system

Abstract

The system according to this embodiment aims to make it easier for users to find desired products through image search based on their image. [Solution] The system according to the embodiment comprises an image input unit, an image generation unit, and an image search unit. The image input unit receives an image from the user. The image generation unit generates an image based on the image input by the image input unit. The image search unit performs a search using the image generated by the image generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to find a desired product in a search within an EC site.

[0005] The system according to the embodiment aims to make it easier for a user to find a desired product through image search based on the user's image.

Means for Solving the Problems

[0006] The system according to the embodiment includes an image input unit, an image generation unit, and an image search unit. The image input unit inputs a user's image. The image generation unit generates an image based on the image input by the image input unit. The image search unit performs a search using the image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to this embodiment makes it easier for users to find the products they want through image search based on their image. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The e-commerce site support system according to an embodiment of the present invention is a system designed to make it easier for users to find the products they want. This system generates an image from the user's image using an image generation AI, and then performs an image search using the generated image, enabling users to quickly and accurately find the products they are looking for. For example, when a user inputs an image as text or a sketch, the system generates an image based on that image using an image generation AI. The generated image is then searched using an image search engine, and related products are displayed. This allows users to find the product that best matches their image. Furthermore, the generated image can also be used as a new product design and sent to the design department to expedite the development of new products. For example, the generated image is sent to the design department and commercialized as a new product. This allows the e-commerce site to respond quickly to user needs and improve the efficiency of product development. As a result, the e-commerce site support system enables users to quickly and accurately find the products they want. Furthermore, by utilizing the generated image as a new product design, the efficiency of product development can be improved.

[0029] The e-commerce site support system according to this embodiment comprises an image input unit, an image generation unit, and an image search unit. The image input unit receives images from the user as input. User images include, but are not limited to, text, sketches, and photographs. The image input unit accepts, for example, text input. When a user inputs an image as text, the image input unit analyzes the text and transmits it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and transmits it to the image generation unit. The image generation unit generates images based on the images input by the image input unit using a generation AI. The generation AI generates images using, for example, technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images include, for example, high-resolution images and images of various styles, but are not limited to these examples. The image generation unit transmits the generated images to the image search unit. The image search unit performs an image search using the generated images. The image search is performed by, for example, similar image search or content-based image search, but is not limited to these examples. The image search unit takes the generated image as input and searches for related products. For example, the image search unit searches for product images similar to the generated image and displays the search results. This allows the user to find the product that best matches their image. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can take the generated image as input and display the search results using an AI model that performs similar image searches. This allows the e-commerce site support system according to the embodiment to quickly and accurately find the product the user wants.

[0030] The image input unit accepts user images as input. These images may include, but are not limited to, text, sketches, and photographs. For example, the image input unit accepts text input. When a user inputs an image as text, the image input unit analyzes the text and sends it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and sends it to the image generation unit. The image input unit provides an intuitive user interface. For example, with text input, users can easily input images using a keyboard or voice input. With voice input, speech recognition technology is used to convert the text and perform analysis. With sketch input, users can use a touchscreen or pen tablet to capture freely drawn sketches as digital data. Furthermore, with photo input, the unit provides a function to upload images from a camera or existing image files, allowing users to input specific images they have. The image input unit integrates these diverse input methods, enabling flexible responses to user needs. For example, if a user enters "blue floral dress" as text, the system analyzes the text using natural language processing technology to extract keywords and features. Similarly, for sketches and photographs, image analysis technology is used to extract features, which are then sent to the image generation unit. This allows the image input unit to accurately capture the user's diverse images and appropriately pass them on to the next processing step.

[0031] The image generation unit uses generative AI to generate images based on images input by the image input unit. The generative AI generates images using technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images may include, but are not limited to, high-resolution images or images of various styles. Specifically, GAN generates realistic images by having two neural networks, a generator and a discriminator, compete. The generator generates an image from random noise, and the discriminator determines whether the image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. On the other hand, VAE generates images by encoding input data into a latent space and decoding new data from that latent space. This allows VAE to generate images of various styles. The image generation unit utilizes these generative AI technologies to generate an image that is closest to the user's image. For example, if the user inputs the text "blue floral dress," the generative AI captures the characteristics of that text and generates an image of a blue floral dress. In addition, for sketches and photographs, the system generates realistic images based on the input image. The generated images are required to be faithful to the user's image and of high quality. The image generation unit sends the generated image to the image search unit, passing it on to the next processing step. In this way, the image generation unit converts the user's image into a concrete form, supporting product searches on e-commerce sites.

[0032] The image search unit performs image searches using the generated images. Image searches are performed using methods such as similar image searches and content-based image searches, but are not limited to these examples. The image search unit takes the generated images as input and searches for related products. For example, the image search unit searches for product images similar to the generated image and displays the search results. Specifically, in similar image searches, the features of the generated image are extracted and compared with existing images in the database. Features include color, shape, texture, etc., and the similarity is calculated by comprehensively evaluating these. In content-based image searches, related products are searched based on the content of the generated image. For example, if the generated image is a "blue floral dress," the system searches the database for products that match those features. The image search unit combines these search methods to quickly find the most relevant products. Furthermore, the image search unit can improve search accuracy using AI. For example, it can utilize an image recognition model using deep learning to extract features of the generated image with high accuracy. This improves the accuracy of search results, allowing users to find the products they are looking for more quickly. Search results are displayed to users in a visually easy-to-understand format, including product images, prices, and detailed information. This allows users to find products that best match their expectations, improving their shopping experience on e-commerce sites. The image search unit continuously improves its search algorithm based on user feedback, enabling it to provide more accurate search results.

