system

Abstract

The system according to this embodiment aims to analyze the user's preferences and past purchase history and suggest the most suitable products and shops. [Solution] The system according to this embodiment comprises an analysis unit, a suggestion unit, a fitting unit, and an explanation unit. The analysis unit analyzes the user's preferences and past purchase history. The suggestion unit suggests the most suitable products and shops based on the analysis results obtained by the analysis unit. The fitting unit virtually tries on the products suggested by the suggestion unit. The explanation unit provides information about the products tried on by the fitting unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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, it has not been fully carried out to propose optimal products and stores based on user preferences and past purchase histories, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user preferences and past purchase histories and propose optimal products and stores.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a suggestion unit, a fitting unit, and an explanation unit. The analysis unit analyzes the user's preferences and past purchase history. The suggestion unit suggests the most suitable products and shops based on the analysis results obtained by the analysis unit. The fitting unit virtually tries on the products suggested by the suggestion unit. The explanation unit provides information about the products tried on by the fitting unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's preferences and past purchase history to suggest the most suitable products and shops. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 MetaShopper system according to an embodiment of the present invention is an AI agent system that enhances the shopping experience within the metaverse. The MetaShopper system analyzes the user's preferences and past purchase history to suggest the most suitable products and shops. Furthermore, the MetaShopper system provides a realistic shopping experience, including virtual try-on and product descriptions. For example, the MetaShopper system recommends products and shops tailored to the user's preferences. The AI ​​analyzes the user's past purchase history and preferences to suggest the most suitable products and shops. Next, the MetaShopper system allows users to try on and preview products using avatars. Generative AI creates 3D models of products and allows the user's avatar to try them on, providing a realistic try-on experience. Furthermore, the MetaShopper system uses AI to provide clear and concise product descriptions and reviews. It uses natural language processing to respond to user questions and requests in real time. Next, the MetaShopper system supports secure transactions of digital items. The AI ​​ensures transaction security, allowing users to purchase NFT items with confidence. Finally, the MetaShopper system provides timely information on sales and new products. The AI ​​notifies users of campaign information at the optimal time based on their interests. In this way, the MetaShopper system personalizes the user's shopping experience and smoothly supports them from product discovery to purchase. This allows users to enjoy a comfortable shopping experience and enables businesses to deliver products effectively. As a result, the MetaShopper system can improve the user's shopping experience.

[0029] The MetaShopper system according to this embodiment comprises an analysis unit, a suggestion unit, a virtual try-on unit, and an explanation unit. The analysis unit analyzes the user's preferences and past purchase history. For example, the analysis unit retrieves the user's past purchase history from a database and analyzes it using AI. The analysis unit can analyze information such as the category, brand, and price range of purchased products in order to identify the user's preferences. The suggestion unit suggests the most suitable products and shops based on the analysis results obtained by the analysis unit. For example, the suggestion unit recommends products and shops based on the user's preferences. The suggestion unit can use AI to select and suggest products and shops that match the user's preferences. The virtual try-on unit virtually tries on the products suggested by the suggestion unit. The virtual try-on unit creates a 3D model of the product using generative AI and has the user's avatar try it on. For example, by having the user's avatar try on the product, the user can check the fit and design of the product. The explanation unit provides information about the products tried on by the virtual try-on unit. The explanation unit uses natural language processing to provide product descriptions and reviews in an easy-to-understand format. The description section provides information such as product features, specifications, and user reviews. The description section can also respond to user questions and requests in real time. As a result, the MetaShopper system according to this embodiment can improve the shopping experience by suggesting optimal products and shops based on the user's preferences and past purchase history, and by providing virtual try-ons and product information.

[0030] The analytics department analyzes user preferences and past purchase history. Specifically, the analytics department retrieves users' past purchase history from a database and analyzes it in detail using AI. The AI ​​analyzes information such as the category, brand, price range, color, size, and material of products that the user has purchased in the past to identify user preferences. For example, if there is a bias towards a particular brand or category among the products a user has purchased in the past, the analytics department can identify that trend. It also analyzes purchase frequency and seasonal purchase patterns to predict what kind of products users prefer at what time of year. Furthermore, it collects information on users' browsing history, favorites lists, and products that were added to the cart but not purchased, to comprehensively understand user preferences. As a result, the analytics department can identify user preferences with high accuracy and provide useful data to the recommendation department, which is the next step.

[0031] The Proposal Department suggests the most suitable products and shops based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department uses AI to select and suggest products and shops that match the user's preferences. The AI ​​extracts the most suitable products from a vast product database based on the user preference data provided by the Analysis Department. For example, if a user has previously preferred purchasing clothing from a particular brand, the AI ​​will prioritize suggesting new arrivals and sale information from that brand. Also, if a user prefers products in a specific price range, the AI ​​will suggest products that match that price range. Furthermore, the Proposal Department can also provide information on nearby shops and online shops, taking into account the user's current location and past browsing history. This allows users to easily find products and shops that suit their preferences, improving their shopping experience.

[0032] The fitting room allows users to virtually try on products suggested by the suggestion room. Specifically, the fitting room uses a generative AI to create 3D models of products and have the user's avatar try them on. The generative AI generates highly accurate 3D models based on product images and dimensional data, and allows the user's avatar to try them on in real time. The user's avatar is created based on the user's body shape and size information, providing an experience close to actual try-on. For example, by virtually trying on suggested clothing, users can check the fit and design of the products. The fitting room can also try on multiple products simultaneously, allowing users to try on different product combinations. This allows users to get the feeling of actually trying on products from the comfort of their homes, reducing anxiety before purchasing.

