system

Abstract

The system according to this embodiment aims to enable users to choose clothing efficiently that suits their preferences and the occasion. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a display unit. The collection unit collects the user's clothing data. The analysis unit analyzes the data collected by the collection unit and identifies the user's preferences. The suggestion unit suggests clothing appropriate for the time, place, and occasion based on the analysis results obtained by the analysis unit. The display unit visually displays the clothing suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, clothing selection that suits the user's preferences and TPO has not been sufficiently carried out, and there is a risk of clothing loss.

[0005] The system according to the embodiment aims to enable waste - free clothing selection that suits the user's preferences and TPO.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a display unit. The data collection unit collects the user's clothing data. The analysis unit analyzes the data collected by the data collection unit to identify the user's preferences. The suggestion unit suggests appropriate clothing for the occasion based on the analysis results obtained by the analysis unit. The display unit visually displays the clothing suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to choose clothing efficiently, tailored to their preferences and the occasion. [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, and the like. The communication I / F controls communication between a plurality of 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) An AI system according to an embodiment of the present invention is a system that reduces clothing waste and proposes the optimal fashion for each individual user. This system collects data on the clothing that the user usually prefers to wear, and the AI ​​analyzes this data to analyze the user's preferences. Based on the analysis results, it proposes the optimal clothing that suits the TPO (Time, Place, Occasion). This proposal includes not only new clothes but also recycled items. Furthermore, the proposed clothing is displayed in a visually easy-to-understand manner so that the user can easily select it. This helps to avoid unnecessary purchases and reduce clothing waste. For example, the system collects data on the clothing that the user usually prefers to wear. This data includes characteristics such as the chosen color, design, silhouette, and material. For example, the colors and designs that the user often wears, and clothing from specific brands are collected as data. This data is input into the AI. Next, the AI ​​analyzes the collected data to analyze the user's preferences. The AI ​​learns the characteristics such as the colors, designs, silhouettes, and materials that the user has chosen and identifies the user's preferences. For example, it analyzes the patterns of colors and designs that the user often chooses to understand the user's preferences. Based on the analysis results, the AI ​​proposes the optimal clothing that suits the TPO. For example, the service suggests outfits suitable for various situations, from everyday wear to special events. Specifically, it suggests outfits for everyday use, sportswear, formal occasions, and more. The suggested outfits are displayed visually in an easy-to-understand way, making it easy for users to select. Furthermore, the suggested outfits include not only new clothes but also recycled items. This allows for choices that are both cost-effective and environmentally conscious. For example, users can reuse unwanted clothes by using recycling shops or flea market apps. This system makes choosing clothes every day easier, more enjoyable, and more efficient for users. It also reduces clothing waste by avoiding unnecessary purchases. For example, when users choose an outfit from the suggested options, they can check whether it is less likely to cause clothing waste. This enables sustainable fashion consumption in today's world where clothing waste is a social problem.This allows the AI ​​system to collect and analyze user clothing data, suggest appropriate attire for different occasions, and display it visually, thereby reducing clothing waste and suggesting the most suitable fashion for each individual user.

[0029] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a display unit. The data collection unit collects the user's clothing data. For example, the data collection unit collects data on the clothing the user usually prefers to wear. For example, the data collection unit can collect data on the colors and designs the user frequently wears, and clothing from specific brands. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials chosen by the user. The analysis unit analyzes the data collected by the data collection unit to identify the user's preferences. For example, the analysis unit can learn the characteristics such as the colors, designs, silhouettes, and materials chosen by the user to identify the user's preferences. For example, the analysis unit can identify the user's preferences using a machine learning algorithm. For example, the analysis unit can identify the user's preferences using data mining techniques. The suggestion unit suggests appropriate clothing for different occasions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest clothing suitable for various situations, from everyday styles to special events. The suggestion unit can suggest clothing suitable for various occasions, such as everyday wear, sportswear, and formal wear for weddings and funerals. The suggestion unit can suggest not only new clothes but also recycled items. The display unit visually displays the clothing suggested by the suggestion unit. The display unit can visually display the suggested clothing in an easy-to-understand manner. The display unit can visually display the suggested clothing using, for example, images, 3D models, animations, etc. As a result, the AI ​​system according to the embodiment can collect and analyze the user's clothing data, suggest clothing appropriate for the time, place, and occasion, and display it visually, thereby reducing clothing waste and suggesting the optimal fashion for each individual user.

[0030] The data collection unit collects user clothing data. Specifically, it collects data on the clothing that users usually prefer to wear. For example, it can collect data on the colors and designs that users frequently wear, as well as clothing from specific brands. The data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials that users choose. This includes data from the user's online shopping history, fashion photos shared on social media, and even data from IoT devices such as smart mirrors and smart closets. This data is collected securely with the user's permission and encrypted to protect privacy. The data collection unit integrates information from these diverse data sources to gain a comprehensive understanding of the user's fashion preferences. Furthermore, the data collection unit can also take into account external factors such as season, weather, and specific events. For example, it can collect seasonal preferences, such as the light clothing a user prefers to wear in summer and the heavier clothing they prefer in winter. The data collection unit can also take into account the user's lifestyle and activity patterns. For example, if a user frequently plays sports, it can collect data on appropriate clothing for that activity. This allows the data collection unit to collect data that meets the diverse needs of users and provide it to the analysis unit.