[0033] The image generation unit can generate images using generative AI. Generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the image generation unit can generate images using a GAN. A GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network distinguishes between the generated image and the actual image. This allows the generative network to generate realistic images that deceive the discriminative network. The image generation unit can also generate images using a VAE. A VAE is a type of autoencoder that converts input data into latent variables and generates new data from those latent variables. This allows the VAE to generate images in various styles. Furthermore, the image generation unit can also generate images based on the user's image using generative AI. For example, the image generation unit can generate an image based on text or a sketch entered by the user. This improves the accuracy of image generation by using generative AI. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input an image entered by the user into the generation AI, which can then generate an image.

[0034] The image search unit can perform image searches using the generated images. Image searches include, but are not limited to, similar image searches and content-based image searches. For example, the image search unit can perform image searches using similar image searches. Similar image searches are methods for searching for images similar to the generated images, for example, by vectorizing the features of the images and calculating the distance between those vectors to evaluate similarity. This allows the image search unit to search for product images similar to the generated images and display the search results. The image search unit can also perform image searches using content-based image searches. Content-based image searches are methods for searching based on the content of the images, for example, by analyzing features such as the color, shape, and texture of the images and performing searches based on those. This allows the image search unit to search for related products based on the content of the generated images and display the search results. Furthermore, the image search unit can use the generated images to quickly and accurately find the products that the user is looking for. For example, the image search unit takes the generated images as input and searches for related products. This improves the accuracy of the search results by using the generated images. Some or all of the processing described above in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can take the generated images as input and use an AI model to perform similar image searches to display the search results.

[0035] The image input unit can accept either text input or sketch input. For example, the image input unit accepts text input. When a user inputs an image as text, the image input unit analyzes the text and sends it to the image generation unit. Text input includes, but is not limited to, input methods using natural language processing techniques. Natural language processing techniques are technologies that analyze text data and understand its content, including, for example, keyword extraction and contextual analysis. This allows the image input unit to accurately analyze the text entered by the user and send it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and sends it to the image generation unit. Sketch input includes, but is not limited to, handwritten input and digital pen input. Handwritten input is a method of scanning a sketch drawn by the user on paper and converting it into digital data, while digital pen input is a method of directly inputting a sketch as digital data using a digital pen. This allows the image input unit to accept a variety of input methods, improving user convenience. Some or all of the processing described above in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can use an AI model to analyze text or sketches entered by the user and send them to the image generation unit.

[0036] The e-commerce support system includes a design utilization unit that uses the generated images as new product designs. The design utilization unit uses the generated images as new product designs. The generated images include, but are not limited to, new designs for clothing, accessories, furniture, etc. The design utilization unit sends the generated images to the design department for commercialization as new products. The design department evaluates the new product designs based on the generated images and makes improvements as necessary. For example, the design department creates a prototype of the new product based on the generated images and evaluates its design. The design department can also plan the manufacturing process for the new product based on the generated images. This improves the efficiency of product development by allowing the design utilization unit to use the generated images as new product designs. Some or all of the above processes in the design utilization unit may be performed using, for example, AI, or not using AI. For example, the design utilization unit can evaluate the new product designs using an AI model that sends the generated images to the design department. This improves the efficiency of product development by allowing the e-commerce support system to use the generated images as new product designs.

[0037] The design utilization unit can send the generated images to the design department, which can then commercialize them as new products. The design utilization unit sends the generated images to the design department. The design department evaluates the design of the new product based on the generated images and makes improvements as needed. For example, the design department can create a prototype of the new product based on the generated images and evaluate its design. The design department can also plan the manufacturing process for the new product based on the generated images. This allows the design utilization unit to quickly commercialize new products by sending the generated images to the design department. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or not. For example, the design utilization unit can evaluate the design of the new product using an AI model that sends the generated images to the design department. This allows the e-commerce site support system to quickly commercialize new products by sending the generated images to the design department.

[0038] The image input unit can analyze the user's past search history and suggest the optimal image input method. For example, the image input unit can suggest relevant image input methods based on keywords the user has frequently searched for in the past. It can also prioritize suggesting input methods the user has used in the past (text, sketch, etc.). Furthermore, it can predict and suggest image input methods to be used at specific times based on the user's past search history. This improves input efficiency by suggesting the optimal input method based on past search history. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's past search history into AI and have the AI ​​suggest the optimal image input method.

[0039] The image input unit can present input candidates based on the user's current areas of interest when an image is input. For example, the image input unit can present relevant image input candidates based on product categories the user has recently searched for. It can also present image input candidates based on trends and fashions that the user is interested in. Furthermore, it can present relevant image input candidates based on the content of product pages the user has viewed. This improves the accuracy of input by presenting input candidates based on the user's current areas of interest. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's current areas of interest into AI and have AI perform the task of presenting input candidates.

[0040] The image input unit can prioritize inputting images that are highly relevant to the user's geographical location when an image is being input. For example, if the user is in a specific region, the image input unit will prioritize inputting images related to that region. If the user is traveling, it can also prioritize inputting images related to the travel destination. Furthermore, if the user is participating in a specific event, it can also prioritize inputting images related to that event. In this way, by considering geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's geographical location information into AI and have the AI ​​input highly relevant images.

[0041] The image input unit can analyze the user's social media activity and input relevant images when an image is input. For example, the image input unit can input relevant images based on images the user has shared on social media. It can also input relevant images based on posts the user has "liked" on social media. Furthermore, it can input relevant images based on posts from accounts the user follows on social media. In this way, relevant images can be input by analyzing social media activity. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's social media activity into AI and have AI perform the input of relevant images.

[0042] The image generation unit can select the optimal image generation algorithm by referring to the user's past search history when generating an image. For example, the image generation unit can select a relevant image generation algorithm based on keywords the user has searched for in the past. It can also prioritize the selection of image generation algorithms that the user has used in the past. Furthermore, it can predict and select an image generation algorithm to be used at a specific time period based on the user's past search history. In this way, the optimal image generation algorithm can be selected by referring to past search history. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's past search history into AI and have the AI ​​select the optimal image generation algorithm.