[0033] The description section provides information about products tried on by the fitting section. Specifically, the description section uses natural language processing to provide clear and concise product descriptions and reviews. By using natural language processing technology, it can automatically extract information such as product features, specifications, and user reviews, and present them in a format that is easy for users to understand. For example, in addition to basic information such as the product's material, size, color options, and washing instructions, it displays reviews and ratings from other users. The description section can also respond to user questions and requests in real time. For example, if a user asks a question about a specific product, the AI ​​will instantly generate an answer and provide it to the user. This allows users to easily obtain detailed product information and make more confident purchasing decisions. Furthermore, the description section also provides information such as product availability, shipping options, and return policies, comprehensively supporting the user's shopping experience.

[0034] The recommendation system can recommend products and shops based on user preferences. For example, it can recommend products and shops considering the user's preferred genres, styles, and brands. The recommendation system can use AI to analyze user preferences and select the most suitable products and shops. The recommendation system can also suggest products and shops that the user might be interested in based on their past purchase and search history. This allows for more personalized recommendations by suggesting products and shops based on user preferences.

[0035] The fitting room unit uses generative AI to create 3D models of products and allows users to virtually try them on using their avatars. For example, the fitting room unit uses generative AI to create 3D models of products. The generative AI receives product images and dimensional information as input and generates the 3D model. By having the user's avatar virtually try on the generated 3D model, the fitting room unit can check the fit and design of the product. For example, by having the user's avatar virtually try on the product, the fitting room unit can also check the size and color variations of the product. In this way, by creating 3D models of products using generative AI and having users' avatars virtually try them on, a realistic virtual try-on experience can be provided.

[0036] The description unit can use natural language processing to provide clear and concise product descriptions and reviews. For example, it can use natural language processing to analyze product features and specifications and summarize them in easy-to-understand text. It can also collect product reviews and extract and provide points of high ratings and areas for improvement. The description unit can also respond to user questions and requests in real time. For example, if a user asks a question about a specific product, the description unit can use natural language processing to analyze the question and provide an appropriate answer. In this way, by using natural language processing to provide clear and concise product descriptions and reviews, it makes it easier for users to understand product information.

[0037] The explanation unit can respond to user questions and requests in real time. For example, if a user asks a question about a product, the explanation unit uses natural language processing to analyze the question and provide an appropriate answer. Based on user requests, the explanation unit can also provide detailed product information and reviews. The explanation unit can use AI to generate and provide the optimal answer to user questions in real time. This allows for real-time responses to user questions and requests, thereby improving user satisfaction.

[0038] The explanation unit can use AI to ensure transaction security, allowing users to purchase NFT items with confidence. For example, the explanation unit can use AI to ensure transaction security. The explanation unit can apply security protocols during transactions to prevent fraudulent transactions. The explanation unit can verify user authentication information to ensure transaction security. The explanation unit can also use AI to monitor transaction history and detect fraudulent transactions. In this way, by using AI to ensure transaction security, users can purchase NFT items with confidence.

[0039] The explanation unit can use AI to notify users of campaign information at the optimal time based on their interests. For example, the explanation unit can use AI to analyze user interests and notify them of campaign information at the optimal time. The explanation unit can determine the timing of campaign information notifications based on the user's behavior history and past responses. The explanation unit can also use AI to analyze user interests in real time and notify them of campaign information at the optimal time. This makes it easier to attract user attention by notifying users of campaign information at the optimal time based on their interests using AI.

[0040] The analytics department can analyze a user's past purchase history and select the most suitable analysis method. For example, the analytics department can apply a specific analysis algorithm based on the product categories that a user frequently purchases. The analytics department can adjust the level of detail of the analysis according to the user's purchase frequency. The analytics department can also analyze seasonal purchase trends from the user's purchase history and make suggestions at the appropriate time. By analyzing a user's past purchase history and selecting the most suitable analysis method, more accurate analysis becomes possible.

[0041] The analytics department can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user has recently moved, the analytics department can prioritize analyzing products related to their new area. If a user has started a new hobby, the analytics department can filter and suggest products related to that hobby. If a user is planning to attend a specific event, the analytics department can also prioritize analyzing products related to that event. By filtering based on the user's current lifestyle and areas of interest, the analytics department can provide more relevant analysis results.

[0042] The analytics department can prioritize analyzing highly relevant data by considering the user's geographical location when analyzing purchase history. For example, if a user lives in a specific region, the analytics department can prioritize analyzing products popular in that region. If a user is traveling, the analytics department can prioritize analyzing products available at their travel destination. If a user is planning to attend a specific event, the analytics department can also prioritize analyzing products related to the region where the event is being held. By prioritizing the analysis of highly relevant data while considering the user's geographical location, more appropriate recommendations can be made.

[0043] The analytics department can analyze users' social media activity and obtain relevant data when analyzing purchase history. For example, the analytics department can prioritize analyzing products that users have shown interest in on social media. The analytics department can also prioritize analyzing products recommended by influencers that users follow. The analytics department can also prioritize analyzing products that are trending in social media groups that users participate in. By analyzing users' social media activity and obtaining relevant data, more accurate analysis becomes possible.

[0044] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, for expensive products, the proposal department will provide a detailed description and review. For everyday products, the proposal department can provide a concise description and key features. For new products, the proposal department can also provide a proposal that emphasizes the features and benefits. By adjusting the level of detail in a proposal based on the importance of the product, it becomes possible to provide users with the most optimal information.

