system

Abstract

The system according to this embodiment aims to provide personalized fashion advice tailored to the user's preferences, body type, and current trends. [Solution] The system according to the embodiment comprises an understanding unit, a trend recognition unit, and a suggestion unit. The understanding unit understands the user's preferences and body type. The trend recognition unit grasps the latest trends based on the information understood by the understanding unit. The suggestion unit provides personalized fashion advice based on the trend information grasped by the trend recognition 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, there is a problem that individualized style proposals are not made for users who are not good at fashion selection.

[0005] The system according to the embodiment aims to provide individualized fashion advice according to the preferences, body types, and trends of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an understanding unit, a trend recognition unit, and a suggestion unit. The understanding unit understands the user's preferences and body type. The trend recognition unit grasps the latest trends based on the information understood by the understanding unit. The suggestion unit provides personalized fashion advice based on the trend information grasped by the trend recognition unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized fashion advice tailored to the user's preferences, body type, and current trends. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion suggestion system according to an embodiment of the present invention is a system that suggests coordinated outfits tailored to the user's preferences, body type, and current trends. This fashion suggestion system understands the user's preferences and body type, grasps the latest trends, and provides personalized fashion advice. For example, the fashion suggestion system's AI agent learns and grows from feedback in order to deeply understand the user's preferences and body type. Next, the fashion suggestion system's AI agent constantly grasps the latest trends and possesses insights equivalent to those of a fashion professional. Then, the fashion suggestion system's AI agent provides personalized fashion advice. The user experience flow involves creating a personalized profile during initial setup and receiving new style suggestions daily. The user can provide feedback to help the AI ​​grow. To improve the generative AI's fashion sense, it understands hobbies using natural language processing and suggests styles that suit the body type using image analysis. This service will be a point of differentiation in the market due to the innovativeness of AI-powered personalization, contributing to increased customer loyalty and the creation of new revenue models. Furthermore, it offers the possibility of rapid response through real-time feedback and global expansion. As a result, the fashion suggestion system can grasp the latest trends based on the user's preferences and body type and provide personalized fashion advice.

[0029] The fashion suggestion system according to this embodiment comprises an understanding unit, a trend-grasping unit, and a suggestion unit. The understanding unit understands the user's preferences and body type. User preferences include, but are not limited to, survey results and past selection history. For example, the understanding unit collects user preferences through surveys and analyzes past selection history. The understanding unit can also collect data such as height, weight, and body fat percentage to understand the user's body type. For example, the understanding unit can input the user's body type data and provide fashion advice based on the body type. The trend-grasping unit grasps the latest trends based on the information understood by the understanding unit. For example, the trend-grasping unit collects data from social media and fashion magazines to grasp the latest trends. For example, the trend-grasping unit analyzes social media posts to grasp the latest fashion trends. The trend-grasping unit can also analyze fashion magazine articles to collect the latest trend information. The suggestion unit provides personalized fashion advice based on the trend information grasped by the trend-grasping unit. For example, the suggestion unit understands the user's tastes using natural language processing and suggests styles that suit the body type using image analysis. For example, the suggestion unit uses natural language processing technology to analyze the user's preferences and image analysis technology to suggest styles that suit the user's body type. The suggestion unit can also provide the user with new style suggestions daily. For instance, it can provide the user with new style suggestions every day to support their fashion choices. This allows the fashion suggestion system according to the embodiment to grasp the latest trends based on the user's preferences and body type, and provide personalized fashion advice.

[0030] The understanding unit understands the user's preferences and body type. User preferences include, but are not limited to, survey results and past selection history. For example, the understanding unit collects user preferences through surveys and analyzes past selection history. Specifically, surveys can ask in detail about the user's preferred colors, styles, brands, and seasonal preferences. In addition, data such as items purchased, products viewed, and saved outfits are collected as past selection history, and by analyzing this data, the user's preferences can be understood more deeply. Furthermore, the understanding unit can collect data such as height, weight, and body fat percentage to understand the user's body type. For example, based on the height and weight data entered by the user, detailed information about the body type can be grasped, and fashion advice based on the body type can be provided. In this way, the understanding unit can collect comprehensive data on the user's preferences and body type, and build a foundation for personalized fashion suggestions. Furthermore, the understanding unit inputs the user's body type data and provides fashion advice based on the body type. For example, based on the height and weight data entered by the user, detailed information about the body type can be grasped, and fashion advice based on the body type can be provided. This allows the understanding unit to collect comprehensive data on the user's preferences and body type, laying the foundation for personalized fashion suggestions.

[0031] The trend-gathering unit grasps the latest trends based on information understood by the understanding unit. For example, the trend-gathering unit collects data from sources such as social media and fashion magazines to grasp the latest trends. Specifically, it analyzes social media posts to understand the latest fashion trends. The trend-gathering unit can also analyze fashion magazine articles to collect the latest trend information. For example, by collecting social media posts and analyzing the content using natural language processing technology, it can identify currently popular styles and items. Furthermore, using image analysis technology, it can extract detailed trend information such as popular colors, designs, and accessories from posted images. Similarly, for fashion magazine articles, it analyzes the content using natural language processing technology to collect the latest trend information. This allows the trend-gathering unit to quickly and accurately grasp the latest fashion trends from a wide range of data sources. Furthermore, the trend-gathering unit can regularly update the collected trend information, always providing the most up-to-date information. This allows the trend-gathering unit to provide users with a foundation for making suggestions that reflect the latest fashion trends.