[0043] The image generation unit can determine the theme of the image to be generated based on the user's current areas of interest during image generation. For example, the image generation unit can determine the theme of relevant images based on the product categories the user has recently searched for. It can also determine the theme of images based on trends and fashions that the user is interested in. Furthermore, it can determine the theme of relevant images based on the content of product pages the user has viewed. This allows for the generation of more relevant images by determining the theme of images based on the user's current areas of interest. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the determination of the image theme.

[0044] The image generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific region, the image generation unit can generate images related to that region. It can also generate images related to the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can generate images related to that event. This allows for the generation of highly relevant images by considering geographical location information. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's geographical location information into the AI ​​and have the AI ​​generate highly relevant images.

[0045] The image generation unit can analyze the user's social media activity and generate relevant images during image generation. For example, the image generation unit can generate relevant images based on images shared by the user on social media. It can also generate relevant images based on posts that the user has "liked" on social media. Furthermore, it can generate relevant images based on posts from accounts that the user follows on social media. In this way, relevant images can be generated by analyzing social media activity. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's social media activity into AI and have AI perform the generation of relevant images.

[0046] The image search unit can select the optimal search algorithm by referring to the user's past search history during an image search. For example, the image search unit can select relevant search algorithms based on keywords the user has searched for in the past. It can also prioritize the selection of search algorithms the user has used in the past. Furthermore, it can predict and select a search algorithm to be used at a specific time period based on the user's past search history. In this way, the optimal search algorithm can be selected by referring to past search history. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's past search history into AI and have the AI ​​perform the selection of the optimal search algorithm.

[0047] The image search unit can filter search results based on the user's current areas of interest during an image search. For example, the image search unit can filter relevant search results based on product categories the user has recently searched for. It can also filter search results based on trends and fads the user is interested in. Furthermore, it can filter relevant search results based on the content of product pages the user has viewed. This allows for the provision of more relevant search results by filtering them based on the user's current areas of interest. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the filtering of search results.

[0048] The image search unit can prioritize displaying highly relevant search results by considering the user's geographical location during an image search. For example, if the user is in a specific region, the image search unit can prioritize displaying search results related to that region. It can also prioritize displaying search results related to the user's travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can prioritize displaying search results related to that event. This allows for the prioritization of highly relevant search results by considering geographical location. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's geographical location information into the AI ​​and have the AI ​​display highly relevant search results.

[0049] The image search unit can analyze a user's social media activity during an image search and display relevant search results. For example, the image search unit can display relevant search results based on images shared by the user on social media. It can also display relevant search results based on posts that the user has "liked" on social media. Furthermore, it can display relevant search results based on posts from accounts that the user follows on social media. In this way, relevant search results can be displayed by analyzing social media activity. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's social media activity into AI and have AI perform the display of relevant search results.

[0050] The design utilization unit can select the optimal design utilization method by referring to the user's past design history when a design is used. For example, the design utilization unit can suggest relevant design utilization methods based on the design styles the user has used in the past. It can also select the optimal design utilization method by referring to designs the user has created in the past. Furthermore, it can predict and suggest design utilization methods to be used at a specific time period based on the user's past design history. In this way, the optimal design utilization method can be selected by referring to past design history. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's past design history into AI and have the AI ​​select the optimal design utilization method.

[0051] The design utilization unit can determine a design theme based on the user's current areas of interest when using a design. For example, the design utilization unit can determine a relevant design theme based on the product categories the user has recently searched for. It can also determine a design theme based on trends and fashions the user is interested in. Furthermore, it can determine a relevant design theme based on the content of product pages the user has viewed. This allows for the provision of more relevant designs by determining the design theme based on the user's current areas of interest. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's current areas of interest into AI and have the AI ​​determine the design theme.

[0052] The design utilization unit can prioritize the use of highly relevant designs by considering the user's geographical location information when using designs. For example, if the user is in a specific region, the design utilization unit can prioritize designs related to that region. It can also prioritize designs related to the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can prioritize designs related to that event. This allows for the priority of highly relevant designs by considering geographical location information. Some or all of the above processing in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's geographical location information into AI and have the AI ​​perform the task of selecting highly relevant designs.

[0053] The design utilization unit can analyze a user's social media activity and utilize relevant designs when a design is used. For example, the design utilization unit can utilize relevant designs based on images shared by the user on social media. It can also utilize relevant designs based on posts that the user has "liked" on social media. Furthermore, it can utilize relevant designs based on posts from accounts that the user follows on social media. In this way, relevant designs can be utilized by analyzing social media activity. Some or all of the above processing in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's social media activity into AI and have the AI ​​perform the utilization of relevant designs.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The e-commerce site support system can also be equipped with a real-time trend analysis unit. This unit analyzes current market trends in real time and makes product recommendations based on the results. For example, it prioritizes suggesting currently popular product categories and designs. Furthermore, the real-time trend analysis unit can also consider the user's past search and purchase history to provide individually optimized recommendations. This ensures that product recommendations are based on the latest market trends, improving user satisfaction.

[0056] The e-commerce site support system can also include a seasonal event support section. This section provides product suggestions tailored to the season or specific events. For example, it suggests products for events such as Christmas or Valentine's Day. Furthermore, the seasonal event support section can consider the user's past purchase history and prioritize suggesting related products. This results in product suggestions that are relevant to the season and events, thereby increasing the user's purchasing intent.