[0031] The analysis unit analyzes data collected by the data collection unit to identify user preferences. Specifically, it can identify user preferences by learning the characteristics of colors, designs, silhouettes, materials, etc., that users have chosen. The analysis unit uses machine learning algorithms to identify user preferences. For example, it can use clustering algorithms to group user preference patterns and compare them with other users who have similar tastes. It can also use deep learning technology to extract user preferences from image data. For example, it can analyze fashion photos uploaded by users and extract characteristics such as color, design, and silhouette. Furthermore, the analysis unit can use data mining technology to discover hidden patterns from users' past purchase and browsing history, allowing it to identify user preferences more accurately. For example, if a user tends to frequently choose a particular brand or design, this information can be used to identify the user's preferences. The analysis unit can also improve the accuracy of its analysis results by incorporating user feedback. For example, by having users evaluate suggested clothing, the analysis unit learns from these evaluations and incorporates them into future suggestions. This allows the analysis unit to continuously learn user preferences and provide more accurate analysis results.

[0032] The suggestion department proposes appropriate attire for different occasions (TPO) based on the analysis results obtained by the analysis department. Specifically, it can propose clothing suitable for various situations, from everyday wear to special events. For example, it can propose clothing for everyday use, sports, formal occasions, and more. The suggestion department selects the optimal clothing based on the user's preferences and lifestyle. For example, if the user is attending a business meeting, it will propose a formal suit or dress, and for a casual weekend, it will propose relaxed attire. In addition, the suggestion department can propose not only new clothing but also recycled items. This allows users to have environmentally conscious options. Furthermore, the suggestion department can also take into account external factors such as the season, weather, and specific events. For example, it will propose waterproof clothing on rainy days and breathable, lightweight clothing on hot summer days. The suggestion department can also improve the accuracy of its suggestions by incorporating user feedback. For example, by having users evaluate the suggested clothing, the department learns from the evaluation and reflects it in future suggestions. This allows the suggestion department to propose the optimal clothing that meets the user's needs and improve user satisfaction.

[0033] The display unit visually displays the clothing suggested by the suggestion unit. Specifically, it can display the suggested clothing in a visually easy-to-understand manner. For example, it can visually display the suggested clothing using images, 3D models, animations, etc. The display unit provides realistic visuals so that the user can feel as if they are actually trying on the suggested clothing. For example, it can create an avatar of the user and dress that avatar in the suggested clothing so that the user can see how it looks on them. In addition, the display unit can use augmented reality (AR) technology to overlay the suggested clothing onto the user's body through their smartphone or tablet. This allows the user to check the fit and appearance of the suggested clothing without actually trying it on. Furthermore, the display unit can also display detailed information about the suggested clothing. For example, it can provide information such as the material of the clothing, size, price, and where to buy it. The display unit can also display links to online shopping sites so that the user can easily purchase the suggested clothing. In this way, the display unit allows the user to visually check the suggested clothing, obtain the necessary information, and easily make a purchase.

[0034] The proposal department can propose not only new clothing but also recycled items. For example, the proposal department can reuse unwanted clothing by using recycling shops or flea market apps. By proposing recycled items, the proposal department can make choices that are both cost-effective and environmentally conscious. The proposal department can clarify specific definitions and criteria for recycled items, such as including used clothing and remade items. This allows for choices that are both cost-effective and environmentally conscious when proposing recycled items.

[0035] The display unit can visually display the suggested clothing in an easy-to-understand manner. For example, the display unit can visually display the suggested clothing using color coding, icons, text descriptions, etc. The display unit can visually display the suggested clothing using images, 3D models, animations, etc. For example, the display unit can visually display the suggested clothing in an easy-to-understand manner so that the user can easily select it. This visually clear display allows the user to easily make a selection.

[0036] The data collection unit can collect data such as the colors and designs that users frequently wear, and the brands of clothing they often wear. For example, the data collection unit can collect data such as the colors and designs that users frequently wear, and the brands of clothing they often wear. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials that users choose. By collecting data such as the colors and designs that users frequently wear, and the brands of clothing they often wear, the data collection unit can more accurately understand the user's preferences. For example, the data collection unit can clarify specific criteria for what is frequently worn, such as how many times a week or how many times a month. This allows for a more accurate understanding of the user's preferences by collecting data such as the colors and designs that users frequently wear, and the brands of clothing they often wear.

