system

Abstract

The system according to this embodiment aims to suggest the most suitable fashion items according to the user's body type, preferences, and purpose. [Solution] The system according to the embodiment comprises a reception unit, a decision unit, a suggestion unit, and an advice unit. The reception unit receives input information about the user's body type, preferences, and purpose. The decision unit determines items according to the user's body type, preferences, and purpose based on the information received by the reception unit. The suggestion unit proposes the items determined by the decision unit. The advice unit advises on combinations of the items determined by the decision unit and the user's owned items.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, sufficient proposals for fashion items according to the user's body shape, preferences, and purposes have not been made, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal fashion item according to the user's body shape, preferences, and purposes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a decision unit, a suggestion unit, and an advice unit. The reception unit receives input information about the user's body type, preferences, and purpose. The decision unit determines items according to the user's body type, preferences, and purpose based on the information received by the reception unit. The suggestion unit proposes the items determined by the decision unit. The advice unit advises on combinations of the items determined by the decision unit and the user's existing items. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable fashion items according to the user's body type, preferences, and purpose. [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 manages communication between multiple computers. Examples of communication standards applicable 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 [[ID=]], 10 according to the first embodiment.

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant fashion item styling advisory system according to an embodiment of the present invention is a system that suggests items according to the user's body type, preferences, and purpose. This system allows the user to input information about their body type, preferences, and purpose, and the AI ​​analyzes this information to determine and suggest items. It also provides advice on combining items with the user's existing possessions and suggests items that take season and trends into consideration. For example, if the user inputs "I'm looking for a dress to wear to a party," the AI ​​analyzes this information and determines a dress that suits the user's body type, preferences, and purpose. The determined dress is suggested to the user, and advice on combining it with their existing black high heels is also provided. Furthermore, in winter, dresses made of warm materials are suggested, and items based on the latest fashion trends are suggested. This mechanism makes it easy for users to find items that suit their body type, preferences, and purpose, and also provides advice on combining them with their existing possessions, thus simplifying fashion coordination. Additionally, suggestions that consider season and trends allow users to always enjoy the latest fashion. Thus, the AI ​​assistant fashion item styling advisory system enables the suggestion of items according to the user's body type, preferences, and purpose, as well as advice on combining them with their existing possessions.

[0029] The AI ​​assistant for styling advisory systems for fashion items according to this embodiment comprises a reception unit, a decision unit, a suggestion unit, and an advice unit. The reception unit receives input information about the user's body type, preferences, and purpose. For example, the user can input information about their body type, preferences, and purpose. The reception unit accepts input from the user, for example, "I'm looking for a dress to wear to a party." The decision unit determines items that suit the user's body type, preferences, and purpose based on the information received by the reception unit. For example, if the user is slim, likes the color red, and is looking for a dress to wear to a party, the decision unit will determine a dress that meets those conditions. The suggestion unit proposes the items determined by the decision unit. For example, the red dress determined by the decision unit is proposed to the user. The advice unit advises on combinations of the items determined by the decision unit with items the user already owns. For example, it advises on combinations of the red dress with black high heels that the user already owns. As a result, the AI ​​assistant for the fashion item styling advisory system according to the embodiment can suggest items that suit the user's body type, preferences, and purpose, and provide advice on how to combine them with items the user already owns.

[0030] The reception desk receives information about the user's body type, preferences, and purpose. Specifically, users can access a dedicated application or website via their smartphone or computer to input their body type information (height, weight, waist size, etc.), preferences (color, style, brand, etc.), and purpose (casual, business, party, etc.). For example, if a user inputs "I'm looking for a dress to wear to a party," the reception desk receives this information and stores it in the database. Furthermore, the reception desk can refer to information previously entered by the user and their purchase history to collect more accurate information. For example, it can infer the user's current preferences based on the color and style of items they have previously purchased. The reception desk can also analyze the information entered by the user in real time and ask additional questions as needed. For example, if a user inputs "a red dress," the reception desk will ask additional questions such as "What kind of dress silhouette do you prefer?" to collect more detailed information. In this way, the reception desk can efficiently collect information that meets the user's needs and provide it as input data for the next step, the decision-making desk.

[0031] The decision-making unit determines items based on the user's body type, preferences, and purpose, using information received by the reception unit. Specifically, it uses an AI algorithm to analyze the user's input information and select the most suitable fashion items. For example, if a user is slim, likes the color red, and is looking for a dress to wear to a party, the decision-making unit searches its database for dresses that meet these criteria and narrows down the options to the best choices. The AI ​​considers silhouettes and designs that suit the user's body type, and further selects colors and styles that match the user's preferences and purpose. For example, it might determine that a fitted silhouette and a design that emphasizes the waistline are suitable for a slim user. It also considers the popularity and ratings of selected items based on past purchase history and reviews from other users. This allows the decision-making unit to determine the most suitable fashion items for the user's needs with high accuracy. Furthermore, the decision-making unit provides the user with detailed information about the selected items (material, size, price, etc.) to help them make their selection. This allows the decision-making unit to efficiently determine the most suitable fashion items based on the user's body type, preferences, and purpose, and provide this data to the suggestion unit, which is the next step.

