system

Abstract

The system according to this embodiment aims to recommend the most suitable fashion items based on the user's preferences, past purchase history, and area trend information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a recommendation unit, a feedback collection unit, and a feedback reflection unit. The reception unit receives input from the user's preferences, past purchase history, and area trend information. The analysis unit analyzes the information entered by the reception unit. The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The feedback collection unit collects feedback on the items recommended by the recommendation unit. The feedback reflection unit reflects the feedback collected by the feedback collection unit in the next recommendation.

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, personalized fashion item recommendations based on a user's preferences, past purchase history, and trend information in an area are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal fashion items based on a user's preferences, past purchase history, and trend information in an area.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a feedback collection unit, and a feedback reflection unit. The reception unit receives input from the user, including their preferences, past purchase history, and area trend information. The analysis unit analyzes the information entered by the reception unit. The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The feedback collection unit collects feedback on the items recommended by the recommendation unit. The feedback reflection unit reflects the feedback collected by the feedback collection unit in the next recommendation. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable fashion items based on the user's preferences, past purchase history, and area trend information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personalized shopping assistant system according to an embodiment of the present invention is a system that provides a personalized shopping assistant based on the user's preferences, past purchase history, and area trend information using generative AI. The personalized shopping assistant system allows the user to input their preferences, past purchase history, and area trend information. The generative AI analyzes this information and recommends the most suitable fashion items to the user. This recommendation more accurately reflects the user's preferences, making it easier for the user to find items that suit their style. For example, if a user likes blue shirts, prefers a casual style, and sneakers are popular in their area, the generative AI will recommend a blue casual shirt and sneakers. This recommendation more accurately reflects the user's preferences, making it easier for the user to find items that suit their style. For example, the user can see the recommended items and think, "These are perfect for my taste." Furthermore, the generative AI updates trend information in real time, always making recommendations based on the latest information. This allows the user to always find items that match the latest trends. This mechanism significantly reduces the effort required for shopping. For example, it eliminates the need for users to search through a vast number of products to find items that suit their preferences. Furthermore, because the generating AI provides recommendations that accurately reflect the user's preferences, users can easily find items that match their style. This improves the user's shopping experience, making online shopping more enjoyable and easier. As a result, personalized shopping assistant systems can provide personalized shopping assistance based on the user's preferences, past purchase history, and local trend information.

[0029] The personalized shopping assistant system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a feedback collection unit, and a feedback reflection unit. The reception unit receives input from the user's preferences, past purchase history, and area trend information. User preferences include, but are not limited to, colors, styles, and brands. For example, the reception unit can receive input from the user such as their favorite colors and styles, information on items they have purchased in the past, and trend information for the area where they live. The analysis unit analyzes the information entered by the reception unit using a generative AI. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The recommendation unit makes recommendations based on, for example, the user's preferences, past purchase history, and area trend information using a generative AI. The recommendation unit can update trend information in real time and always make recommendations based on the latest information. The feedback collection unit collects feedback on items recommended by the recommendation unit. For example, the feedback collection unit can collect ratings and comments on items actually purchased by the user, as well as post-purchase surveys. The feedback reflection unit incorporates the feedback collected by the feedback collection unit into future recommendations. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of future recommendations. As a result, the personalized shopping assistant system according to this embodiment can provide a personalized shopping assistant based on the user's preferences, past purchase history, and area trend information.

[0030] The reception desk inputs user preferences, past purchase history, and area trend information. User preferences include, but are not limited to, colors, styles, and brands. Specifically, users can input their favorite colors, styles, and preferences for specific brands in detail through a dedicated interface. For example, if a user inputs "I like blue casual styles," the reception desk saves this information to the database. Past purchase history is also important data, and the system can automatically retrieve and save information about items the user has previously purchased. This includes purchase date and time, details of the purchased item, price, and purchase location. Furthermore, area trend information is also an important element, and the system collects and saves the latest fashion trends in the user's area. This allows the reception desk to comprehensively understand the user's individual preferences, past behavior, and regional trends, and provide accurate data to the analysis department in the next step.

[0031] The analysis unit uses generative AI to analyze the information entered by the reception unit. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, the generative AI receives user preferences, past purchase history, and area trend information as input data, and integrates and analyzes this data. For example, it uses data mining techniques to extract the user's past purchase patterns and reveals user preference trends through statistical analysis. It also uses machine learning algorithms to build a predictive model based on user preferences and trend information to predict items that are likely to be purchased next. Based on these analysis results, the generative AI generates data to recommend the most suitable fashion items to the user. Furthermore, the analysis unit updates the data in real time, allowing it to always perform analysis based on the latest information. This enables the analysis unit to quickly respond to changes in user preferences and trends and provide highly accurate recommendations.

[0032] The recommendation department recommends the most suitable fashion items to the user based on information analyzed by the analysis department. For example, the recommendation department uses generative AI to make recommendations based on the user's preferences, past purchase history, and area trend information. Specifically, the generative AI selects the most suitable items for the user based on the data provided by the analysis department and generates a recommendation list. The recommendation list includes items in colors, styles, and brands that match the user's preferences, and also takes into account the latest area trends. For example, if a user prefers a casual style, the recommendation department will create a list centered on casual items and present it to the user. Furthermore, the recommendation department updates trend information in real time, ensuring that recommendations are always based on the latest information. This allows users to always receive recommendations based on the latest trends, providing a more satisfying shopping experience. In addition, the recommendation department can continuously improve its recommendation algorithm based on user feedback, thereby improving the accuracy of future recommendations.

[0033] The Feedback Collection Unit collects feedback on items recommended by the Recommendation Unit. For example, the Feedback Collection Unit can collect ratings and comments on items actually purchased by users, as well as post-purchase surveys. Specifically, after a user purchases a recommended item, they can input ratings and comments through a dedicated interface. Ratings include satisfaction with the item's quality, design, and price, while comments include specific feedback and suggestions for improvement. Furthermore, feedback on the user's overall shopping experience can be collected through post-purchase surveys. This allows the Feedback Collection Unit to gain a detailed understanding of users' real opinions and impressions, collecting valuable data to reflect in future recommendations. Additionally, the Feedback Collection Unit stores the collected feedback in a database, making it accessible to the Analysis Unit and Recommendation Unit. This improves the overall system accuracy and user satisfaction.

[0034] The feedback reflection unit incorporates the feedback collected by the feedback collection unit into the next recommendation. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of the next recommendation. Specifically, the feedback reflection unit analyzes user ratings and comments and adjusts the parameters of the recommendation algorithm. For example, if a particular item receives a low rating, it may be removed from the recommendation list or its recommendation frequency may be reduced. Also, if a user gives a high rating to a particular brand or style, the algorithm may be adjusted to prioritize recommending items from that brand or style. Furthermore, the feedback reflection unit can detect new trends and changes in preferences based on user feedback and incorporate them into the recommendation algorithm. This allows the feedback reflection unit to quickly respond to changes in user preferences and trends and always provide the optimal recommendation. As a result, the personalized shopping assistant system according to this embodiment can provide a personalized shopping assistant based on user preferences, past purchase history, and area trend information.