[0032] The Proposal Department provides personalized fashion advice based on trend information gathered by the Trend Analysis Department. For example, the Proposal Department understands the user's preferences using natural language processing and suggests styles that suit their body type using image analysis. Specifically, it uses natural language processing technology to analyze the user's preferences and identify the styles and items they like. For instance, it can analyze the user's responses to questionnaires and past selection history to gain a detailed understanding of their tastes and preferences. Furthermore, it uses image analysis technology to suggest styles that suit the user's body type. For example, based on the body type data provided by the user, it can suggest clothing and outfits that fit their body shape. This allows the Proposal Department to provide personalized fashion advice based on the user's preferences and body type. The Proposal Department can also provide users with new style suggestions daily. For example, it can offer users new style suggestions every day to support their fashion choices. This allows the Proposal Department to constantly provide users with new fashion ideas and enrich their fashion lives. Additionally, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the system can collect user feedback and results from actually trying out suggested styles, and use this information to improve the suggestion algorithm. This allows the suggestion team to provide users with more appropriate and satisfying fashion advice.

[0033] The understanding unit can learn and grow from user feedback. For example, the understanding unit can collect and learn from user feedback through surveys and reviews. It can also analyze user usage history and learn from the feedback. For example, the understanding unit can analyze user preferences based on their usage history and deepen its understanding. As a result, the accuracy of the understanding unit improves by learning from user feedback. Some or all of the above processes in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user feedback data into a generating AI and have the generating AI perform feedback learning.

[0034] The trend recognition unit can collect data from social media and fashion magazines to grasp the latest trends. For example, the trend recognition unit can collect social media post data to grasp the latest fashion trends. The trend recognition unit can also collect articles from fashion magazines to grasp the latest trend information. For example, the trend recognition unit can analyze social media post data to grasp the latest trends. The trend recognition unit can also analyze articles from fashion magazines to gather the latest trend information. In this way, by collecting data from social media and fashion magazines, the latest trends can be grasped. Some or all of the above processing in the trend recognition unit may be performed using AI, for example, or without AI. For example, the trend recognition unit can input social media post data into a generating AI and have the generating AI perform the task of grasping the latest trends.

[0035] The suggestion unit can understand the user's hobbies using natural language processing and suggest styles that suit their body type using image analysis. For example, the suggestion unit can analyze the user's hobbies using natural language processing technology and suggest styles that suit the user's body type using image analysis technology. For example, the suggestion unit can analyze the user's hobbies using natural language processing technology and suggest styles that suit those hobbies. The suggestion unit can also analyze the user's body type using image analysis technology and suggest styles that suit their body type. In this way, by using natural language processing and image analysis, it is possible to suggest styles that suit the user's hobbies and body type. 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 hobby data into a generating AI and have the generating AI perform the analysis of the hobbies. The suggestion unit can also input the user's body type data into a generating AI and have the generating AI perform the suggestion of styles that suit their body type.

[0036] The suggestion unit can provide users with new style suggestions on a daily basis. For example, the suggestion unit can provide users with new style suggestions every day to support their fashion choices. Alternatively, the suggestion unit can provide new style suggestions only on weekdays. For example, the suggestion unit can provide users with new style suggestions only on weekdays to support their fashion choices. Furthermore, the suggestion unit can provide new style suggestions at specific times of day. For example, the suggestion unit can provide users with new style suggestions every morning to support their fashion choices. In this way, by providing new style suggestions daily, it is possible to support users in their fashion choices. 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 style data into a generating AI and have the generating AI generate new style suggestions.

[0037] The suggestion unit can improve the accuracy of its suggestions based on user feedback. For example, the suggestion unit can collect user feedback and improve the accuracy of its suggestions. It can also analyze user feedback and improve the accuracy of its suggestions. For example, the suggestion unit can improve the accuracy of its suggestions based on user feedback. This allows for the provision of more appropriate fashion advice by improving the accuracy of suggestions based on user feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user feedback data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0038] The understanding unit can analyze the user's past fashion selection history and select the optimal understanding method. For example, the understanding unit can analyze the user's past fashion selection history to grasp their preferred trends. The understanding unit can also analyze the user's disliked trends based on styles the user has avoided in the past. For example, the understanding unit can analyze preferred trends based on styles the user has chosen in the past. The understanding unit can also analyze seasonal preferences based on styles the user has chosen in a particular season. In this way, the optimal understanding method can be selected by analyzing the user's past fashion selection history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's past fashion selection history data into a generating AI and have the generating AI select the optimal understanding method.

[0039] The understanding unit can filter information on preferences and body type based on the user's lifestyle and occupation. For example, the understanding unit can filter information on preferences and body type based on the user's lifestyle. It can also filter information on preferences and body type based on the user's occupation. For example, if the user is an office worker, the understanding unit can suggest a business casual style. If the user has an active lifestyle, the understanding unit can also suggest a sporty style. This allows for more appropriate fashion advice to be provided by filtering information based on the user's lifestyle and occupation. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's lifestyle and occupation data into a generating AI and have the generating AI perform the information filtering.

[0040] The understanding unit can prioritize understanding highly relevant information based on the user's geographical location. For example, the understanding unit prioritizes understanding highly relevant information based on the user's geographical location. The understanding unit can also provide highly relevant fashion advice based on the user's geographical location. For example, if the user lives in a cold region, the understanding unit will prioritize understanding information about warm clothing. Also, if the user lives in an urban area, the understanding unit can prioritize understanding information about urban styles. This allows for the provision of more appropriate fashion advice by prioritizing the understanding of highly relevant information based on the user's geographical location. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority understanding of highly relevant information.