[0032] The suggestion unit proposes items selected by the decision unit. Specifically, it presents items to the user in a visually easy-to-understand manner. For example, if a red dress selected by the decision unit is proposed to the user, the suggestion unit will display images and detailed information about the dress on the application or website. Furthermore, the suggestion unit can provide a function that simulates how the user would look wearing the item. For example, if a user uploads a photo of themselves, the AI ​​will virtually have the selected dress try on the photo, allowing the user to visually confirm how it would look when actually worn. The suggestion unit also provides coordination examples and styling advice related to the selected item. For example, it can suggest accessories and shoes that would match the red dress, which can be used as a reference when the user considers a total coordination. In this way, the suggestion unit can propose items to the user in a visual and concrete manner, expanding the range of choices. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, users can input ratings and comments on the proposed items, which can be reflected in future suggestions. In this way, the suggestion unit can propose the most suitable items to the user and improve satisfaction.

[0033] The advice unit advises on combinations of items selected by the decision unit with the user's existing items. Specifically, it presents example outfits combining items the user already owns with newly suggested items. For example, if the advice unit advises combining a user's existing black high heels with a red dress, it will provide images of the outfit and styling tips. Furthermore, the advice unit manages a database of the user's owned items and can suggest optimal combinations based on previously purchased and favorite items. For example, it will consider combinations of accessories and bags the user has previously purchased with newly suggested dresses. The advice unit also provides styling advice according to the season and trends. For example, in winter, it will suggest coats and scarves that match a red dress and advise on styling that is in line with trends. This allows the advice unit to provide styling that makes the most of the items the user owns and considers combinations with newly suggested items. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, users can input ratings and comments on the advised outfits, which can be reflected in future advice. This allows the advice unit to provide users with optimal styling advice and improve their satisfaction.

[0034] The decision-making unit can determine items based on the season and trends. For example, the decision-making unit will suggest a dress made of warm material during the winter season. The decision-making unit can also suggest items based on the latest fashion trends. For example, the decision-making unit will suggest items that suit the user based on the latest fashion trends. This makes it possible to suggest items according to the season and trends. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the decision-making unit can input data on the season and trends into a generative AI, which can then analyze that data to determine items.

[0035] The suggestion unit can propose the selected items to the user. For example, the suggestion unit can propose a red dress selected by the decision unit to the user. The suggestion unit can also provide an interface for proposing selected items to the user. For example, the suggestion unit can provide an interface for the user to review and select the proposed items. This allows the suggested items to be proposed to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the items selected by the decision unit into the AI, and the AI ​​can propose those items to the user.

[0036] The advice unit can advise on combinations between a determined item and an item the user already owns. For example, the advice unit might advise on a combination of black high heels and a red dress that the user already owns. The advice unit can also provide an interface for advising on combinations with items the user already owns. For example, the advice unit can take the user's owned items as input and advise on combinations with those items. This allows it to advise on combinations with owned items. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the determined item and owned items into the AI, which can then analyze the data and advise on combinations.

[0037] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can automatically complete information such as body type, preferences, and purpose that the user has previously entered. The reception desk can also learn the user's frequently used input patterns and automatically suggest them during subsequent input. Furthermore, the reception desk can predict information related to specific events from the user's past input history, simplifying the input process. This reduces the user's input effort by providing an auto-completion function based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze that data to provide the auto-completion function.

[0038] The reception desk can display relevant fashion advice in real time based on the user's input. For example, the reception desk can suggest relevant fashion items in real time based on the user's body type and preferences. The reception desk can also provide appropriate styling advice in real time according to the user's stated purpose. Furthermore, the reception desk can display fashion advice that is appropriate for the season and current trends in real time based on the information the user has entered. This supports the user's choices by providing fashion advice in real time based on the user's input. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input into a generating AI, which can then analyze the content and display fashion advice in real time.

[0039] The reception desk can provide region-specific fashion information based on the user's geographical location information at the time of input. For example, if the user is in a specific region, the reception desk can suggest fashion items that are suitable for the climate and culture of that region. Furthermore, if the user is traveling, the reception desk can also provide region-specific fashion information for the destination. Additionally, if the user is in a specific city, the reception desk can suggest fashion items that reflect the latest trends in that city. In this way, region-specific fashion information can be provided by considering the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then analyze that information to provide region-specific fashion information.

[0040] The reception desk can analyze a user's social media activity and prompt them to input relevant fashion items. For example, the reception desk can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts shared by the user on social media and suggest relevant fashion items. Furthermore, it can suggest appropriate fashion items based on events and groups the user participates in on social media. This allows the reception desk to prompt users to input relevant fashion items by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the data and prompt the user to input relevant fashion items.

[0041] The decision-making unit can analyze the user's past selection history to determine more personalized items. For example, it can analyze the trends of items the user has previously selected and suggest items of a similar style. It can also suggest related items based on the user's past purchase history. Furthermore, it can prioritize suggesting items of specific brands or designs based on the user's past selection history. This improves user satisfaction by suggesting personalized items based on past selection history. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input the user's past selection history data into a generating AI, which can then analyze that data to determine personalized items.

[0042] The decision unit can analyze the user's body shape data in detail and select items that provide the optimal fit. For example, the decision unit can suggest items with the optimal size and fit based on the user's body shape data. The decision unit can also suggest customizable items tailored to the user's body shape. Furthermore, the decision unit can analyze the user's body shape data and select items with designs suitable for specific body types. In this way, by analyzing the user's body shape data in detail, it can provide items with the optimal fit. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's body shape data into a generating AI, which can then analyze the data and select items with the optimal fit.

[0043] The decision-making unit can determine items that reflect region-specific trends by considering the user's geographical location. For example, if the user is in a specific region, the decision-making unit can suggest items that reflect the latest trends in that region. Furthermore, if the user is traveling, the decision-making unit can suggest fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the decision-making unit can suggest items that suit the climate and culture of that city. This allows the system to suggest items that reflect region-specific trends by considering the user's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's geographical location information into a generating AI, which can then analyze that information to determine items that reflect region-specific trends.

