation unit can provide a design description that uses a lot of technical terms to a user who is knowledgeable about fashion. The generation unit can also provide a design description in simple language to a user who is not knowledgeable about fashion. For example, the generation unit can adjust the use of optimal technical terms based on the user's past selection history. This allows for the provision of a more understandable design by adjusting the use of design terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can adjust the use of technical terms using a generation AI that adjusts the use of design terminology according to the user's level of expertise when generating a design.

[0087] The search unit can analyze a user's emotions and adjust search criteria based on the analyzed user emotions. The search unit, for example, estimates the user's emotions and adjusts the search criteria based on the estimated user emotions. For example, the search unit provides a wide range of search results when the user is relaxed. The search unit can also prioritize the most relevant search results when the user is in a hurry. For example, the search unit provides simple, highly visible search results when the user is stressed. This allows for adjusting the search criteria based on the user's emotions to provide more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can adjust the search criteria using AI that estimates the user's emotions and adjusts the search criteria based on the estimated user emotions.

[0088] The search unit can improve search accuracy by taking into account the interrelationships between clothes during a search. The search unit can improve search accuracy by taking into account the interrelationships between clothes during a search. For example, the search unit can prioritize searching for clothes of the same brand. The search unit can also prioritize searching for clothes of the same style. For example, the search unit can prioritize searching for clothes of the same color. By taking into account the interrelationships between clothes, more accurate search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can improve search accuracy by using AI that takes into account the interrelationships between clothes during a search.

[0089] The search unit can perform a search taking into account attribute information of the person who submitted the clothing. For example, the search unit can perform a search taking into account attribute information of the person who submitted the clothing. For example, if the person who submitted the clothing is a professional designer, the search unit can prioritize searching for designs by that person. Furthermore, if the person who submitted the clothing is a popular brand, the search unit can also prioritize searching for designs by that person. For example, if the person who submitted the clothing matches the user's preferences, the search unit can prioritize searching for designs by that person. In this way, by taking into account the attribute information of the person who submitted the clothing, more relevant search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account attribute information of the person who submitted the clothing.

[0090] The search unit can weight the search based on the frequency of submission of clothing during a search. The search unit, for example, weights the search based on the frequency of submission of clothing during a search. For example, the search unit prioritizes searches for designs that are submitted more frequently. The search unit can also search for designs that are submitted less frequently later. For example, the search unit adjusts the display order of search results based on the submission frequency. By weighting the search based on the frequency of submission of clothing in this way, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can weight the search using AI that weights the search based on the frequency of submission of clothing during a search.

[0091] The search unit can analyze a user's emotions and adjust the order in which search results are displayed based on the analyzed user's emotions. The search unit, for example, estimates the user's emotions and adjusts the order in which search results are displayed based on the estimated user's emotions. For example, if the user is relaxed, the search unit provides a wide range of search results and displays them in a random order. The search unit can also prioritize displaying the most relevant search results if the user is in a hurry. For example, if the user is stressed, the search unit prioritizes displaying simple and highly visible search results. This allows for adjusting the order in which search results are displayed based on the user's emotions to provide more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or without AI. For example, the search unit can adjust the order in which search results are displayed using AI that estimates the user's emotions and adjusts the order in which search results are displayed based on the estimated user's emotions.

[0092] The search unit can perform a search taking into account the geographical distribution of clothes. For example, the search unit can perform a search taking into account the geographical distribution of clothes. For example, the search unit can prioritize searching for clothes in stores close to the user's current location. Furthermore, if the user is searching at a travel destination, the search unit can also prioritize searching for clothes in stores in that area. For example, if the user prefers a fashion style from a specific area, the search unit can prioritize searching for clothes from that area. In this way, by taking the geographical distribution of clothes into account, more relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account the geographical distribution of clothes.

[0093] The search unit can improve search accuracy by referring to literature related to clothing during a search. For example, the search unit can improve search accuracy by referring to literature related to clothing during a search. For example, the search unit can adjust search results based on articles in related fashion magazines. The search unit can also scrutinize search results by referring to related academic papers. For example, the search unit can adjust search results based on related online reviews. By referring to literature related to clothing, more accurate search results can be provided. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can improve search accuracy by using AI to improve search accuracy by referring to literature related to clothing during a search.

[0094] The search unit can perform a search taking into account the market value of the clothes. For example, the search unit can perform a search taking into account the market value of the clothes. For example, the search unit can prioritize searching for expensive clothes. The search unit can also prioritize searching for affordable clothes. For example, the search unit can adjust the display order of search results based on the market value. This makes it possible to provide more appropriate search results by taking into account the market value of the clothes. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can perform a search using AI that performs a search taking into account the market value of the clothes.