[0057] The e-commerce site support system can also include a personalized advertising section. This section displays individually optimized ads based on the user's past search and purchase history. For example, it prioritizes displaying ads related to product categories the user has previously shown interest in. Furthermore, the personalized advertising section can analyze the user's current areas of interest in real time and display relevant ads. This ensures that users see ads that are more relevant to them, improving advertising effectiveness.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The image input unit receives the user's image. The user's image can include text, sketches, photographs, etc. The image input unit accepts text and sketch input, analyzes the input image, and sends it to the image generation unit. Step 2: The image generation unit uses a generation AI to generate images based on the images input by the image input unit. The generation AI employs technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images may include, for example, high-resolution images and images in various styles. Step 3: The image search unit performs an image search using the generated images. The image search is performed using methods such as similar image search or content-based image search. The image search unit takes the generated images as input, searches for related products, and displays the search results.

[0060] (Example of form 2) The e-commerce site support system according to an embodiment of the present invention is a system designed to make it easier for users to find the products they want. This system generates an image from the user's image using an image generation AI, and then performs an image search using the generated image, enabling users to quickly and accurately find the products they are looking for. For example, when a user inputs an image as text or a sketch, the system generates an image based on that image using an image generation AI. The generated image is then searched using an image search engine, and related products are displayed. This allows users to find the product that best matches their image. Furthermore, the generated image can also be used as a new product design and sent to the design department to expedite the development of new products. For example, the generated image is sent to the design department and commercialized as a new product. This allows the e-commerce site to respond quickly to user needs and improve the efficiency of product development. As a result, the e-commerce site support system enables users to quickly and accurately find the products they want. Furthermore, by utilizing the generated image as a new product design, the efficiency of product development can be improved.

[0061] The e-commerce site support system according to this embodiment comprises an image input unit, an image generation unit, and an image search unit. The image input unit receives images from the user as input. User images include, but are not limited to, text, sketches, and photographs. The image input unit accepts, for example, text input. When a user inputs an image as text, the image input unit analyzes the text and transmits it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and transmits it to the image generation unit. The image generation unit generates images based on the images input by the image input unit using a generation AI. The generation AI generates images using, for example, technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images include, for example, high-resolution images and images of various styles, but are not limited to these examples. The image generation unit transmits the generated images to the image search unit. The image search unit performs an image search using the generated images. The image search is performed by, for example, similar image search or content-based image search, but is not limited to these examples. The image search unit takes the generated image as input and searches for related products. For example, the image search unit searches for product images similar to the generated image and displays the search results. This allows the user to find the product that best matches their image. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can take the generated image as input and display the search results using an AI model that performs similar image searches. This allows the e-commerce site support system according to the embodiment to quickly and accurately find the product the user wants.

[0062] The image input unit accepts user images as input. These images may include, but are not limited to, text, sketches, and photographs. For example, the image input unit accepts text input. When a user inputs an image as text, the image input unit analyzes the text and sends it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and sends it to the image generation unit. The image input unit provides an intuitive user interface. For example, with text input, users can easily input images using a keyboard or voice input. With voice input, speech recognition technology is used to convert the text and perform analysis. With sketch input, users can use a touchscreen or pen tablet to capture freely drawn sketches as digital data. Furthermore, with photo input, the unit provides a function to upload images from a camera or existing image files, allowing users to input specific images they have. The image input unit integrates these diverse input methods, enabling flexible responses to user needs. For example, if a user enters "blue floral dress" as text, the system analyzes the text using natural language processing technology to extract keywords and features. Similarly, for sketches and photographs, image analysis technology is used to extract features, which are then sent to the image generation unit. This allows the image input unit to accurately capture the user's diverse images and appropriately pass them on to the next processing step.

[0063] The image generation unit uses generative AI to generate images based on images input by the image input unit. The generative AI generates images using technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images may include, but are not limited to, high-resolution images or images of various styles. Specifically, GAN generates realistic images by having two neural networks, a generator and a discriminator, compete. The generator generates an image from random noise, and the discriminator determines whether the image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. On the other hand, VAE generates images by encoding input data into a latent space and decoding new data from that latent space. This allows VAE to generate images of various styles. The image generation unit utilizes these generative AI technologies to generate an image that is closest to the user's image. For example, if the user inputs the text "blue floral dress," the generative AI captures the characteristics of that text and generates an image of a blue floral dress. In addition, for sketches and photographs, the system generates realistic images based on the input image. The generated images are required to be faithful to the user's image and of high quality. The image generation unit sends the generated image to the image search unit, passing it on to the next processing step. In this way, the image generation unit converts the user's image into a concrete form, supporting product searches on e-commerce sites.

[0064] The image search unit performs image searches using the generated images. Image searches are performed using methods such as similar image searches and content-based image searches, but are not limited to these examples. The image search unit takes the generated images as input and searches for related products. For example, the image search unit searches for product images similar to the generated image and displays the search results. Specifically, in similar image searches, the features of the generated image are extracted and compared with existing images in the database. Features include color, shape, texture, etc., and the similarity is calculated by comprehensively evaluating these. In content-based image searches, related products are searched based on the content of the generated image. For example, if the generated image is a "blue floral dress," the system searches the database for products that match those features. The image search unit combines these search methods to quickly find the most relevant products. Furthermore, the image search unit can improve search accuracy using AI. For example, it can utilize an image recognition model using deep learning to extract features of the generated image with high accuracy. This improves the accuracy of search results, allowing users to find the products they are looking for more quickly. Search results are displayed to users in a visually easy-to-understand format, including product images, prices, and detailed information. This allows users to find products that best match their expectations, improving their shopping experience on e-commerce sites. The image search unit continuously improves its search algorithm based on user feedback, enabling it to provide more accurate search results.

[0065] The image generation unit can generate images using generative AI. Generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the image generation unit can generate images using a GAN. A GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network distinguishes between the generated image and the actual image. This allows the generative network to generate realistic images that deceive the discriminative network. The image generation unit can also generate images using a VAE. A VAE is a type of autoencoder that converts input data into latent variables and generates new data from those latent variables. This allows the VAE to generate images in various styles. Furthermore, the image generation unit can also generate images based on the user's image using generative AI. For example, the image generation unit can generate an image based on text or a sketch entered by the user. This improves the accuracy of image generation by using generative AI. Some or all of the above-described processes in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input an image entered by the user into the generation AI, which can then generate an image.