[0037] The analysis unit can learn the characteristics of the user, such as the colors, designs, silhouettes, and materials, and identify the user's preferences. For example, the analysis unit can learn the characteristics of the user, such as the colors, designs, silhouettes, and materials, and identify the user's preferences. The analysis unit can identify the user's preferences, for example, using machine learning algorithms. The analysis unit can identify the user's preferences, for example, using data mining techniques. The analysis unit can clarify specific methods for learning features, for example, including machine learning algorithms and data mining techniques. This allows the analysis unit to identify the user's preferences by learning the characteristics of the user, such as the colors, designs, silhouettes, and materials.

[0038] The suggestion department can propose clothing suitable for various occasions, such as everyday wear, sportswear, and formal events. For example, the suggestion department can propose clothing that suits a wide range of situations, from everyday styles to special events. For example, the suggestion department can propose clothing suitable for various occasions, such as everyday wear, sportswear, and formal events. The suggestion department can clearly define specific examples of various occasions, such as everyday wear, sports, and formal events. This allows users to select the most appropriate clothing for the time, place, and occasion by suggesting clothing suitable for various situations.

[0039] The data collection unit can analyze the user's past clothing data and select the optimal collection method. For example, the data collection unit can prioritize collecting data on clothing that the user has frequently worn in the past. For example, the data collection unit can collect data related to specific seasons or events from the user's past clothing data. For example, the data collection unit can analyze the user's past clothing data and collect data on specific brands or designs. This allows the optimal collection method to be selected by analyzing the user's past clothing data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past clothing data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter clothing data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting work-related clothing data based on the user's current lifestyle. For example, the data collection unit can collect data on specific styles and trends based on the user's areas of interest. For example, the data collection unit can collect data on specific materials and colors based on the user's lifestyle and areas of interest. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting clothing data. For example, if the user lives in a cold region, the data collection unit can prioritize the collection of winter clothing data. For example, if the user lives in an urban area, the data collection unit can prioritize the collection of business casual clothing data. For example, if the user lives in a resort area, the data collection unit can prioritize the collection of resort wear data. This allows for the collection of more relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting clothing data. For example, the data collection unit can prioritize collecting data on clothing that the user frequently shares on social media. For example, the data collection unit can collect data based on the styles of fashion influencers that the user follows. For example, the data collection unit can collect relevant clothing data based on information about events and parties that the user attends. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the clothing data during the analysis. For example, the analysis unit can perform a detailed analysis of clothing data for important events. For example, the analysis unit can perform a simplified analysis of everyday clothing data. For example, the analysis unit can perform a detailed analysis of clothing data for a specific brand or design. By adjusting the level of detail of the analysis based on the importance of the clothing data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the clothing data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of clothing data during analysis. For example, the analysis unit can apply a casual wear analysis algorithm to casual wear data. For example, the analysis unit can apply a formal wear analysis algorithm to formal wear data. For example, the analysis unit can apply a sportswear analysis algorithm to sportswear data. By applying different analysis algorithms depending on the category of clothing data, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of clothing data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of clothing data during the analysis. For example, the analysis unit can prioritize the analysis of the most recent clothing data. For example, the analysis unit can prioritize the analysis of clothing data according to the season. For example, the analysis unit can prioritize the analysis of clothing data related to a specific event. By determining the priority of analysis based on the submission timing of clothing data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the clothing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of clothing data during the analysis. For example, the analysis unit can prioritize the analysis of data that is highly relevant to the user's preferences. For example, the analysis unit can prioritize the analysis of data that is highly relevant based on the user's past selection history. For example, the analysis unit can prioritize the analysis of data that is highly relevant based on the user's current areas of interest. By adjusting the order of analysis based on the relevance of clothing data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of clothing data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, it can provide detailed suggestions for clothing for important events, concise suggestions for everyday clothing, and detailed suggestions for clothing from specific brands or designs. By adjusting the level of detail based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the clothing into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0048] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm for casual wear to casual wear. For example, the suggestion unit can apply a suggestion algorithm for formal wear to formal wear. For example, the suggestion unit can apply a suggestion algorithm for sportswear to sportswear. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the clothing category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0049] The proposal department can prioritize proposals based on the timing of clothing submissions. For example, the proposal department can prioritize the latest clothing. For example, the proposal department can prioritize seasonal clothing. For example, the proposal department can prioritize clothing related to a specific event. By prioritizing proposals based on the timing of clothing submissions, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of clothing submissions into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant to the user's preferences. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant based on the user's past selection history. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant based on the user's current areas of interest. By adjusting the order of suggestions based on the relevance of the clothing items, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the clothing items into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0051] The display unit can select the optimal display method by referring to the user's past selection history when displaying information. For example, the display unit can prioritize display styles that the user has previously preferred. For example, the display unit can provide highly relevant display methods based on the user's past selection history. For example, the display unit can analyze the user's past selection history and provide the optimal display method. This allows for the selection of a more appropriate display method by referring to the user's past selection history. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past selection history into a generating AI and have the generating AI select the optimal display method.