[0095] The providing unit can analyze the user's emotions and adjust the display method of the information to be provided based on the analyzed user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the display method of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a concise display method that focuses on the main points. For example, if the user is feeling stressed, the providing unit provides a simple display method with high visibility. This allows for adjusting the display method of the information to be provided based on the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can adjust the display method of the information to be provided using an AI that estimates the user's emotions and adjusts the display method of the information to be provided based on the estimated user's emotions.

[0096] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit can provide the optimal display method based on display methods that the user has preferred in the past. The providing unit can also provide the optimal display method by eliminating display methods that the user has avoided in the past. For example, the providing unit can analyze the user's past operation history and suggest the optimal display method. In this way, by referring to the user's past operation history, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can select the display method by using AI that selects the optimal display method by referring to the user's past operation history when providing the display.

[0097] The providing unit can customize the display content according to the user's current task at the time of providing. The providing unit, for example, customizes the display content according to the user's current task at the time of providing. For example, when the user is shopping, the providing unit can prioritize displaying information about related products. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information about travel destinations. For example, when the user is at work, the providing unit can prioritize displaying information related to work. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using AI that customizes the display content according to the user's current task at the time of providing.

[0098] The providing unit can analyze the user's emotions and adjust the operation procedures for the information to be provided based on the analyzed user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the operation procedures for the information to be provided based on the estimated user's emotions. For example, the providing unit provides detailed operation procedures when the user is relaxed. The providing unit can also provide concise operation procedures when the user is in a hurry. For example, the providing unit provides simple and intuitive operation procedures when the user is stressed. This allows for adjusting the operation procedures for the information to be provided based on the user's emotions, thereby providing more appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can adjust the operation procedures using an AI that estimates the user's emotions and adjusts the operation procedures for the information to be provided based on the estimated user's emotions.

[0099] The providing unit can select the optimal display method by taking into account the user's device information at the time of providing. For example, the providing unit selects the optimal display method by taking into account the user's device information at the time of providing. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide a more appropriate display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method by using AI that selects the optimal display method by taking into account the user's device information at the time of providing.

[0100] The providing unit can make the display content multilingual according to the user's language setting at the time of providing. The providing unit, for example, can make the display content multilingual according to the user's language setting at the time of providing. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual according to the user's language setting at the time of providing.

[0101] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide related information at the time of providing the information using AI that analyzes the user's social media activity and provides related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, search unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives text input from a user via the reception device 38 of the smart device 14. The generation unit generates clothing designs using a generation AI via the specific processing unit 290 of the data processing device 12. The search unit searches the database of an online shop via the specific processing unit 290 of the data processing device 12 and suggests similar clothing. The provision unit provides information about the searched clothing to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, search unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input from the user via the microphone 238 of the smart glasses 214. The generation unit generates clothing designs using a generation AI via the specific processing unit 290 of the data processing device 12. The search unit searches the database of an online shop via the specific processing unit 290 of the data processing device 12 and suggests similar clothing. The provision unit provides information about the searched clothing to the user via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, search unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user via the microphone 238 of the headset type terminal 314. The generation unit generates clothing designs using a generation AI by the specific processing unit 290 of the data processing device 12. The search unit searches the database of an online shop by the specific processing unit 290 of the data processing device 12 and suggests similar clothing. The provision unit provides information about the searched clothing to the user via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user via the microphone 238 of the robot 414. The generation unit generates clothing designs using a generation AI by the specific processing unit 290 of the data processing device 12. The search unit searches the database of an online shop by the specific processing unit 290 of the data processing device 12 and suggests similar clothing. The provision unit provides information about the clothing searched for by the speaker 240 of the robot 414 to the user.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can analyze the user's past purchase history and understand the user's design preferences. For example, the reception unit can suggest related designs based on the color and style of clothes the user has previously purchased. If the user prefers a particular brand, the reception unit can also preferentially suggest new designs from that brand. Furthermore, the reception unit can suggest new designs by eliminating design features that the user has avoided in the past. This makes it possible to make more personalized design suggestions by utilizing the user's past purchase history.

[0104] The generation unit can analyze the user's emotions and adjust the color tone of the design based on the analyzed user's emotions. For example, the generation unit can generate a design with soft colors when the user is relaxed. The generation unit can also generate a design with bright colors when the user is excited. Furthermore, the generation unit can also generate a design with subdued colors when the user is stressed. In this way, by adjusting the color tone of the design based on the user's emotions, a more appropriate design can be provided.

[0105] The search unit can prioritize searching for clothes that can be purchased at nearby stores based on the user's current location information. For example, if the user is in a specific area, the search unit can prioritize suggesting clothes that are available at stores in that area. Also, if the user is traveling, the search unit can suggest clothes that can be purchased at stores in the user's travel destination. Furthermore, if the user is attending a specific event, the search unit can suggest clothes that are suitable for that event. This makes it possible to provide more relevant search results by utilizing the user's location information.

