system

Abstract

The system according to this embodiment aims to suggest the most suitable clothing based on the user's preferences and schedule. [Solution] The system according to the embodiment comprises a reception unit, a learning unit, a suggestion unit, and a provision unit. The reception unit receives the user's basic information. The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The suggestion unit suggests the most suitable clothing based on the weather forecast and schedule, based on the information learned by the learning unit. The provision unit provides the clothing suggested by the suggestion unit to the user.

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, it is difficult to propose an optimal outfit based on the user's preferences and schedule, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal outfit based on the user's preferences and schedule.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a learning unit, a suggestion unit, and a provision unit. The reception unit receives the user's basic information. The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The suggestion unit suggests the most suitable clothing based on the weather forecast and schedule, based on the information learned by the learning unit. The provision unit provides the clothing suggested by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable outfit based on the user's preferences and schedule. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal fashion advisor AI system according to an embodiment of the present invention is a system that supports busy business people in choosing their clothes. This system takes the user's basic information (age, gender, occupation, residential area, etc.) as input, learns the user's preferences and past clothing history, and provides personalized advice. Furthermore, it suggests the optimal outfit in real time based on weather forecasts and schedules. For example, it suggests waterproof clothing on rainy days and formal attire on days with important business meetings. It also suggests outfits that take into account how easily clothes get dirty, reducing the burden of dry cleaning costs. Moreover, it enhances user affinity by providing interactive advice using chat and avatars. This mechanism allows users to efficiently choose their clothes even on busy mornings, achieving smart attire appropriate for the occasion. For example, based on the basic information entered by the user, the system learns the user's preferences and past clothing history and provides personalized advice. For example, based on weather forecasts and schedules, the system suggests the optimal outfit in real time. For example, it suggests waterproof clothing on rainy days and formal attire on days with important business meetings. Furthermore, the system suggests outfits that take into account how easily clothes get dirty, reducing the burden of dry cleaning costs. Furthermore, the system enhances user engagement by providing interactive advice through chat and avatars. This allows users to efficiently choose their outfits even on busy mornings, enabling them to achieve smart attire appropriate for the occasion. Based on the user's basic information, the AI ​​personal fashion advisor system can then provide personalized clothing suggestions.

[0029] The personal fashion advisor AI system according to this embodiment comprises a reception unit, a learning unit, a suggestion unit, and a provision unit. The reception unit inputs the user's basic information. The user's basic information includes, but is not limited to, name, age, gender, and address. The reception unit, for example, stores the basic information entered by the user in a database. The reception unit can also update the user's basic information in real time. For example, if the user enters new information, the reception unit immediately reflects that information. The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The learning unit, for example, analyzes the types and frequency of clothes the user has worn in the past. The learning unit can also learn the user's preferred colors and styles. For example, the learning unit identifies the brands and designs the user likes. The suggestion unit suggests the most suitable outfit based on the weather forecast and schedule, based on the information learned by the learning unit. The suggestion unit, for example, obtains weather forecast data and suggests an outfit that matches the user's schedule. The suggestion unit can also make suggestions considering the user's preferences and past clothing history. For example, the suggestion unit proposes clothing based on the user's preferred colors and styles. The delivery unit provides the clothing suggested by the suggestion unit to the user. The delivery unit provides interactive advice, for example, using chat or an avatar. The delivery unit can also provide real-time feedback to the user. For example, the delivery unit provides an interface in which the user can input their opinion on the suggested clothing. As a result, the personal fashion advisor AI system according to this embodiment can make personalized clothing suggestions based on the user's basic information.