[0066] The image search unit can perform image searches using the generated images. Image searches include, but are not limited to, similar image searches and content-based image searches. For example, the image search unit can perform image searches using similar image searches. Similar image searches are methods for searching for images similar to the generated images, for example, by vectorizing the features of the images and calculating the distance between those vectors to evaluate similarity. This allows the image search unit to search for product images similar to the generated images and display the search results. The image search unit can also perform image searches using content-based image searches. Content-based image searches are methods for searching based on the content of the images, for example, by analyzing features such as the color, shape, and texture of the images and performing searches based on those. This allows the image search unit to search for related products based on the content of the generated images and display the search results. Furthermore, the image search unit can use the generated images to quickly and accurately find the products that the user is looking for. For example, the image search unit takes the generated images as input and searches for related products. This improves the accuracy of the search results by using the generated images. Some or all of the processing described above in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can take the generated images as input and use an AI model to perform similar image searches to display the search results.

[0067] The image input unit can accept either text input or sketch input. For example, the image input unit accepts text input. When a user inputs an image as text, the image input unit analyzes the text and sends it to the image generation unit. Text input includes, but is not limited to, input methods using natural language processing techniques. Natural language processing techniques are technologies that analyze text data and understand its content, including, for example, keyword extraction and contextual analysis. This allows the image input unit to accurately analyze the text entered by the user and send it to the image generation unit. The image input unit can also accept sketch input. When a user inputs an image as a sketch, the image input unit analyzes the sketch and sends it to the image generation unit. Sketch input includes, but is not limited to, handwritten input and digital pen input. Handwritten input is a method of scanning a sketch drawn by the user on paper and converting it into digital data, while digital pen input is a method of directly inputting a sketch as digital data using a digital pen. This allows the image input unit to accept a variety of input methods, improving user convenience. Some or all of the processing described above in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can use an AI model to analyze text or sketches entered by the user and send them to the image generation unit.

[0068] The e-commerce support system includes a design utilization unit that uses the generated images as new product designs. The design utilization unit uses the generated images as new product designs. The generated images include, but are not limited to, new designs for clothing, accessories, furniture, etc. The design utilization unit sends the generated images to the design department for commercialization as new products. The design department evaluates the new product designs based on the generated images and makes improvements as necessary. For example, the design department creates a prototype of the new product based on the generated images and evaluates its design. The design department can also plan the manufacturing process for the new product based on the generated images. This improves the efficiency of product development by allowing the design utilization unit to use the generated images as new product designs. Some or all of the above processes in the design utilization unit may be performed using, for example, AI, or not using AI. For example, the design utilization unit can evaluate the new product designs using an AI model that sends the generated images to the design department. This improves the efficiency of product development by allowing the e-commerce support system to use the generated images as new product designs.

[0069] The design utilization unit can send the generated images to the design department, which can then commercialize them as new products. The design utilization unit sends the generated images to the design department. The design department evaluates the design of the new product based on the generated images and makes improvements as needed. For example, the design department can create a prototype of the new product based on the generated images and evaluate its design. The design department can also plan the manufacturing process for the new product based on the generated images. This allows the design utilization unit to quickly commercialize new products by sending the generated images to the design department. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or not. For example, the design utilization unit can evaluate the design of the new product using an AI model that sends the generated images to the design department. This allows the e-commerce site support system to quickly commercialize new products by sending the generated images to the design department.

[0070] The image input unit can estimate the user's emotions and adjust the image input interface based on the estimated emotions. For example, if the user is stressed, the image input unit can provide a simple interface and minimize the input steps. If the user is relaxed, it can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick image input. This allows for a more comfortable input experience by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image input unit may be performed using AI or not. For example, the image input unit can input user emotion data into the generative AI and have the generative AI perform emotion-based interface adjustments.

[0071] The image input unit can analyze the user's past search history and suggest the optimal image input method. For example, the image input unit can suggest relevant image input methods based on keywords the user has frequently searched for in the past. It can also prioritize suggesting input methods the user has used in the past (text, sketch, etc.). Furthermore, it can predict and suggest image input methods to be used at specific times based on the user's past search history. This improves input efficiency by suggesting the optimal input method based on past search history. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's past search history into AI and have the AI ​​suggest the optimal image input method.

[0072] The image input unit can present input candidates based on the user's current areas of interest when an image is input. For example, the image input unit can present relevant image input candidates based on product categories the user has recently searched for. It can also present image input candidates based on trends and fashions that the user is interested in. Furthermore, it can present relevant image input candidates based on the content of product pages the user has viewed. This improves the accuracy of input by presenting input candidates based on the user's current areas of interest. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's current areas of interest into AI and have AI perform the task of presenting input candidates.

[0073] The image input unit can estimate the user's emotions and determine the priority of input images based on the estimated emotions. For example, if the user is excited, the image input unit may prioritize displaying visually stimulating images. If the user is relaxed, it may also prioritize displaying images with calming colors. Furthermore, if the user is stressed, it may prioritize displaying simple and highly visible images. In this way, by prioritizing images based on the user's emotions, more appropriate images can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image input unit may be performed using AI, or not using AI. For example, the image input unit can input user emotion data into the generative AI and have the generative AI perform the determination of image priorities based on emotions.

[0074] The image input unit can prioritize inputting images that are highly relevant to the user's geographical location when an image is being input. For example, if the user is in a specific region, the image input unit will prioritize inputting images related to that region. If the user is traveling, it can also prioritize inputting images related to the travel destination. Furthermore, if the user is participating in a specific event, it can also prioritize inputting images related to that event. In this way, by considering geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's geographical location information into AI and have the AI ​​input highly relevant images.