[0106] The providing unit can analyze the user's emotions and adjust the display format of the information to be provided based on the analyzed user's emotions. For example, if the user is relaxed, the providing unit can provide a display format including detailed information. If the user is in a hurry, the providing unit can also provide a concise display format that focuses on the main points. Furthermore, if the user is feeling stressed, the providing unit can also provide a simple, highly visible display format. In this way, by adjusting the display format of the information to be provided based on the user's emotions, more appropriate information can be provided.

[0107] The generation unit can improve the accuracy of the design based on the user's past design selection history. For example, the generation unit generates a new design based on the features of designs that the user has previously preferred. The generation unit can also generate a new design by eliminating the features of designs that the user has previously avoided. Furthermore, the generation unit can analyze the user's past design selection history and propose an optimal design. In this way, the user's past design selection history can be utilized to improve the accuracy of the design.

[0108] The reception unit can analyze the user's emotions and customize the text input interface based on the analyzed user's emotions. For example, the reception unit can provide a simple and intuitive interface when the user is feeling stressed. The reception unit can also provide detailed input options when the user is relaxed. Furthermore, the reception unit can prioritize voice input when the user is in a hurry, allowing the user to quickly input clothing characteristics. In this way, a more appropriate input environment can be provided by customizing the text input interface according to the user's emotions.

[0109] The search unit can analyze the user's past search history and provide optimal search results. For example, the search unit can prioritize relevant search results based on the characteristics of clothing that the user has previously searched for. The search unit can also provide new search results by eliminating search results that the user has avoided in the past. Furthermore, the search unit can analyze the user's past search history and apply an optimal search algorithm. This makes it possible to provide more accurate search results by utilizing the user's past search history.

[0110] The providing unit can analyze the user's emotions and determine the priority of information to be provided based on the analyzed user's emotions. For example, when the user is excited, the providing unit can prioritize displaying important information. When the user is relaxed, the providing unit can also prioritize displaying detailed information. Furthermore, when the user is feeling stressed, the providing unit can also prioritize displaying concise, to-the-point information. In this way, by determining the priority of information to be provided based on the user's emotions, more appropriate information can be provided.

[0111] The generation unit can adjust the design by taking into account the user's current fashion trends when generating the design. For example, the generation unit can generate a related design based on the style of clothes recently purchased by the user. The generation unit can also refer to the style of a fashion influencer the user follows on social media. Furthermore, the generation unit can generate a related design based on fashion items the user has searched for in the past. This makes it possible to provide a more appropriate design by taking into account the user's current fashion trends.

[0112] The providing unit can analyze the user's emotions and adjust the display method of the information to be provided based on the analyzed user's emotions. For example, when the user is relaxed, the providing unit can provide a display method including detailed information. When the user is in a hurry, the providing unit can also provide a concise display method that focuses on the main points. Furthermore, when the user is feeling stressed, the providing unit can also provide a simple display method with high visibility. In this way, by adjusting the display method of the information to be provided based on the user's emotions, more appropriate information can be provided.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives text from the user that includes the characteristics of the clothing. For example, the user might enter text such as, "I want clothing with a specific color, shape, and style." This information is then input into the generation AI. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate a clothing design. The generation AI analyzes the text using natural language processing technology and extracts the characteristics of the clothing desired by the user. For example, the generation AI uses a text generation AI (e.g., LLM) to extract the characteristics of the clothing desired by the user and generate a design by combining elements such as color, shape, and style. The generation unit can also use a multimodal generation AI to generate clothing designs. Step 3: The search unit searches for similar clothes based on the design generated by the generation unit. For example, the search unit searches a database of an online shop based on the generated design and suggests similar clothes. Step 4: The providing unit provides the clothes searched for by the searching unit to the user. For example, the providing unit provides information about the searched clothes to the user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 receiving unit that receives text including clothing characteristics from a user; a generation unit that analyzes the text received by the reception unit and generates a clothing design; a search unit that searches for similar clothes based on the design generated by the generation unit; a providing unit that provides the clothes searched for by the searching unit to the user. A system characterized by:

2. The generation unit Generative AI extracts the characteristics of the clothes the user desires and generates a design by combining elements of color, shape, and style.

2. The system of claim 1.

3. The search unit Based on the generated design, it searches the online shop database and suggests similar clothes.

2. The system of claim 1.

4. The providing unit Provide users with information about the clothes they searched for 2. The system of claim 1.

5. The generation unit Generate original clothing designs based on the user's preferences and provide information for creating clothing based on those designs.

2. The system of claim 1.

6. The reception unit Analyze user emotions and adjust the timing of text input based on the analyzed user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past text input history and select the appropriate input method 2. The system of claim 1.

8. The reception unit As you type, filter based on your current fashion trends or interests 2. The system of claim 1.

Images