[0041] The understanding unit can analyze a user's social media activity and understand relevant information. For example, the understanding unit can analyze a user's social media activity and understand relevant information. The understanding unit can also grasp a user's preferences based on their social media activity. For example, the understanding unit can analyze posts from fashion influencers that a user follows and understand their preferences. The understanding unit can also analyze fashion-related posts that a user has shared and understand their preferred style. In this way, relevant information can be understood by analyzing a user's social media activity. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user social media activity data into a generating AI and have the generating AI perform the understanding of relevant information.

[0042] The trend analysis unit can predict current trends by referring to past trend data. For example, the trend analysis unit can predict seasonal trends based on past trend data. The trend analysis unit can also predict trends related to specific events or occasions based on past trend data. For example, the trend analysis unit can predict fluctuations in the popularity of specific fashion items based on past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0043] The trend analysis unit can apply different trend analysis methods to each fashion category. For example, the trend analysis unit can utilize social media post data for casual fashion trend analysis. It can also utilize fashion magazine data for business fashion trend analysis. For example, the trend analysis unit can utilize sports event data for sports fashion trend analysis. By applying different trend analysis methods to each fashion category, more accurate trend analysis becomes possible. Some or all of the above-described processes in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input data for each fashion category into a generating AI and have the generating AI execute the application of trend analysis methods.

[0044] The trend analysis unit can analyze changes in trends based on the timing of trend information collection. For example, the trend analysis unit can collect seasonal trend information and analyze changes in trends. It can also collect trend information before and after specific events or occasions and analyze changes in trends. For example, the trend analysis unit can collect trend information before and after large-scale events such as fashion weeks and analyze changes in trends. This allows for the provision of more accurate trend information by analyzing changes in trends based on the timing of trend information collection. Some or all of the above-described processes in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input trend information collection timing data into a generating AI and have the generating AI perform the analysis of changes in trends.

[0045] The trend analysis unit can analyze trends by referring to relevant market data. For example, the trend analysis unit can analyze trends by referring to sales data in the fashion market. It can also analyze trends by referring to sales data of fashion items. For example, the trend analysis unit can analyze trends by referring to fashion-related search data. This allows for more accurate trend analysis by referring to relevant market data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input relevant market data into a generating AI and have the generating AI perform the trend analysis.

[0046] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it will provide detailed suggestions for items worn at important events. Conversely, it can provide concise suggestions for items worn daily. For example, it will provide detailed suggestions tailored to the season for seasonal items. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide more appropriate fashion advice. 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 fashion item importance data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0047] The suggestion unit can apply different suggestion algorithms depending on the fashion category. For example, in the case of casual fashion, the suggestion unit can apply an algorithm that suggests a relaxed style. Similarly, in the case of business fashion, it can apply an algorithm that suggests a formal style. For example, in the case of sports fashion, the suggestion unit can apply an algorithm that suggests a style that emphasizes functionality. By applying different suggestion algorithms depending on the fashion category, more appropriate fashion advice can be provided. 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 fashion category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0048] The proposal department can prioritize proposals based on the timing of fashion item submissions. For example, the proposal department can prioritize suggesting seasonally appropriate items to coincide with seasonal changes. It can also prioritize suggesting relevant items before specific events or occasions. For instance, it can prioritize suggesting the latest items to coincide with new product launches. This allows for more appropriate fashion advice to be provided by prioritizing proposals based on the timing of fashion item submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input fashion item submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of fashion items. For example, the suggestion unit may prioritize suggesting outfits related to items selected by the user. It can also prioritize suggesting items related to the user's preferences. For example, the suggestion unit may prioritize suggesting items that suit the user's body type. By adjusting the order of suggestions based on the relevance of fashion items, more appropriate fashion advice can be provided. 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 fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The understanding unit can provide fashion advice based not only on the user's preferences and body type, but also on the user's life events. For example, it can understand the user's important events such as weddings and graduations and suggest appropriate fashion. It can also suggest styles tailored to special occasions such as the user's birthday or anniversary. Furthermore, it can suggest fashion suitable for the climate and culture of the travel destination based on the user's travel plans. This allows for the provision of personalized fashion advice based on the user's life events.

[0052] The understanding unit can prioritize understanding highly relevant information based on the user's geographical location. For example, if the user lives in a cold climate, the understanding unit will prioritize information on warm clothing. Similarly, if the user lives in an urban area, it can prioritize information on urban styles. Furthermore, if the user lives in a beach resort, it can prioritize information on resort fashion. This allows the system to provide more appropriate fashion advice by prioritizing highly relevant information based on the user's geographical location.

[0053] The understanding unit can analyze a user's social media activity and understand relevant information. For example, it can analyze posts from fashion influencers a user follows to understand their preferences. It can also analyze fashion-related posts a user shares to understand their preferred style. Furthermore, it can analyze the activities of fashion communities a user participates in to understand relevant information. In this way, by analyzing a user's social media activity, relevant information can be understood.

[0054] The trend analysis unit can predict current trends by referring to past trend data. For example, it can predict seasonal trends based on past trend data. It can also predict trends related to specific events or occasions based on past trend data. Furthermore, it can predict fluctuations in the popularity of specific fashion items based on past trend data. In this way, current trends can be predicted by referring to past trend data.

[0055] The suggestion department can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it will provide detailed suggestions for items worn at important events. Conversely, it can provide concise suggestions for items worn daily. Furthermore, for seasonal items, it can provide detailed suggestions tailored to the season. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide more appropriate fashion advice.