[0045] The suggestion function can apply different suggestion algorithms depending on the product category. For example, for fashion items, it can make suggestions based on the user's style. For electronic devices, it can make suggestions based on the user's technical needs. For food products, it can make suggestions based on the user's dietary restrictions and preferences. By applying different suggestion algorithms depending on the product category, it becomes possible to make the most optimal suggestions for the user.

[0046] The proposal department can prioritize proposals based on the timing of product submission. For example, it can prioritize new products. It can also make limited-time proposals for products on sale. For seasonal products, it can make proposals tailored to the season. By prioritizing proposals based on the timing of product submission, it becomes possible to provide users with the most optimal information.

[0047] The suggestion function can adjust the order of suggestions based on the relevance of the products. For example, it can prioritize suggesting highly relevant products based on the user's past purchase history. It can also prioritize suggesting highly relevant products based on the user's current interests. Furthermore, it can prioritize suggesting highly relevant products based on the user's preferences. By adjusting the order of suggestions based on the relevance of the products, it becomes possible to provide the user with the most optimal information.

[0048] The fitting room system can analyze the user's past fitting history to select the optimal fitting method during the fitting process. For example, the fitting room system can suggest the optimal fitting method based on the items the user has tried on in the past. The fitting room system can also suggest a fitting method based on the user's preferred style from their past fitting history. The fitting room system can also analyze the user's past fitting history to suggest the most efficient fitting method. By analyzing the user's past fitting history and selecting the optimal fitting method, the system enables the user to have the best possible fitting experience.

[0049] The fitting room system can customize the fitting experience based on the user's current lifestyle. For example, if the user is at home, the system can provide a fitting experience at home. If the user is out, the system can provide a fitting experience on a mobile device. If the user is planning to attend a specific event, the system can also provide a fitting experience tailored to that event. By customizing the fitting experience based on the user's current lifestyle, the system can provide the optimal fitting experience for the user.

[0050] The fitting room system can select the optimal fitting method by considering the user's geographical location during the fitting process. For example, if a user lives in a specific region, the fitting room system can prioritize letting them try on products popular in that region. If a user is traveling, the fitting room system can prioritize letting them try on products available at their travel destination. If a user is planning to attend a specific event, the fitting room system can also prioritize letting them try on products related to the region where the event is being held. By selecting the optimal fitting method based on the user's geographical location, the system can provide the user with the best possible fitting experience.

[0051] The fitting room system can analyze a user's social media activity during the fitting process and suggest fitting options accordingly. For example, it can prioritize allowing users to try on items they've shown interest in on social media. It can also prioritize allowing users to try on items recommended by influencers they follow. Furthermore, it can prioritize allowing users to try on items that are trending in social media groups they belong to. By analyzing a user's social media activity and suggesting fitting options based on that analysis, the system can provide the optimal fitting experience for each user.

[0052] The explanation unit can select the most appropriate explanation method by referring to the user's past question history during the explanation process. For example, the explanation unit can prioritize providing relevant information based on the user's past questions. The explanation unit can provide information, including detailed explanations, from the user's past question history. The explanation unit can also analyze the user's past question history and provide the most efficient explanation method. This makes it possible to provide the user with the most optimal information by selecting the most appropriate explanation method by referring to the user's past question history.

[0053] The explanation section can customize the content of the explanation based on the user's current interests. For example, if the user has shown interest in a particular category, the explanation section will prioritize providing information related to that category. The explanation section can also provide relevant information based on keywords the user has recently searched for. If the user is planning to attend a particular event, the explanation section can also provide information related to that event. This allows for the provision of information that is best suited to the user by customizing the content of the explanation based on the user's current interests.

[0054] The explanation function can select the most appropriate explanation method by considering the user's geographical location. For example, if the user lives in a specific region, the explanation function can prioritize providing information about products popular in that region. If the user is traveling, the explanation function can prioritize providing information about products available at their travel destination. If the user is planning to attend a specific event, the explanation function can also prioritize providing information related to the region where the event is being held. By selecting the most appropriate explanation method considering the user's geographical location, it becomes possible to provide the user with the most relevant information.

[0055] The explanation function can analyze the user's social media activity and suggest content for the explanation. For example, the explanation function can prioritize providing information about products the user has shown interest in on social media. It can also prioritize providing information about products recommended by influencers the user follows. Furthermore, it can prioritize providing information about products that are trending in social media groups the user participates in. This allows the system to provide the most relevant information to the user by analyzing their social media activity and suggesting content for the explanation.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analytics department can consider a user's current health status and fitness level when analyzing their past purchase history. For example, if a user has recently become interested in fitness, health-related products and services can be prioritized in the analysis. If a user has set specific health goals, products related to those goals can be suggested. If a user is on a specific diet, foods and supplements suitable for those restrictions can also be analyzed. This allows for more relevant suggestions by analyzing based on the user's health status and fitness level.

[0058] The analytics department can consider users' life events when analyzing their past purchase history. For example, if a user is getting married, it can prioritize analyzing products and services related to weddings. If a user starts a new job, it can suggest products related to that job. If a user is planning to move, it can analyze products related to their new residence. By conducting analysis based on users' life events, it becomes possible to make more relevant suggestions.

[0059] The recommendation system can analyze a user's past purchase history and take into account their seasonal preferences. For example, if a user prefers a particular type of product in the summer, it can suggest products appropriate for that season. If a user prefers a particular brand in the winter, it can prioritize suggesting products from that brand. If a user enjoys a particular activity in the spring, it can suggest products related to that activity. This allows for more personalized recommendations based on the user's seasonal preferences.

