system

Abstract

The system according to this embodiment provides a system that efficiently determines and makes searchable whether or not there are pockets in images of women's clothing. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs an analysis process to analyze an image, a determination process to determine whether or not there is a pocket based on the image analyzed by the analysis process, an assignment process to assign a "pocket present" tag if it is determined by the determination process that there is a pocket, a size analysis process to analyze the size of the pocket, and a generation process to generate an image based on the information analyzed by the size analysis process.

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 conventional technology, there is a problem that it is difficult to efficiently determine whether there are pockets in women's clothing and make it searchable.

[0005] The system according to the embodiment aims to efficiently determine the presence or absence of pockets from an image of women's clothing and make it searchable.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a determination unit, a tagging unit, a size analysis unit, and a generation unit. The analysis unit analyzes an image. The determination unit determines whether or not there is a pocket based on the image analyzed by the analysis unit. The tagging unit assigns a "pocket present" tag if the determination unit determines that there is a pocket. The size analysis unit analyzes the size of the pocket. The generation unit generates an image based on the information analyzed by the size analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently determine and search for the presence or absence of pockets from images of women's clothing. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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, a 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 system according to an embodiment of the present invention is a system for solving the problem that many women's clothes lack pockets or have insufficient pockets. This system uses AI to analyze images of women's clothes listed on e-commerce sites and determine whether or not they have pockets. If there are pockets, the system automatically assigns a "pockets" tag. Furthermore, the AI ​​analyzes the size of the pockets, and if they are large enough to hold, for example, a smartphone, it generates an image based on that information. This image is intended to allow users to visually confirm the size of the pockets. Users can efficiently search for women's clothes with pockets by selecting the "pockets" tag on the search page of the e-commerce site. As a result, the system enables female users to efficiently find clothes with pockets and can meet the demand for women's clothes with pockets without increasing manufacturers' costs.

[0029] The system according to the embodiment comprises an analysis unit, a determination unit, a tagging unit, a size analysis unit, and a generation unit. The analysis unit analyzes an image. For example, the analysis unit analyzes an image of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine whether or not there is a pocket in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. The determination unit determines whether or not there is a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether or not the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. The tagging unit assigns a "pocket present" tag when the determination unit determines that there is a pocket. For example, the tagging unit automatically assigns a "pocket present" tag to an image that has been determined to have a pocket. The tagging unit can set the timing and criteria for tag assignment. The size analysis unit analyzes the size of the pocket. For example, the size analysis unit measures the size of the pocket and determines whether or not it is large enough to fit a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. The generation unit generates an image based on the information analyzed by the size analysis unit. The generation unit generates an image that, for example, allows the size of the pocket to be visually confirmed. The generation unit can display a smartphone inside the pocket in the generated image. This allows the system to efficiently find clothes with pockets for female users. The elements of the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit operate sequentially in relation to each other. For example, the analysis unit analyzes the image, the determination unit determines whether there is a pocket, the tagging unit assigns a tag, the size analysis unit analyzes the size of the pocket, and the generation unit generates an image. This allows the system to efficiently find clothes with pockets for female users.

[0030] The analysis unit analyzes images. For example, it analyzes images of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine the presence or absence of pockets in the image. Specifically, the analysis unit utilizes deep learning-based image recognition technology to detect specific regions within the image. For example, it uses a convolutional neural network (CNN) to extract features within the image and identify the shape and texture of pockets. Furthermore, the analysis unit compares multiple images and enhances the training data using data augmentation technology to determine the presence or absence of pockets with high accuracy. This allows the analysis unit to accurately detect pockets even under different angles and lighting conditions. In addition, the analysis unit performs noise reduction and contrast adjustment as image preprocessing to improve analysis accuracy. As a result, the analysis unit can efficiently and accurately analyze images of women's clothing listed on e-commerce sites and determine the presence or absence of pockets.

[0031] The determination unit determines the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. Specifically, the determination unit makes a determination based on criteria such as the shape, location, and size of the pocket. For example, it sets criteria such as the pocket being rectangular, located in a specific position, and being above a certain size, and determines the presence or absence of a pocket based on these criteria. Furthermore, the determination unit uses a machine learning model to learn the characteristics of pockets and improve the accuracy of the determination. For example, it uses algorithms such as support vector machines (SVM) or random forests to classify the presence or absence of pockets. The determination unit can also receive feedback from the analysis unit and modify the determination result. As a result, the determination unit can determine with high accuracy whether the area detected by the analysis unit is a pocket, and improve the accuracy of the entire system.

[0032] The tagging unit assigns the "Pockets Present" tag when the detection unit determines that a pocket is present. For example, the tagging unit automatically assigns the "Pockets Present" tag to images that have been determined to have pockets. Specifically, the tagging unit receives the detection result from the detection unit and performs the process of assigning tags to the corresponding images. The tagging unit can set the timing and criteria for tag assignment. For example, it can be set whether to assign the tag immediately after the presence or absence of a pocket is determined, or whether to assign the tag when certain conditions are met. Furthermore, the tagging unit can customize the type and content of the tags. For example, in addition to the "Pockets Present" tag, it can assign detailed tags such as "Large Pocket Present" or "Smartphone-Compatible Pocket Present." This allows the tagging unit to enable users to efficiently search for clothes with pockets, improving the usability of the system.

[0033] The size analysis unit analyzes the size of the pocket. For example, the size analysis unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. Specifically, the size analysis unit uses an algorithm to detect the pocket area in the image and measure the length and width of that area. For example, it uses image processing technology to extract the pocket's outline and calculate the length and area of ​​that outline. The size analysis unit can also simulate whether a smartphone will actually fit, taking into account the shape and position information of the pocket. For example, it uses an algorithm that virtually inserts a model of a smartphone into the pocket and evaluates its fit. In this way, the size analysis unit can accurately analyze the size of the pocket and provide information to help the user determine whether they can store their smartphone.

[0034] The generation unit generates images based on the information analyzed by the size analysis unit. For example, the generation unit generates images that allow users to visually confirm the size of a pocket. Specifically, to show the size of the pocket, the generation unit inserts a model of a smartphone into the image and displays how it fits inside. For example, a model of a smartphone is generated using 3D modeling technology and placed in the pocket. The generation unit can also add annotations and highlights to the image to emphasize the size and shape of the pocket. This allows users to intuitively understand the size of the pocket. Furthermore, the generation unit can automatically upload the generated image to an e-commerce site and display it on the product page. This allows users to visually confirm the size of the pocket when browsing the product page, making it easier to make a purchase decision.

[0035] The generation unit can generate an image based on the information that the pocket is large enough to hold a smartphone. For example, the generation unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. The generation unit uses an algorithm to measure the size of the pocket. After measuring the size of the pocket, the generation unit generates an image based on that information. The generation unit can display the smartphone inside the pocket in the generated image. This allows the user to visually confirm the size of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the information on the measured size of the pocket into a generation AI, and the generation AI can generate an image.