[0056] The understanding unit can filter information on preferences and body type based on the user's lifestyle and occupation. For example, if the user is an office worker, the understanding unit will suggest a business casual style. If the user has an active lifestyle, it can also suggest a sporty style. Furthermore, if the user is engaged in a creative profession, it can suggest a unique and individual style. This allows for more appropriate fashion advice by filtering information based on the user's lifestyle and occupation.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The understanding unit understands the user's preferences and body type. User preferences include survey results and past selection history. The understanding unit collects user preferences through surveys and analyzes past selection history. In addition, the understanding unit collects data such as height, weight, and body fat percentage to understand the user's body type. Step 2: The trend identification unit grasps the latest trends based on the information understood by the understanding unit. The trend identification unit collects data from social media and fashion magazines to grasp the latest trends. For example, it analyzes social media posts to grasp the latest fashion trends. It can also analyze fashion magazine articles to collect the latest trend information. Step 3: The suggestion department provides personalized fashion advice based on trend information gathered by the trend analysis department. The suggestion department understands the user's preferences using natural language processing and suggests styles that suit the user's body type using image analysis. For example, it uses natural language processing technology to analyze the user's preferences and image analysis technology to suggest styles that suit the user's body type. The suggestion department can also provide the user with new style suggestions on a daily basis.

[0059] (Example of form 2) The fashion suggestion system according to an embodiment of the present invention is a system that suggests coordinated outfits tailored to the user's preferences, body type, and current trends. This fashion suggestion system understands the user's preferences and body type, grasps the latest trends, and provides personalized fashion advice. For example, the fashion suggestion system's AI agent learns and grows from feedback in order to deeply understand the user's preferences and body type. Next, the fashion suggestion system's AI agent constantly grasps the latest trends and possesses insights equivalent to those of a fashion professional. Then, the fashion suggestion system's AI agent provides personalized fashion advice. The user experience flow involves creating a personalized profile during initial setup and receiving new style suggestions daily. The user can provide feedback to help the AI ​​grow. To improve the generative AI's fashion sense, it understands hobbies using natural language processing and suggests styles that suit the body type using image analysis. This service will be a point of differentiation in the market due to the innovativeness of AI-powered personalization, contributing to increased customer loyalty and the creation of new revenue models. Furthermore, it offers the possibility of rapid response through real-time feedback and global expansion. As a result, the fashion suggestion system can grasp the latest trends based on the user's preferences and body type and provide personalized fashion advice.

[0060] The fashion suggestion system according to this embodiment comprises an understanding unit, a trend-grasping unit, and a suggestion unit. The understanding unit understands the user's preferences and body type. User preferences include, but are not limited to, survey results and past selection history. For example, the understanding unit collects user preferences through surveys and analyzes past selection history. The understanding unit can also collect data such as height, weight, and body fat percentage to understand the user's body type. For example, the understanding unit can input the user's body type data and provide fashion advice based on the body type. The trend-grasping unit grasps the latest trends based on the information understood by the understanding unit. For example, the trend-grasping unit collects data from social media and fashion magazines to grasp the latest trends. For example, the trend-grasping unit analyzes social media posts to grasp the latest fashion trends. The trend-grasping unit can also analyze fashion magazine articles to collect the latest trend information. The suggestion unit provides personalized fashion advice based on the trend information grasped by the trend-grasping unit. For example, the suggestion unit understands the user's tastes using natural language processing and suggests styles that suit the body type using image analysis. For example, the suggestion unit uses natural language processing technology to analyze the user's preferences and image analysis technology to suggest styles that suit the user's body type. The suggestion unit can also provide the user with new style suggestions daily. For instance, it can provide the user with new style suggestions every day to support their fashion choices. This allows the fashion suggestion system according to the embodiment to grasp the latest trends based on the user's preferences and body type, and provide personalized fashion advice.

[0061] The understanding unit understands the user's preferences and body type. User preferences include, but are not limited to, survey results and past selection history. For example, the understanding unit collects user preferences through surveys and analyzes past selection history. Specifically, surveys can ask in detail about the user's preferred colors, styles, brands, and seasonal preferences. In addition, data such as items purchased, products viewed, and saved outfits are collected as past selection history, and by analyzing this data, the user's preferences can be understood more deeply. Furthermore, the understanding unit can collect data such as height, weight, and body fat percentage to understand the user's body type. For example, based on the height and weight data entered by the user, detailed information about the body type can be grasped, and fashion advice based on the body type can be provided. In this way, the understanding unit can collect comprehensive data on the user's preferences and body type, and build a foundation for personalized fashion suggestions. Furthermore, the understanding unit inputs the user's body type data and provides fashion advice based on the body type. For example, based on the height and weight data entered by the user, detailed information about the body type can be grasped, and fashion advice based on the body type can be provided. This allows the understanding unit to collect comprehensive data on the user's preferences and body type, laying the foundation for personalized fashion suggestions.

[0062] The trend-gathering unit grasps the latest trends based on information understood by the understanding unit. For example, the trend-gathering unit collects data from sources such as social media and fashion magazines to grasp the latest trends. Specifically, it analyzes social media posts to understand the latest fashion trends. The trend-gathering unit can also analyze fashion magazine articles to collect the latest trend information. For example, by collecting social media posts and analyzing the content using natural language processing technology, it can identify currently popular styles and items. Furthermore, using image analysis technology, it can extract detailed trend information such as popular colors, designs, and accessories from posted images. Similarly, for fashion magazine articles, it analyzes the content using natural language processing technology to collect the latest trend information. This allows the trend-gathering unit to quickly and accurately grasp the latest fashion trends from a wide range of data sources. Furthermore, the trend-gathering unit can regularly update the collected trend information, always providing the most up-to-date information. This allows the trend-gathering unit to provide users with a foundation for making suggestions that reflect the latest fashion trends.