[0060] The fitting room system can analyze a user's past fitting history and take into account changes in their body shape. For example, if a user loses weight, it can suggest products that suit their new body shape. If a user gains muscle, it can suggest products that are suitable for that body shape. If a user achieves a specific fitness goal, it can also suggest products related to that goal. This allows for a more appropriate fitting experience by suggesting products based on changes in the user's body shape.

[0061] The explanation section can consider the user's learning style when analyzing the user's past question history. For example, if the user prefers visual information, explanations can be provided that make extensive use of images and videos. If the user prefers text-based information, detailed written explanations can be provided. If the user prefers interactive information, explanations that include quizzes and simulations can be provided. This allows for more effective information delivery by tailoring explanations to the user's learning style.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analytics department analyzes user preferences and past purchase history. Specifically, it retrieves users' past purchase history from a database and analyzes it using AI. The analytics department analyzes information such as the category, brand, and price range of purchased products to identify user preferences. Step 2: The Proposal Department proposes the most suitable products and shops based on the analysis results obtained by the Analysis Department. The Proposal Department uses AI to select and propose products and shops that match the user's preferences. Step 3: The fitting department virtually tries on the products suggested by the proposal department. The fitting department uses generative AI to create 3D models of the products and has the user's avatar try them on. This allows the user to check the fit and design of the products. Step 4: The description section provides information about the product tried on by the fitting section. The description section uses natural language processing to provide clear and concise product descriptions and reviews. The description section provides information such as product features, specifications, and user reviews, and can also respond to user questions and requests in real time.

[0064] (Example of form 2) The MetaShopper system according to an embodiment of the present invention is an AI agent system that enhances the shopping experience within the metaverse. The MetaShopper system analyzes the user's preferences and past purchase history to suggest the most suitable products and shops. Furthermore, the MetaShopper system provides a realistic shopping experience, including virtual try-on and product descriptions. For example, the MetaShopper system recommends products and shops tailored to the user's preferences. The AI ​​analyzes the user's past purchase history and preferences to suggest the most suitable products and shops. Next, the MetaShopper system allows users to try on and preview products using avatars. Generative AI creates 3D models of products and allows the user's avatar to try them on, providing a realistic try-on experience. Furthermore, the MetaShopper system uses AI to provide clear and concise product descriptions and reviews. It uses natural language processing to respond to user questions and requests in real time. Next, the MetaShopper system supports secure transactions of digital items. The AI ​​ensures transaction security, allowing users to purchase NFT items with confidence. Finally, the MetaShopper system provides timely information on sales and new products. The AI ​​notifies users of campaign information at the optimal time based on their interests. In this way, the MetaShopper system personalizes the user's shopping experience and smoothly supports them from product discovery to purchase. This allows users to enjoy a comfortable shopping experience and enables businesses to deliver products effectively. As a result, the MetaShopper system can improve the user's shopping experience.

[0065] The MetaShopper system according to this embodiment comprises an analysis unit, a suggestion unit, a virtual try-on unit, and an explanation unit. The analysis unit analyzes the user's preferences and past purchase history. For example, the analysis unit retrieves the user's past purchase history from a database and analyzes it using AI. The analysis unit can analyze information such as the category, brand, and price range of purchased products in order to identify the user's preferences. The suggestion unit suggests the most suitable products and shops based on the analysis results obtained by the analysis unit. For example, the suggestion unit recommends products and shops based on the user's preferences. The suggestion unit can use AI to select and suggest products and shops that match the user's preferences. The virtual try-on unit virtually tries on the products suggested by the suggestion unit. The virtual try-on unit creates a 3D model of the product using generative AI and has the user's avatar try it on. For example, by having the user's avatar try on the product, the user can check the fit and design of the product. The explanation unit provides information about the products tried on by the virtual try-on unit. The explanation unit uses natural language processing to provide product descriptions and reviews in an easy-to-understand format. The description section provides information such as product features, specifications, and user reviews. The description section can also respond to user questions and requests in real time. As a result, the MetaShopper system according to this embodiment can improve the shopping experience by suggesting optimal products and shops based on the user's preferences and past purchase history, and by providing virtual try-ons and product information.

[0066] The analytics department analyzes user preferences and past purchase history. Specifically, the analytics department retrieves users' past purchase history from a database and analyzes it in detail using AI. The AI ​​analyzes information such as the category, brand, price range, color, size, and material of products that the user has purchased in the past to identify user preferences. For example, if there is a bias towards a particular brand or category among the products a user has purchased in the past, the analytics department can identify that trend. It also analyzes purchase frequency and seasonal purchase patterns to predict what kind of products users prefer at what time of year. Furthermore, it collects information on users' browsing history, favorites lists, and products that were added to the cart but not purchased, to comprehensively understand user preferences. As a result, the analytics department can identify user preferences with high accuracy and provide useful data to the recommendation department, which is the next step.

[0067] The Proposal Department suggests the most suitable products and shops based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department uses AI to select and suggest products and shops that match the user's preferences. The AI ​​extracts the most suitable products from a vast product database based on the user preference data provided by the Analysis Department. For example, if a user has previously preferred purchasing clothing from a particular brand, the AI ​​will prioritize suggesting new arrivals and sale information from that brand. Also, if a user prefers products in a specific price range, the AI ​​will suggest products that match that price range. Furthermore, the Proposal Department can also provide information on nearby shops and online shops, taking into account the user's current location and past browsing history. This allows users to easily find products and shops that suit their preferences, improving their shopping experience.