[0036] The tagging unit can automatically assign a "pocket present" tag if a pocket is present. For example, the tagging unit automatically assigns a "pocket present" tag to images that are determined to have a pocket. The tagging unit can set the timing and criteria for tagging. This eliminates the need for manufacturers to manually assign tags. Some or all of the above-described processes in the tagging unit may be performed using AI, for example, or without AI. For example, the tagging unit can input images that are determined to have a pocket into the AI, and the AI ​​can automatically assign a "pocket present" tag.

[0037] The analysis unit can analyze images of women's clothing listed on e-commerce sites. For example, the analysis unit analyzes images of women's clothing listed on e-commerce sites. The analysis unit uses an image analysis algorithm to determine whether or not there are pockets in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. In this way, the presence or absence of pockets can be determined by analyzing images of women's clothing listed on e-commerce sites. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input images of women's clothing listed on e-commerce sites into a generating AI, and the generating AI can analyze the images.

[0038] The determination unit can determine the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. This allows for the automatic determination of the presence or absence of a pocket. Some or all of the above-described processes in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input the area detected by the analysis unit to the generating AI, and the generating AI can determine the presence or absence of a pocket.

[0039] The size analysis unit can analyze the size of a pocket. For example, the size analysis unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. This allows the user to visually confirm the size of the pocket by analyzing it. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input the information on the measured size of the pocket into a generative AI, which can then analyze the size of the pocket.

[0040] The generation unit can generate an image so that the user can visually confirm the size of the pocket. For example, the generation unit measures the size of the pocket and generates an image based on that information. The generation unit can display how a smartphone fits in the pocket in the generated image. This allows the user to intuitively understand the size of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the information on the measured size of the pocket into a generation AI, and the generation AI can generate an image.

[0041] The analysis unit can improve the accuracy of image analysis by considering the design and color of the clothing. For example, the analysis unit recognizes the design pattern of the clothing and identifies the location of pockets. The analysis unit analyzes differences in color and clarifies the boundaries of pockets. The analysis unit adjusts the analysis algorithm according to the complexity of the design. This improves the accuracy of the analysis by considering the design and color of the clothing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input information about the design and color of the clothing into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0042] The analysis unit can more accurately determine the presence or absence of a pocket by integrating and analyzing multiple images during image analysis. For example, the analysis unit integrates images taken from multiple angles to determine the presence or absence of a pocket. The analysis unit analyzes different parts of the images to confirm the presence or absence of a pocket. The analysis unit eliminates overlapping parts of the images to perform accurate analysis. As a result, the presence or absence of a pocket can be determined more accurately by integrating and analyzing multiple images. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input multiple images into a generative AI, which can then integrate and analyze the images.

[0043] The analysis unit can improve the accuracy of image analysis by considering the material information of the clothing. For example, the analysis unit can analyze the reflective properties of the clothing material to identify the location of pockets. The analysis unit can analyze the texture of the material to clarify the boundaries of pockets. The analysis unit adjusts the analysis algorithm according to the type of material. This improves the accuracy of the analysis by considering the material information of the clothing. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the material information of the clothing into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0044] The analysis unit can prioritize the analysis of highly relevant images by referring to the user's past search history during image analysis. For example, the analysis unit can prioritize the analysis of images of clothing that the user has searched for in the past. The analysis unit identifies highly relevant images from the user's past search history and prioritizes their analysis. The analysis unit analyzes the user's search patterns and prioritizes the analysis of the most suitable images. In this way, by referring to the user's past search history, highly relevant images can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past search history into a generative AI, which can then identify highly relevant images and prioritize their analysis.

[0045] The determination unit can improve its determination accuracy by considering the shape and location of the pocket when determining the presence or absence of a pocket. For example, the determination unit analyzes the shape of the pocket to make an accurate determination. The determination unit identifies the location of the pocket to improve the determination accuracy. The determination unit analyzes the shape and location of the pocket in an integrated manner to improve the determination accuracy. In this way, the determination accuracy is improved by considering the shape and location of the pocket. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input information on the shape and location of the pocket into the generating AI, which can then make adjustments to improve the determination accuracy.

[0046] The determination unit can improve its determination accuracy by considering the image resolution when determining the presence or absence of a pocket. For example, the determination unit can improve its determination accuracy by prioritizing the analysis of high-resolution images. The determination unit uses an algorithm that can accurately determine even low-resolution images. The determination unit adjusts the determination criteria according to the image resolution. This improves the determination accuracy by considering the image resolution. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the determination unit can input image resolution information to the generative AI, which can then make adjustments to improve the determination accuracy.

[0047] The judgment unit can improve its judgment accuracy by considering the brand information of the clothing when determining whether or not there is a pocket. For example, the judgment unit analyzes the design patterns of each brand and determines whether or not there is a pocket. The judgment unit adjusts the judgment criteria considering the characteristics of the brand. The judgment unit accurately determines whether or not there is a pocket based on the brand information. As a result, the judgment accuracy is improved by considering the brand information of the clothing. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input brand information into a generative AI, and the generative AI can make adjustments to improve the judgment accuracy.

[0048] The judgment unit can improve its judgment accuracy by referring to the user's past purchase history when determining whether or not a garment has pockets. For example, the judgment unit determines whether or not a garment has pockets based on the design of clothes the user has purchased in the past. The judgment unit identifies highly relevant clothing from the user's purchase history and improves judgment accuracy. The judgment unit analyzes the user's purchase patterns and uses the optimal judgment criteria. This improves judgment accuracy by referring to the user's past purchase history. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the user's past purchase history into a generative AI, which can then identify highly relevant clothing and improve judgment accuracy.

[0049] The tagging unit can assign detailed information to tags while considering the number and location of pockets. For example, the tagging unit can analyze the number of pockets and assign detailed information to the tags. The tagging unit can identify the location of the pockets and assign detailed information to the tags. The tagging unit can analyze the number and location of pockets in an integrated manner and assign detailed information to the tags. In this way, detailed information is assigned to tags by considering the number and location of pockets. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input information on the number and location of pockets to a generation AI, and the generation AI can assign detailed information to the tags.

[0050] The tagging unit can assign detailed information to tags while considering the material and design of the pocket. For example, the tagging unit can analyze the material of the pocket and assign detailed information to the tag. The tagging unit can identify the design of the pocket and assign detailed information to the tag. The tagging unit can analyze the material and design of the pocket together and assign detailed information to the tag. In this way, detailed information is assigned to the tag by considering the material and design of the pocket. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input information about the material and design of the pocket into a generation AI, and the generation AI can assign detailed information to the tag.