[0035] The reception desk allows users to input information tailored to their preferred colors and styles, seasons, and events. For example, users can input information such as "I like blue shirts," "I prefer casual styles in the summer," and "I want to wear a formal dress for Christmas." This allows for more personalized recommendations by providing users with information tailored to their preferred colors, styles, seasons, and events. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the information entered by the user into the AI, which can then automatically analyze the information and complete the input.

[0036] The analysis unit can perform analysis while also considering the purchase history and ratings of other users. For example, the analysis unit performs analysis based on information such as items that other users have given high ratings to or items that are frequently purchased. For example, the analysis unit prioritizes recommending items that other users have given high ratings to. The analysis unit can also adjust the recommendation results by referring to items that other users frequently purchase. Furthermore, the analysis unit can analyze the purchase history of other users to grasp trends and improve the accuracy of the analysis. For example, the analysis unit grasps current trends based on the purchase history of other users and recommends the most suitable items to the user. This improves the accuracy of the analysis by considering the purchase history and ratings of other users. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the purchase history and ratings of other users into the generation AI, which automatically performs the analysis and outputs the recommendation results.

[0037] The recommendation unit can update trend information in real time and make recommendations based on the latest information. For example, the recommendation unit collects the latest news articles and social media posts from the internet and updates the trend information. For example, the recommendation unit collects the latest fashion magazines and blog posts and makes recommendations based on the trend information. The recommendation unit can also analyze social media posts to understand current trends and make recommendations. Furthermore, the recommendation unit can update trend information in real time and always make recommendations based on the latest information. For example, the recommendation unit collects the latest news articles and social media posts from the internet in real time and updates the trend information. This allows for real-time updates of trend information, ensuring that recommendations are always based on the latest information. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the latest news articles and social media posts from the internet into the generation AI, which automatically updates the trend information and outputs recommendation results.

[0038] The feedback collection unit can collect feedback on items that users have actually purchased. For example, the feedback collection unit can collect user ratings, comments, and post-purchase surveys for items purchased by users. For instance, the feedback collection unit can provide a function for users to rate purchased items with stars. It can also provide a function for users to enter comments on purchased items. Furthermore, the feedback collection unit can collect user feedback through post-purchase surveys. For example, the feedback collection unit can collect user feedback on the usability and satisfaction level of purchased items through post-purchase surveys. By collecting feedback on items actually purchased by users, this can be reflected in future recommendations. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input user-generated feedback into an AI, which can then automatically analyze and collect the feedback.

[0039] The feedback reflection unit can incorporate the collected feedback into the next recommendation. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of the next recommendation. For example, the feedback reflection unit can adjust the algorithm to prioritize recommending items that users have given high ratings to. The feedback reflection unit can also adjust the algorithm to exclude items that users have given low ratings to from the recommendation. Furthermore, the feedback reflection unit can also adjust the recommendation algorithm based on user comments and survey results. For example, the feedback reflection unit can adjust the recommendation algorithm based on user comments and survey results to improve the accuracy of the next recommendation. In this way, the accuracy of the recommendation is improved by incorporating the collected feedback into the next recommendation. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the collected feedback into AI, and the AI ​​can automatically analyze the feedback and adjust the recommendation algorithm.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display the user's preferred colors and styles that they have frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information tailored to specific seasons or events based on the user's past input history. This allows the reception desk to suggest the optimal input method and improve input efficiency by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history into an AI, which can then automatically suggest the optimal input method.

[0041] The reception desk can customize input fields based on the user's current fashion trends and areas of interest during input. For example, the reception desk can suggest relevant input fields based on fashion items the user has recently been interested in. It can also customize input fields by referencing the styles of fashion influencers the user follows. Furthermore, the reception desk can suggest appropriate fashion items for events the user plans to attend. This allows for more accurate input by customizing input fields based on the user's current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the input fields.

[0042] The input system can prioritize displaying highly relevant input items by considering the user's geographical location during input. For example, if the user is in a specific region, the input system will prioritize displaying fashion items popular in that region. Furthermore, if the user is traveling, the input system can customize input items based on trend information for their travel destination. Additionally, if the user has plans to attend a specific event, the input system can prioritize displaying fashion items related to that event. This improves input efficiency by prioritizing the display of highly relevant input items while considering the user's geographical location. Some or all of the above processing in the input system may be performed using AI, or not. For example, the input system can input the user's geographical location into the AI, which can then automatically prioritize displaying highly relevant input items.

[0043] The reception desk can analyze the user's social media activity during input and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on fashion items the user has "liked" or commented on on social media. It can also analyze posts from brands and influencers the user follows and suggest relevant input fields. Furthermore, the reception desk can customize input fields based on trend information from fashion communities the user participates in. This allows the reception desk to analyze the user's social media activity, suggest relevant input fields, and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then automatically suggest relevant input fields.

[0044] The analysis unit can improve analysis accuracy by thoroughly analyzing the user's past purchase history during analysis. For example, the analysis unit can improve analysis accuracy based on the color and style of items the user has purchased in the past. The analysis unit can also adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, the analysis unit can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. For example, the analysis unit can improve analysis accuracy based on the color and style of items the user has purchased in the past. Furthermore, the analysis unit can adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, the analysis unit can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. In this way, analysis accuracy is improved by thoroughly analyzing the user's past purchase history. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the user's past purchase history into the generation AI, which can then automatically perform the analysis and improve analysis accuracy.

[0045] The analysis unit can customize its analysis methods based on the user's current fashion trends and areas of interest during analysis. For example, the analysis unit can customize its analysis methods based on the fashion items the user has recently become interested in. It can also adjust its analysis methods by referencing the styles of fashion influencers the user follows. Furthermore, the analysis unit can analyze appropriate fashion items to match events the user plans to attend. This improves analysis accuracy by customizing the analysis methods based on the user's current fashion trends and areas of interest. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's current fashion trends and areas of interest into the generative AI, which can then automatically customize the analysis methods.

[0046] The analysis unit can improve its analysis accuracy by referring to the purchase history and ratings of other users during the analysis process. For example, the analysis unit can improve its analysis accuracy based on items that other users have given high ratings to. Furthermore, the analysis unit can adjust its analysis results by referring to items that other users frequently purchase. In addition, the analysis unit can improve its analysis accuracy by analyzing the purchase history of other users and grasping trends. For example, the analysis unit can improve its analysis accuracy based on items that other users have given high ratings to. Furthermore, the analysis unit can adjust its analysis results by referring to items that other users frequently purchase. Furthermore, the analysis unit can improve its analysis accuracy by analyzing the purchase history of other users and grasping trends. This allows the analysis unit to improve its accuracy by referring to the purchase history and ratings of other users. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the purchase history and ratings of other users into the generation AI, which can then automatically perform the analysis and improve its accuracy.