[0052] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows for the selection of a more appropriate display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, it can prioritize collecting data on clothing the user has purchased in the past. It can collect data related to specific seasons or events from the user's past purchase history. It can analyze the user's past purchase history and collect data on specific brands or designs. This allows the optimal data collection method to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI and have the generating AI select the optimal data collection method.

[0055] The data collection unit can collect data based on the user's lifestyle. For example, if the user enjoys outdoor activities, it can prioritize collecting data on outdoor wear. If the user values ​​business attire, it can prioritize collecting data on business wear. If the user prefers casual wear, it can prioritize collecting data on casual wear. By collecting data based on the user's lifestyle, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle data into a generating AI and have the generating AI collect highly relevant data.

[0056] The analysis unit can analyze the user's past selection history and select the optimal analysis method. For example, it can prioritize the analysis of data on clothing the user has previously selected. It can analyze data related to specific seasons or events from the user's past selection history. It can analyze data on specific brands or designs by analyzing the user's past selection history. In this way, the optimal analysis method can be selected by analyzing the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past selection history into a generating AI and have the generating AI select the optimal analysis method.

[0057] The suggestion unit can adjust its suggestions based on the user's geographical location. For example, if the user lives in a cold region, it can prioritize suggesting winter clothing. If the user lives in an urban area, it can prioritize suggesting business casual attire. If the user lives in a resort area, it can prioritize suggesting resort wear. This allows for more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI adjust the suggestions to be more relevant.

[0058] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. This allows for the selection of a more appropriate display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0059] The suggestion unit can analyze a user's social media activity and make relevant suggestions. For example, it can prioritize suggesting clothing data that the user frequently shares on social media. It can also make suggestions based on the styles of fashion influencers the user follows. It can suggest relevant clothing based on information about events and parties the user attends. In this way, relevant suggestions become possible by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant suggestions.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the user's clothing data. For example, the data collection unit collects data on the clothing the user usually likes to wear. For example, the data collection unit can collect data on the colors and designs the user frequently wears, and clothing from specific brands. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials chosen by the user. Step 2: The analysis unit analyzes the data collected by the collection unit to identify user preferences. The analysis unit can identify user preferences by, for example, learning the characteristics of the colors, designs, silhouettes, and materials chosen by the user. The analysis unit can identify user preferences by, for example, using machine learning algorithms. The analysis unit can identify user preferences by, for example, using data mining techniques. Step 3: The proposal department proposes appropriate attire for the time, place, and occasion based on the analysis results obtained by the analysis department. The proposal department can propose attire suitable for various situations, such as everyday wear to special events. The proposal department can propose attire suitable for various occasions, such as everyday wear, sportswear, and formal wear for weddings and funerals. The proposal department can propose not only new clothing but also recycled items. Step 4: The display unit visually displays the clothing proposed by the proposal unit. The display unit can, for example, visually display the proposed clothing in an easy-to-understand manner. The display unit can, for example, visually display the proposed clothing using images, 3D models, animations, etc.

[0062] (Example of form 2) An AI system according to an embodiment of the present invention is a system that reduces clothing waste and proposes the optimal fashion for each individual user. This system collects data on the clothing that the user usually prefers to wear, and the AI ​​analyzes this data to analyze the user's preferences. Based on the analysis results, it proposes the optimal clothing that suits the TPO (Time, Place, Occasion). This proposal includes not only new clothes but also recycled items. Furthermore, the proposed clothing is displayed in a visually easy-to-understand manner so that the user can easily select it. This helps to avoid unnecessary purchases and reduce clothing waste. For example, the system collects data on the clothing that the user usually prefers to wear. This data includes characteristics such as the chosen color, design, silhouette, and material. For example, the colors and designs that the user often wears, and clothing from specific brands are collected as data. This data is input into the AI. Next, the AI ​​analyzes the collected data to analyze the user's preferences. The AI ​​learns the characteristics such as the colors, designs, silhouettes, and materials that the user has chosen and identifies the user's preferences. For example, it analyzes the patterns of colors and designs that the user often chooses to understand the user's preferences. Based on the analysis results, the AI ​​proposes the optimal clothing that suits the TPO. For example, the service suggests outfits suitable for various situations, from everyday wear to special events. Specifically, it suggests outfits for everyday use, sportswear, formal occasions, and more. The suggested outfits are displayed visually in an easy-to-understand way, making it easy for users to select. Furthermore, the suggested outfits include not only new clothes but also recycled items. This allows for choices that are both cost-effective and environmentally conscious. For example, users can reuse unwanted clothes by using recycling shops or flea market apps. This system makes choosing clothes every day easier, more enjoyable, and more efficient for users. It also reduces clothing waste by avoiding unnecessary purchases. For example, when users choose an outfit from the suggested options, they can check whether it is less likely to cause clothing waste. This enables sustainable fashion consumption in today's world where clothing waste is a social problem.This allows the AI ​​system to collect and analyze user clothing data, suggest appropriate attire for different occasions, and display it visually, thereby reducing clothing waste and suggesting the most suitable fashion for each individual user.