[0044] The decision-making unit can analyze a user's social media activity and determine relevant trending items. For example, the unit can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts shared by the user on social media and suggest relevant trending items. Furthermore, it can suggest appropriate trending items based on events and groups the user participates in on social media. This allows the system to suggest trending items relevant to the user by analyzing their social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media activity data into a generating AI, which can then analyze the data to determine relevant trending items.

[0045] The proposal unit can analyze the user's past responses and select the most suitable proposal method when making a proposal. For example, the proposal unit can prioritize using proposal methods that the user has responded favorably to in the past. It can also avoid proposal methods that the user has rejected in the past and try alternative methods. Furthermore, the proposal unit can analyze the user's past responses and select the most effective proposal method. This improves user satisfaction by selecting the optimal proposal method based on past responses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past response data into a generating AI, which can then analyze the data and select the most suitable proposal method.

[0046] The suggestion unit can apply different suggestion algorithms depending on the user's body type and preferences when making suggestions. For example, the suggestion unit can use an algorithm that suggests items with the optimal fit based on the user's body type data. It can also apply a customizable suggestion algorithm based on the user's preferences. Furthermore, the suggestion unit can combine different suggestion algorithms depending on the user's body type and preferences. This allows for more personalized suggestions by applying a suggestion algorithm tailored to the user's body type and preferences. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's body type data and preference data into a generating AI, which can then analyze the data and apply different suggestion algorithms.

[0047] The suggestion unit can prioritize suggesting region-specific items by considering the user's geographical location. For example, if the user is in a specific region, the suggestion unit can suggest items that reflect the latest trends in that region. Furthermore, if the user is traveling, the suggestion unit can suggest fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the suggestion unit can suggest items that suit the climate and culture of that city. This allows for the priority suggestion of region-specific items by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For instance, the suggestion unit can input the user's geographical location information into a generating AI, which can then analyze that information to prioritize suggesting region-specific items.

[0048] The suggestion unit can analyze a user's social media activity and suggest relevant items when making suggestions. For example, the suggestion unit can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts the user has shared on social media and suggest relevant items. Furthermore, the suggestion unit can suggest appropriate items based on events and groups the user participates in on social media. In this way, by analyzing social media activity, it is possible to suggest items relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant items.

[0049] The advice unit can analyze the user's past outfit history and suggest the optimal combination when providing advice. For example, the advice unit can suggest the optimal outfit based on combinations of items the user has previously preferred to wear. The advice unit can also suggest combinations suitable for specific events based on the user's past outfit history. Furthermore, the advice unit can analyze the user's past outfit history and suggest combinations that match the season and current trends. This improves user satisfaction by suggesting optimal combinations based on past outfit history. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past outfit history data into a generating AI, which can then analyze the data and suggest the optimal combination.

[0050] The advice unit can apply different advice algorithms depending on the user's body type and preferences when providing advice. For example, the advice unit can use an algorithm that suggests items with the optimal fit based on the user's body type data. It can also apply customizable advice algorithms based on the user's preferences. Furthermore, the advice unit can combine different advice algorithms depending on the user's body type and preferences. This allows for more personalized advice by applying advice algorithms tailored to the user's body type and preferences. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's body type data and preference data into a generating AI, which can then analyze the data and apply different advice algorithms.

[0051] The advice unit can prioritize suggesting region-specific combinations by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can suggest combinations that reflect the latest trends in that region. Furthermore, if the user is traveling, the advice unit can suggest combinations of fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the advice unit can suggest combinations of items that suit the climate and culture of that city. This allows the advice unit to prioritize suggesting region-specific combinations by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location information into a generating AI, which can then analyze that information to prioritize suggesting region-specific combinations.

[0052] The advice unit can analyze the user's social media activity and suggest relevant combinations when providing advice. For example, the advice unit can suggest relevant item combinations based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts the user has shared on social media and suggest relevant item combinations. Furthermore, it can suggest appropriate item combinations based on events and groups the user participates in on social media. This allows the advice unit to suggest combinations relevant to the user by analyzing their social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant combinations.

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

[0054] The reception department can receive information about the user's body type, preferences, and purpose, as well as lifestyle information. For example, if a user has an active lifestyle, the reception department can receive this information, and the decision department can take this information into consideration when deciding on items. The suggestion department can suggest items suitable for an active lifestyle, and the advice department can advise on combinations with items the user already owns that are suitable for an active lifestyle. This makes it possible to suggest items and advise on combinations that are tailored to the user's lifestyle.

[0055] The suggestion function can propose items considering the user's body type, preferences, and purpose, as well as their health condition. For example, if a user has allergies, the suggestion function will propose items made from allergy-friendly materials. It can also propose items suitable for a specific health condition. Furthermore, it can propose items to help the user maintain their health. This allows for item suggestions tailored to the user's health condition.

[0056] The reception department can receive information about the user's body type, preferences, and purpose, as well as their budget. For example, if a user is looking for items within a specific budget, the reception department can receive this information, and the decision department can then consider this information to determine the items. The suggestion department can propose items that are purchasable within the budget, and the advice department can advise on the best combinations within that budget. This allows for the suggestion of items and advice on combinations that are tailored to the user's budget.