[0051] The tagging unit can assign detailed tag information while considering the clothing category information. For example, the tagging unit analyzes the clothing category and assigns detailed information to the tag. The tagging unit adjusts the detailed tag information based on the category information. The tagging unit integrates and analyzes the category information and assigns detailed information to the tag. In this way, detailed tag information is assigned by considering the clothing category information. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input clothing category information into a generation AI, and the generation AI can assign detailed tag information.

[0052] The tagging unit can, when assigning tags, refer to the user's past search history to prioritize assigning highly relevant tags. For example, the tagging unit identifies highly relevant tags from the user's past search history and assigns them preferentially. The tagging unit analyzes the user's search patterns and assigns the most appropriate tags preferentially. The tagging unit automatically assigns highly relevant tags based on the user's past search history. This allows for the preferential assignment of highly relevant tags by referring to the user's past search history. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input the user's past search history into a generation AI, which can then identify highly relevant tags and assign them preferentially.

[0053] The size analysis unit can improve the accuracy of its analysis by considering the shape and location of the pocket when analyzing its size. For example, the size analysis unit analyzes the shape of the pocket and measures its exact size. The size analysis unit identifies the location of the pocket and improves the accuracy of the analysis. The size analysis unit analyzes the shape and location of the pocket in an integrated manner and measures its exact size. As a result, the accuracy of the analysis is improved by considering the shape and location of the pocket. Some or all of the above processes in the size analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the size analysis unit can input information on the shape and location of the pocket into the generating AI, which can then make adjustments to improve the accuracy of the analysis.

[0054] The size analysis unit can improve the accuracy of its analysis by considering the image resolution when analyzing the size of a pocket. For example, the size analysis unit prioritizes the analysis of high-resolution images to accurately measure the size of the pocket. The size analysis unit uses an algorithm that can accurately analyze even low-resolution images. The size analysis unit adjusts the analysis algorithm according to the image resolution. This improves the accuracy of the analysis by considering the image resolution. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input image resolution information to the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0055] The size analysis unit can improve the accuracy of its analysis of pocket size by considering the clothing brand information. For example, the size analysis unit analyzes the design patterns of each brand and accurately measures the pocket size. The size analysis unit adjusts the analysis algorithm considering the characteristics of the brand. The size analysis unit accurately analyzes the pocket size based on the brand information. As a result, the accuracy of the analysis is improved by considering the clothing brand information. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input brand information into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0056] The size analysis unit can improve the accuracy of its analysis by referring to the user's past purchase history when analyzing pocket size. For example, the size analysis unit analyzes pocket size based on the design of clothes the user has purchased in the past. The size analysis unit identifies highly relevant clothes from the user's purchase history and improves the accuracy of the analysis. The size analysis unit analyzes the user's purchase patterns and uses the optimal analysis algorithm. This improves the accuracy of the analysis by referring to the user's past purchase history. Some or all of the above processes in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input the user's past purchase history into a generative AI, which can then identify highly relevant clothes and improve the accuracy of the analysis.

[0057] The generation unit can improve generation accuracy by considering the shape and position of the pockets during image generation. For example, the generation unit analyzes the shape of the pockets and generates accurate images. The generation unit identifies the position of the pockets and improves generation accuracy. The generation unit analyzes the shape and position of the pockets in an integrated manner and generates accurate images. As a result, generation accuracy is improved by considering the shape and position of the pockets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the shape and position of the pockets into the generation AI, which can then make adjustments to improve generation accuracy.

[0058] The generation unit can improve generation accuracy by considering the material and design of the pocket during image generation. For example, the generation unit analyzes the material of the pocket and generates an accurate image. The generation unit identifies the design of the pocket and improves generation accuracy. The generation unit analyzes the material and design of the pocket together and generates an accurate image. In this way, generation accuracy is improved by considering the material and design of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the material and design of the pocket into the generation AI, which can then make adjustments to improve generation accuracy.

[0059] The generation unit can improve generation accuracy by considering clothing category information during image generation. For example, the generation unit analyzes clothing categories and generates accurate images. The generation unit adjusts the generation algorithm based on the category information. The generation unit integrates and analyzes the category information to generate accurate images. This improves generation accuracy by considering clothing category information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input clothing category information into the generation AI, which can then make adjustments to improve generation accuracy.

[0060] The generation unit can prioritize generating highly relevant images by referring to the user's past search history during image generation. For example, the generation unit identifies highly relevant images from the user's past search history and generates them preferentially. The generation unit analyzes the user's search patterns and prioritizes generating the most suitable images. The generation unit automatically generates highly relevant images based on the user's past search history. This allows for the priority generation of highly relevant images by referring to the user's past search history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past search history into a generation AI, which can then identify and prioritize the generation of highly relevant images.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The analysis unit can improve the accuracy of image analysis by considering the design and color of the clothing. For example, the analysis unit recognizes the design pattern of the clothing and identifies the location of pockets. It analyzes differences in color and clarifies the boundaries of the pockets. It adjusts the analysis algorithm according to the complexity of the design. In this way, the accuracy of the analysis is improved by considering the design and color of the clothing. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information about the design and color of the clothing into a generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0063] The determination unit can improve its determination accuracy by considering the shape and location of the pocket when determining the presence or absence of a pocket. For example, the determination unit analyzes the shape of the pocket to make an accurate determination. It identifies the location of the pocket to improve the determination accuracy. It analyzes the shape and location of the pocket in an integrated manner to improve the determination accuracy. In this way, the determination accuracy is improved by considering the shape and location of the pocket. Some or all of the above processing in the determination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the determination unit can input information on the shape and location of the pocket into the generation AI, which can then make adjustments to improve the determination accuracy.

[0064] The size analysis unit can improve the accuracy of its analysis by considering the shape and location of the pocket when analyzing its size. For example, the size analysis unit analyzes the shape of the pocket and measures its exact size. It identifies the location of the pocket and improves the accuracy of the analysis. It analyzes the shape and location of the pocket in an integrated manner and measures its exact size. In this way, the accuracy of the analysis is improved by considering the shape and location of the pocket. Some or all of the above processes in the size analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the size analysis unit can input information on the shape and location of the pocket into the generation AI, which can then make adjustments to improve the accuracy of the analysis.

[0065] The generation unit can improve generation accuracy by considering the shape and position of the pockets during image generation. For example, the generation unit analyzes the shape of the pockets and generates an accurate image. It identifies the position of the pockets and improves generation accuracy. It analyzes the shape and position of the pockets in an integrated manner and generates an accurate image. In this way, generation accuracy is improved by considering the shape and position of the pockets. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input information on the shape and position of the pockets into the generation AI, which can then make adjustments to improve generation accuracy.