[0063] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a display unit. The data collection unit collects the user's clothing data. For example, the data collection unit collects data on the clothing the user usually prefers to wear. For example, the data collection unit can collect data on the colors and designs the user frequently wears, and clothing from specific brands. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials chosen by the user. The analysis unit analyzes the data collected by the data collection unit to identify the user's preferences. For example, the analysis unit can learn the characteristics such as the colors, designs, silhouettes, and materials chosen by the user to identify the user's preferences. For example, the analysis unit can identify the user's preferences using a machine learning algorithm. For example, the analysis unit can identify the user's preferences using data mining techniques. The suggestion unit suggests appropriate clothing for different occasions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest clothing suitable for various situations, from everyday styles to special events. The suggestion unit can suggest clothing suitable for various occasions, such as everyday wear, sportswear, and formal wear for weddings and funerals. The suggestion unit can suggest not only new clothes but also recycled items. The display unit visually displays the clothing suggested by the suggestion unit. The display unit can visually display the suggested clothing in an easy-to-understand manner. The display unit can visually display the suggested clothing using, for example, images, 3D models, animations, etc. As a result, the AI ​​system according to the embodiment can collect and analyze the user's clothing data, suggest clothing appropriate for the time, place, and occasion, and display it visually, thereby reducing clothing waste and suggesting the optimal fashion for each individual user.

[0064] The data collection unit collects user clothing data. Specifically, it collects data on the clothing that users usually prefer to wear. For example, it can collect data on the colors and designs that users frequently wear, as well as clothing from specific brands. The data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials that users choose. This includes data from the user's online shopping history, fashion photos shared on social media, and even data from IoT devices such as smart mirrors and smart closets. This data is collected securely with the user's permission and encrypted to protect privacy. The data collection unit integrates information from these diverse data sources to gain a comprehensive understanding of the user's fashion preferences. Furthermore, the data collection unit can also take into account external factors such as season, weather, and specific events. For example, it can collect seasonal preferences, such as the light clothing a user prefers to wear in summer and the heavier clothing they prefer in winter. The data collection unit can also take into account the user's lifestyle and activity patterns. For example, if a user frequently plays sports, it can collect data on appropriate clothing for that activity. This allows the data collection unit to collect data that meets the diverse needs of users and provide it to the analysis unit.

[0065] The analysis unit analyzes data collected by the data collection unit to identify user preferences. Specifically, it can identify user preferences by learning the characteristics of colors, designs, silhouettes, materials, etc., that users have chosen. The analysis unit uses machine learning algorithms to identify user preferences. For example, it can use clustering algorithms to group user preference patterns and compare them with other users who have similar tastes. It can also use deep learning technology to extract user preferences from image data. For example, it can analyze fashion photos uploaded by users and extract characteristics such as color, design, and silhouette. Furthermore, the analysis unit can use data mining technology to discover hidden patterns from users' past purchase and browsing history, allowing it to identify user preferences more accurately. For example, if a user tends to frequently choose a particular brand or design, this information can be used to identify the user's preferences. The analysis unit can also improve the accuracy of its analysis results by incorporating user feedback. For example, by having users evaluate suggested clothing, the analysis unit learns from these evaluations and incorporates them into future suggestions. This allows the analysis unit to continuously learn user preferences and provide more accurate analysis results.

[0066] The suggestion department proposes appropriate attire for different occasions (TPO) based on the analysis results obtained by the analysis department. Specifically, it can propose clothing suitable for various situations, from everyday wear to special events. For example, it can propose clothing for everyday use, sports, formal occasions, and more. The suggestion department selects the optimal clothing based on the user's preferences and lifestyle. For example, if the user is attending a business meeting, it will propose a formal suit or dress, and for a casual weekend, it will propose relaxed attire. In addition, the suggestion department can propose not only new clothing but also recycled items. This allows users to have environmentally conscious options. Furthermore, the suggestion department can also take into account external factors such as the season, weather, and specific events. For example, it will propose waterproof clothing on rainy days and breathable, lightweight clothing on hot summer days. The suggestion department can also improve the accuracy of its suggestions by incorporating user feedback. For example, by having users evaluate the suggested clothing, the department learns from the evaluation and reflects it in future suggestions. This allows the suggestion department to propose the optimal clothing that meets the user's needs and improve user satisfaction.

[0067] The display unit visually displays the clothing suggested by the suggestion unit. Specifically, it can display the suggested clothing in a visually easy-to-understand manner. For example, it can visually display the suggested clothing using images, 3D models, animations, etc. The display unit provides realistic visuals so that the user can feel as if they are actually trying on the suggested clothing. For example, it can create an avatar of the user and dress that avatar in the suggested clothing so that the user can see how it looks on them. In addition, the display unit can use augmented reality (AR) technology to overlay the suggested clothing onto the user's body through their smartphone or tablet. This allows the user to check the fit and appearance of the suggested clothing without actually trying it on. Furthermore, the display unit can also display detailed information about the suggested clothing. For example, it can provide information such as the material of the clothing, size, price, and where to buy it. The display unit can also display links to online shopping sites so that the user can easily purchase the suggested clothing. In this way, the display unit allows the user to visually check the suggested clothing, obtain the necessary information, and easily make a purchase.