[0057] The suggestion function can propose items considering the user's body type, preferences, purpose, and occupation. For example, if the user is a business person, the suggestion function will propose items suitable for business settings. If the user is in a creative profession, it can also propose items suitable for that profession. Furthermore, if the user is participating in an event related to a specific profession, it can propose items suitable for that event. This makes it possible to suggest items tailored to the user's occupation.

[0058] The advice function can suggest combinations based on the user's body type, preferences, and purpose, as well as their hobbies. For example, if a user enjoys outdoor activities, the advice function will suggest combinations of items suitable for outdoor activities. Similarly, if a user is attending a music festival, it can suggest combinations of items suitable for that event. Furthermore, if a user is attending an event related to a specific hobby, it can suggest combinations of items suitable for that event. This allows for advice on combinations tailored to the user's hobbies.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts information about the user's body type, preferences, and purpose. For example, the user can enter information about their body type, preferences, and purpose. The reception desk accepts, for example, the user's input such as "I'm looking for a dress to wear to a party." Step 2: The decision-making unit determines items based on the user's body type, preferences, and purpose, using the information received by the reception unit. For example, if the user is slim, likes the color red, and is looking for a dress to wear to a party, the decision-making unit will determine a dress that meets those criteria. Step 3: The suggestion unit proposes the item decided by the decision unit. For example, the red dress decided by the decision unit is proposed to the user. Step 4: The advice section advises on combinations of items determined by the decision section with items the user already owns. For example, it might advise a combination of black high heels and a red dress that the user already owns.

[0061] (Example of form 2) The AI ​​assistant fashion item styling advisory system according to an embodiment of the present invention is a system that suggests items according to the user's body type, preferences, and purpose. This system allows the user to input information about their body type, preferences, and purpose, and the AI ​​analyzes this information to determine and suggest items. It also provides advice on combining items with the user's existing possessions and suggests items that take season and trends into consideration. For example, if the user inputs "I'm looking for a dress to wear to a party," the AI ​​analyzes this information and determines a dress that suits the user's body type, preferences, and purpose. The determined dress is suggested to the user, and advice on combining it with their existing black high heels is also provided. Furthermore, in winter, dresses made of warm materials are suggested, and items based on the latest fashion trends are suggested. This mechanism makes it easy for users to find items that suit their body type, preferences, and purpose, and also provides advice on combining them with their existing possessions, thus simplifying fashion coordination. Additionally, suggestions that consider season and trends allow users to always enjoy the latest fashion. Thus, the AI ​​assistant fashion item styling advisory system enables the suggestion of items according to the user's body type, preferences, and purpose, as well as advice on combining them with their existing possessions.

[0062] The AI ​​assistant for styling advisory systems for fashion items according to this embodiment comprises a reception unit, a decision unit, a suggestion unit, and an advice unit. The reception unit receives input information about the user's body type, preferences, and purpose. For example, the user can input information about their body type, preferences, and purpose. The reception unit accepts input from the user, for example, "I'm looking for a dress to wear to a party." The decision unit determines items that suit the user's body type, preferences, and purpose based on the information received by the reception unit. For example, if the user is slim, likes the color red, and is looking for a dress to wear to a party, the decision unit will determine a dress that meets those conditions. The suggestion unit proposes the items determined by the decision unit. For example, the red dress determined by the decision unit is proposed to the user. The advice unit advises on combinations of the items determined by the decision unit with items the user already owns. For example, it advises on combinations of the red dress with black high heels that the user already owns. As a result, the AI ​​assistant for the fashion item styling advisory system according to the embodiment can suggest items that suit the user's body type, preferences, and purpose, and provide advice on how to combine them with items the user already owns.

[0063] The reception desk receives information about the user's body type, preferences, and purpose. Specifically, users can access a dedicated application or website via their smartphone or computer to input their body type information (height, weight, waist size, etc.), preferences (color, style, brand, etc.), and purpose (casual, business, party, etc.). For example, if a user inputs "I'm looking for a dress to wear to a party," the reception desk receives this information and stores it in the database. Furthermore, the reception desk can refer to information previously entered by the user and their purchase history to collect more accurate information. For example, it can infer the user's current preferences based on the color and style of items they have previously purchased. The reception desk can also analyze the information entered by the user in real time and ask additional questions as needed. For example, if a user inputs "a red dress," the reception desk will ask additional questions such as "What kind of dress silhouette do you prefer?" to collect more detailed information. In this way, the reception desk can efficiently collect information that meets the user's needs and provide it as input data for the next step, the decision-making desk.

[0064] The decision-making unit determines items based on the user's body type, preferences, and purpose, using information received by the reception unit. Specifically, it uses an AI algorithm to analyze the user's input information and select the most suitable fashion items. For example, if a user is slim, likes the color red, and is looking for a dress to wear to a party, the decision-making unit searches its database for dresses that meet these criteria and narrows down the options to the best choices. The AI ​​considers silhouettes and designs that suit the user's body type, and further selects colors and styles that match the user's preferences and purpose. For example, it might determine that a fitted silhouette and a design that emphasizes the waistline are suitable for a slim user. It also considers the popularity and ratings of selected items based on past purchase history and reviews from other users. This allows the decision-making unit to determine the most suitable fashion items for the user's needs with high accuracy. Furthermore, the decision-making unit provides the user with detailed information about the selected items (material, size, price, etc.) to help them make their selection. This allows the decision-making unit to efficiently determine the most suitable fashion items based on the user's body type, preferences, and purpose, and provide this data to the suggestion unit, which is the next step.