[0066] The tagging unit can assign detailed information to tags while considering the number and location of pockets. For example, the tagging unit analyzes the number of pockets and assigns detailed information to the tags. It identifies the location of the pockets and assigns detailed information to the tags. It analyzes the number and location of pockets in an integrated manner and assigns detailed information to the tags. In this way, detailed information is assigned to tags by considering the number and location of pockets. Some or all of the above processing in the tagging unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the tagging unit can input information on the number and location of pockets to a generation AI, and the generation AI can assign detailed information to the tags.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes the image. For example, the analysis unit analyzes an image of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine whether or not there are pockets in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. Step 2: The determination unit determines the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. Step 3: The tagging unit assigns the "Pocket Present" tag if the judgment unit determines that a pocket exists. For example, the tagging unit automatically assigns the "Pocket Present" tag to images that have been determined to have a pocket. The tagging unit can set the timing and criteria for tag assignment. Step 4: The size analysis unit analyzes the size of the pocket. The size analysis unit measures the size of the pocket, for example, and determines whether it is large enough to hold a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. Step 5: The generation unit generates an image based on the information analyzed by the size analysis unit. For example, the generation unit generates an image that allows the size of a pocket to be visually confirmed. The generation unit can display a smartphone inside the pocket in the generated image.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system for solving the problem that many women's clothes lack pockets or have insufficient pockets. This system uses AI to analyze images of women's clothes listed on e-commerce sites and determine whether or not they have pockets. If there are pockets, the system automatically assigns a "pockets" tag. Furthermore, the AI ​​analyzes the size of the pockets, and if they are large enough to hold, for example, a smartphone, it generates an image based on that information. This image is intended to allow users to visually confirm the size of the pockets. Users can efficiently search for women's clothes with pockets by selecting the "pockets" tag on the search page of the e-commerce site. As a result, the system enables female users to efficiently find clothes with pockets and can meet the demand for women's clothes with pockets without increasing manufacturers' costs.

[0070] The system according to the embodiment comprises an analysis unit, a determination unit, a tagging unit, a size analysis unit, and a generation unit. The analysis unit analyzes an image. For example, the analysis unit analyzes an image of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine whether or not there is a pocket in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. The determination unit determines whether or not there is a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether or not the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. The tagging unit assigns a "pocket present" tag when the determination unit determines that there is a pocket. For example, the tagging unit automatically assigns a "pocket present" tag to an image that has been determined to have a pocket. The tagging unit can set the timing and criteria for tag assignment. The size analysis unit analyzes the size of the pocket. For example, the size analysis unit measures the size of the pocket and determines whether or not it is large enough to fit a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. The generation unit generates an image based on the information analyzed by the size analysis unit. The generation unit generates an image that, for example, allows the size of the pocket to be visually confirmed. The generation unit can display a smartphone inside the pocket in the generated image. This allows the system to efficiently find clothes with pockets for female users. The elements of the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit operate sequentially in relation to each other. For example, the analysis unit analyzes the image, the determination unit determines whether there is a pocket, the tagging unit assigns a tag, the size analysis unit analyzes the size of the pocket, and the generation unit generates an image. This allows the system to efficiently find clothes with pockets for female users.

[0071] The analysis unit analyzes images. For example, it analyzes images of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine the presence or absence of pockets in the image. Specifically, the analysis unit utilizes deep learning-based image recognition technology to detect specific regions within the image. For example, it uses a convolutional neural network (CNN) to extract features within the image and identify the shape and texture of pockets. Furthermore, the analysis unit compares multiple images and enhances the training data using data augmentation technology to determine the presence or absence of pockets with high accuracy. This allows the analysis unit to accurately detect pockets even under different angles and lighting conditions. In addition, the analysis unit performs noise reduction and contrast adjustment as image preprocessing to improve analysis accuracy. As a result, the analysis unit can efficiently and accurately analyze images of women's clothing listed on e-commerce sites and determine the presence or absence of pockets.

[0072] The determination unit determines the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. Specifically, the determination unit makes a determination based on criteria such as the shape, location, and size of the pocket. For example, it sets criteria such as the pocket being rectangular, located in a specific position, and being above a certain size, and determines the presence or absence of a pocket based on these criteria. Furthermore, the determination unit uses a machine learning model to learn the characteristics of pockets and improve the accuracy of the determination. For example, it uses algorithms such as support vector machines (SVM) or random forests to classify the presence or absence of pockets. The determination unit can also receive feedback from the analysis unit and modify the determination result. As a result, the determination unit can determine with high accuracy whether the area detected by the analysis unit is a pocket, and improve the accuracy of the entire system.

[0073] The tagging unit assigns the "Pockets Present" tag when the detection unit determines that a pocket is present. For example, the tagging unit automatically assigns the "Pockets Present" tag to images that have been determined to have pockets. Specifically, the tagging unit receives the detection result from the detection unit and performs the process of assigning tags to the corresponding images. The tagging unit can set the timing and criteria for tag assignment. For example, it can be set whether to assign the tag immediately after the presence or absence of a pocket is determined, or whether to assign the tag when certain conditions are met. Furthermore, the tagging unit can customize the type and content of the tags. For example, in addition to the "Pockets Present" tag, it can assign detailed tags such as "Large Pocket Present" or "Smartphone-Compatible Pocket Present." This allows the tagging unit to enable users to efficiently search for clothes with pockets, improving the usability of the system.

[0074] The size analysis unit analyzes the size of the pocket. For example, the size analysis unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. Specifically, the size analysis unit uses an algorithm to detect the pocket area in the image and measure the length and width of that area. For example, it uses image processing technology to extract the pocket's outline and calculate the length and area of ​​that outline. The size analysis unit can also simulate whether a smartphone will actually fit, taking into account the shape and position information of the pocket. For example, it uses an algorithm that virtually inserts a model of a smartphone into the pocket and evaluates its fit. In this way, the size analysis unit can accurately analyze the size of the pocket and provide information to help the user determine whether they can store their smartphone.

[0075] The generation unit generates images based on the information analyzed by the size analysis unit. For example, the generation unit generates images that allow users to visually confirm the size of a pocket. Specifically, to show the size of the pocket, the generation unit inserts a model of a smartphone into the image and displays how it fits inside. For example, a model of a smartphone is generated using 3D modeling technology and placed in the pocket. The generation unit can also add annotations and highlights to the image to emphasize the size and shape of the pocket. This allows users to intuitively understand the size of the pocket. Furthermore, the generation unit can automatically upload the generated image to an e-commerce site and display it on the product page. This allows users to visually confirm the size of the pocket when browsing the product page, making it easier to make a purchase decision.

[0076] The generation unit can generate an image based on the information that the pocket is large enough to hold a smartphone. For example, the generation unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. The generation unit uses an algorithm to measure the size of the pocket. After measuring the size of the pocket, the generation unit generates an image based on that information. The generation unit can display the smartphone inside the pocket in the generated image. This allows the user to visually confirm the size of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the information on the measured size of the pocket into a generation AI, and the generation AI can generate an image.