[0047] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze popular fashion items in that region. Furthermore, if the user is traveling, the analysis unit can perform analysis based on trend information for the travel destination. Additionally, if the user is planning to attend a specific event, the analysis unit can analyze fashion items related to that event. This improves analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then automatically perform the analysis and improve analysis accuracy.

[0048] The recommendation unit can improve recommendation accuracy by analyzing the user's past purchase history in detail when making recommendations. For example, the recommendation unit can improve recommendation accuracy based on the color and style of items the user has purchased in the past. The recommendation unit can also adjust recommendation results by considering the user's ratings of items purchased in the past. Furthermore, the recommendation unit can improve recommendation accuracy by analyzing the frequency of use of items purchased in the past. For example, the recommendation unit can improve recommendation accuracy based on the color and style of items the user has purchased in the past. Furthermore, the recommendation unit can adjust recommendation results by considering the user's ratings of items purchased in the past. Furthermore, the recommendation unit can also improve recommendation accuracy by analyzing the frequency of use of items purchased in the past. In this way, recommendation accuracy is improved by analyzing the user's past purchase history in detail. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the user's past purchase history into a generating AI, which then automatically analyzes the data to improve recommendation accuracy.

[0049] The recommendation unit can customize its recommendation methods based on the user's current fashion trends and areas of interest. For example, it can customize its recommendation methods based on fashion items the user has recently been interested in. It can also adjust its recommendation methods by referencing the styles of fashion influencers the user follows. Furthermore, it can recommend appropriate fashion items to match events the user plans to attend. This improves recommendation accuracy by customizing the recommendation method based on the user's current fashion trends and areas of interest. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation system can input the user's current fashion trends and areas of interest into a generating AI, which can then automatically customize the recommendation method.

[0050] The recommendation unit can improve recommendation accuracy by referring to other users' purchase history and ratings. For example, the recommendation unit can improve recommendation accuracy based on items that other users have given high ratings to. Furthermore, the recommendation unit can adjust recommendation results by referring to items that other users frequently purchase. In addition, the recommendation unit can improve recommendation accuracy by analyzing other users' purchase history and identifying trends. For example, the recommendation unit can improve recommendation accuracy based on items that other users have given high ratings to. Furthermore, the recommendation unit can adjust recommendation results by referring to items that other users frequently purchase. Furthermore, the recommendation unit can improve recommendation accuracy by analyzing other users' purchase history and identifying trends. This allows the recommendation unit to improve recommendation accuracy by referring to other users' purchase history and ratings. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input other users' purchase history and ratings into its generating AI, which then automatically analyzes the data to improve recommendation accuracy.

[0051] The recommendation unit can make recommendations while considering the user's geographical location. For example, if the user is in a specific region, the recommendation unit can recommend fashion items popular in that region. Furthermore, if the user is traveling, the recommendation unit can make recommendations based on trend information for their travel destination. Additionally, if the user is planning to attend a specific event, the recommendation unit can recommend fashion items related to that event. This improves recommendation accuracy by considering the user's geographical location. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the user's geographical location information into a generating AI, which then automatically analyzes the data and makes recommendations.

[0052] The feedback collection unit can improve collection accuracy by analyzing the user's past feedback history in detail when collecting feedback. For example, the feedback collection unit can improve collection accuracy based on the content of feedback previously provided by the user. The feedback collection unit can also adjust the collection method considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback collection unit can improve collection accuracy by analyzing the frequency of feedback previously provided by the user. For example, the feedback collection unit can improve collection accuracy based on the content of feedback previously provided by the user. Furthermore, the feedback collection unit can adjust the collection method considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback collection unit can improve collection accuracy by analyzing the frequency of feedback previously provided by the user. This improves collection accuracy by analyzing the user's past feedback history in detail. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI, which can then automatically analyze and improve collection accuracy.

[0053] The feedback collection unit can customize its collection methods based on the user's current fashion trends and areas of interest when collecting feedback. For example, the feedback collection unit can customize its collection methods based on the fashion items the user has recently become interested in. It can also adjust its collection methods by referring to the styles of fashion influencers the user follows. Furthermore, the feedback collection unit can suggest appropriate feedback collection methods to match events the user plans to attend. This improves collection accuracy by customizing the collection methods based on the user's current fashion trends and areas of interest. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the collection methods.

[0054] The feedback collection unit can collect feedback while considering the user's geographical location information. For example, if the user is in a specific region, the feedback collection unit will prioritize collecting feedback from that region. Furthermore, if the user is traveling, the feedback collection unit can collect feedback based on their travel destination. Additionally, if the user is planning to attend a specific event, the feedback collection unit can prioritize collecting feedback related to that event. This improves the accuracy of data collection by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the user's geographical location information into an AI, which can then automatically analyze and collect feedback.

[0055] The feedback reflection unit can improve the accuracy of feedback reflection by analyzing the user's past feedback history in detail when reflecting feedback. For example, the feedback reflection unit can improve the accuracy of reflection based on the content of feedback previously provided by the user. The feedback reflection unit can also adjust the reflection method by considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback reflection unit can also improve the accuracy of reflection by analyzing the frequency of feedback previously provided by the user. For example, the feedback reflection unit can improve the accuracy of reflection based on the content of feedback previously provided by the user. Furthermore, the feedback reflection unit can adjust the reflection method by considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback reflection unit can also improve the accuracy of reflection by analyzing the frequency of feedback previously provided by the user. In this way, the accuracy of reflection is improved by analyzing the user's past feedback history in detail. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's past feedback history into AI, and the AI ​​can automatically analyze it to improve the accuracy of reflection.

[0056] The feedback reflection unit can customize the reflection method based on the user's current fashion trends and areas of interest when reflecting feedback. For example, the feedback reflection unit can customize the reflection method based on the fashion items the user has recently become interested in. It can also adjust the reflection method by referring to the styles of fashion influencers the user follows. Furthermore, the feedback reflection unit can suggest an appropriate feedback reflection method to match the events the user plans to attend. This improves the accuracy of the reflection by customizing the reflection method based on the user's current fashion trends and areas of interest. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the reflection method.

[0057] The feedback reflection unit can reflect feedback while considering the user's geographical location information. For example, if the user is in a specific region, the feedback reflection unit will prioritize reflecting feedback from that region. Furthermore, if the user is traveling, the feedback reflection unit can reflect feedback based on feedback from their travel destination. Additionally, if the user is planning to attend a specific event, the feedback reflection unit can prioritize reflecting feedback related to that event. This improves the accuracy of feedback reflection by considering the user's geographical location information. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's geographical location information into AI, which can then automatically analyze and reflect the feedback.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display preferred colors and styles that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information tailored to specific seasons or events based on the user's past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method and improve input efficiency. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into the AI, which can then automatically suggest the optimal input method.

[0060] The reception desk can customize input fields based on the user's current fashion trends and areas of interest during input. For example, it can suggest relevant input fields based on fashion items the user has recently been interested in. It can also customize input fields by referencing the styles of fashion influencers the user follows. Furthermore, it can suggest appropriate fashion items for events the user plans to attend. This allows for more accurate input by customizing input fields based on the user's current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the input fields.