[0065] The suggestion unit proposes items selected by the decision unit. Specifically, it presents items to the user in a visually easy-to-understand manner. For example, if a red dress selected by the decision unit is proposed to the user, the suggestion unit will display images and detailed information about the dress on the application or website. Furthermore, the suggestion unit can provide a function that simulates how the user would look wearing the item. For example, if a user uploads a photo of themselves, the AI ​​will virtually have the selected dress try on the photo, allowing the user to visually confirm how it would look when actually worn. The suggestion unit also provides coordination examples and styling advice related to the selected item. For example, it can suggest accessories and shoes that would match the red dress, which can be used as a reference when the user considers a total coordination. In this way, the suggestion unit can propose items to the user in a visual and concrete manner, expanding the range of choices. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, users can input ratings and comments on the proposed items, which can be reflected in future suggestions. In this way, the suggestion unit can propose the most suitable items to the user and improve satisfaction.

[0066] The advice unit advises on combinations of items selected by the decision unit with the user's existing items. Specifically, it presents example outfits combining items the user already owns with newly suggested items. For example, if the advice unit advises combining a user's existing black high heels with a red dress, it will provide images of the outfit and styling tips. Furthermore, the advice unit manages a database of the user's owned items and can suggest optimal combinations based on previously purchased and favorite items. For example, it will consider combinations of accessories and bags the user has previously purchased with newly suggested dresses. The advice unit also provides styling advice according to the season and trends. For example, in winter, it will suggest coats and scarves that match a red dress and advise on styling that is in line with trends. This allows the advice unit to provide styling that makes the most of the items the user owns and considers combinations with newly suggested items. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, users can input ratings and comments on the advised outfits, which can be reflected in future advice. This allows the advice unit to provide users with optimal styling advice and improve their satisfaction.

[0067] The decision-making unit can determine items based on the season and trends. For example, the decision-making unit will suggest a dress made of warm material during the winter season. The decision-making unit can also suggest items based on the latest fashion trends. For example, the decision-making unit will suggest items that suit the user based on the latest fashion trends. This makes it possible to suggest items according to the season and trends. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the decision-making unit can input data on the season and trends into a generative AI, which can then analyze that data to determine items.

[0068] The suggestion unit can propose the selected items to the user. For example, the suggestion unit can propose a red dress selected by the decision unit to the user. The suggestion unit can also provide an interface for proposing selected items to the user. For example, the suggestion unit can provide an interface for the user to review and select the proposed items. This allows the suggested items to be proposed to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the items selected by the decision unit into the AI, and the AI ​​can propose those items to the user.

[0069] The advice unit can advise on combinations between a determined item and an item the user already owns. For example, the advice unit might advise on a combination of black high heels and a red dress that the user already owns. The advice unit can also provide an interface for advising on combinations with items the user already owns. For example, the advice unit can take the user's owned items as input and advise on combinations with those items. This allows it to advise on combinations with owned items. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the determined item and owned items into the AI, which can then analyze the data and advise on combinations.

[0070] The reception desk can estimate the user's emotions and modify the input interface design based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This improves the user's input experience by changing the input interface design according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze the data and modify the input interface design.

[0071] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can automatically complete information such as body type, preferences, and purpose that the user has previously entered. The reception desk can also learn the user's frequently used input patterns and automatically suggest them during subsequent input. Furthermore, the reception desk can predict information related to specific events from the user's past input history, simplifying the input process. This reduces the user's input effort by providing an auto-completion function based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze that data to provide the auto-completion function.

[0072] The reception desk can display relevant fashion advice in real time based on the user's input. For example, the reception desk can suggest relevant fashion items in real time based on the user's body type and preferences. The reception desk can also provide appropriate styling advice in real time according to the user's stated purpose. Furthermore, the reception desk can display fashion advice that is appropriate for the season and current trends in real time based on the information the user has entered. This supports the user's choices by providing fashion advice in real time based on the user's input. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input into a generating AI, which can then analyze the content and display fashion advice in real time.

[0073] The reception desk can estimate the user's emotions and dynamically change the priority of input content based on the estimated emotions. For example, if the reception desk is stressed, it will prioritize the input of important information and postpone other information. If the user is relaxed, the reception desk can also encourage the input of detailed information and offer customizable options. Furthermore, if the user is in a hurry, the reception desk can prioritize the input of the most important information and provide suggestions quickly. This improves the user's input experience by changing the priority of input content according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze that data and dynamically change the priority of input content.

[0074] The reception desk can provide region-specific fashion information based on the user's geographical location information at the time of input. For example, if the user is in a specific region, the reception desk can suggest fashion items that are suitable for the climate and culture of that region. Furthermore, if the user is traveling, the reception desk can also provide region-specific fashion information for the destination. Additionally, if the user is in a specific city, the reception desk can suggest fashion items that reflect the latest trends in that city. In this way, region-specific fashion information can be provided by considering the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then analyze that information to provide region-specific fashion information.

[0075] The reception desk can analyze a user's social media activity and prompt them to input relevant fashion items. For example, the reception desk can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts shared by the user on social media and suggest relevant fashion items. Furthermore, it can suggest appropriate fashion items based on events and groups the user participates in on social media. This allows the reception desk to prompt users to input relevant fashion items by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the data and prompt the user to input relevant fashion items.

[0076] The decision-making unit can estimate the user's emotions and dynamically change the item selection criteria based on the estimated emotions. For example, if the user is stressed, the decision-making unit will prioritize selecting simple and comfortable items. If the user is relaxed, the decision-making unit may also select colorful and unique items. Furthermore, if the user is in a hurry, the decision-making unit may prioritize items that can be selected quickly. This allows for the selection of more appropriate items by changing the item selection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input user emotion data into a generative AI, which can analyze that data and dynamically change the item selection criteria.