[0068] The proposal department can propose not only new clothing but also recycled items. For example, the proposal department can reuse unwanted clothing by using recycling shops or flea market apps. By proposing recycled items, the proposal department can make choices that are both cost-effective and environmentally conscious. The proposal department can clarify specific definitions and criteria for recycled items, such as including used clothing and remade items. This allows for choices that are both cost-effective and environmentally conscious when proposing recycled items.

[0069] The display unit can visually display the suggested clothing in an easy-to-understand manner. For example, the display unit can visually display the suggested clothing using color coding, icons, text descriptions, etc. The display unit can visually display the suggested clothing using images, 3D models, animations, etc. For example, the display unit can visually display the suggested clothing in an easy-to-understand manner so that the user can easily select it. This visually clear display allows the user to easily make a selection.

[0070] The data collection unit can collect data such as the colors and designs that users frequently wear, and the brands of clothing they often wear. For example, the data collection unit can collect data such as the colors and designs that users frequently wear, and the brands of clothing they often wear. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials that users choose. By collecting data such as the colors and designs that users frequently wear, and the brands of clothing they often wear, the data collection unit can more accurately understand the user's preferences. For example, the data collection unit can clarify specific criteria for what is frequently worn, such as how many times a week or how many times a month. This allows for a more accurate understanding of the user's preferences by collecting data such as the colors and designs that users frequently wear, and the brands of clothing they often wear.

[0071] The analysis unit can learn the characteristics of the user, such as the colors, designs, silhouettes, and materials, and identify the user's preferences. For example, the analysis unit can learn the characteristics of the user, such as the colors, designs, silhouettes, and materials, and identify the user's preferences. The analysis unit can identify the user's preferences, for example, using machine learning algorithms. The analysis unit can identify the user's preferences, for example, using data mining techniques. The analysis unit can clarify specific methods for learning features, for example, including machine learning algorithms and data mining techniques. This allows the analysis unit to identify the user's preferences by learning the characteristics of the user, such as the colors, designs, silhouettes, and materials.

[0072] The suggestion department can propose clothing suitable for various occasions, such as everyday wear, sportswear, and formal events. For example, the suggestion department can propose clothing that suits a wide range of situations, from everyday styles to special events. For example, the suggestion department can propose clothing suitable for various occasions, such as everyday wear, sportswear, and formal events. The suggestion department can clearly define specific examples of various occasions, such as everyday wear, sports, and formal events. This allows users to select the most appropriate clothing for the time, place, and occasion by suggesting clothing suitable for various situations.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of clothing data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can collect data immediately to accurately reflect their preferences. For example, if the user is in a hurry, the data collection unit can collect the necessary data in a short amount of time. By adjusting the collection timing based on the user's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The data collection unit can analyze the user's past clothing data and select the optimal collection method. For example, the data collection unit can prioritize collecting data on clothing that the user has frequently worn in the past. For example, the data collection unit can collect data related to specific seasons or events from the user's past clothing data. For example, the data collection unit can analyze the user's past clothing data and collect data on specific brands or designs. This allows the optimal collection method to be selected by analyzing the user's past clothing data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past clothing data into a generating AI and have the generating AI select the optimal collection method.