[0061] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if the user is in a specific region, it can prioritize displaying fashion items popular in that region. Furthermore, if the user is traveling, it can customize input fields based on trend information in their travel destination. Additionally, if the user has plans to attend a specific event, it can prioritize displaying fashion items related to that event. This improves input efficiency by prioritizing the display of highly relevant input fields based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then automatically prioritize displaying highly relevant input fields.

[0062] The analysis unit can improve analysis accuracy by thoroughly analyzing the user's past purchase history during the analysis process. For example, it can improve analysis accuracy based on the color and style of items the user has purchased in the past. It can also adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, it can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. In this way, analysis accuracy is improved by thoroughly analyzing the user's past purchase history. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the user's past purchase history into the generation AI, which can then automatically perform the analysis and improve analysis accuracy.

[0063] The recommendation unit can improve recommendation accuracy by referring to other users' purchase history and ratings during the recommendation process. For example, it can improve recommendation accuracy based on items that other users have given high ratings to. It can also adjust recommendation results by referring to items that other users frequently purchase. Furthermore, it can analyze other users' purchase history to understand trends and improve recommendation accuracy. In this way, recommendation accuracy is improved by referring to other users' purchase history and ratings. Some or all of the above processes in the recommendation unit are performed using a generation AI. For example, the recommendation unit can input other users' purchase history and ratings into the generation AI, which can then automatically analyze the data and improve recommendation accuracy.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk inputs the user's preferences, past purchase history, and area trend information. User preferences include colors, styles, brands, etc. For example, users can input their favorite colors and styles, information on items they have purchased in the past, and trend information for the area where they live. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit performs the analysis using methods such as generative AI, data mining, statistical analysis, and machine learning algorithms. Step 3: The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The recommendation unit uses a generation AI to make recommendations based on the user's preferences, past purchase history, and area trend information. The recommendation unit updates trend information in real time, so it can always make recommendations based on the latest information. Step 4: The Feedback Collection Unit collects feedback on items recommended by the Recommendation Unit. The Feedback Collection Unit can collect ratings and comments on items actually purchased by users, as well as post-purchase surveys. Step 5: The feedback reflection unit incorporates the feedback collected by the feedback collection unit into the next recommendation. Based on the collected feedback, the feedback reflection unit can adjust the recommendation algorithm to improve the accuracy of the next recommendation.

[0066] (Example of form 2) The personalized shopping assistant system according to an embodiment of the present invention is a system that provides a personalized shopping assistant based on the user's preferences, past purchase history, and area trend information using generative AI. The personalized shopping assistant system allows the user to input their preferences, past purchase history, and area trend information. The generative AI analyzes this information and recommends the most suitable fashion items to the user. This recommendation more accurately reflects the user's preferences, making it easier for the user to find items that suit their style. For example, if a user likes blue shirts, prefers a casual style, and sneakers are popular in their area, the generative AI will recommend a blue casual shirt and sneakers. This recommendation more accurately reflects the user's preferences, making it easier for the user to find items that suit their style. For example, the user can see the recommended items and think, "These are perfect for my taste." Furthermore, the generative AI updates trend information in real time, always making recommendations based on the latest information. This allows the user to always find items that match the latest trends. This mechanism significantly reduces the effort required for shopping. For example, it eliminates the need for users to search through a vast number of products to find items that suit their preferences. Furthermore, because the generating AI provides recommendations that accurately reflect the user's preferences, users can easily find items that match their style. This improves the user's shopping experience, making online shopping more enjoyable and easier. As a result, personalized shopping assistant systems can provide personalized shopping assistance based on the user's preferences, past purchase history, and local trend information.

[0067] The personalized shopping assistant system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a feedback collection unit, and a feedback reflection unit. The reception unit receives input from the user's preferences, past purchase history, and area trend information. User preferences include, but are not limited to, colors, styles, and brands. For example, the reception unit can receive input from the user such as their favorite colors and styles, information on items they have purchased in the past, and trend information for the area where they live. The analysis unit analyzes the information entered by the reception unit using a generative AI. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The recommendation unit makes recommendations based on, for example, the user's preferences, past purchase history, and area trend information using a generative AI. The recommendation unit can update trend information in real time and always make recommendations based on the latest information. The feedback collection unit collects feedback on items recommended by the recommendation unit. For example, the feedback collection unit can collect ratings and comments on items actually purchased by the user, as well as post-purchase surveys. The feedback reflection unit incorporates the feedback collected by the feedback collection unit into future recommendations. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of future recommendations. As a result, the personalized shopping assistant system according to this embodiment can provide a personalized shopping assistant based on the user's preferences, past purchase history, and area trend information.

[0068] The reception desk inputs user preferences, past purchase history, and area trend information. User preferences include, but are not limited to, colors, styles, and brands. Specifically, users can input their favorite colors, styles, and preferences for specific brands in detail through a dedicated interface. For example, if a user inputs "I like blue casual styles," the reception desk saves this information to the database. Past purchase history is also important data, and the system can automatically retrieve and save information about items the user has previously purchased. This includes purchase date and time, details of the purchased item, price, and purchase location. Furthermore, area trend information is also an important element, and the system collects and saves the latest fashion trends in the user's area. This allows the reception desk to comprehensively understand the user's individual preferences, past behavior, and regional trends, and provide accurate data to the analysis department in the next step.

[0069] The analysis unit uses generative AI to analyze the information entered by the reception unit. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, the generative AI receives user preferences, past purchase history, and area trend information as input data, and integrates and analyzes this data. For example, it uses data mining techniques to extract the user's past purchase patterns and reveals user preference trends through statistical analysis. It also uses machine learning algorithms to build a predictive model based on user preferences and trend information to predict items that are likely to be purchased next. Based on these analysis results, the generative AI generates data to recommend the most suitable fashion items to the user. Furthermore, the analysis unit updates the data in real time, allowing it to always perform analysis based on the latest information. This enables the analysis unit to quickly respond to changes in user preferences and trends and provide highly accurate recommendations.

[0070] The recommendation department recommends the most suitable fashion items to the user based on information analyzed by the analysis department. For example, the recommendation department uses generative AI to make recommendations based on the user's preferences, past purchase history, and area trend information. Specifically, the generative AI selects the most suitable items for the user based on the data provided by the analysis department and generates a recommendation list. The recommendation list includes items in colors, styles, and brands that match the user's preferences, and also takes into account the latest area trends. For example, if a user prefers a casual style, the recommendation department will create a list centered on casual items and present it to the user. Furthermore, the recommendation department updates trend information in real time, ensuring that recommendations are always based on the latest information. This allows users to always receive recommendations based on the latest trends, providing a more satisfying shopping experience. In addition, the recommendation department can continuously improve its recommendation algorithm based on user feedback, thereby improving the accuracy of future recommendations.