[0075] The image input unit can analyze the user's social media activity and input relevant images when an image is input. For example, the image input unit can input relevant images based on images the user has shared on social media. It can also input relevant images based on posts the user has "liked" on social media. Furthermore, it can input relevant images based on posts from accounts the user follows on social media. In this way, relevant images can be input by analyzing social media activity. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's social media activity into AI and have AI perform the input of relevant images.

[0076] The image generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the image generation unit can generate images with soft tones. If the user is excited, it can also generate images with vibrant tones. Furthermore, if the user is stressed, it can generate images with a simple and calming style. By adjusting the image style based on the user's emotions, more appropriate images can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the image style based on the emotion.

[0077] The image generation unit can select the optimal image generation algorithm by referring to the user's past search history when generating an image. For example, the image generation unit can select a relevant image generation algorithm based on keywords the user has searched for in the past. It can also prioritize the selection of image generation algorithms that the user has used in the past. Furthermore, it can predict and select an image generation algorithm to be used at a specific time period based on the user's past search history. In this way, the optimal image generation algorithm can be selected by referring to past search history. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's past search history into AI and have the AI ​​select the optimal image generation algorithm.

[0078] The image generation unit can determine the theme of the image to be generated based on the user's current areas of interest during image generation. For example, the image generation unit can determine the theme of relevant images based on the product categories the user has recently searched for. It can also determine the theme of images based on trends and fashions that the user is interested in. Furthermore, it can determine the theme of relevant images based on the content of product pages the user has viewed. This allows for the generation of more relevant images by determining the theme of images based on the user's current areas of interest. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the determination of the image theme.

[0079] The image generation unit can estimate the user's emotions and adjust the color tone of the generated image based on the estimated user emotions. For example, if the user is relaxed, the image generation unit can generate an image with soft colors. If the user is excited, it can also generate an image with vibrant colors. Furthermore, if the user is stressed, it can generate an image with simple and calm colors. In this way, by adjusting the color tone of the image based on the user's emotions, a more appropriate image can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input user emotion data into the generation AI and have the generation AI perform emotion-based color tone adjustments.

[0080] The image generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific region, the image generation unit can generate images related to that region. It can also generate images related to the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can generate images related to that event. This allows for the generation of highly relevant images by considering geographical location information. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's geographical location information into the AI ​​and have the AI ​​generate highly relevant images.

[0081] The image generation unit can analyze the user's social media activity and generate relevant images during image generation. For example, the image generation unit can generate relevant images based on images shared by the user on social media. It can also generate relevant images based on posts that the user has "liked" on social media. Furthermore, it can generate relevant images based on posts from accounts that the user follows on social media. In this way, relevant images can be generated by analyzing social media activity. Some or all of the above processing in the image generation unit may be performed using AI, for example, or without AI. For example, the image generation unit can input the user's social media activity into AI and have AI perform the generation of relevant images.

[0082] The image search unit can estimate the user's emotions and adjust the display method of search results based on the estimated emotions. For example, if the user is nervous, the image search unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image search unit may be performed using AI, for example, or not using AI. For example, the image search unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of search results based on emotions.

[0083] The image search unit can select the optimal search algorithm by referring to the user's past search history during an image search. For example, the image search unit can select relevant search algorithms based on keywords the user has searched for in the past. It can also prioritize the selection of search algorithms the user has used in the past. Furthermore, it can predict and select a search algorithm to be used at a specific time period based on the user's past search history. In this way, the optimal search algorithm can be selected by referring to past search history. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's past search history into AI and have the AI ​​perform the selection of the optimal search algorithm.

[0084] The image search unit can filter search results based on the user's current areas of interest during an image search. For example, the image search unit can filter relevant search results based on product categories the user has recently searched for. It can also filter search results based on trends and fads the user is interested in. Furthermore, it can filter relevant search results based on the content of product pages the user has viewed. This allows for the provision of more relevant search results by filtering them based on the user's current areas of interest. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the filtering of search results.

[0085] The image search unit can estimate the user's emotions and determine the priority of search results based on the estimated emotions. For example, if the user is excited, the image search unit may prioritize displaying visually stimulating search results. If the user is relaxed, it may also prioritize displaying search results with calming colors. Furthermore, if the user is stressed, it may prioritize displaying simple and highly visible search results. This allows for the provision of more appropriate search results by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image search unit may be performed using AI, or not. For example, the image search unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based search result prioritization.

[0086] The image search unit can prioritize displaying highly relevant search results by considering the user's geographical location during an image search. For example, if the user is in a specific region, the image search unit can prioritize displaying search results related to that region. It can also prioritize displaying search results related to the user's travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can prioritize displaying search results related to that event. This allows for the prioritization of highly relevant search results by considering geographical location. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's geographical location information into the AI ​​and have the AI ​​display highly relevant search results.

[0087] The image search unit can analyze a user's social media activity during an image search and display relevant search results. For example, the image search unit can display relevant search results based on images shared by the user on social media. It can also display relevant search results based on posts that the user has "liked" on social media. Furthermore, it can display relevant search results based on posts from accounts that the user follows on social media. In this way, relevant search results can be displayed by analyzing social media activity. Some or all of the above processing in the image search unit may be performed using AI, for example, or without AI. For example, the image search unit can input the user's social media activity into AI and have AI perform the display of relevant search results.

[0088] The design utilization unit can estimate the user's emotions and adjust how the design is used based on those emotions. For example, if the user is relaxed, the design utilization unit can suggest a design with soft colors. If the user is excited, it can suggest a design with vibrant colors. Furthermore, if the user is stressed, it can suggest a simple and calming design. In this way, by adjusting how the design is used based on the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design utilization unit may be performed using AI, for example, or not using AI. For example, the design utilization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of how the design is used based on the emotion.