[0030] The reception desk inputs the user's basic information. This basic information includes, but is not limited to, name, age, gender, and address. The reception desk stores the basic information entered by the user in a database. The reception desk can also update the user's basic information in real time. For example, if a user enters new information, the reception desk immediately reflects that information. Specifically, the reception desk provides a form for entering information through the user interface and has a function to automatically save the data entered by the user to the database. When a user updates information, an algorithm is implemented that compares the existing data with the new data and updates the database as necessary. Furthermore, the reception desk is equipped with security features that encrypt data and control access to protect user privacy. For example, the user's personal information is encrypted and can only be accessed by authenticated users. The reception desk also has a function to validate the input content to prevent user input errors. For example, it checks the format of email addresses and confirms the input of required fields to support the entry of accurate information. In this way, the reception desk can manage user basic information accurately and securely, improving the reliability of the entire system.

[0031] The learning unit learns the user's preferences and past clothing history based on information entered by the reception unit. For example, the learning unit analyzes the types and frequency of clothes the user has worn in the past. The learning unit can also learn the user's preferred colors and styles. For example, the learning unit can identify the brands and designs the user likes. Specifically, the learning unit uses machine learning algorithms to analyze the user's past choices and behavioral patterns and model the user's preferences. For example, it collects data on clothes the user has purchased in the past and extracts trends in the user's preferences based on that data. The learning unit can also collect information from external data sources such as the user's social media posts and online shopping history to perform more accurate learning. Furthermore, the learning unit continuously improves its learning model by incorporating user feedback. For example, when a user evaluates suggested outfits, that evaluation is incorporated as learning data and reflected in future suggestions. This allows the learning unit to provide personalized suggestions that match the user's preferences and needs.

[0032] The suggestion unit proposes the most suitable clothing based on weather forecasts and schedules, using information learned by the learning unit. For example, the suggestion unit can acquire weather forecast data and propose clothing that matches the user's schedule. The suggestion unit can also make suggestions considering the user's preferences and past clothing history. For example, it can suggest clothing based on the colors and styles the user prefers. Specifically, the suggestion unit uses a weather forecast API to acquire the latest weather information and selects appropriate clothing based on that information. It can also link with the user's calendar app and suggest clothing according to scheduled events and activities. Furthermore, the suggestion unit has an algorithm that selects clothing that the user is likely to like, based on the user's past clothing history and preference data. For example, it considers the colors and styles the user has preferred to wear in the past and proposes clothing with similar characteristics. The suggestion unit can also make suggestions that take seasons and trends into account. For example, it can suggest clothing that incorporates the latest fashion trends or clothing made of materials and designs appropriate for the season. In this way, the suggestion unit can provide optimal clothing suggestions that match the user's needs and preferences, thereby increasing user satisfaction.

[0033] The service provider provides users with clothing suggestions proposed by the suggestion provider. The service provider offers interactive advice, for example, using chat or avatars. The service provider can also provide real-time feedback to users. For example, the service provider provides an interface where users can input their opinions on the suggested clothing. Specifically, the service provider provides a chatbot where users can input questions and comments about the suggested clothing, and responds immediately to user inquiries and requests. In addition, by providing visual advice using avatars, users can visually confirm the suggested clothing. Furthermore, the service provider can collect user feedback and use that information to improve the suggestions. For example, by having users rate the suggested clothing, this rating is incorporated as training data and reflected in future suggestions. The service provider can also provide customized advice tailored to the user's preferences and needs. For example, it can suggest clothing suitable for specific events or situations, or provide advice tailored to the user's body type and style. In this way, the service provider can provide personalized advice to users and increase user satisfaction.

[0034] The suggestion unit includes an acquisition unit that obtains weather forecasts and schedule information. For example, the suggestion unit can acquire weather forecast data and suggest clothing that matches the user's schedule. The suggestion unit can acquire weather forecast data in real time, for example, by using a weather forecast API. The suggestion unit can also acquire schedule information from the user's calendar app or schedule management tool. For example, the suggestion unit can acquire schedule information from the user's calendar app and suggest the most appropriate clothing based on that information. In this way, the suggestion unit can suggest the most appropriate clothing based on weather forecasts and schedules.