[0071] The Feedback Collection Unit collects feedback on items recommended by the Recommendation Unit. For example, the Feedback Collection Unit can collect ratings and comments on items actually purchased by users, as well as post-purchase surveys. Specifically, after a user purchases a recommended item, they can input ratings and comments through a dedicated interface. Ratings include satisfaction with the item's quality, design, and price, while comments include specific feedback and suggestions for improvement. Furthermore, feedback on the user's overall shopping experience can be collected through post-purchase surveys. This allows the Feedback Collection Unit to gain a detailed understanding of users' real opinions and impressions, collecting valuable data to reflect in future recommendations. Additionally, the Feedback Collection Unit stores the collected feedback in a database, making it accessible to the Analysis Unit and Recommendation Unit. This improves the overall system accuracy and user satisfaction.

[0072] The feedback reflection unit incorporates the feedback collected by the feedback collection unit into the next recommendation. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of the next recommendation. Specifically, the feedback reflection unit analyzes user ratings and comments and adjusts the parameters of the recommendation algorithm. For example, if a particular item receives a low rating, it may be removed from the recommendation list or its recommendation frequency may be reduced. Also, if a user gives a high rating to a particular brand or style, the algorithm may be adjusted to prioritize recommending items from that brand or style. Furthermore, the feedback reflection unit can detect new trends and changes in preferences based on user feedback and incorporate them into the recommendation algorithm. This allows the feedback reflection unit to quickly respond to changes in user preferences and trends and always provide the optimal recommendation. As a result, the personalized shopping assistant system according to this embodiment can provide a personalized shopping assistant based on user preferences, past purchase history, and area trend information.

[0073] The reception desk allows users to input information tailored to their preferred colors and styles, seasons, and events. For example, users can input information such as "I like blue shirts," "I prefer casual styles in the summer," and "I want to wear a formal dress for Christmas." This allows for more personalized recommendations by providing users with information tailored to their preferred colors, styles, seasons, and events. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the information entered by the user into the AI, which can then automatically analyze the information and complete the input.

[0074] The analysis unit can perform analysis while also considering the purchase history and ratings of other users. For example, the analysis unit performs analysis based on information such as items that other users have given high ratings to or items that are frequently purchased. For example, the analysis unit prioritizes recommending items that other users have given high ratings to. The analysis unit can also adjust the recommendation results by referring to items that other users frequently purchase. Furthermore, the analysis unit can analyze the purchase history of other users to grasp trends and improve the accuracy of the analysis. For example, the analysis unit grasps current trends based on the purchase history of other users and recommends the most suitable items to the user. This improves the accuracy of the analysis by considering the purchase history and ratings of other users. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the purchase history and ratings of other users into the generation AI, which automatically performs the analysis and outputs the recommendation results.

[0075] The recommendation unit can update trend information in real time and make recommendations based on the latest information. For example, the recommendation unit collects the latest news articles and social media posts from the internet and updates the trend information. For example, the recommendation unit collects the latest fashion magazines and blog posts and makes recommendations based on the trend information. The recommendation unit can also analyze social media posts to understand current trends and make recommendations. Furthermore, the recommendation unit can update trend information in real time and always make recommendations based on the latest information. For example, the recommendation unit collects the latest news articles and social media posts from the internet in real time and updates the trend information. This allows for real-time updates of trend information, ensuring that recommendations are always based on the latest information. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the latest news articles and social media posts from the internet into the generation AI, which automatically updates the trend information and outputs recommendation results.

[0076] The feedback collection unit can collect feedback on items that users have actually purchased. For example, the feedback collection unit can collect user ratings, comments, and post-purchase surveys for items purchased by users. For instance, the feedback collection unit can provide a function for users to rate purchased items with stars. It can also provide a function for users to enter comments on purchased items. Furthermore, the feedback collection unit can collect user feedback through post-purchase surveys. For example, the feedback collection unit can collect user feedback on the usability and satisfaction level of purchased items through post-purchase surveys. By collecting feedback on items actually purchased by users, this can be reflected in future recommendations. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input user-generated feedback into an AI, which can then automatically analyze and collect the feedback.

[0077] The feedback reflection unit can incorporate the collected feedback into the next recommendation. For example, the feedback reflection unit can adjust the recommendation algorithm based on the collected feedback to improve the accuracy of the next recommendation. For example, the feedback reflection unit can adjust the algorithm to prioritize recommending items that users have given high ratings to. The feedback reflection unit can also adjust the algorithm to exclude items that users have given low ratings to from the recommendation. Furthermore, the feedback reflection unit can also adjust the recommendation algorithm based on user comments and survey results. For example, the feedback reflection unit can adjust the recommendation algorithm based on user comments and survey results to improve the accuracy of the next recommendation. In this way, the accuracy of the recommendation is improved by incorporating the collected feedback into the next recommendation. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the collected feedback into AI, and the AI ​​can automatically analyze the feedback and adjust the recommendation algorithm.

[0078] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This reduces user stress and improves input efficiency by adjusting the display of the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generating AI, which can then automatically estimate emotions and adjust the interface display method.

[0079] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display the user's preferred colors and styles that they have frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information tailored to specific seasons or events based on the user's past input history. This allows the reception desk to suggest the optimal input method and improve input efficiency by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history into an AI, which can then automatically suggest the optimal input method.

[0080] The reception desk can customize input fields based on the user's current fashion trends and areas of interest during input. For example, the reception desk can suggest relevant input fields based on fashion items the user has recently been interested in. It can also customize input fields by referencing the styles of fashion influencers the user follows. Furthermore, the reception desk can suggest appropriate fashion items for events the user plans to attend. This allows for more accurate input by customizing input fields based on the user's current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the input fields.

[0081] The reception desk can estimate the user's emotions and prioritize input fields based on those emotions. For example, if the user is nervous, the reception desk can prioritize displaying important input fields and postpone other fields. If the user is relaxed, the reception desk can also display detailed input fields in order, allowing the user to choose freely. Furthermore, if the user is in a hurry, the reception desk can display only the most important input fields, enabling quick completion. This improves input efficiency by prioritizing input fields according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generating AI, which can then automatically estimate emotions and determine the priority of the input items.

[0082] The input system can prioritize displaying highly relevant input items by considering the user's geographical location during input. For example, if the user is in a specific region, the input system will prioritize displaying fashion items popular in that region. Furthermore, if the user is traveling, the input system can customize input items based on trend information for their travel destination. Additionally, if the user has plans to attend a specific event, the input system can prioritize displaying fashion items related to that event. This improves input efficiency by prioritizing the display of highly relevant input items while considering the user's geographical location. Some or all of the above processing in the input system may be performed using AI, or not. For example, the input system can input the user's geographical location into the AI, which can then automatically prioritize displaying highly relevant input items.