[0077] The tagging unit can automatically assign a "pocket present" tag if a pocket is present. For example, the tagging unit automatically assigns a "pocket present" tag to images that are determined to have a pocket. The tagging unit can set the timing and criteria for tagging. This eliminates the need for manufacturers to manually assign tags. Some or all of the above-described processes in the tagging unit may be performed using AI, for example, or without AI. For example, the tagging unit can input images that are determined to have a pocket into the AI, and the AI ​​can automatically assign a "pocket present" tag.

[0078] The analysis unit can analyze images of women's clothing listed on e-commerce sites. For example, the analysis unit analyzes images of women's clothing listed on e-commerce sites. The analysis unit uses an image analysis algorithm to determine whether or not there are pockets in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. In this way, the presence or absence of pockets can be determined by analyzing images of women's clothing listed on e-commerce sites. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input images of women's clothing listed on e-commerce sites into a generating AI, and the generating AI can analyze the images.

[0079] The determination unit can determine the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. This allows for the automatic determination of the presence or absence of a pocket. Some or all of the above-described processes in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input the area detected by the analysis unit to the generating AI, and the generating AI can determine the presence or absence of a pocket.

[0080] The size analysis unit can analyze the size of a pocket. For example, the size analysis unit measures the size of the pocket and determines whether it is large enough to hold a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. This allows the user to visually confirm the size of the pocket by analyzing it. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input the information on the measured size of the pocket into a generative AI, which can then analyze the size of the pocket.

[0081] The generation unit can generate an image so that the user can visually confirm the size of the pocket. For example, the generation unit measures the size of the pocket and generates an image based on that information. The generation unit can display how a smartphone fits in the pocket in the generated image. This allows the user to intuitively understand the size of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the information on the measured size of the pocket into a generation AI, and the generation AI can generate an image.

[0082] The analysis unit can estimate the user's emotions and adjust the timing of image analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit performs the analysis quickly and provides the results promptly. If the user is relaxed, the analysis unit performs a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit performs a simplified analysis and provides the results quickly. This allows for more appropriate analysis by adjusting the timing of image analysis according to 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-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the timing of image analysis can be adjusted based on the results.

[0083] The analysis unit can improve the accuracy of image analysis by considering the design and color of the clothing. For example, the analysis unit recognizes the design pattern of the clothing and identifies the location of pockets. The analysis unit analyzes differences in color and clarifies the boundaries of pockets. The analysis unit adjusts the analysis algorithm according to the complexity of the design. This improves the accuracy of the analysis by considering the design and color of the clothing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input information about the design and color of the clothing into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0084] The analysis unit can more accurately determine the presence or absence of a pocket by integrating and analyzing multiple images during image analysis. For example, the analysis unit integrates images taken from multiple angles to determine the presence or absence of a pocket. The analysis unit analyzes different parts of the images to confirm the presence or absence of a pocket. The analysis unit eliminates overlapping parts of the images to perform accurate analysis. As a result, the presence or absence of a pocket can be determined more accurately by integrating and analyzing multiple images. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input multiple images into a generative AI, which can then integrate and analyze the images.

[0085] The analysis unit can estimate the user's emotions and determine the priority of images to analyze based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize analyzing important images. If the user is relaxed, the analysis unit will analyze all images equally for a detailed analysis. If the user is stressed, the analysis unit will perform a simplified analysis and provide the results quickly. This allows for more appropriate analysis by determining the priority of images to analyze 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the analysis unit can determine the priority of images to analyze based on the results.

[0086] The analysis unit can improve the accuracy of image analysis by considering the material information of the clothing. For example, the analysis unit can analyze the reflective properties of the clothing material to identify the location of pockets. The analysis unit can analyze the texture of the material to clarify the boundaries of pockets. The analysis unit adjusts the analysis algorithm according to the type of material. This improves the accuracy of the analysis by considering the material information of the clothing. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the material information of the clothing into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0087] The analysis unit can prioritize the analysis of highly relevant images by referring to the user's past search history during image analysis. For example, the analysis unit can prioritize the analysis of images of clothing that the user has searched for in the past. The analysis unit identifies highly relevant images from the user's past search history and prioritizes their analysis. The analysis unit analyzes the user's search patterns and prioritizes the analysis of the most suitable images. In this way, by referring to the user's past search history, highly relevant images can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past search history into a generative AI, which can then identify highly relevant images and prioritize their analysis.

[0088] The judgment unit can estimate the user's emotions and adjust the criteria for determining whether or not there is a pocket based on the estimated emotions. For example, if the user is stressed, the judgment unit uses a simple criterion. If the user is relaxed, the judgment unit uses a detailed criterion. If the user is in a hurry, the judgment unit uses a rapid criterion. This allows for a more accurate determination by adjusting the criteria for determining whether or not there is a pocket 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 judgment unit may be performed using a generative AI, or not using a generative AI. For example, the judgment unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the criteria for determining whether or not there is a pocket can be adjusted based on the result.

[0089] The determination unit can improve its determination accuracy by considering the shape and location of the pocket when determining the presence or absence of a pocket. For example, the determination unit analyzes the shape of the pocket to make an accurate determination. The determination unit identifies the location of the pocket to improve the determination accuracy. The determination unit analyzes the shape and location of the pocket in an integrated manner to improve the determination accuracy. In this way, the determination accuracy is improved by considering the shape and location of the pocket. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input information on the shape and location of the pocket into the generating AI, which can then make adjustments to improve the determination accuracy.

[0090] The determination unit can improve its determination accuracy by considering the image resolution when determining the presence or absence of a pocket. For example, the determination unit can improve its determination accuracy by prioritizing the analysis of high-resolution images. The determination unit uses an algorithm that can accurately determine even low-resolution images. The determination unit adjusts the determination criteria according to the image resolution. This improves the determination accuracy by considering the image resolution. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the determination unit can input image resolution information to the generative AI, which can then make adjustments to improve the determination accuracy.

[0091] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit provides a simple and highly visible display method. If the user is relaxed, the judgment unit provides a display method that includes detailed information. If the user is in a hurry, the judgment unit provides a display method that gets straight to the point. By adjusting the display method of the judgment result according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using a generative AI, or not using a generative AI. For example, the judgment unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the display method of the judgment result can be adjusted based on the result.

[0092] The judgment unit can improve its judgment accuracy by considering the brand information of the clothing when determining whether or not there is a pocket. For example, the judgment unit analyzes the design patterns of each brand and determines whether or not there is a pocket. The judgment unit adjusts the judgment criteria considering the characteristics of the brand. The judgment unit accurately determines whether or not there is a pocket based on the brand information. As a result, the judgment accuracy is improved by considering the brand information of the clothing. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input brand information into a generative AI, and the generative AI can make adjustments to improve the judgment accuracy.