[0068] The fitting room allows users to virtually try on products suggested by the suggestion room. Specifically, the fitting room uses a generative AI to create 3D models of products and have the user's avatar try them on. The generative AI generates highly accurate 3D models based on product images and dimensional data, and allows the user's avatar to try them on in real time. The user's avatar is created based on the user's body shape and size information, providing an experience close to actual try-on. For example, by virtually trying on suggested clothing, users can check the fit and design of the products. The fitting room can also try on multiple products simultaneously, allowing users to try on different product combinations. This allows users to get the feeling of actually trying on products from the comfort of their homes, reducing anxiety before purchasing.

[0069] The description section provides information about products tried on by the fitting section. Specifically, the description section uses natural language processing to provide clear and concise product descriptions and reviews. By using natural language processing technology, it can automatically extract information such as product features, specifications, and user reviews, and present them in a format that is easy for users to understand. For example, in addition to basic information such as the product's material, size, color options, and washing instructions, it displays reviews and ratings from other users. The description section can also respond to user questions and requests in real time. For example, if a user asks a question about a specific product, the AI ​​will instantly generate an answer and provide it to the user. This allows users to easily obtain detailed product information and make more confident purchasing decisions. Furthermore, the description section also provides information such as product availability, shipping options, and return policies, comprehensively supporting the user's shopping experience.

[0070] The recommendation system can recommend products and shops based on user preferences. For example, it can recommend products and shops considering the user's preferred genres, styles, and brands. The recommendation system can use AI to analyze user preferences and select the most suitable products and shops. The recommendation system can also suggest products and shops that the user might be interested in based on their past purchase and search history. This allows for more personalized recommendations by suggesting products and shops based on user preferences.

[0071] The fitting room unit uses generative AI to create 3D models of products and allows users to virtually try them on using their avatars. For example, the fitting room unit uses generative AI to create 3D models of products. The generative AI receives product images and dimensional information as input and generates the 3D model. By having the user's avatar virtually try on the generated 3D model, the fitting room unit can check the fit and design of the product. For example, by having the user's avatar virtually try on the product, the fitting room unit can also check the size and color variations of the product. In this way, by creating 3D models of products using generative AI and having users' avatars virtually try them on, a realistic virtual try-on experience can be provided.

[0072] The description unit can use natural language processing to provide clear and concise product descriptions and reviews. For example, it can use natural language processing to analyze product features and specifications and summarize them in easy-to-understand text. It can also collect product reviews and extract and provide points of high ratings and areas for improvement. The description unit can also respond to user questions and requests in real time. For example, if a user asks a question about a specific product, the description unit can use natural language processing to analyze the question and provide an appropriate answer. In this way, by using natural language processing to provide clear and concise product descriptions and reviews, it makes it easier for users to understand product information.

[0073] The explanation unit can respond to user questions and requests in real time. For example, if a user asks a question about a product, the explanation unit uses natural language processing to analyze the question and provide an appropriate answer. Based on user requests, the explanation unit can also provide detailed product information and reviews. The explanation unit can use AI to generate and provide the optimal answer to user questions in real time. This allows for real-time responses to user questions and requests, thereby improving user satisfaction.

[0074] The explanation unit can use AI to ensure transaction security, allowing users to purchase NFT items with confidence. For example, the explanation unit can use AI to ensure transaction security. The explanation unit can apply security protocols during transactions to prevent fraudulent transactions. The explanation unit can verify user authentication information to ensure transaction security. The explanation unit can also use AI to monitor transaction history and detect fraudulent transactions. In this way, by using AI to ensure transaction security, users can purchase NFT items with confidence.

[0075] The explanation unit can use AI to notify users of campaign information at the optimal time based on their interests. For example, the explanation unit can use AI to analyze user interests and notify them of campaign information at the optimal time. The explanation unit can determine the timing of campaign information notifications based on the user's behavior history and past responses. The explanation unit can also use AI to analyze user interests in real time and notify them of campaign information at the optimal time. This makes it easier to attract user attention by notifying users of campaign information at the optimal time based on their interests using AI.

[0076] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can delay the analysis until the user is relaxed. If the user is agitated, the analysis unit can start immediately and provide quick suggestions. If the user is tired, the analysis unit can perform the analysis at night and notify the user of the results the following morning. By adjusting the timing of the analysis based on the user's emotions, it becomes possible to perform the analysis at the optimal time according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The analytics department can analyze a user's past purchase history and select the most suitable analysis method. For example, the analytics department can apply a specific analysis algorithm based on the product categories that a user frequently purchases. The analytics department can adjust the level of detail of the analysis according to the user's purchase frequency. The analytics department can also analyze seasonal purchase trends from the user's purchase history and make suggestions at the appropriate time. By analyzing a user's past purchase history and selecting the most suitable analysis method, more accurate analysis becomes possible.

[0078] The analytics department can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user has recently moved, the analytics department can prioritize analyzing products related to their new area. If a user has started a new hobby, the analytics department can filter and suggest products related to that hobby. If a user is planning to attend a specific event, the analytics department can also prioritize analyzing products related to that event. By filtering based on the user's current lifestyle and areas of interest, the analytics department can provide more relevant analysis results.

[0079] The analysis unit can estimate the user's emotions and determine the priority of data to analyze based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the latest trending products. If the user is relaxed, the analysis unit can prioritize analyzing data related to products purchased in the past. If the user is stressed, the analysis unit can also prioritize analyzing products with relaxing effects. This allows for optimal data analysis tailored to the user's situation by prioritizing data analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The analytics department can prioritize analyzing highly relevant data by considering the user's geographical location when analyzing purchase history. For example, if a user lives in a specific region, the analytics department can prioritize analyzing products popular in that region. If a user is traveling, the analytics department can prioritize analyzing products available at their travel destination. If a user is planning to attend a specific event, the analytics department can also prioritize analyzing products related to the region where the event is being held. By prioritizing the analysis of highly relevant data while considering the user's geographical location, more appropriate recommendations can be made.