[0083] The reception desk can analyze the user's social media activity during input and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on fashion items the user has "liked" or commented on on social media. It can also analyze posts from brands and influencers the user follows and suggest relevant input fields. Furthermore, the reception desk can customize input fields based on trend information from fashion communities the user participates in. This allows the reception desk to analyze the user's social media activity, suggest relevant input fields, and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then automatically suggest relevant input fields.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more recommendations. If the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize the most important recommendations. Furthermore, if the user is excited, the analysis unit can adjust the analysis algorithm to provide visually appealing recommendations. This improves analysis accuracy by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then automatically estimate emotions and adjust the analysis algorithm.

[0085] The analysis unit can improve analysis accuracy by thoroughly analyzing the user's past purchase history during analysis. For example, the analysis unit can improve analysis accuracy based on the color and style of items the user has purchased in the past. The analysis unit can also adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, the analysis unit can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. For example, the analysis unit can improve analysis accuracy based on the color and style of items the user has purchased in the past. Furthermore, the analysis unit can adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, the analysis unit can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. In this way, analysis accuracy is improved by thoroughly analyzing the user's past purchase history. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the user's past purchase history into the generation AI, which can then automatically perform the analysis and improve analysis accuracy.

[0086] The analysis unit can customize its analysis methods based on the user's current fashion trends and areas of interest during analysis. For example, the analysis unit can customize its analysis methods based on the fashion items the user has recently become interested in. It can also adjust its analysis methods by referencing the styles of fashion influencers the user follows. Furthermore, the analysis unit can analyze appropriate fashion items to match events the user plans to attend. This improves analysis accuracy by customizing the analysis methods based on the user's current fashion trends and areas of interest. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's current fashion trends and areas of interest into the generative AI, which can then automatically customize the analysis methods.

[0087] The analysis unit can improve its analysis accuracy by referring to the purchase history and ratings of other users during the analysis process. For example, the analysis unit can improve its analysis accuracy based on items that other users have given high ratings to. Furthermore, the analysis unit can adjust its analysis results by referring to items that other users frequently purchase. In addition, the analysis unit can improve its analysis accuracy by analyzing the purchase history of other users and grasping trends. For example, the analysis unit can improve its analysis accuracy based on items that other users have given high ratings to. Furthermore, the analysis unit can adjust its analysis results by referring to items that other users frequently purchase. Furthermore, the analysis unit can improve its analysis accuracy by analyzing the purchase history of other users and grasping trends. This allows the analysis unit to improve its accuracy by referring to the purchase history and ratings of other users. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the purchase history and ratings of other users into the generation AI, which can then automatically perform the analysis and improve its accuracy.

[0088] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze popular fashion items in that region. Furthermore, if the user is traveling, the analysis unit can perform analysis based on trend information for the travel destination. Additionally, if the user is planning to attend a specific event, the analysis unit can analyze fashion items related to that event. This improves analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then automatically perform the analysis and improve analysis accuracy.

[0089] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. It can also provide quick recommendations if the user is in a hurry. Furthermore, if the user is excited, the recommendation system can adjust the way recommendations are presented to provide visually appealing recommendations. This improves user satisfaction by adjusting the recommendation presentation according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the recommendation unit is performed using a generative AI. For example, the recommendation unit can input the user's facial expression data into the generative AI, which can then automatically estimate the emotion and adjust the way the recommendations are presented.

[0090] The recommendation unit can improve recommendation accuracy by analyzing the user's past purchase history in detail when making recommendations. For example, the recommendation unit can improve recommendation accuracy based on the color and style of items the user has purchased in the past. The recommendation unit can also adjust recommendation results by considering the user's ratings of items purchased in the past. Furthermore, the recommendation unit can improve recommendation accuracy by analyzing the frequency of use of items purchased in the past. For example, the recommendation unit can improve recommendation accuracy based on the color and style of items the user has purchased in the past. Furthermore, the recommendation unit can adjust recommendation results by considering the user's ratings of items purchased in the past. Furthermore, the recommendation unit can also improve recommendation accuracy by analyzing the frequency of use of items purchased in the past. In this way, recommendation accuracy is improved by analyzing the user's past purchase history in detail. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the user's past purchase history into a generating AI, which then automatically analyzes the data to improve recommendation accuracy.

[0091] The recommendation unit can customize its recommendation methods based on the user's current fashion trends and areas of interest. For example, it can customize its recommendation methods based on fashion items the user has recently been interested in. It can also adjust its recommendation methods by referencing the styles of fashion influencers the user follows. Furthermore, it can recommend appropriate fashion items to match events the user plans to attend. This improves recommendation accuracy by customizing the recommendation method based on the user's current fashion trends and areas of interest. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation system can input the user's current fashion trends and areas of interest into a generating AI, which can then automatically customize the recommendation method.

[0092] The recommendation system can estimate the user's emotions and prioritize recommendations based on those emotions. For example, if the user is stressed, the recommendation system will prioritize important recommendations and postpone others. If the user is relaxed, the recommendation system can also display detailed recommendations in order, allowing the user to choose freely. Furthermore, if the user is in a hurry, the recommendation system can display only the most important recommendations, allowing the user to complete the recommendation process quickly. This improves user satisfaction by prioritizing recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input user facial expression data into the generative AI, which can then automatically estimate emotions and determine recommendation priorities.

[0093] The recommendation unit can improve recommendation accuracy by referring to other users' purchase history and ratings. For example, the recommendation unit can improve recommendation accuracy based on items that other users have given high ratings to. Furthermore, the recommendation unit can adjust recommendation results by referring to items that other users frequently purchase. In addition, the recommendation unit can improve recommendation accuracy by analyzing other users' purchase history and identifying trends. For example, the recommendation unit can improve recommendation accuracy based on items that other users have given high ratings to. Furthermore, the recommendation unit can adjust recommendation results by referring to items that other users frequently purchase. Furthermore, the recommendation unit can improve recommendation accuracy by analyzing other users' purchase history and identifying trends. This allows the recommendation unit to improve recommendation accuracy by referring to other users' purchase history and ratings. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input other users' purchase history and ratings into its generating AI, which then automatically analyzes the data to improve recommendation accuracy.

[0094] The recommendation unit can make recommendations while considering the user's geographical location. For example, if the user is in a specific region, the recommendation unit can recommend fashion items popular in that region. Furthermore, if the user is traveling, the recommendation unit can make recommendations based on trend information for their travel destination. Additionally, if the user is planning to attend a specific event, the recommendation unit can recommend fashion items related to that event. This improves recommendation accuracy by considering the user's geographical location. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the user's geographical location information into a generating AI, which then automatically analyzes the data and makes recommendations.

[0095] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit can provide questions requesting detailed feedback. If the user is in a hurry, the feedback collection unit can also provide questions requesting concise feedback. Furthermore, if the user is excited, the feedback collection unit can provide a visually appealing feedback collection interface. This improves the quality of feedback by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user facial expression data into a generating AI, which can automatically estimate emotions and adjust the feedback collection method.