[0063] The Proposal Department provides personalized fashion advice based on trend information gathered by the Trend Analysis Department. For example, the Proposal Department understands the user's preferences using natural language processing and suggests styles that suit their body type using image analysis. Specifically, it uses natural language processing technology to analyze the user's preferences and identify the styles and items they like. For instance, it can analyze the user's responses to questionnaires and past selection history to gain a detailed understanding of their tastes and preferences. Furthermore, it uses image analysis technology to suggest styles that suit the user's body type. For example, based on the body type data provided by the user, it can suggest clothing and outfits that fit their body shape. This allows the Proposal Department to provide personalized fashion advice based on the user's preferences and body type. The Proposal Department can also provide users with new style suggestions daily. For example, it can offer users new style suggestions every day to support their fashion choices. This allows the Proposal Department to constantly provide users with new fashion ideas and enrich their fashion lives. Additionally, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the system can collect user feedback and results from actually trying out suggested styles, and use this information to improve the suggestion algorithm. This allows the suggestion team to provide users with more appropriate and satisfying fashion advice.

[0064] The understanding unit can learn and grow from user feedback. For example, the understanding unit can collect and learn from user feedback through surveys and reviews. It can also analyze user usage history and learn from the feedback. For example, the understanding unit can analyze user preferences based on their usage history and deepen its understanding. As a result, the accuracy of the understanding unit improves by learning from user feedback. Some or all of the above processes in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user feedback data into a generating AI and have the generating AI perform feedback learning.

[0065] The trend recognition unit can collect data from social media and fashion magazines to grasp the latest trends. For example, the trend recognition unit can collect social media post data to grasp the latest fashion trends. The trend recognition unit can also collect articles from fashion magazines to grasp the latest trend information. For example, the trend recognition unit can analyze social media post data to grasp the latest trends. The trend recognition unit can also analyze articles from fashion magazines to gather the latest trend information. In this way, by collecting data from social media and fashion magazines, the latest trends can be grasped. Some or all of the above processing in the trend recognition unit may be performed using AI, for example, or without AI. For example, the trend recognition unit can input social media post data into a generating AI and have the generating AI perform the task of grasping the latest trends.

[0066] The suggestion unit can understand the user's hobbies using natural language processing and suggest styles that suit their body type using image analysis. For example, the suggestion unit can analyze the user's hobbies using natural language processing technology and suggest styles that suit the user's body type using image analysis technology. For example, the suggestion unit can analyze the user's hobbies using natural language processing technology and suggest styles that suit those hobbies. The suggestion unit can also analyze the user's body type using image analysis technology and suggest styles that suit their body type. In this way, by using natural language processing and image analysis, it is possible to suggest styles that suit the user's hobbies and body type. 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 hobby data into a generating AI and have the generating AI perform the analysis of the hobbies. The suggestion unit can also input the user's body type data into a generating AI and have the generating AI perform the suggestion of styles that suit their body type.

[0067] The suggestion unit can provide users with new style suggestions on a daily basis. For example, the suggestion unit can provide users with new style suggestions every day to support their fashion choices. Alternatively, the suggestion unit can provide new style suggestions only on weekdays. For example, the suggestion unit can provide users with new style suggestions only on weekdays to support their fashion choices. Furthermore, the suggestion unit can provide new style suggestions at specific times of day. For example, the suggestion unit can provide users with new style suggestions every morning to support their fashion choices. In this way, by providing new style suggestions daily, it is possible to support users in their fashion choices. 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 style data into a generating AI and have the generating AI generate new style suggestions.

[0068] The suggestion unit can improve the accuracy of its suggestions based on user feedback. For example, the suggestion unit can collect user feedback and improve the accuracy of its suggestions. It can also analyze user feedback and improve the accuracy of its suggestions. For example, the suggestion unit can improve the accuracy of its suggestions based on user feedback. This allows for the provision of more appropriate fashion advice by improving the accuracy of suggestions based on user feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user feedback data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0069] The understanding unit can estimate the user's emotions and deepen its understanding of their preferences and body type based on the estimated emotions. For example, the understanding unit can estimate the user's emotions using facial recognition technology to deepen its understanding of their preferences and body type. The understanding unit can also estimate the user's emotions using voice analysis technology to deepen its understanding of their preferences and body type. For example, the understanding unit can analyze the user's facial expression data to estimate their emotions. The understanding unit can also analyze the user's voice data to estimate their emotions. This allows for a deeper understanding of preferences and body type based on the user's emotions, enabling the provision of more appropriate fashion advice. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The understanding unit can analyze the user's past fashion selection history and select the optimal understanding method. For example, the understanding unit can analyze the user's past fashion selection history to grasp their preferred trends. The understanding unit can also analyze the user's disliked trends based on styles the user has avoided in the past. For example, the understanding unit can analyze preferred trends based on styles the user has chosen in the past. The understanding unit can also analyze seasonal preferences based on styles the user has chosen in a particular season. In this way, the optimal understanding method can be selected by analyzing the user's past fashion selection history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's past fashion selection history data into a generating AI and have the generating AI select the optimal understanding method.