[0077] The decision-making unit can analyze the user's past selection history to determine more personalized items. For example, it can analyze the trends of items the user has previously selected and suggest items of a similar style. It can also suggest related items based on the user's past purchase history. Furthermore, it can prioritize suggesting items of specific brands or designs based on the user's past selection history. This improves user satisfaction by suggesting personalized items based on past selection history. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input the user's past selection history data into a generating AI, which can then analyze that data to determine personalized items.

[0078] The decision unit can analyze the user's body shape data in detail and select items that provide the optimal fit. For example, the decision unit can suggest items with the optimal size and fit based on the user's body shape data. The decision unit can also suggest customizable items tailored to the user's body shape. Furthermore, the decision unit can analyze the user's body shape data and select items with designs suitable for specific body types. In this way, by analyzing the user's body shape data in detail, it can provide items with the optimal fit. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's body shape data into a generating AI, which can then analyze the data and select items with the optimal fit.

[0079] The decision-making unit can estimate the user's emotions and dynamically change the order in which items are suggested based on the estimated emotions. For example, if the user is stressed, the decision-making unit may suggest relaxing items first. If the user is relaxed, the decision-making unit may also suggest colorful and unique items first. Furthermore, if the user is in a hurry, the decision-making unit may suggest items that can be selected quickly first. This allows for the suggestion of items in a more appropriate order by changing the order of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input user emotion data into a generative AI, which can analyze the data and dynamically change the order in which items are suggested.

[0080] The decision-making unit can determine items that reflect region-specific trends by considering the user's geographical location. For example, if the user is in a specific region, the decision-making unit can suggest items that reflect the latest trends in that region. Furthermore, if the user is traveling, the decision-making unit can suggest fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the decision-making unit can suggest items that suit the climate and culture of that city. This allows the system to suggest items that reflect region-specific trends by considering the user's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's geographical location information into a generating AI, which can then analyze that information to determine items that reflect region-specific trends.

[0081] The decision-making unit can analyze a user's social media activity and determine relevant trending items. For example, the unit can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts shared by the user on social media and suggest relevant trending items. Furthermore, it can suggest appropriate trending items based on events and groups the user participates in on social media. This allows the system to suggest trending items relevant to the user by analyzing their social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media activity data into a generating AI, which can then analyze the data to determine relevant trending items.

[0082] The suggestion unit can estimate the user's emotions and dynamically change the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and intuitive suggestions. If the user is relaxed, it can offer suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can offer concise and quick suggestions. By changing the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze that data and dynamically change the way suggestions are presented.

[0083] The proposal unit can analyze the user's past responses and select the most suitable proposal method when making a proposal. For example, the proposal unit can prioritize using proposal methods that the user has responded favorably to in the past. It can also avoid proposal methods that the user has rejected in the past and try alternative methods. Furthermore, the proposal unit can analyze the user's past responses and select the most effective proposal method. This improves user satisfaction by selecting the optimal proposal method based on past responses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past response data into a generating AI, which can then analyze the data and select the most suitable proposal method.

[0084] The suggestion unit can apply different suggestion algorithms depending on the user's body type and preferences when making suggestions. For example, the suggestion unit can use an algorithm that suggests items with the optimal fit based on the user's body type data. It can also apply a customizable suggestion algorithm based on the user's preferences. Furthermore, the suggestion unit can combine different suggestion algorithms depending on the user's body type and preferences. This allows for more personalized suggestions by applying a suggestion algorithm tailored to the user's body type and preferences. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's body type data and preference data into a generating AI, which can then analyze the data and apply different suggestion algorithms.

[0085] The suggestion unit can estimate the user's emotions and dynamically change the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will first suggest items that promote relaxation. If the user is relaxed, the suggestion unit may first suggest colorful and unique items. Furthermore, if the user is in a hurry, the suggestion unit may first suggest items that can be selected quickly. By changing the priority of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can analyze that data and dynamically change the priority of suggestions.

[0086] The suggestion unit can prioritize suggesting region-specific items by considering the user's geographical location. For example, if the user is in a specific region, the suggestion unit can suggest items that reflect the latest trends in that region. Furthermore, if the user is traveling, the suggestion unit can suggest fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the suggestion unit can suggest items that suit the climate and culture of that city. This allows for the priority suggestion of region-specific items by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For instance, the suggestion unit can input the user's geographical location information into a generating AI, which can then analyze that information to prioritize suggesting region-specific items.

[0087] The suggestion unit can analyze a user's social media activity and suggest relevant items when making suggestions. For example, the suggestion unit can suggest relevant items based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts the user has shared on social media and suggest relevant items. Furthermore, the suggestion unit can suggest appropriate items based on events and groups the user participates in on social media. In this way, by analyzing social media activity, it is possible to suggest items relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant items.

[0088] The advice unit can estimate the user's emotions and dynamically change the way it expresses advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide simple and intuitive advice. If the user is relaxed, the advice unit can also provide advice that includes detailed information. Furthermore, if the user is in a hurry, the advice unit can provide concise and quick advice. This allows for more appropriate advice by changing the way it expresses advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generative AI, which can analyze that data and dynamically change the way it expresses advice.

[0089] The advice unit can analyze the user's past outfit history and suggest the optimal combination when providing advice. For example, the advice unit can suggest the optimal outfit based on combinations of items the user has previously preferred to wear. The advice unit can also suggest combinations suitable for specific events based on the user's past outfit history. Furthermore, the advice unit can analyze the user's past outfit history and suggest combinations that match the season and current trends. This improves user satisfaction by suggesting optimal combinations based on past outfit history. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past outfit history data into a generating AI, which can then analyze the data and suggest the optimal combination.