[0081] The analytics department can analyze users' social media activity and obtain relevant data when analyzing purchase history. For example, the analytics department can prioritize analyzing products that users have shown interest in on social media. The analytics department can also prioritize analyzing products recommended by influencers that users follow. The analytics department can also prioritize analyzing products that are trending in social media groups that users participate in. By analyzing users' social media activity and obtaining relevant data, more accurate analysis becomes possible.

[0082] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can offer suggestions with detailed explanations. If the user is in a hurry, it can offer concise and to-the-point suggestions. If the user is excited, it can also offer visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. 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.

[0083] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, for expensive products, the proposal department will provide a detailed description and review. For everyday products, the proposal department can provide a concise description and key features. For new products, the proposal department can also provide a proposal that emphasizes the features and benefits. By adjusting the level of detail in a proposal based on the importance of the product, it becomes possible to provide users with the most optimal information.

[0084] The suggestion function can apply different suggestion algorithms depending on the product category. For example, for fashion items, it can make suggestions based on the user's style. For electronic devices, it can make suggestions based on the user's technical needs. For food products, it can make suggestions based on the user's dietary restrictions and preferences. By applying different suggestion algorithms depending on the product category, it becomes possible to make the most optimal suggestions for the user.

[0085] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion function will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion function can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion function can also provide a visually appealing suggestion. By adjusting the length of the suggestion based on the user's emotions, it is possible to provide the user with the most appropriate information. 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.

[0086] The proposal department can prioritize proposals based on the timing of product submission. For example, it can prioritize new products. It can also make limited-time proposals for products on sale. For seasonal products, it can make proposals tailored to the season. By prioritizing proposals based on the timing of product submission, it becomes possible to provide users with the most optimal information.

[0087] The suggestion function can adjust the order of suggestions based on the relevance of the products. For example, it can prioritize suggesting highly relevant products based on the user's past purchase history. It can also prioritize suggesting highly relevant products based on the user's current interests. Furthermore, it can prioritize suggesting highly relevant products based on the user's preferences. By adjusting the order of suggestions based on the relevance of the products, it becomes possible to provide the user with the most optimal information.

[0088] The fitting room unit can estimate the user's emotions and adjust the fitting process based on those emotions. For example, if the user is relaxed, the fitting room unit can provide a detailed fitting experience. If the user is in a hurry, the fitting room unit can provide a concise fitting experience. If the user is excited, the fitting room unit can also provide a visually appealing fitting experience. By adjusting the fitting process based on the user's emotions, the optimal fitting experience for the user can be achieved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The fitting room system can analyze the user's past fitting history to select the optimal fitting method during the fitting process. For example, the fitting room system can suggest the optimal fitting method based on the items the user has tried on in the past. The fitting room system can also suggest a fitting method based on the user's preferred style from their past fitting history. The fitting room system can also analyze the user's past fitting history to suggest the most efficient fitting method. By analyzing the user's past fitting history and selecting the optimal fitting method, the system enables the user to have the best possible fitting experience.

[0090] The fitting room system can customize the fitting experience based on the user's current lifestyle. For example, if the user is at home, the system can provide a fitting experience at home. If the user is out, the system can provide a fitting experience on a mobile device. If the user is planning to attend a specific event, the system can also provide a fitting experience tailored to that event. By customizing the fitting experience based on the user's current lifestyle, the system can provide the optimal fitting experience for the user.

[0091] The fitting room system can estimate the user's emotions and prioritize items for fitting based on those emotions. For example, if the user is excited, the fitting room system can prioritize letting them try on the latest trendy items. If the user is relaxed, the fitting room system can prioritize letting them try on items related to items they have previously purchased. If the user is stressed, the fitting room system can also prioritize letting them try on items with a relaxing effect. By prioritizing items based on the user's emotions, the system can provide the optimal fitting experience for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The fitting room system can select the optimal fitting method by considering the user's geographical location during the fitting process. For example, if a user lives in a specific region, the fitting room system can prioritize letting them try on products popular in that region. If a user is traveling, the fitting room system can prioritize letting them try on products available at their travel destination. If a user is planning to attend a specific event, the fitting room system can also prioritize letting them try on products related to the region where the event is being held. By selecting the optimal fitting method based on the user's geographical location, the system can provide the user with the best possible fitting experience.

[0093] The fitting room system can analyze a user's social media activity during the fitting process and suggest fitting options accordingly. For example, it can prioritize allowing users to try on items they've shown interest in on social media. It can also prioritize allowing users to try on items recommended by influencers they follow. Furthermore, it can prioritize allowing users to try on items that are trending in social media groups they belong to. By analyzing a user's social media activity and suggesting fitting options based on that analysis, the system can provide the optimal fitting experience for each user.

[0094] The explanation unit can estimate the user's emotions and adjust its explanation based on those emotions. For example, if the user is nervous, the explanation unit can provide a simple and easy-to-understand explanation. If the user is relaxed, the explanation unit can provide a detailed explanation. If the user is in a hurry, the explanation unit can also provide a concise explanation. This allows for the provision of optimal information to the user by adjusting the explanation based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The explanation unit can select the most appropriate explanation method by referring to the user's past question history during the explanation process. For example, the explanation unit can prioritize providing relevant information based on the user's past questions. The explanation unit can provide information, including detailed explanations, from the user's past question history. The explanation unit can also analyze the user's past question history and provide the most efficient explanation method. This makes it possible to provide the user with the most optimal information by selecting the most appropriate explanation method by referring to the user's past question history.