[0035] The proposal department will suggest outfits that take into account how easily clothes get dirty. For example, the proposal department will suggest clothing that is less likely to get dirty, taking into account the material, color, and environment in which it will be worn. For example, the proposal department can suggest clothing made of stain-resistant materials. The proposal department can also suggest clothing in colors that do not show dirt easily. For example, the proposal department can suggest dark-colored clothing that does not show dirt easily. Furthermore, the proposal department can suggest clothing that is less likely to get dirty depending on the environment in which it will be worn. For example, the proposal department can suggest stain-resistant clothing to users who spend a lot of time outdoors. This allows the proposal department to make suggestions that take into account how easily clothes get dirty.

[0036] The service provider includes a dialogue unit that provides advice interactively using chat and avatars. For example, the service provider can interact with users using a chatbot and provide clothing advice. For example, when a user enters a question into the chatbot, the chatbot will provide appropriate clothing advice. The service provider can also provide advice interactively using avatars. For example, the service provider can provide an interface in which an avatar gives clothing advice to the user. This enables the service provider to provide interactive advice to users.

[0037] The learning unit includes a feedback unit that collects user feedback and improves the accuracy of suggestions. The learning unit provides, for example, an interface where users can input their opinions on suggested clothing. When a user inputs feedback on suggested clothing, the learning unit collects this information and improves the accuracy of suggestions. Furthermore, the learning unit can also improve the suggestion algorithm based on user feedback. For example, the learning unit analyzes user feedback and adjusts the suggestion algorithm. This allows the learning unit to improve the accuracy of suggestions based on user feedback.

[0038] The reception desk analyzes the user's past basic information input history and selects the optimal input method. For example, the reception desk automatically displays basic information that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk predicts and suggests basic information to be used during specific time periods based on the user's past input history. This allows the reception desk to select the optimal input method 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.