[0071] The understanding unit can filter information on preferences and body type based on the user's lifestyle and occupation. For example, the understanding unit can filter information on preferences and body type based on the user's lifestyle. It can also filter information on preferences and body type based on the user's occupation. For example, if the user is an office worker, the understanding unit can suggest a business casual style. If the user has an active lifestyle, the understanding unit can also suggest a sporty style. This allows for more appropriate fashion advice to be provided by filtering information based on the user's lifestyle and occupation. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's lifestyle and occupation data into a generating AI and have the generating AI perform the information filtering.

[0072] The understanding unit can estimate the user's emotions and determine the priority of information to understand based on the estimated emotions. For example, the understanding unit can estimate the user's emotions using facial recognition technology and determine the priority of information to understand. Alternatively, the understanding unit can estimate the user's emotions using voice analysis technology and determine the priority of information to understand. For example, if the user is stressed, the understanding unit will prioritize information that promotes relaxation. Similarly, if the user is happy, the understanding unit can prioritize positive information. By prioritizing information based on the user's emotions, more appropriate information can be understood. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI, or not. For example, the understanding unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0073] The understanding unit can prioritize understanding highly relevant information based on the user's geographical location. For example, the understanding unit prioritizes understanding highly relevant information based on the user's geographical location. The understanding unit can also provide highly relevant fashion advice based on the user's geographical location. For example, if the user lives in a cold region, the understanding unit will prioritize understanding information about warm clothing. Also, if the user lives in an urban area, the understanding unit can prioritize understanding information about urban styles. This allows for the provision of more appropriate fashion advice by prioritizing the understanding of highly relevant information based on the user's geographical location. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority understanding of highly relevant information.

[0074] The understanding unit can analyze a user's social media activity and understand relevant information. For example, the understanding unit can analyze a user's social media activity and understand relevant information. The understanding unit can also grasp a user's preferences based on their social media activity. For example, the understanding unit can analyze posts from fashion influencers that a user follows and understand their preferences. The understanding unit can also analyze fashion-related posts that a user has shared and understand their preferred style. In this way, relevant information can be understood by analyzing a user's social media activity. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user social media activity data into a generating AI and have the generating AI perform the understanding of relevant information.

[0075] The trend identification unit can estimate the user's emotions and adjust the method of collecting trend information based on the estimated user emotions. For example, the trend identification unit can estimate the user's emotions using facial recognition technology and adjust the method of collecting trend information. Alternatively, the trend identification unit can estimate the user's emotions using voice analysis technology and adjust the method of collecting trend information. For example, if the user is relaxed, the trend identification unit can collect trend information at a leisurely pace. Conversely, if the user is in a hurry, the trend identification unit can also collect trend information quickly. By adjusting the method of collecting trend information based on the user's emotions, more appropriate trend information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the trend identification unit may be performed using AI, for example, or without AI. For example, the trend identification unit can input user emotion data into a generative AI and have the generative AI adjust the method of collecting trend information.

[0076] The trend analysis unit can predict current trends by referring to past trend data. For example, the trend analysis unit can predict seasonal trends based on past trend data. The trend analysis unit can also predict trends related to specific events or occasions based on past trend data. For example, the trend analysis unit can predict fluctuations in the popularity of specific fashion items based on past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0077] The trend analysis unit can apply different trend analysis methods to each fashion category. For example, the trend analysis unit can utilize social media post data for casual fashion trend analysis. It can also utilize fashion magazine data for business fashion trend analysis. For example, the trend analysis unit can utilize sports event data for sports fashion trend analysis. By applying different trend analysis methods to each fashion category, more accurate trend analysis becomes possible. Some or all of the above-described processes in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input data for each fashion category into a generating AI and have the generating AI execute the application of trend analysis methods.

[0078] The trend recognition unit can estimate the user's emotions and adjust the display method of trend information based on the estimated user emotions. For example, the trend recognition unit can estimate the user's emotions using facial recognition technology and adjust the display method of trend information. Alternatively, the trend recognition unit can estimate the user's emotions using voice analysis technology and adjust the display method of trend information. For example, if the user is tense, the trend recognition unit can provide a simple and highly visible display method. If the user is relaxed, the trend recognition unit can also provide a display method that includes detailed information. By adjusting the display method of trend information based on the user's emotions, more appropriate trend information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the trend recognition unit may be performed using AI, for example, or without AI. For example, the trend recognition unit can input user emotion data into a generative AI and have the generative AI adjust the display method of trend information.

[0079] The trend analysis unit can analyze changes in trends based on the timing of trend information collection. For example, the trend analysis unit can collect seasonal trend information and analyze changes in trends. It can also collect trend information before and after specific events or occasions and analyze changes in trends. For example, the trend analysis unit can collect trend information before and after large-scale events such as fashion weeks and analyze changes in trends. This allows for the provision of more accurate trend information by analyzing changes in trends based on the timing of trend information collection. Some or all of the above-described processes in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input trend information collection timing data into a generating AI and have the generating AI perform the analysis of changes in trends.

[0080] The trend analysis unit can analyze trends by referring to relevant market data. For example, the trend analysis unit can analyze trends by referring to sales data in the fashion market. It can also analyze trends by referring to sales data of fashion items. For example, the trend analysis unit can analyze trends by referring to fashion-related search data. This allows for more accurate trend analysis by referring to relevant market data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input relevant market data into a generating AI and have the generating AI perform the trend analysis.