[0096] The explanation section can customize the content of the explanation based on the user's current interests. For example, if the user has shown interest in a particular category, the explanation section will prioritize providing information related to that category. The explanation section can also provide relevant information based on keywords the user has recently searched for. If the user is planning to attend a particular event, the explanation section can also provide information related to that event. This allows for the provision of information that is best suited to the user by customizing the content of the explanation based on the user's current interests.

[0097] The explanation unit can estimate the user's emotions and determine the priority of explanations based on the estimated emotions. For example, if the user is excited, the explanation unit can prioritize providing the latest trend information. If the user is relaxed, the explanation unit can prioritize providing information related to products they have purchased in the past. If the user is stressed, the explanation unit can also prioritize providing information with a relaxing effect. In this way, by determining the priority of explanations based on the user's emotions, it becomes possible to provide the user with the most optimal information. 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.

[0098] The explanation function can select the most appropriate explanation method by considering the user's geographical location. For example, if the user lives in a specific region, the explanation function can prioritize providing information about products popular in that region. If the user is traveling, the explanation function can prioritize providing information about products available at their travel destination. If the user is planning to attend a specific event, the explanation function can also prioritize providing information related to the region where the event is being held. By selecting the most appropriate explanation method considering the user's geographical location, it becomes possible to provide the user with the most relevant information.

[0099] The explanation function can analyze the user's social media activity and suggest content for the explanation. For example, the explanation function can prioritize providing information about products the user has shown interest in on social media. It can also prioritize providing information about products recommended by influencers the user follows. Furthermore, it can prioritize providing information about products that are trending in social media groups the user participates in. This allows the system to provide the most relevant information to the user by analyzing their social media activity and suggesting content for the explanation.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The suggestion function can estimate the user's emotions and customize the content of suggestions based on those emotions. For example, if the user is stressed, it can suggest products or services that have a relaxing effect. If the user is excited, it can suggest the latest trendy products or limited-edition items. If the user is relaxed, it can provide suggestions that include detailed product descriptions and reviews. By customizing the content of suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user.

[0102] The analytics department can consider a user's current health status and fitness level when analyzing their past purchase history. For example, if a user has recently become interested in fitness, health-related products and services can be prioritized in the analysis. If a user has set specific health goals, products related to those goals can be suggested. If a user is on a specific diet, foods and supplements suitable for those restrictions can also be analyzed. This allows for more relevant suggestions by analyzing based on the user's health status and fitness level.

[0103] The fitting room system can estimate the user's emotions and provide fitting room feedback based on those emotions. For example, if the user is confident, highlighting positive feedback can increase their purchase intent. If the user is feeling anxious, highlighting the product's advantages and positive reviews from other users can provide reassurance. If the user is excited, highlighting the product's unique features and exclusivity can encourage purchase. By providing fitting room feedback based on the user's emotions, the system can create the optimal fitting room experience for the user.

[0104] The explanation section can estimate the user's emotions and adjust the tone of the explanation based on those emotions. For example, if the user is relaxed, the explanation can be delivered in a casual and friendly tone. If the user is in a hurry, the explanation can be delivered in a concise and to-the-point tone. If the user is excited, the explanation can be delivered in an energetic and engaging tone. By adjusting the tone of the explanation based on the user's emotions, it becomes possible to provide the user with the most relevant information.

[0105] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion can be delayed to make the user more receptive to the information. If the user is excited, the suggestion can be delivered immediately to maintain their interest. If the user is stressed, the suggestion can be adjusted to allow them to receive the suggestion in a relaxed state. By adjusting the timing of suggestions based on the user's emotions, it becomes possible to provide the most optimal suggestions for the user.

[0106] The analytics department can consider users' life events when analyzing their past purchase history. For example, if a user is getting married, it can prioritize analyzing products and services related to weddings. If a user starts a new job, it can suggest products related to that job. If a user is planning to move, it can analyze products related to their new residence. By conducting analysis based on users' life events, it becomes possible to make more relevant suggestions.

[0107] The recommendation system can analyze a user's past purchase history and take into account their seasonal preferences. For example, if a user prefers a particular type of product in the summer, it can suggest products appropriate for that season. If a user prefers a particular brand in the winter, it can prioritize suggesting products from that brand. If a user enjoys a particular activity in the spring, it can suggest products related to that activity. This allows for more personalized recommendations based on the user's seasonal preferences.

[0108] The fitting room system can analyze a user's past fitting history and take into account changes in their body shape. For example, if a user loses weight, it can suggest products that suit their new body shape. If a user gains muscle, it can suggest products that are suitable for that body shape. If a user achieves a specific fitness goal, it can also suggest products related to that goal. This allows for a more appropriate fitting experience by suggesting products based on changes in the user's body shape.

[0109] The explanation section can consider the user's learning style when analyzing the user's past question history. For example, if the user prefers visual information, explanations can be provided that make extensive use of images and videos. If the user prefers text-based information, detailed written explanations can be provided. If the user prefers interactive information, explanations that include quizzes and simulations can be provided. This allows for more effective information delivery by tailoring explanations to the user's learning style.