[0093] The judgment unit can improve its judgment accuracy by referring to the user's past purchase history when determining whether or not a garment has pockets. For example, the judgment unit determines whether or not a garment has pockets based on the design of clothes the user has purchased in the past. The judgment unit identifies highly relevant clothing from the user's purchase history and improves judgment accuracy. The judgment unit analyzes the user's purchase patterns and uses the optimal judgment criteria. This improves judgment accuracy by referring to the user's past purchase history. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the user's past purchase history into a generative AI, which can then identify highly relevant clothing and improve judgment accuracy.

[0094] The tagging unit can estimate the user's emotions and adjust the timing of tagging based on the estimated emotions. For example, if the user is stressed, the tagging unit will tag quickly. If the user is relaxed, the tagging unit will tag after performing a detailed analysis. If the user is in a hurry, the tagging unit will perform a simple analysis and tag quickly. By adjusting the timing of tagging according to the user's emotions, more appropriate tagging becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the tagging unit may be performed using a generative AI, or not using a generative AI. For example, the tagging unit can input user emotion data into a generative AI, the generative AI will estimate the emotions, and the timing of tagging can be adjusted based on the result.

[0095] The tagging unit can assign detailed information to tags while considering the number and location of pockets. For example, the tagging unit can analyze the number of pockets and assign detailed information to the tags. The tagging unit can identify the location of the pockets and assign detailed information to the tags. The tagging unit can analyze the number and location of pockets in an integrated manner and assign detailed information to the tags. In this way, detailed information is assigned to tags by considering the number and location of pockets. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input information on the number and location of pockets to a generation AI, and the generation AI can assign detailed information to the tags.

[0096] The tagging unit can assign detailed information to tags while considering the material and design of the pocket. For example, the tagging unit can analyze the material of the pocket and assign detailed information to the tag. The tagging unit can identify the design of the pocket and assign detailed information to the tag. The tagging unit can analyze the material and design of the pocket together and assign detailed information to the tag. In this way, detailed information is assigned to the tag by considering the material and design of the pocket. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input information about the material and design of the pocket into a generation AI, and the generation AI can assign detailed information to the tag.

[0097] The tagging unit can estimate the user's emotions and adjust the tag display method based on the estimated emotions. For example, if the user is nervous, the tagging unit provides a simple and highly visible display method. If the user is relaxed, the tagging unit provides a display method that includes detailed information. If the user is in a hurry, the tagging unit provides a display method that gets straight to the point. By adjusting the tag display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the tagging unit may be performed using a generative AI, or not using a generative AI. For example, the tagging unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the tag display method can be adjusted based on the result.

[0098] The tagging unit can assign detailed tag information while considering the clothing category information. For example, the tagging unit analyzes the clothing category and assigns detailed information to the tag. The tagging unit adjusts the detailed tag information based on the category information. The tagging unit integrates and analyzes the category information and assigns detailed information to the tag. In this way, detailed tag information is assigned by considering the clothing category information. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input clothing category information into a generation AI, and the generation AI can assign detailed tag information.

[0099] The tagging unit can, when assigning tags, refer to the user's past search history to prioritize assigning highly relevant tags. For example, the tagging unit identifies highly relevant tags from the user's past search history and assigns them preferentially. The tagging unit analyzes the user's search patterns and assigns the most appropriate tags preferentially. The tagging unit automatically assigns highly relevant tags based on the user's past search history. This allows for the preferential assignment of highly relevant tags by referring to the user's past search history. Some or all of the above processing in the tagging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the tagging unit can input the user's past search history into a generation AI, which can then identify highly relevant tags and assign them preferentially.

[0100] The size analysis unit can estimate the user's emotions and adjust the timing of the pocket size analysis based on the estimated emotions. For example, if the user is stressed, the size analysis unit performs a rapid analysis. If the user is relaxed, the size analysis unit performs a detailed analysis. If the user is in a hurry, the size analysis unit performs a simplified analysis and provides results quickly. This allows for more appropriate analysis by adjusting the timing of the pocket size analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the size analysis unit may be performed using a generative AI, or not using a generative AI. For example, the size analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the timing of the pocket size analysis can be adjusted based on the results.

[0101] The size analysis unit can improve the accuracy of its analysis by considering the shape and location of the pocket when analyzing its size. For example, the size analysis unit analyzes the shape of the pocket and measures its exact size. The size analysis unit identifies the location of the pocket and improves the accuracy of the analysis. The size analysis unit analyzes the shape and location of the pocket in an integrated manner and measures its exact size. As a result, the accuracy of the analysis is improved by considering the shape and location of the pocket. Some or all of the above processes in the size analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the size analysis unit can input information on the shape and location of the pocket into the generating AI, which can then make adjustments to improve the accuracy of the analysis.

[0102] The size analysis unit can improve the accuracy of its analysis by considering the image resolution when analyzing the size of a pocket. For example, the size analysis unit prioritizes the analysis of high-resolution images to accurately measure the size of the pocket. The size analysis unit uses an algorithm that can accurately analyze even low-resolution images. The size analysis unit adjusts the analysis algorithm according to the image resolution. This improves the accuracy of the analysis by considering the image resolution. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input image resolution information to the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0103] The size analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the size analysis unit provides a simple and highly visible display method. If the user is relaxed, the size analysis unit provides a display method that includes detailed information. If the user is in a hurry, the size analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the size analysis unit may be performed using a generative AI, or not using a generative AI. For example, the size analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the display method of the analysis results can be adjusted based on the result.

[0104] The size analysis unit can improve the accuracy of its analysis of pocket size by considering the clothing brand information. For example, the size analysis unit analyzes the design patterns of each brand and accurately measures the pocket size. The size analysis unit adjusts the analysis algorithm considering the characteristics of the brand. The size analysis unit accurately analyzes the pocket size based on the brand information. As a result, the accuracy of the analysis is improved by considering the clothing brand information. Some or all of the above processing in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input brand information into the generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0105] The size analysis unit can improve the accuracy of its analysis by referring to the user's past purchase history when analyzing pocket size. For example, the size analysis unit analyzes pocket size based on the design of clothes the user has purchased in the past. The size analysis unit identifies highly relevant clothes from the user's purchase history and improves the accuracy of the analysis. The size analysis unit analyzes the user's purchase patterns and uses the optimal analysis algorithm. This improves the accuracy of the analysis by referring to the user's past purchase history. Some or all of the above processes in the size analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the size analysis unit can input the user's past purchase history into a generative AI, which can then identify highly relevant clothes and improve the accuracy of the analysis.

[0106] The generation unit can estimate the user's emotions and adjust the representation of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit generates images that progress at a leisurely pace. If the user is in a hurry, the generation unit generates images that emphasize the shortest route. If the user is excited, the generation unit generates images with visually stimulating effects. By adjusting the representation of the generated images according to the user's emotions, more appropriate images are 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 generation unit may be performed using a generation AI or not. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotions, and the representation of the generated images can be adjusted based on the result.