[0081] The proposal unit can estimate the user's emotions and adjust the way fashion advice is presented based on the estimated emotions. For example, the proposal unit can estimate the user's emotions using facial recognition technology and adjust the way fashion advice is presented. Alternatively, the proposal unit can estimate the user's emotions using voice analysis technology and adjust the way fashion advice is presented. For example, if the user is nervous, the proposal unit can provide simple and easily understandable advice. If the user is relaxed, the proposal unit can also provide advice that includes detailed information. By adjusting the way fashion advice is presented based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI adjust the way fashion advice is presented.

[0082] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it will provide detailed suggestions for items worn at important events. Conversely, it can provide concise suggestions for items worn daily. For example, it will provide detailed suggestions tailored to the season for seasonal items. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide more appropriate fashion advice. 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 fashion item importance data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0083] The suggestion unit can apply different suggestion algorithms depending on the fashion category. For example, in the case of casual fashion, the suggestion unit can apply an algorithm that suggests a relaxed style. Similarly, in the case of business fashion, it can apply an algorithm that suggests a formal style. For example, in the case of sports fashion, the suggestion unit can apply an algorithm that suggests a style that emphasizes functionality. By applying different suggestion algorithms depending on the fashion category, more appropriate fashion advice can be provided. 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 fashion category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition technology and adjust the length of the suggestion. Alternatively, the suggestion unit can estimate the user's emotions using voice analysis technology and adjust the length of the suggestion. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. Conversely, if the user is relaxed, the suggestion unit can provide a longer suggestion that includes detailed explanations. By adjusting the length of the suggestion based on the user's emotions, more appropriate fashion advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0085] The proposal department can prioritize proposals based on the timing of fashion item submissions. For example, the proposal department can prioritize suggesting seasonally appropriate items to coincide with seasonal changes. It can also prioritize suggesting relevant items before specific events or occasions. For instance, it can prioritize suggesting the latest items to coincide with new product launches. This allows for more appropriate fashion advice to be provided by prioritizing proposals based on the timing of fashion item submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input fashion item submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of fashion items. For example, the suggestion unit may prioritize suggesting outfits related to items selected by the user. It can also prioritize suggesting items related to the user's preferences. For example, the suggestion unit may prioritize suggesting items that suit the user's body type. By adjusting the order of suggestions based on the relevance of fashion items, more appropriate fashion advice can be provided. 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 fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The understanding unit can provide fashion advice based not only on the user's preferences and body type, but also on the user's life events. For example, it can understand the user's important events such as weddings and graduations and suggest appropriate fashion. It can also suggest styles tailored to special occasions such as the user's birthday or anniversary. Furthermore, it can suggest fashion suitable for the climate and culture of the travel destination based on the user's travel plans. This allows for the provision of personalized fashion advice based on the user's life events.

[0089] The trend analysis unit can estimate the user's emotions and adjust the method of collecting trend information based on those emotions. For example, if the user is relaxed, the trend analysis unit will collect trend information at a leisurely pace. If the user is in a hurry, the trend analysis unit can also collect trend information quickly. Furthermore, if the user is excited, the trend analysis unit can prioritize collecting the latest trend information. By adjusting the method of collecting trend information based on the user's emotions, more relevant trend information can be collected.

[0090] The suggestion function can estimate the user's emotions and adjust the way fashion advice is presented based on those emotions. For example, if the user is feeling stressed, the suggestion function will provide simple, easy-to-understand advice. If the user is relaxed, it can also provide advice with more detailed information. Furthermore, if the user is excited, it can provide colorful, visually appealing advice. By adjusting the way fashion advice is presented based on the user's emotions, it can provide more appropriate advice.

[0091] The understanding unit can prioritize understanding highly relevant information based on the user's geographical location. For example, if the user lives in a cold climate, the understanding unit will prioritize information on warm clothing. Similarly, if the user lives in an urban area, it can prioritize information on urban styles. Furthermore, if the user lives in a beach resort, it can prioritize information on resort fashion. This allows the system to provide more appropriate fashion advice by prioritizing highly relevant information based on the user's geographical location.

[0092] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, to-the-point suggestions. If the user is relaxed, it can provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, it can provide highly entertaining suggestions. By adjusting the length of suggestions based on the user's emotions, it can provide more appropriate fashion advice.

[0093] The understanding unit can analyze a user's social media activity and understand relevant information. For example, it can analyze posts from fashion influencers a user follows to understand their preferences. It can also analyze fashion-related posts a user shares to understand their preferred style. Furthermore, it can analyze the activities of fashion communities a user participates in to understand relevant information. In this way, by analyzing a user's social media activity, relevant information can be understood.

[0094] The trend analysis unit can predict current trends by referring to past trend data. For example, it can predict seasonal trends based on past trend data. It can also predict trends related to specific events or occasions based on past trend data. Furthermore, it can predict fluctuations in the popularity of specific fashion items based on past trend data. In this way, current trends can be predicted by referring to past trend data.

[0095] The suggestion department can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it will provide detailed suggestions for items worn at important events. Conversely, it can provide concise suggestions for items worn daily. Furthermore, for seasonal items, it can provide detailed suggestions tailored to the season. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide more appropriate fashion advice.

[0096] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion function will prioritize suggesting relaxing styles. Similarly, if the user is happy, it can prioritize suggesting positive styles. Furthermore, if the user is excited, it can prioritize suggesting energetic styles. By prioritizing suggestions based on the user's emotions, it can provide more appropriate fashion advice.