[0096] The feedback collection unit can improve collection accuracy by analyzing the user's past feedback history in detail when collecting feedback. For example, the feedback collection unit can improve collection accuracy based on the content of feedback previously provided by the user. The feedback collection unit can also adjust the collection method considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback collection unit can improve collection accuracy by analyzing the frequency of feedback previously provided by the user. For example, the feedback collection unit can improve collection accuracy based on the content of feedback previously provided by the user. Furthermore, the feedback collection unit can adjust the collection method considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback collection unit can improve collection accuracy by analyzing the frequency of feedback previously provided by the user. This improves collection accuracy by analyzing the user's past feedback history in detail. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI, which can then automatically analyze and improve collection accuracy.

[0097] The feedback collection unit can customize its collection methods based on the user's current fashion trends and areas of interest when collecting feedback. For example, the feedback collection unit can customize its collection methods based on the fashion items the user has recently become interested in. It can also adjust its collection methods by referring to the styles of fashion influencers the user follows. Furthermore, the feedback collection unit can suggest appropriate feedback collection methods to match events the user plans to attend. This improves collection accuracy by customizing the collection methods based on the user's current fashion trends and areas of interest. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the collection methods.

[0098] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is nervous, the feedback collection unit can prioritize collecting important feedback and postpone other feedback. If the user is relaxed, the feedback collection unit can collect detailed feedback in order, allowing the user to choose freely. Furthermore, if the user is in a hurry, the feedback collection unit can collect only the most important feedback, allowing for quick completion of the collection. This improves the quality of feedback by prioritizing feedback collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user facial expression data into a generating AI, which can then automatically estimate emotions and determine the priority of feedback collection.

[0099] The feedback collection unit can collect feedback while considering the user's geographical location information. For example, if the user is in a specific region, the feedback collection unit will prioritize collecting feedback from that region. Furthermore, if the user is traveling, the feedback collection unit can collect feedback based on their travel destination. Additionally, if the user is planning to attend a specific event, the feedback collection unit can prioritize collecting feedback related to that event. This improves the accuracy of data collection by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the user's geographical location information into an AI, which can then automatically analyze and collect feedback.

[0100] The feedback reflection unit can estimate the user's emotions and adjust the method of feedback reflection based on the estimated user emotions. For example, if the user is relaxed, the feedback reflection unit will reflect detailed feedback. Also, if the user is in a hurry, the feedback reflection unit can reflect feedback quickly. Furthermore, if the user is excited, the feedback reflection unit can provide a visually appealing method of feedback reflection. For example, if the user is relaxed, the feedback reflection unit will reflect detailed feedback. Also, if the user is in a hurry, the feedback reflection unit can reflect feedback quickly. Furthermore, if the user is excited, the feedback reflection unit can provide a visually appealing method of feedback reflection. This improves the accuracy of feedback reflection by adjusting the method of feedback reflection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the feedback reflection unit may be performed using AI or not using AI. For example, the feedback reflection unit can input the user's facial expression data into a generating AI, which can automatically estimate emotions and adjust the method of feedback reflection.

[0101] The feedback reflection unit can improve the accuracy of feedback reflection by analyzing the user's past feedback history in detail when reflecting feedback. For example, the feedback reflection unit can improve the accuracy of reflection based on the content of feedback previously provided by the user. The feedback reflection unit can also adjust the reflection method by considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback reflection unit can also improve the accuracy of reflection by analyzing the frequency of feedback previously provided by the user. For example, the feedback reflection unit can improve the accuracy of reflection based on the content of feedback previously provided by the user. Furthermore, the feedback reflection unit can adjust the reflection method by considering the evaluation of the feedback previously provided by the user. Furthermore, the feedback reflection unit can also improve the accuracy of reflection by analyzing the frequency of feedback previously provided by the user. In this way, the accuracy of reflection is improved by analyzing the user's past feedback history in detail. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's past feedback history into AI, and the AI ​​can automatically analyze it to improve the accuracy of reflection.

[0102] The feedback reflection unit can customize the reflection method based on the user's current fashion trends and areas of interest when reflecting feedback. For example, the feedback reflection unit can customize the reflection method based on the fashion items the user has recently become interested in. It can also adjust the reflection method by referring to the styles of fashion influencers the user follows. Furthermore, the feedback reflection unit can suggest an appropriate feedback reflection method to match the events the user plans to attend. This improves the accuracy of the reflection by customizing the reflection method based on the user's current fashion trends and areas of interest. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the reflection method.

[0103] The feedback reflection unit can estimate the user's emotions and determine the priority of feedback reflection based on the estimated emotions. For example, if the user is tense, the feedback reflection unit will prioritize important feedback and postpone other feedback. If the user is relaxed, the feedback reflection unit can also reflect detailed feedback in order, allowing the user to choose freely. Furthermore, if the user is in a hurry, the feedback reflection unit can reflect only the most important feedback, allowing for quick completion of the reflection process. This improves the accuracy of feedback reflection by determining the priority of feedback reflection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input user facial expression data into a generating AI, which can then automatically estimate emotions and determine the priority of feedback reflection.

[0104] The feedback reflection unit can reflect feedback while considering the user's geographical location information. For example, if the user is in a specific region, the feedback reflection unit will prioritize reflecting feedback from that region. Furthermore, if the user is traveling, the feedback reflection unit can reflect feedback based on feedback from their travel destination. Additionally, if the user is planning to attend a specific event, the feedback reflection unit can prioritize reflecting feedback related to that event. This improves the accuracy of feedback reflection by considering the user's geographical location information. Some or all of the above processing in the feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's geographical location information into AI, which can then automatically analyze and reflect the feedback.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide more recommendations. If the user is in a hurry, it can perform a quick analysis and prioritize the most important recommendations. Furthermore, if the user is excited, it can adjust the analysis algorithm to provide visually appealing recommendations. In this way, the accuracy of the analysis is improved by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user facial expression data into the generative AI, which can automatically estimate emotions and adjust the analysis algorithm.

[0107] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, it can provide detailed recommendations. If the user is in a hurry, it can provide quick recommendations. Furthermore, if the user is excited, it can adjust the way recommendations are presented to provide visually appealing recommendations. This improves user satisfaction by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 recommendation unit is performed using generative AI. For example, the recommendation unit can input user facial expression data into the generative AI, which can automatically estimate emotions and adjust the way recommendations are presented.

[0108] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, it can provide questions requesting detailed feedback. If the user is in a hurry, it can provide questions requesting concise feedback. Furthermore, if the user is excited, it can provide a visually appealing feedback collection interface. This improves the quality of feedback by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user facial expression data into a generative AI, which can automatically estimate emotions and adjust the feedback collection method.

[0109] The feedback reflection unit can estimate the user's emotions and adjust the feedback reflection method based on the estimated user emotions. For example, if the user is relaxed, it can reflect detailed feedback. If the user is in a hurry, it can also reflect feedback quickly. Furthermore, if the user is excited, it can provide a visually appealing feedback reflection method. This improves the accuracy of the feedback by adjusting the feedback reflection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 feedback reflection unit may be performed using AI or not. For example, the feedback reflection unit can input the user's facial expression data into the generative AI, which can automatically estimate emotions and adjust the feedback reflection method.