[0107] The generation unit can improve generation accuracy by considering the shape and position of the pockets during image generation. For example, the generation unit analyzes the shape of the pockets and generates accurate images. The generation unit identifies the position of the pockets and improves generation accuracy. The generation unit analyzes the shape and position of the pockets in an integrated manner and generates accurate images. As a result, generation accuracy is improved by considering the shape and position of the pockets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the shape and position of the pockets into the generation AI, which can then make adjustments to improve generation accuracy.

[0108] The generation unit can improve generation accuracy by considering the material and design of the pocket during image generation. For example, the generation unit analyzes the material of the pocket and generates an accurate image. The generation unit identifies the design of the pocket and improves generation accuracy. The generation unit analyzes the material and design of the pocket together and generates an accurate image. In this way, generation accuracy is improved by considering the material and design of the pocket. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the material and design of the pocket into the generation AI, which can then make adjustments to improve generation accuracy.

[0109] The generation unit can estimate the user's emotions and adjust the display method of the generated images based on the estimated emotions. For example, if the user is nervous, the generation unit provides a simple and highly visible display method. If the user is relaxed, the generation unit provides a display method that includes detailed information. If the user is in a hurry, the generation unit provides a display method that gets straight to the point. By adjusting the display method of the generated images according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotions, and the display method of the generated images can be adjusted based on the result.

[0110] The generation unit can improve generation accuracy by considering clothing category information during image generation. For example, the generation unit analyzes clothing categories and generates accurate images. The generation unit adjusts the generation algorithm based on the category information. The generation unit integrates and analyzes the category information to generate accurate images. This improves generation accuracy by considering clothing category information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input clothing category information into the generation AI, which can then make adjustments to improve generation accuracy.

[0111] The generation unit can prioritize generating highly relevant images by referring to the user's past search history during image generation. For example, the generation unit identifies highly relevant images from the user's past search history and generates them preferentially. The generation unit analyzes the user's search patterns and prioritizes generating the most suitable images. The generation unit automatically generates highly relevant images based on the user's past search history. This allows for the priority generation of highly relevant images by referring to the user's past search history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past search history into a generation AI, which can then identify and prioritize the generation of highly relevant images.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit performs a rapid analysis and provides results quickly. If the user is relaxed, the analysis unit performs a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit performs a simplified analysis and provides results quickly. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For example, the analysis unit can input user emotion data into a generative AI, which estimates the emotions, and then adjust the analysis algorithm based on the results.

[0114] The judgment unit can estimate the user's emotions and adjust the criteria for determining whether or not there is a pocket based on the estimated emotions. For example, if the user is stressed, the judgment unit uses a simple criterion. If the user is relaxed, the judgment unit uses a detailed criterion. If the user is in a hurry, the judgment unit uses a rapid criterion. This allows for more accurate determination by adjusting the criteria for determining whether or not there is a pocket according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For example, the judgment unit can input the user's emotion data into a generative AI, which estimates the emotion, and then adjust the criteria for determining whether or not there is a pocket based on the result.

[0115] The tagging unit can estimate the user's emotions and adjust the timing of tagging based on the estimated emotions. For example, if the user is stressed, the tagging unit will tag quickly. If the user is relaxed, the tagging unit will tag after performing a detailed analysis. If the user is in a hurry, the tagging unit will perform a simple analysis and tag quickly. This allows for more appropriate tagging by adjusting the timing of tagging according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For example, the tagging unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the timing of tagging based on the results.

[0116] The generation unit can estimate the user's emotions and adjust the way it represents the generated images based on those emotions. For example, if the user is relaxed, the generation unit will generate images that progress at a leisurely pace. If the user is in a hurry, the generation unit will generate images that emphasize the shortest route. If the user is excited, the generation unit will generate images with visually stimulating effects. In this way, by adjusting the way it represents the generated images according to the user's emotions, more appropriate images can be generated. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the generation unit can input user emotion data into a generation AI, which will estimate the emotions and adjust the way it represents the generated images based on the result.

[0117] The generation unit can estimate the user's emotions and adjust the display method of the generated images based on the estimated emotions. For example, if the user is nervous, the generation unit provides a simple and highly visible display method. If the user is relaxed, the generation unit provides a display method that includes detailed information. If the user is in a hurry, the generation unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the generated images according to the user's emotions, more appropriate displays become possible. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the generation unit can input user emotion data into the generation AI, the generation AI can estimate the emotions, and the display method of the generated images can be adjusted based on the result.

[0118] The analysis unit can improve the accuracy of image analysis by considering the design and color of the clothing. For example, the analysis unit recognizes the design pattern of the clothing and identifies the location of pockets. It analyzes differences in color and clarifies the boundaries of the pockets. It adjusts the analysis algorithm according to the complexity of the design. In this way, the accuracy of the analysis is improved by considering the design and color of the clothing. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information about the design and color of the clothing into a generative AI, which can then make adjustments to improve the accuracy of the analysis.

[0119] The determination unit can improve its determination accuracy by considering the shape and location of the pocket when determining the presence or absence of a pocket. For example, the determination unit analyzes the shape of the pocket to make an accurate determination. It identifies the location of the pocket to improve the determination accuracy. It analyzes the shape and location of the pocket in an integrated manner to improve the determination accuracy. In this way, the determination accuracy is improved by considering the shape and location of the pocket. Some or all of the above processing in the determination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the determination unit can input information on the shape and location of the pocket into the generation AI, which can then make adjustments to improve the determination accuracy.

[0120] The size analysis unit can improve the accuracy of its analysis by considering the shape and location of the pocket when analyzing its size. For example, the size analysis unit analyzes the shape of the pocket and measures its exact size. It identifies the location of the pocket and improves the accuracy of the analysis. It analyzes the shape and location of the pocket in an integrated manner and measures its exact size. In this way, the accuracy of the analysis is improved by considering the shape and location of the pocket. Some or all of the above processes in the size analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the size analysis unit can input information on the shape and location of the pocket into the generation AI, which can then make adjustments to improve the accuracy of the analysis.

[0121] The generation unit can improve generation accuracy by considering the shape and position of the pockets during image generation. For example, the generation unit analyzes the shape of the pockets and generates an accurate image. It identifies the position of the pockets and improves generation accuracy. It analyzes the shape and position of the pockets in an integrated manner and generates an accurate image. In this way, generation accuracy is improved by considering the shape and position of the pockets. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input information on the shape and position of the pockets into the generation AI, which can then make adjustments to improve generation accuracy.