[0090] The advice unit can apply different advice algorithms depending on the user's body type and preferences when providing advice. For example, the advice unit can use an algorithm that suggests items with the optimal fit based on the user's body type data. It can also apply customizable advice algorithms based on the user's preferences. Furthermore, the advice unit can combine different advice algorithms depending on the user's body type and preferences. This allows for more personalized advice by applying advice algorithms tailored to the user's body type and preferences. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's body type data and preference data into a generating AI, which can then analyze the data and apply different advice algorithms.

[0091] The advice unit can estimate the user's emotions and dynamically change the priority of advice based on the estimated emotions. For example, if the user is stressed, the advice unit may first suggest combinations of items that promote relaxation. If the user is relaxed, the advice unit may first suggest combinations of colorful and unique items. Furthermore, if the user is in a hurry, the advice unit may first suggest combinations of items that can be selected quickly. This allows for more appropriate advice by changing the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generative AI, which can analyze that data and dynamically change the priority of advice.

[0092] The advice unit can prioritize suggesting region-specific combinations by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can suggest combinations that reflect the latest trends in that region. Furthermore, if the user is traveling, the advice unit can suggest combinations of fashion items specific to the region they are visiting. Additionally, if the user is in a specific city, the advice unit can suggest combinations of items that suit the climate and culture of that city. This allows the advice unit to prioritize suggesting region-specific combinations by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location information into a generating AI, which can then analyze that information to prioritize suggesting region-specific combinations.

[0093] The advice unit can analyze the user's social media activity and suggest relevant combinations when providing advice. For example, the advice unit can suggest relevant item combinations based on the styles of fashion influencers the user follows on social media. It can also analyze photos and posts the user has shared on social media and suggest relevant item combinations. Furthermore, it can suggest appropriate item combinations based on events and groups the user participates in on social media. This allows the advice unit to suggest combinations relevant to the user by analyzing their social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant combinations.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception department can receive information about the user's body type, preferences, and purpose, as well as lifestyle information. For example, if a user has an active lifestyle, the reception department can receive this information, and the decision department can take this information into consideration when deciding on items. The suggestion department can suggest items suitable for an active lifestyle, and the advice department can advise on combinations with items the user already owns that are suitable for an active lifestyle. This makes it possible to suggest items and advise on combinations that are tailored to the user's lifestyle.

[0096] The decision-making unit can estimate the user's emotions and dynamically change the color and design of items based on those emotions. For example, if the user is stressed, the unit will select items with calming colors and simple designs. If the user is relaxed, it can select items with bright colors and unique designs. Furthermore, if the user is in a hurry, it can prioritize items that can be selected quickly. This allows for the selection of more appropriate items by changing the color and design of items according to the user's emotions.

[0097] The suggestion function can propose items considering the user's body type, preferences, and purpose, as well as their health condition. For example, if a user has allergies, the suggestion function will propose items made from allergy-friendly materials. It can also propose items suitable for a specific health condition. Furthermore, it can propose items to help the user maintain their health. This allows for item suggestions tailored to the user's health condition.

[0098] The advice unit can estimate the user's emotions and dynamically change the content of the advice based on those emotions. For example, if the user is stressed, the advice unit will provide simple and intuitive advice. If the user is relaxed, it can provide advice that includes more detailed information. Furthermore, if the user is in a hurry, it can provide concise and quick advice. By changing the content of the advice according to the user's emotions, it becomes possible to provide more appropriate advice.

[0099] The reception department can receive information about the user's body type, preferences, and purpose, as well as their budget. For example, if a user is looking for items within a specific budget, the reception department can receive this information, and the decision department can then consider this information to determine the items. The suggestion department can propose items that are purchasable within the budget, and the advice department can advise on the best combinations within that budget. This allows for the suggestion of items and advice on combinations that are tailored to the user's budget.

[0100] The reception desk can estimate the user's emotions and dynamically change the priority of input content based on that estimation. For example, if the user is stressed, it will prioritize the input of important information and postpone other information. If the user is relaxed, it can encourage the input of detailed information and offer customizable options. Furthermore, if the user is in a hurry, it can prioritize the input of the most important information and provide quick suggestions. This improves the user's input experience by changing the priority of input content according to the user's emotions.

[0101] The suggestion function can propose items considering the user's body type, preferences, purpose, and occupation. For example, if the user is a business person, the suggestion function will propose items suitable for business settings. If the user is in a creative profession, it can also propose items suitable for that profession. Furthermore, if the user is participating in an event related to a specific profession, it can propose items suitable for that event. This makes it possible to suggest items tailored to the user's occupation.

[0102] The decision-making unit can estimate the user's emotions and dynamically change the item selection criteria based on those emotions. For example, if the user is stressed, it will prioritize simple and comfortable items. If the user is relaxed, it may also select colorful and unique items. Furthermore, if the user is in a hurry, it may prioritize items that can be selected quickly. By changing the item selection criteria according to the user's emotions, it is possible to select more appropriate items.

[0103] The advice function can suggest combinations based on the user's body type, preferences, and purpose, as well as their hobbies. For example, if a user enjoys outdoor activities, the advice function will suggest combinations of items suitable for outdoor activities. Similarly, if a user is attending a music festival, it can suggest combinations of items suitable for that event. Furthermore, if a user is attending an event related to a specific hobby, it can suggest combinations of items suitable for that event. This allows for advice on combinations tailored to the user's hobbies.