[0110] The suggestion function can estimate the user's emotions and adjust the frequency of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced to avoid burdening the user. If the user is excited, the frequency of suggestions can be increased to maintain their interest. If the user is relaxed, suggestions can be made at an appropriate frequency to provide the user with the most relevant information. In this way, by adjusting the frequency of suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The analytics department analyzes user preferences and past purchase history. Specifically, it retrieves users' past purchase history from a database and analyzes it using AI. The analytics department analyzes information such as the category, brand, and price range of purchased products to identify user preferences. Step 2: The Proposal Department proposes the most suitable products and shops based on the analysis results obtained by the Analysis Department. The Proposal Department uses AI to select and propose products and shops that match the user's preferences. Step 3: The fitting department virtually tries on the products suggested by the proposal department. The fitting department uses generative AI to create 3D models of the products and has the user's avatar try them on. This allows the user to check the fit and design of the products. Step 4: The description section provides information about the product tried on by the fitting section. The description section uses natural language processing to provide clear and concise product descriptions and reviews. The description section provides information such as product features, specifications, and user reviews, and can also respond to user questions and requests in real time.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, proposal unit, fitting unit, and explanation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which retrieves the user's past purchase history from the database 24 and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable products and shops based on the analysis results. The fitting unit is implemented by the control unit 46A of the smart device 14, which creates a 3D model of the product using generation AI and allows the user's avatar to try it on. The explanation unit is implemented by the control unit 46A of the smart device 14, which provides product descriptions and reviews using natural language processing. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, proposal unit, fitting unit, and explanation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which retrieves the user's past purchase history from the database 24 and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable products and shops based on the analysis results. The fitting unit is implemented by the control unit 46A of the smart glasses 214, which creates a 3D model of the product using generation AI and allows the user's avatar to try it on. The explanation unit is implemented by the control unit 46A of the smart glasses 214, which provides product descriptions and reviews using natural language processing. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, proposal unit, fitting unit, and explanation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which retrieves the user's past purchase history from the database 24 and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable products and shops based on the analysis results. The fitting unit is implemented by the control unit 46A of the headset terminal 314, which creates a 3D model of the product using generation AI and allows the user's avatar to try it on. The explanation unit is implemented by the control unit 46A of the headset terminal 314, which provides product descriptions and reviews using natural language processing. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the analysis unit, proposal unit, fitting unit, and explanation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which retrieves the user's past purchase history from the database 24 and analyzes it using AI. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable products and shops based on the analysis results. The fitting unit is implemented by, for example, the control unit 46A of the robot 414, which creates a 3D model of the product using generative AI and allows the user's avatar to try it on. The explanation unit is implemented by, for example, the control unit 46A of the robot 414, which provides product descriptions and reviews using natural language processing. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The analytics department analyzes user preferences and past purchase history, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable products and shops. A fitting room for virtually trying on the products proposed by the aforementioned proposal room, An explanatory unit that provides information about the product tried on by the fitting unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, Recommend products and shops based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The fitting area is, Using generative AI, a 3D model of the product is created, and the user's avatar is made to virtually try it on. The system described in Appendix 1, characterized by the features described herein. (Note 4) The above explanatory section is, We use natural language processing to provide easy-to-understand summaries of product descriptions and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 5) The above explanatory section is, Responding to user questions and requests in real time The system described in Appendix 1, characterized by the features described herein. (Note 6) The above explanatory section is, Using AI to ensure transaction security and allowing users to purchase NFT items with peace of mind. The system described in Appendix 1, characterized by the features described herein. (Note 7) The above explanatory section is, Using AI, campaign information is notified to users at the optimal time based on their interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Analyze the user's past purchase history and select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When analyzing purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates user sentiment and prioritizes data to analyze based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing purchase history, the system prioritizes analyzing highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing purchase history, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fitting area is, The system estimates the user's emotions and adjusts the fitting process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fitting area is, During the fitting process, the system analyzes the user's past fitting history to select the optimal fitting method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The fitting area is, During the try-on process, the method of trying on clothes is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The fitting area is, The system estimates the user's emotions and determines the priority of try-on based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The fitting area is, During the fitting process, the system selects the optimal fitting method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The fitting area is, During the try-on process, we analyze the user's social media activity and suggest ways to try on clothes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The above explanatory section is, It estimates the user's emotions and adjusts the explanation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The above explanatory section is, During the explanation, the system will refer to the user's past question history to select the most appropriate explanation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The above explanatory section is, During the explanation, customize the content of the explanation based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 29) The above explanatory section is, It estimates the user's emotions and determines the priority of explanations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The above explanatory section is, During the explanation, the optimal explanation method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The above explanatory section is, During the explanation, we analyze the user's social media activity and suggest content for the explanation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. The analytics department analyzes user preferences and past purchase history, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable products and shops. A fitting room for virtually trying on the products proposed by the aforementioned proposal room, An explanatory unit that provides information about the product tried on by the fitting unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, Recommend products and shops based on user preferences. The system according to feature 1.

3. The fitting area is, Using generative AI, a 3D model of the product is created, and the user's avatar is made to virtually try it on. The system according to feature 1.

4. The above explanatory section is, We use natural language processing to provide easy-to-understand summaries of product descriptions and reviews. The system according to feature 1.

5. The above explanatory section is, Responding to user questions and requests in real time The system according to feature 1.

6. The above explanatory section is, Using AI to ensure transaction security, users can purchase NFT items with peace of mind. The system according to feature 1.

7. The above explanatory section is, Using AI, campaign information is notified to users at the optimal time based on their interests. The system according to feature 1.

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

9. The aforementioned analysis unit is Analyze the user's past purchase history and select the most suitable analysis method. The system according to feature 1.

10. The aforementioned analysis unit is When analyzing purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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