[0122] The tagging unit can assign detailed information to tags while considering the number and location of pockets. For example, the tagging unit analyzes the number of pockets and assigns detailed information to the tags. It identifies the location of the pockets and assigns detailed information to the tags. It analyzes the number and location of pockets in an integrated manner and assigns detailed information to the tags. In this way, detailed information is assigned to tags by considering the number and location of pockets. Some or all of the above processing in the tagging unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the tagging unit can input information on the number and location of pockets to a generation AI, and the generation AI can assign detailed information to the tags.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis unit analyzes the image. For example, the analysis unit analyzes an image of women's clothing listed on an e-commerce site. The analysis unit uses an image analysis algorithm to determine whether or not there are pockets in the image. For example, the analysis unit detects a specific area in the image and determines whether or not that area is a pocket. Step 2: The determination unit determines the presence or absence of a pocket based on the image analyzed by the analysis unit. For example, the determination unit determines whether the area detected by the analysis unit is a pocket. The determination unit makes a determination based on criteria such as the shape, position, and size of the pocket. Step 3: The tagging unit assigns the "Pocket Present" tag if the judgment unit determines that a pocket exists. For example, the tagging unit automatically assigns the "Pocket Present" tag to images that have been determined to have a pocket. The tagging unit can set the timing and criteria for tag assignment. Step 4: The size analysis unit analyzes the size of the pocket. The size analysis unit measures the size of the pocket, for example, and determines whether it is large enough to hold a smartphone. The size analysis unit uses an algorithm to measure the size of the pocket. Step 5: The generation unit generates an image based on the information analyzed by the size analysis unit. For example, the generation unit generates an image that allows the size of a pocket to be visually confirmed. The generation unit can display a smartphone inside the pocket in the generated image.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the image using the camera 42 of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The determination unit determines the presence or absence of a pocket using the identification processing unit 290 of the data processing unit 12. The tagging unit assigns a "pocket present" tag using the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The size analysis unit analyzes the size of the pocket using the identification processing unit 290 of the data processing unit 12. The generation unit generates an image using the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the image using the camera 42 of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The determination unit determines the presence or absence of a pocket using the identification processing unit 290 of the data processing unit 12. The tagging unit assigns a "pocket present" tag using the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The size analysis unit analyzes the size of the pocket using the identification processing unit 290 of the data processing unit 12. The generation unit generates an image using the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the image using the camera 42 of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The determination unit determines the presence or absence of a pocket using the identification processing unit 290 of the data processing unit 12. The tagging unit assigns a "pocket present" tag using the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The size analysis unit analyzes the size of the pocket using the identification processing unit 290 of the data processing unit 12. The generation unit generates an image using the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the analysis unit, determination unit, tagging unit, size analysis unit, and generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the image using the camera 42 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The determination unit determines the presence or absence of a pocket using the specific processing unit 290 of the data processing unit 12. The tagging unit assigns a "pocket present" tag using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The size analysis unit analyzes the size of the pocket using the specific processing unit 290 of the data processing unit 12. The generation unit generates an image using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) An analysis unit that analyzes images, A determination unit that determines the presence or absence of a pocket based on the image analyzed by the aforementioned analysis unit, A tagging unit that assigns a "Pockets present" tag when the determination unit determines that there are pockets, A size analysis unit that analyzes the size of the pocket, The system comprises a generation unit that generates an image based on the information analyzed by the size analysis unit. A system characterized by the following features. (Note 2) The generating unit is If the pocket is large enough to fit a smartphone, an image will be generated based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned tagging unit, Automatically add the "Pockets present" tag if a pocket is found. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze images of women's clothing listed on e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 5) The determination unit, The presence or absence of a pocket is determined based on the image analyzed by the aforementioned analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned size analysis unit, Analyze the size of the pocket The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Generate images so that users can visually confirm the size of the pockets. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing images, consider the design and color of the clothing to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, By integrating and analyzing multiple images during image analysis, the presence or absence of pockets can be determined more accurately. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of images to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing images, consider the material information of the clothing to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During image analysis, the system prioritizes analyzing images that are highly relevant by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, The system estimates the user's emotions and adjusts the criteria for determining whether or not there is a pocket based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When determining the presence or absence of a pocket, the shape and location of the pocket are taken into consideration to improve the accuracy of the determination. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, When determining the presence or absence of a pocket, the image resolution is taken into consideration to improve the accuracy of the determination. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, When determining whether or not a garment has pockets, we improve the accuracy of the determination by taking into account the clothing's brand information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When determining whether or not a pocket is present, we improve the accuracy of the determination by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tagging unit, It estimates the user's emotions and adjusts the timing of tagging based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tagging unit, When assigning tags, the number and location of pockets are taken into consideration when adding detailed information to the tags. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tagging unit, When adding tags, the material and design of the pocket are taken into consideration when adding detailed information to the tags. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tagging unit, It estimates the user's sentiment and adjusts how tags are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tagging unit, When assigning tags, the tag details are added while taking into account the clothing category information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tagging unit, When assigning tags, the system prioritizes assigning highly relevant tags by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned size analysis unit, The system estimates the user's emotions and adjusts the timing of the pocket size analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned size analysis unit, When analyzing pocket size, consider the shape and location of the pocket to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned size analysis unit, When analyzing pocket size, consider image resolution to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned size analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned size analysis unit, When analyzing pocket size, consider clothing brand information to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned size analysis unit, When analyzing pocket size, we improve the accuracy of the analysis by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is It estimates the user's emotions and adjusts the way images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating images, the shape and position of the pockets are taken into consideration to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating images, the material and design of the pockets are taken into consideration to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is It estimates the user's emotions and adjusts how images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating images, consider clothing category information to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is When generating images, the system prioritizes generating highly relevant images by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 analysis unit that analyzes images, A determination unit that determines the presence or absence of a pocket based on the image analyzed by the aforementioned analysis unit, A tagging unit that attaches a pocket tag when the determination unit determines that there is a pocket, A size analysis unit that analyzes the size of the pocket, The system comprises a generation unit that generates an image based on the information analyzed by the size analysis unit. A system characterized by the following features.

2. The generating unit is If the pocket is large enough to fit a smartphone, an image will be generated based on that information. The system according to feature 1.

3. The aforementioned tagging unit, Automatically add a "Pockets" tag if a pocket is present. The system according to feature 1.

4. The aforementioned analysis unit, Analyze images of women's clothing listed on e-commerce sites. The system according to feature 1.

5. The determination unit, The presence or absence of a pocket is determined based on the image analyzed by the aforementioned analysis unit. The system according to feature 1.

6. The aforementioned size analysis unit, Analyze the size of the pocket The system according to feature 1.

7. The generating unit is Generate images so that users can visually confirm the size of the pockets. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of image analysis based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing images, consider the design and color of the clothing to improve the accuracy of the analysis. The system according to feature 1.

10. The aforementioned analysis unit, By integrating and analyzing multiple images during image analysis, the presence or absence of pockets can be determined more accurately. The system according to feature 1.

Images