[0089] The design utilization unit can select the optimal design utilization method by referring to the user's past design history when a design is used. For example, the design utilization unit can suggest relevant design utilization methods based on the design styles the user has used in the past. It can also select the optimal design utilization method by referring to designs the user has created in the past. Furthermore, it can predict and suggest design utilization methods to be used at a specific time period based on the user's past design history. In this way, the optimal design utilization method can be selected by referring to past design history. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's past design history into AI and have the AI ​​select the optimal design utilization method.

[0090] The design utilization unit can determine a design theme based on the user's current areas of interest when using a design. For example, the design utilization unit can determine a relevant design theme based on the product categories the user has recently searched for. It can also determine a design theme based on trends and fashions the user is interested in. Furthermore, it can determine a relevant design theme based on the content of product pages the user has viewed. This allows for the provision of more relevant designs by determining the design theme based on the user's current areas of interest. Some or all of the above processes in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's current areas of interest into AI and have the AI ​​determine the design theme.

[0091] The design application unit can estimate the user's emotions and determine design priorities based on those emotions. For example, if the user is excited, the design application unit may prioritize displaying visually stimulating designs. If the user is relaxed, it may prioritize displaying designs with calming colors. Furthermore, if the user is stressed, it may prioritize displaying simple and highly visible designs. This allows for the provision of more appropriate designs by prioritizing designs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design application unit may be performed using AI, or not. For example, the design application unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based design priorities.

[0092] The design utilization unit can prioritize the use of highly relevant designs by considering the user's geographical location information when using designs. For example, if the user is in a specific region, the design utilization unit can prioritize designs related to that region. It can also prioritize designs related to the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, it can prioritize designs related to that event. This allows for the priority of highly relevant designs by considering geographical location information. Some or all of the above processing in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's geographical location information into AI and have the AI ​​perform the task of selecting highly relevant designs.

[0093] The design utilization unit can analyze a user's social media activity and utilize relevant designs when a design is used. For example, the design utilization unit can utilize relevant designs based on images shared by the user on social media. It can also utilize relevant designs based on posts that the user has "liked" on social media. Furthermore, it can utilize relevant designs based on posts from accounts that the user follows on social media. In this way, relevant designs can be utilized by analyzing social media activity. Some or all of the above processing in the design utilization unit may be performed using AI, for example, or without AI. For example, the design utilization unit can input the user's social media activity into AI and have the AI ​​perform the utilization of relevant designs.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The e-commerce site support system can also be equipped with a voice input unit. This unit allows users to input images using their voice. For example, if a user describes an image related to a product verbally, the voice input unit analyzes the voice and converts it into text data. The converted text data is then sent to the image generation unit and used as the basis for image generation. Furthermore, the voice input unit can estimate the user's emotions and adjust the voice input interface based on the estimated emotions. For example, if the user is nervous, the voice input unit can provide simple instructions; if the user is relaxed, it can provide detailed instructions. This improves user convenience and provides a wider variety of input methods through the use of voice input.

[0096] The image generation unit may further include a style conversion unit. The style conversion unit can change the style of the generated image. For example, it can convert the generated image to an anime style or an oil painting style. The style conversion unit can also estimate the user's emotions and select a style based on those emotions. For example, if the user is relaxed, it can select a style with soft tones, and if the user is excited, it can select a style with vivid tones. This improves the diversity of the generated images and allows for the provision of images with styles that match the user's emotions.

[0097] The image search unit can also include a filtering unit. The filtering unit can filter search results based on user preferences. For example, it can prioritize displaying highly relevant products based on products the user has previously purchased or viewed. The filtering unit can also estimate the user's emotions and adjust the filtering criteria based on those emotions. For example, if the user is stressed, it can prioritize displaying simple, highly visible products, and if the user is relaxed, it can prioritize displaying products containing detailed information. This allows for the provision of search results tailored to the user's preferences and emotions.

[0098] The image input unit can further include a gesture input unit. The gesture input unit allows the user to input images using hand movements and gestures. For example, if the user draws a specific shape with their hand, the gesture input unit recognizes that shape and sends it to the image generation unit. The gesture input unit can also estimate the user's emotions and adjust the gesture input interface based on the estimated emotions. For example, if the user is tense, it can prioritize simple gestures, and if the user is relaxed, it can accept complex gestures. This improves user convenience and provides a wider variety of input methods when using gesture input.

[0099] The design utilization unit can also include a customization suggestion unit. This unit can offer customization suggestions to the user based on the generated image. For example, it can suggest changes to the color or material of the generated clothing design. Furthermore, the customization suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For instance, if the user is relaxed, it can suggest detailed customization options; if the user is in a hurry, it can suggest simpler customization options. This ensures that customization suggestions are tailored to the user's needs, improving the efficiency of product development.

[0100] The e-commerce site support system can also include a review analysis unit. This unit analyzes product reviews previously posted by users and makes product recommendations based on the results. For example, it prioritizes recommending products similar to those highly rated by users. Furthermore, the review analysis unit can estimate the user's emotions from the content of their reviews and adjust the recommendations based on these estimations. For instance, if a user expresses positive emotions, it can suggest related new products; if they express negative emotions, it can suggest improved products. This allows for more appropriate product recommendations by leveraging users' past reviews.

[0101] The e-commerce site support system can also be equipped with a real-time trend analysis unit. This unit analyzes current market trends in real time and makes product recommendations based on the results. For example, it prioritizes suggesting currently popular product categories and designs. Furthermore, the real-time trend analysis unit can also consider the user's past search and purchase history to provide individually optimized recommendations. This ensures that product recommendations are based on the latest market trends, improving user satisfaction.

[0102] The e-commerce site support system can also include a seasonal event support section. This section provides product suggestions tailored to the season or specific events. For example, it suggests products for events such as Christmas or Valentine's Day. Furthermore, the seasonal event support section can consider the user's past purchase history and prioritize suggesting related products. This results in product suggestions that are relevant to the season and events, thereby increasing the user's purchasing intent.