[0039] The reception desk customizes input fields based on the user's current lifestyle and areas of interest when basic information is entered. For example, if the user is busy, the reception desk will display only the minimum necessary input fields. If the user has a particular area of ​​interest, the reception desk will add input fields related to that area. The reception desk will also adjust the order of the input fields according to the user's lifestyle. This allows the reception desk to customize input fields according to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0040] The reception desk prioritizes inputting highly relevant information when users enter basic information, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk prioritizes inputting basic information related to that region. For example, if a user is traveling, the reception desk prioritizes inputting basic information related to their travel destination. Furthermore, if a user is planning to move, the reception desk prioritizes inputting basic information related to their new place of residence. This enables the reception desk to input highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The reception desk analyzes the user's social media activity and inputs relevant information when basic information is entered. For example, the reception desk automatically inputs basic information based on information the user has shared on social media. For example, the reception desk prompts the user to input basic information related to their areas of interest from their social media activity. The reception desk also prompts the user to input relevant basic information based on their social media friendships. This enables the reception desk to input relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0042] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit adjusts parameters to improve learning accuracy based on past learning data. The learning unit also analyzes past learning data to identify areas for improvement in the learning algorithm. This enables the learning unit to optimize the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0043] The learning unit adjusts the accuracy of its learning based on the level of detail of the user's clothing history during the learning process. For example, the learning unit increases the accuracy of learning when the user's clothing history is detailed. For example, the learning unit adjusts the accuracy of learning when the user's clothing history is concise. The learning unit also adjusts the parameters of the learning algorithm according to the level of detail of the user's clothing history. This allows the learning unit to adjust the accuracy of learning according to the level of detail of the clothing history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0044] The learning unit weights the training data based on the user's lifestyle during training. For example, if the user is a business person, the learning unit increases the weight of training data related to business scenes. For example, if the user has a casual lifestyle, the learning unit increases the weight of training data related to casual clothing. The learning unit also adjusts the weighting of the training data according to the user's lifestyle. This allows the learning unit to weight the training data according to the user's lifestyle. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0045] The learning unit improves the accuracy of its learning by referring to the user's purchase history during the learning process. For example, the learning unit improves the accuracy of its learning based on the user's purchase history. For example, the learning unit improves the accuracy of its learning by analyzing the user's preferences from their purchase history. The learning unit also adjusts the parameters of its learning algorithm by referring to the user's purchase history. This allows the learning unit to improve the accuracy of its learning based on the purchase history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0046] The proposal unit adjusts the accuracy of its proposals based on the level of detail of the weather forecast and schedule. For example, the proposal unit increases the accuracy of its proposals when the weather forecast is detailed. The proposal unit adjusts the accuracy of its proposals according to the level of detail of the schedule. The proposal unit also improves the accuracy of its proposals based on the level of detail of the weather forecast and schedule. This allows the proposal unit to adjust the accuracy of its proposals according to the level of detail of the weather forecast and schedule. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0047] The suggestion unit determines the priority of suggestions based on the user's past clothing history when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the user's past clothing history. For example, the suggestion unit analyzes the user's preferences from their past clothing history and determines the priority of suggestions. The suggestion unit also adjusts the priority of suggestions by referring to the user's past clothing history. This enables the suggestion unit to determine the priority of suggestions based on past clothing history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0048] The suggestion unit, when making a suggestion, takes into account the user's geographical location information to propose the most suitable clothing. For example, if the user lives in a specific region, the suggestion unit will suggest clothing appropriate for that region. For example, if the user is traveling, the suggestion unit will suggest clothing appropriate for the travel destination. Furthermore, if the user is planning to move, the suggestion unit will suggest clothing appropriate for the new place of residence. This enables the suggestion unit to propose the most suitable clothing based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0049] The suggestion unit analyzes the user's social media activity and suggests relevant clothing when making suggestions. For example, the suggestion unit analyzes the user's preferences from their social media activity and suggests relevant clothing. For example, the suggestion unit suggests relevant clothing based on the user's social media friendships. The suggestion unit also suggests relevant clothing based on information shared on the user's social media. This enables the suggestion unit to suggest relevant clothing based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0050] The delivery unit selects the optimal delivery method by referring to the user's past clothing delivery history at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the clothing delivery methods the user has preferred in the past. For example, the delivery unit analyzes the user's preference trends from their past clothing delivery history and selects the optimal delivery method. The delivery unit also adjusts the parameters of the delivery method by referring to the user's past clothing delivery history. This enables the delivery unit to select the optimal delivery method based on past clothing delivery history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0051] The service provider customizes the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider selects a simple delivery method. For example, if the user is relaxed, the service provider selects a detailed delivery method. The service provider also adjusts the parameters of the delivery method according to the user's living situation. This allows the service provider to customize the delivery method according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0052] The service provider, at the time of delivery, provides the most suitable clothing considering the user's geographical