[0097] The understanding unit can filter information on preferences and body type based on the user's lifestyle and occupation. For example, if the user is an office worker, the understanding unit will suggest a business casual style. If the user has an active lifestyle, it can also suggest a sporty style. Furthermore, if the user is engaged in a creative profession, it can suggest a unique and individual style. This allows for more appropriate fashion advice by filtering information based on the user's lifestyle and occupation.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The understanding unit understands the user's preferences and body type. User preferences include survey results and past selection history. The understanding unit collects user preferences through surveys and analyzes past selection history. In addition, the understanding unit collects data such as height, weight, and body fat percentage to understand the user's body type. Step 2: The trend identification unit grasps the latest trends based on the information understood by the understanding unit. The trend identification unit collects data from social media and fashion magazines to grasp the latest trends. For example, it analyzes social media posts to grasp the latest fashion trends. It can also analyze fashion magazine articles to collect the latest trend information. Step 3: The suggestion department provides personalized fashion advice based on trend information gathered by the trend analysis department. The suggestion department understands the user's preferences using natural language processing and suggests styles that suit the user's body type using image analysis. For example, it uses natural language processing technology to analyze the user's preferences and image analysis technology to suggest styles that suit the user's body type. The suggestion department can also provide the user with new style suggestions on a daily basis.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the understanding unit, trend-gathering unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the understanding unit is implemented by the control unit 46A of the smart device 14 and collects and analyzes user preferences and body shape data. The trend-gathering unit is implemented by the identification processing unit 290 of the data processing device 12 and collects and analyzes the latest trend information from social media and fashion magazines. The suggestion unit is implemented by the control unit 46A of the smart device 14 and provides personalized fashion advice using natural language processing and image analysis techniques. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the understanding unit, trend-gathering unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the smart glasses 214, which collects and analyzes the user's preferences and body shape data. The trend-gathering unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects and analyzes the latest trend information from social media and fashion magazines. The suggestion unit is implemented by the control unit 46A of the smart glasses 214, which provides personalized fashion advice using natural language processing and image analysis techniques. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the understanding unit, trend-gathering unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the headset terminal 314, which collects and analyzes user preferences and body shape data. The trend-gathering unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects and analyzes the latest trend information from social media and fashion magazines. The suggestion unit is implemented by the control unit 46A of the headset terminal 314, which provides personalized fashion advice using natural language processing and image analysis techniques. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the understanding unit, trend-gathering unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the robot 414 and collects and analyzes user preferences and body shape data. The trend-gathering unit is implemented by the identification processing unit 290 of the data processing unit 12 and collects and analyzes the latest trend information from social media and fashion magazines. The suggestion unit is implemented by the control unit 46A of the robot 414 and provides personalized fashion advice using natural language processing and image analysis techniques. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) An understanding unit that understands the user's preferences and body type, A trend-gathering unit that grasps the latest trends based on the information understood by the aforementioned understanding unit, The system includes a suggestion unit that provides personalized fashion advice based on trend information obtained by the trend recognition unit. A system characterized by the following features. (Note 2) The aforementioned understanding unit is, Learn and grow from user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The trend recognition unit, We collect data from social media and fashion magazines to understand the latest trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Using natural language processing to understand the user's preferences and image analysis to suggest styles that suit their body type. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Providing users with new style suggestions on a daily basis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We will improve the accuracy of our suggestions based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned understanding unit is, It estimates the user's emotions and deepens its understanding of their preferences and body type based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned understanding unit is, Analyze the user's past fashion choices and select the most appropriate method of understanding them. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned understanding unit is, Filter information on preferences and body type based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned understanding unit is, It estimates the user's emotions and prioritizes the information to understand based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned understanding unit is, Prioritize understanding the most relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned understanding unit is, Analyze users' social media activity and understand relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The trend recognition unit, We estimate user sentiment and adjust the method of collecting trend information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The trend recognition unit, Predicting current trends by referring to past trend data The system described in Appendix 1, characterized by the features described herein. (Note 15) The trend recognition unit, Applying different trend analysis methods to each fashion category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The trend recognition unit, It estimates user sentiment and adjusts how trend information is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The trend recognition unit, Analyze changes in trends based on when trend information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The trend recognition unit, Analyze trends by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way fashion advice is 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, Adjust the level of detail in the suggestions based on the importance of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Apply different suggestion algorithms depending on the fashion 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, Prioritize proposals based on the submission timing of fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Adjust the order of suggestions based on the relevance of the fashion items. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An understanding unit that understands the user's preferences and body type, A trend-gathering unit that grasps the latest trends based on the information understood by the aforementioned understanding unit, The system includes a suggestion unit that provides personalized fashion advice based on trend information obtained by the trend recognition unit. A system characterized by the following features.

2. The aforementioned understanding unit is, Learn and grow from user feedback. The system according to feature 1.

3. The trend recognition unit, We collect data from social media and fashion magazines to understand the latest trends. The system according to feature 1.

4. The aforementioned proposal section is, Using natural language processing to understand the user's preferences and image analysis to suggest styles that suit their body type. The system according to feature 1.

5. The aforementioned proposal section is, Providing users with new style suggestions on a daily basis. The system according to feature 1.

6. The aforementioned proposal section is, We will improve the accuracy of our suggestions based on user feedback. The system according to feature 1.

7. The aforementioned understanding unit is, It estimates the user's emotions and deepens its understanding of their preferences and body type based on those estimated emotions. The system according to feature 1.

8. The aforementioned understanding unit is, Analyze the user's past fashion choices and select the most appropriate method of understanding them. The system according to feature 1.

Images