[0103] The e-commerce site support system can also include a personalized advertising section. This section displays individually optimized ads based on the user's past search and purchase history. For example, it prioritizes displaying ads related to product categories the user has previously shown interest in. Furthermore, the personalized advertising section can analyze the user's current areas of interest in real time and display relevant ads. This ensures that users see ads that are more relevant to them, improving advertising effectiveness.

[0104] The e-commerce site support system can also be equipped with a user feedback collection unit. This unit collects feedback from users and uses the results to improve the system. For example, it can collect user-suggested improvements and requests to enhance the system's functionality and interface. Furthermore, the user feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For instance, it can request detailed feedback when the user is relaxed and brief feedback when the user is in a hurry. This allows for system improvements that reflect user opinions, leading to increased user satisfaction.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The image input unit receives the user's image. The user's image can include text, sketches, photographs, etc. The image input unit accepts text and sketch input, analyzes the input image, and sends it to the image generation unit. Step 2: The image generation unit uses a generation AI to generate images based on the images input by the image input unit. The generation AI employs technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder). The generated images may include, for example, high-resolution images and images in various styles. Step 3: The image search unit performs an image search using the generated images. The image search is performed using methods such as similar image search or content-based image search. The image search unit takes the generated images as input, searches for related products, and displays the search results.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] For example, the image input unit is implemented by the receiving device 38 of the smart device 14. For example, text input is received using the touch panel 38A, and sketch input is received using the camera 42. The image generation unit is implemented by the specific processing unit 290 of the data processing device 12, and generates images using generation AI. The image search unit is implemented by the specific processing unit 290 of the data processing device 12, and performs image searches using the generated images. The design utilization unit is implemented by the specific processing unit 290 of the data processing device 12, and transmits the generated images to the design department. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] For example, the image input unit is implemented by the microphone 238 and camera 42 of the smart glasses 214. For example, text input is accepted as voice input using the microphone 238, and sketch input is accepted using the camera 42. The image generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and generates images using generation AI. The image search unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and performs image searches using the generated images. The design utilization unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and transmits the generated images to the design department. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] For example, the image input unit is implemented by the microphone 238 and camera 42 of the headset terminal 314. For example, text input is accepted as voice input using the microphone 238, and sketch input is accepted using the camera 42. The image generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and generates images using generation AI. The image search unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and performs image searches using the generated images. The design utilization unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and transmits the generated images to the design department. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] For example, the image input unit is implemented by the microphone 238 and camera 42 of the robot 414. For example, text input is received as voice input using the microphone 238, and sketch input is received using the camera 42. The image generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and generates images using generation AI. The image search unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and performs image searches using the generated images. The design utilization unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and transmits the generated images to the design department. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) An image input section for inputting the user's image, An image generation unit that generates an image based on an image input by the aforementioned image input unit, An image search unit that performs a search using the image generated by the image generation unit, Equipped with A system characterized by the following features. (Note 2) The image generation unit, Generate images using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned image search unit, Perform an image search using the generated images. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned image input unit is Accepts text input or sketch input. The system described in Appendix 1, characterized by the features described herein. (Note 5) The image generation unit, It includes a design utilization section that uses the generated images as new product designs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned design utilization section is, The generated images are sent to the design department for commercialization as a new product. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned image input unit is It estimates the user's emotions and adjusts the image input interface based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned image input unit is It analyzes the user's past search history and suggests the optimal image input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned image input unit is When an image is entered, input candidates are presented based on the user's current area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned image input unit is It estimates the user's emotions and determines the priority of the input images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned image input unit is When inputting images, the system prioritizes inputting images that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned image input unit is When inputting images, the system analyzes the user's social media activity and inputs relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The image generation unit, It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The image generation unit, When generating images, the system selects the optimal image generation algorithm by referring to the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The image generation unit, When generating images, the theme of the generated images is determined based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The image generation unit, It estimates the user's emotions and adjusts the color tone of the generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The image generation unit, When generating images, the system considers the user's geographical location to generate highly relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The image generation unit, When generating images, the system analyzes the user's social media activity and generates relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned image search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned image search unit, When performing an image search, the system selects the optimal search algorithm by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned image search unit, When performing an image search, the search results are filtered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned image search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned image search unit, When performing an image search, the system prioritizes displaying more relevant search results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned image search unit, When performing an image search, the system analyzes the user's social media activity and displays relevant search results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned design utilization section is, It estimates user emotions and adjusts how the design is used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned design utilization section is, When a design is used, the system selects the most suitable design usage method by referring to the user's past design history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned design utilization section is, When using the design, the design theme is determined based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned design utilization section is, We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned design utilization section is, When using designs, the system prioritizes the use of highly relevant designs, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned design utilization section is, When using designs, we analyze users' social media activity and utilize relevant designs. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An image input section for inputting the user's image, An image generation unit that generates an image based on an image input by the aforementioned image input unit, An image search unit that performs a search using the image generated by the image generation unit, Equipped with A system characterized by the following features.

2. The image generation unit, Generate images using AI. The system according to feature 1.

3. The aforementioned image search unit, Perform an image search using the generated image. The system according to feature 1.

4. The aforementioned image input unit is Accepts text input or sketch input. The system according to feature 1.

5. The image generation unit, It includes a design utilization section that uses the generated images as new product designs. The system according to feature 1.

6. The aforementioned design utilization section is, The generated images are sent to the design department for commercialization as a new product. The system according to claim 5, characterized in that it is the same as described in claim 5.

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

8. The aforementioned image input unit is It analyzes the user's past search history and suggests the optimal image input method. The system according to feature 1.

Images