[0075] The data collection unit can filter clothing data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting work-related clothing data based on the user's current lifestyle. For example, the data collection unit can collect data on specific styles and trends based on the user's areas of interest. For example, the data collection unit can collect data on specific materials and colors based on the user's lifestyle and areas of interest. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of clothing data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data on relaxing clothing. For example, if the user is relaxed, the data collection unit can prioritize collecting data on everyday clothing. For example, if the user is in a hurry, the data collection unit can prioritize collecting data on work attire. This allows for more appropriate data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting clothing data. For example, if the user lives in a cold region, the data collection unit can prioritize the collection of winter clothing data. For example, if the user lives in an urban area, the data collection unit can prioritize the collection of business casual clothing data. For example, if the user lives in a resort area, the data collection unit can prioritize the collection of resort wear data. This allows for the collection of more relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0078] The data collection unit can analyze the user's social media activity and collect relevant data when collecting clothing data. For example, the data collection unit can prioritize collecting data on clothing that the user frequently shares on social media. For example, the data collection unit can collect data based on the styles of fashion influencers that the user follows. For example, the data collection unit can collect relevant clothing data based on information about events and parties that the user attends. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit can provide quick analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the clothing data during the analysis. For example, the analysis unit can perform a detailed analysis of clothing data for important events. For example, the analysis unit can perform a simplified analysis of everyday clothing data. For example, the analysis unit can perform a detailed analysis of clothing data for a specific brand or design. By adjusting the level of detail of the analysis based on the importance of the clothing data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the clothing data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of clothing data during analysis. For example, the analysis unit can apply a casual wear analysis algorithm to casual wear data. For example, the analysis unit can apply a formal wear analysis algorithm to formal wear data. For example, the analysis unit can apply a sportswear analysis algorithm to sportswear data. By applying different analysis algorithms depending on the category of clothing data, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of clothing data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is stressed, the analysis unit can provide a concise analysis. For example, if the user is in a hurry, the analysis unit can provide a rapid analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can determine the priority of analysis based on the submission timing of clothing data during the analysis. For example, the analysis unit can prioritize the analysis of the most recent clothing data. For example, the analysis unit can prioritize the analysis of clothing data according to the season. For example, the analysis unit can prioritize the analysis of clothing data related to a specific event. By determining the priority of analysis based on the submission timing of clothing data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the clothing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of clothing data during the analysis. For example, the analysis unit can prioritize the analysis of data that is highly relevant to the user's preferences. For example, the analysis unit can prioritize the analysis of data that is highly relevant based on the user's past selection history. For example, the analysis unit can prioritize the analysis of data that is highly relevant based on the user's current areas of interest. By adjusting the order of analysis based on the relevance of clothing data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of clothing data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is stressed, the suggestion function can provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion function can provide suggestions quickly. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, it can provide detailed suggestions for clothing for important events, concise suggestions for everyday clothing, and detailed suggestions for clothing from specific brands or designs. By adjusting the level of detail based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the clothing into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0087] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm for casual wear to casual wear. For example, the suggestion unit can apply a suggestion algorithm for formal wear to formal wear. For example, the suggestion unit can apply a suggestion algorithm for sportswear to sportswear. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the clothing category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0088] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is stressed, the suggestion unit can provide concise suggestions. For example, if the user is in a hurry, the suggestion unit can provide quick suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The proposal department can prioritize proposals based on the timing of clothing submissions. For example, the proposal department can prioritize the latest clothing. For example, the proposal department can prioritize seasonal clothing. For example, the proposal department can prioritize clothing related to a specific event. By prioritizing proposals based on the timing of clothing submissions, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of clothing submissions into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0090] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant to the user's preferences. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant based on the user's past selection history. For example, the suggestion unit can prioritize suggesting clothing items that are highly relevant based on the user's current areas of interest. By adjusting the order of suggestions based on the relevance of the clothing items, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the clothing items into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0091] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit can provide a detailed display. For example, if the user is stressed, the display unit can provide a concise and to-the-point display. For example, if the user is in a hurry, the display unit can provide a rapid display. By adjusting the display method based on 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. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The display unit can select the optimal display method by referring to the user's past selection history when displaying information. For example, the display unit can prioritize display styles that the user has previously preferred. For example, the display unit can provide highly relevant display methods based on the user's past selection history. For example, the display unit can analyze the user's past selection history and provide the optimal display method. This allows for the selection of a more appropriate display method by referring to the user's past selection history. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past selection history into a generating AI and have the generating AI select the optimal display method.

[0093] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit can prioritize displaying detailed information. For example, if the user is stressed, the display unit can prioritize displaying concise information. For example, if the user is in a hurry, the display unit can prioritize providing information that needs to be displayed quickly. This allows for more appropriate display by determining the display priority based on 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.

[0094] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows for the selection of a more appropriate display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The suggestion function can estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing clothing. If the user is relaxed, it can suggest everyday or casual clothing. If the user is in a hurry, it can suggest clothing that is easy to put on. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, it can prioritize collecting data on clothing the user has purchased in the past. It can collect data related to specific seasons or events from the user's past purchase history. It can analyze the user's past purchase history and collect data on specific brands or designs. This allows the optimal data collection method to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI and have the generating AI select the optimal data collection method.

[0098] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize analyzing data on relaxing clothing. If the user is relaxed, it can prioritize analyzing data on everyday clothing. If the user is in a hurry, it can prioritize analyzing data on work attire. By prioritizing analysis based on the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, suggestions can be made when the user is relaxed. If the user is relaxed, suggestions can be made immediately. If the user is in a hurry, suggestions can be made quickly. By adjusting the timing of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The display unit can estimate the user's emotions and adjust the display content based on the estimated emotions. For example, if the user is stressed, it can provide relaxing display content. If the user is relaxed, it can provide detailed display content. If the user is in a hurry, it can provide concise display content. By adjusting the display content based on the user's emotions, more appropriate displays become possible. 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.

[0101] The data collection unit can collect data based on the user's lifestyle. For example, if the user enjoys outdoor activities, it can prioritize collecting data on outdoor wear. If the user values ​​business attire, it can prioritize collecting data on business wear. If the user prefers casual wear, it can prioritize collecting data on casual wear. By collecting data based on the user's lifestyle, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle data into a generating AI and have the generating AI collect highly relevant data.