[0110] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick information entry. This reduces user stress and improves input efficiency by adjusting the display of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can automatically estimate emotions and adjust the display of the interface.

[0111] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display preferred colors and styles that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information tailored to specific seasons or events based on the user's past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method and improve input efficiency. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into the AI, which can then automatically suggest the optimal input method.

[0112] The reception desk can customize input fields based on the user's current fashion trends and areas of interest during input. For example, it can suggest relevant input fields based on fashion items the user has recently been interested in. It can also customize input fields by referencing the styles of fashion influencers the user follows. Furthermore, it can suggest appropriate fashion items for events the user plans to attend. This allows for more accurate input by customizing input fields based on the user's current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current fashion trends and areas of interest into the AI, which can then automatically customize the input fields.

[0113] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if the user is in a specific region, it can prioritize displaying fashion items popular in that region. Furthermore, if the user is traveling, it can customize input fields based on trend information in their travel destination. Additionally, if the user has plans to attend a specific event, it can prioritize displaying fashion items related to that event. This improves input efficiency by prioritizing the display of highly relevant input fields based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then automatically prioritize displaying highly relevant input fields.

[0114] The analysis unit can improve analysis accuracy by thoroughly analyzing the user's past purchase history during the analysis process. For example, it can improve analysis accuracy based on the color and style of items the user has purchased in the past. It can also adjust the analysis results by considering the user's ratings of items purchased in the past. Furthermore, it can improve analysis accuracy by analyzing the frequency of use of items purchased in the past. In this way, analysis accuracy is improved by thoroughly analyzing the user's past purchase history. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the user's past purchase history into the generation AI, which can then automatically perform the analysis and improve analysis accuracy.

[0115] The recommendation unit can improve recommendation accuracy by referring to other users' purchase history and ratings during the recommendation process. For example, it can improve recommendation accuracy based on items that other users have given high ratings to. It can also adjust recommendation results by referring to items that other users frequently purchase. Furthermore, it can analyze other users' purchase history to understand trends and improve recommendation accuracy. In this way, recommendation accuracy is improved by referring to other users' purchase history and ratings. Some or all of the above processes in the recommendation unit are performed using a generation AI. For example, the recommendation unit can input other users' purchase history and ratings into the generation AI, which can then automatically analyze the data and improve recommendation accuracy.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The reception desk inputs the user's preferences, past purchase history, and area trend information. User preferences include colors, styles, brands, etc. For example, users can input their favorite colors and styles, information on items they have purchased in the past, and trend information for the area where they live. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit performs the analysis using methods such as generative AI, data mining, statistical analysis, and machine learning algorithms. Step 3: The recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the analysis unit. The recommendation unit uses a generation AI to make recommendations based on the user's preferences, past purchase history, and area trend information. The recommendation unit updates trend information in real time, so it can always make recommendations based on the latest information. Step 4: The Feedback Collection Unit collects feedback on items recommended by the Recommendation Unit. The Feedback Collection Unit can collect ratings and comments on items actually purchased by users, as well as post-purchase surveys. Step 5: The feedback reflection unit incorporates the feedback collected by the feedback collection unit into the next recommendation. Based on the collected feedback, the feedback reflection unit can adjust the recommendation algorithm to improve the accuracy of the next recommendation.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, feedback collection unit, and feedback reflection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and inputs the user's preferences, past purchase history, and area trend information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using generated AI. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user based on the analyzed information. The feedback collection unit is implemented by the reception device 38 of the smart device 14 and collects feedback on the recommended items. The feedback reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the collected feedback in the next recommendation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, feedback collection unit, and feedback reflection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's preferences, past purchase history, and area trend information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using generated AI. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user based on the analyzed information. The feedback collection unit is implemented by the microphone 238 of the smart glasses 214 and collects feedback on the recommended items. The feedback reflection unit is implemented by the specific processing unit 290 of the data processing unit 12 and reflects the collected feedback in the next recommendation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, feedback collection unit, and feedback reflection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's preferences, past purchase history, and area trend information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information using generated AI. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user based on the analyzed information. The feedback collection unit is implemented by, for example, the microphone 238 of the headset terminal 314 and collects feedback on the recommended items. The feedback reflection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the collected feedback in the next recommendation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, feedback collection unit, and feedback reflection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and inputs the user's preferences, past purchase history, and area trend information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information using generated AI. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user based on the analyzed information. The feedback collection unit is implemented by, for example, the microphone 238 of the robot 414 and collects feedback on the recommended items. The feedback reflection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the collected feedback in the next recommendation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) A reception area where users input their preferences, past purchase history, and area trend information, An analysis unit analyzes the information input by the reception unit, A recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the aforementioned analysis unit, A feedback collection unit collects feedback on items recommended by the recommendation unit, The system includes a feedback reflection unit that reflects the feedback collected by the feedback collection unit in the next recommendation. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter information such as the user's preferred colors and styles, as well as information related to the season and events. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The analysis also takes into account the purchase history and ratings of other users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, We update trending information in real time and provide recommendations based on the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback collection unit is Collect feedback from users about items they have actually purchased. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback reflection unit is The collected feedback will be used to improve future recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter data, the input fields are customized based on their current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter data, the system prioritizes displaying the most relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by thoroughly analyzing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the purchase history and ratings of other users. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, When making recommendations, we analyze the user's past purchase history in detail to improve recommendation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, the recommendation method is customized based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, we improve the accuracy of our recommendations by referencing other users' purchase history and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, the system takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback collection unit is When collecting feedback, we analyze the user's past feedback history in detail to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback collection unit is When collecting feedback, the collection method is customized based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback collection unit is When collecting feedback, the user's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback reflection unit is We estimate the user's emotions and adjust the feedback process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback reflection unit is When incorporating feedback, we analyze the user's past feedback history in detail to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback reflection unit is When incorporating feedback, the method of reflection is customized based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback reflection unit is The system estimates the user's emotions and prioritizes the incorporation of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback reflection unit is When incorporating feedback, the user's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their preferences, past purchase history, and area trend information, An analysis unit analyzes the information input by the reception unit, A recommendation unit recommends the most suitable fashion items to the user based on the information analyzed by the aforementioned analysis unit, A feedback collection unit collects feedback on items recommended by the recommendation unit, The system includes a feedback reflection unit that reflects the feedback collected by the feedback collection unit in the next recommendation. A system characterized by the following features.

2. The aforementioned reception unit is Enter information such as the user's preferred colors and styles, as well as information related to the season and events. The system according to feature 1.

3. The aforementioned analysis unit, The analysis also takes into account the purchase history and ratings of other users. The system according to feature 1.

4. The recommendation unit is, We update trending information in real time and provide recommendations based on the latest information. The system according to feature 1.

5. The aforementioned feedback collection unit is Collect feedback from users about items they have actually purchased. The system according to feature 1.

6. The aforementioned feedback reflection unit is The collected feedback will be used to improve future recommendations. The system according to feature 1.

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

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

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