[0104] The suggestion function can estimate the user's emotions and dynamically change the priority of suggestions based on those emotions. For example, if the user is stressed, it will first suggest items that promote relaxation. If the user is relaxed, it can also first suggest colorful and unique items. Furthermore, if the user is in a hurry, it can first suggest items that can be selected quickly. By changing the priority of suggestions according to the user's emotions, it becomes possible to provide more appropriate suggestions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk accepts information about the user's body type, preferences, and purpose. For example, the user can enter information about their body type, preferences, and purpose. The reception desk accepts, for example, the user's input such as "I'm looking for a dress to wear to a party." Step 2: The decision-making unit determines items based on the user's body type, preferences, and purpose, using the information received by the reception unit. For example, if the user is slim, likes the color red, and is looking for a dress to wear to a party, the decision-making unit will determine a dress that meets those criteria. Step 3: The suggestion unit proposes the item decided by the decision unit. For example, the red dress decided by the decision unit is proposed to the user. Step 4: The advice section advises on combinations of items determined by the decision section with items the user already owns. For example, it might advise a combination of black high heels and a red dress that the user already owns.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] For example, the reception unit is implemented by the reception device 38 of the smart device 14, which can input information about the user's body type, preferences, and purpose. For example, it can accept input from the user such as "I'm looking for a dress to wear to a party." The decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines items according to the user's body type, preferences, and purpose based on the information received by the reception unit. The suggestion unit is implemented by the output device 40 of the smart device 14, which suggests the items determined by the decision unit to the user. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, which advises on combinations of items determined by the decision unit and items the user already owns. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, and can receive information about the user's body type, preferences, and purpose. For example, it can receive input such as "I'm looking for a dress to wear to a party." The decision unit is implemented by the identification processing unit 290 of the data processing device 12, and based on the information received by the reception unit, it determines items that suit the user's body type, preferences, and purpose. The suggestion unit is implemented by the speaker 240 of the smart glasses 214, and suggests the items determined by the decision unit to the user. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, and advises on combinations of items determined by the decision unit and items the user already owns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, and can receive information about the user's body type, preferences, and purpose. For example, it can receive input such as "I'm looking for a dress to wear to a party." The decision unit is implemented by the identification processing unit 290 of the data processing device 12, and based on the information received by the reception unit, it determines items that suit the user's body type, preferences, and purpose. The suggestion unit is implemented by the speaker 240 of the headset terminal 314, and suggests the items determined by the decision unit to the user. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, and advises on combinations of items determined by the decision unit and items the user already owns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] For example, the reception unit is implemented by the microphone 238 of the robot 414, which can input information about the user's body type, preferences, and purpose. For example, it can accept input such as "I'm looking for a dress to wear to a party." The decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines items according to the user's body type, preferences, and purpose based on the information received by the reception unit. The suggestion unit is implemented by the speaker 240 of the robot 414, which suggests the items determined by the decision unit to the user. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, which advises on combinations of items determined by the decision unit and items the user already owns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception area that accepts input of information about the user's body type, preferences, and purpose, Based on the information received by the reception unit, a decision unit determines items according to the user's body type, preferences, and purpose. A proposal unit proposes items determined by the aforementioned determination unit, The system includes an advice unit that advises on combinations of items determined by the determination unit and owned items. A system characterized by the following features. (Note 2) The aforementioned determination unit, The aforementioned items are determined based on the season and trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The selected items are suggested to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, It advises on the combination of the selected item and the items you already own. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and modifies the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past input history and provides an auto-completion function to reduce the effort required for input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Based on user input, relevant fashion advice is displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Based on the user's geographical location, region-specific fashion information is provided at the time of input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze users' social media activity and prompt them to input relevant fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned determination unit, It estimates the user's emotions and dynamically changes the item selection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned determination unit, We analyze the user's past selection history to determine more personalized items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned determination unit, The system analyzes the user's body shape data in detail and selects items that provide the optimal fit. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned determination unit, It estimates the user's emotions and dynamically changes the order in which items are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned determination unit, We take the user's geographical location into consideration to determine items that reflect region-specific trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned determination unit, Analyze users' social media activity and determine relevant trending items. 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 dynamically changes the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we analyze the user's past responses and select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's body type and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and dynamically changes the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, the system prioritizes suggesting region-specific items, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and suggest relevant items. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the user's emotions and dynamically changes the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, the system analyzes the user's past outfit history and suggests the optimal combination. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's body type and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, It estimates the user's emotions and dynamically changes the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, the system prioritizes suggesting region-specific combinations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, we analyze the user's social media activity and suggest relevant combinations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts input of information about the user's body type, preferences, and purpose, Based on the information received by the reception unit, a decision unit determines items according to the user's body type, preferences, and purpose. A proposal unit proposes items determined by the aforementioned determination unit, The system includes an advice unit that advises on combinations of items determined by the determination unit and owned items. A system characterized by the following features.

2. The aforementioned determination unit, The aforementioned items are determined based on the season and trends. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and modifies the input interface design based on the estimated user emotions. The system according to feature 1.

4. The aforementioned reception unit is The system analyzes the user's past input history and provides an auto-completion function to reduce the effort required for input. The system according to feature 1.

5. The aforementioned reception unit is Based on the user's input, relevant fashion advice is displayed in real time. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and dynamically changes the priority of input content based on the estimated user emotions. The system according to feature 1.

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