[0102] The analysis unit can analyze the user's past selection history and select the optimal analysis method. For example, it can prioritize the analysis of data on clothing the user has previously selected. It can analyze data related to specific seasons or events from the user's past selection history. It can analyze data on specific brands or designs by analyzing the user's past selection history. In this way, the optimal analysis method can be selected by analyzing the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past selection history into a generating AI and have the generating AI select the optimal analysis method.

[0103] The suggestion unit can adjust its suggestions based on the user's geographical location. For example, if the user lives in a cold region, it can prioritize suggesting winter clothing. If the user lives in an urban area, it can prioritize suggesting business casual attire. If the user lives in a resort area, it can prioritize suggesting resort wear. This allows for more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI adjust the suggestions to be more relevant.

[0104] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. This allows for the selection of a more appropriate display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0105] The suggestion unit can analyze a user's social media activity and make relevant suggestions. For example, it can prioritize suggesting clothing data that the user frequently shares on social media. It can also make suggestions based on the styles of fashion influencers the user follows. It can suggest relevant clothing based on information about events and parties the user attends. In this way, relevant suggestions become possible by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant suggestions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects the user's clothing data. For example, the data collection unit collects data on the clothing the user usually likes to wear. For example, the data collection unit can collect data on the colors and designs the user frequently wears, and clothing from specific brands. For example, the data collection unit can collect characteristics such as the colors, designs, silhouettes, and materials chosen by the user. Step 2: The analysis unit analyzes the data collected by the collection unit to identify user preferences. The analysis unit can identify user preferences by, for example, learning the characteristics of the colors, designs, silhouettes, and materials chosen by the user. The analysis unit can identify user preferences by, for example, using machine learning algorithms. The analysis unit can identify user preferences by, for example, using data mining techniques. Step 3: The proposal department proposes appropriate attire for the time, place, and occasion based on the analysis results obtained by the analysis department. The proposal department can propose attire suitable for various situations, such as everyday wear to special events. The proposal department can propose attire suitable for various occasions, such as everyday wear, sportswear, and formal wear for weddings and funerals. The proposal department can propose not only new clothing but also recycled items. Step 4: The display unit visually displays the clothing proposed by the proposal unit. The display unit can, for example, visually display the proposed clothing in an easy-to-understand manner. The display unit can, for example, visually display the proposed clothing using images, 3D models, animations, etc.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's clothing data using the camera 42 and reception device 38 of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's preferences. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests clothing appropriate for the time, place, and occasion based on the analysis results. The display unit visually displays the suggested clothing using, for example, the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's clothing data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's preferences. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests clothing appropriate for the time, place, and occasion based on the analysis results. The display unit visually displays the suggested clothing using, for example, the speaker 240 and display of the smart glasses 214. 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.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's clothing data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's preferences. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests clothing appropriate for the time, place, and occasion based on the analysis results. The display unit visually displays the suggested clothing using, for example, the display 343 and speaker 240 of the headset terminal 314. 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.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[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 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.

[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 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.

[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 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and display unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's clothing data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's preferences. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests clothing appropriate for the time, place, and occasion based on the analysis results. The display unit visually displays the suggested clothing using, for example, the speaker 240 or display device of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A collection unit that collects user clothing data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user preferences, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes clothing appropriate for the time, place, and occasion. The system includes a display unit that visually displays the clothing proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We offer not only new clothes but also recycled items. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The suggested outfits are displayed in a visually easy-to-understand manner. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system collects data on the colors and designs that users frequently wear, as well as clothing from specific brands. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It learns the characteristics of the user, such as the colors, designs, silhouettes, and materials, to identify the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We offer clothing suitable for various occasions, including everyday wear, sportswear, and formal events such as weddings and funerals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of clothing data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past clothing data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting clothing data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the clothing data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting clothing data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting clothing data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the clothing data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of clothing data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the clothing data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of clothing data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline for clothing-related submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying content, the system refers to the user's past selection history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 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. A collection unit that collects user clothing data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user preferences, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes clothing appropriate for the time, place, and occasion. The system includes a display unit that visually displays the clothing proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We offer not only new clothes but also recycled items. The system according to feature 1.

3. The aforementioned display unit is The suggested outfits are displayed in a visually easy-to-understand manner. The system according to feature 1.

4. The aforementioned collection unit is The system collects data on the colors and designs that users frequently wear, as well as clothing from specific brands. The system according to feature 1.

5. The aforementioned analysis unit, It learns the characteristics of the user, such as the colors, designs, silhouettes, and materials, to identify the user's preferences. The system according to feature 1.

6. The aforementioned proposal section is, We offer clothing suitable for various occasions, including everyday wear, sportswear, and formal events such as weddings and funerals. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of clothing data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past clothing data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting clothing data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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