location. For example, if the user lives in a specific region, the service provider provides clothing suitable for that region. For example, if the user is traveling, the service provider provides clothing suitable for the travel destination. Furthermore, if the user is planning to move, the service provider provides clothing suitable for the new place of residence. This enables the service provider to provide the most suitable clothing based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0053] The service provider analyzes the user's social media activity at the time of service provision and provides relevant clothing. For example, the service provider analyzes the user's preferences from their social media activity and provides relevant clothing. For example, the service provider provides relevant clothing based on the user's social media friendships. The service provider also provides relevant clothing based on information shared on the user's social media. This enables the service provider to provide relevant clothing based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0054] The acquisition unit analyzes the user's past acquisition history when acquiring weather forecasts or schedules and selects the optimal acquisition method. For example, the acquisition unit selects the optimal acquisition method based on the weather forecast or schedule acquisition methods the user has preferred in the past. For example, the acquisition unit analyzes the user's preferred trends from their past acquisition history and selects the optimal acquisition method. The acquisition unit also adjusts the parameters of the acquisition method by referring to the user's past acquisition history. This enables the acquisition unit to select the optimal acquisition method based on past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0055] The acquisition unit filters weather forecasts and schedules based on the user's current lifestyle and areas of interest. For example, if the user is busy, the acquisition unit will acquire only the minimum necessary weather forecasts and schedules. If the user has a specific area of ​​interest, the acquisition unit will acquire weather forecasts and schedules related to that area. The acquisition unit also adjusts the order in which it acquires weather forecasts and schedules according to the user's lifestyle. This allows the acquisition unit to filter weather forecasts and schedules according to the user's lifestyle and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0056] The data acquisition unit prioritizes acquiring highly relevant information, taking into account the user's geographical location, when acquiring weather forecasts and schedules. For example, if the user lives in a specific region, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to that region. For example, if the user is traveling, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to the travel destination. Furthermore, if the user is planning to move, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to the new place of residence. This enables the data acquisition unit to acquire highly relevant information based on geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0057] The acquisition unit analyzes the user's social media activity and obtains relevant information when acquiring weather forecasts and schedules. For example, the acquisition unit analyzes the user's preferences from their social media activity and obtains relevant weather forecasts and schedules. For example, the acquisition unit obtains relevant weather forecasts and schedules based on the user's social media friendships. The acquisition unit also obtains relevant weather forecasts and schedules based on information shared on the user's social media. This enables the acquisition unit to acquire relevant information based on social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0058] The dialogue unit selects the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit selects the optimal dialogue method based on the dialogue method the user has preferred in the past. For example, the dialogue unit analyzes the user's preferences from their past dialogue history and selects the optimal dialogue method. The dialogue unit also adjusts the parameters of the dialogue method by referring to the user's past dialogue history. This enables the dialogue unit to select the optimal dialogue method based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0059] The dialogue unit customizes the means of dialogue based on the user's current life situation during the conversation. For example, if the user is busy, the dialogue unit selects a concise dialogue method. For example, if the user is relaxed, the dialogue unit selects a detailed dialogue method. The dialogue unit also adjusts the parameters of the dialogue method according to the user's life situation. This allows the dialogue unit to customize the dialogue method according to the user's life situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0060] The dialogue unit selects the optimal dialogue method during a conversation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the dialogue unit selects a dialogue method appropriate for that region. For example, if the user is traveling, the dialogue unit selects a dialogue method appropriate for the travel destination. Furthermore, if the user is planning to move, the dialogue unit selects a dialogue method appropriate for the new place of residence. This enables the dialogue unit to select the optimal dialogue method based on geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0061] The dialogue unit analyzes the user's social media activity during a conversation and conducts relevant dialogue. For example, the dialogue unit analyzes the user's preferences from their social media activity and conducts relevant dialogue. For example, the dialogue unit conducts relevant dialogue based on the user's social media friendships. Furthermore, the dialogue unit conducts relevant dialogue based on information shared on the user's social media. This enables the dialogue unit to conduct relevant dialogue based on social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0062] The feedback unit selects the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit selects the optimal collection method based on the user's preferred feedback collection method in the past. For example, the feedback unit analyzes the user's preferred trends from their past feedback history and selects the optimal collection method. The feedback unit also adjusts the parameters of the collection method by referring to the user's past feedback history. This enables the feedback unit to select the optimal collection method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0063] The feedback unit customizes the means of collecting feedback based on the user's current living situation. For example, if the user is busy, the feedback unit selects a simple collection method. For example, if the user is relaxed, the feedback unit selects a detailed collection method. The feedback unit also adjusts the parameters of the collection method according to the user's living situation. This allows the feedback unit to customize the collection method according to the user's living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0064] The feedback unit selects the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the feedback unit selects a feedback collection method suitable for that region. For example, if the user is traveling, the feedback unit selects a feedback collection method suitable for the travel destination. Furthermore, if the user is planning to move, the feedback unit selects a feedback collection method suitable for the new place of residence. This enables the feedback unit to select the optimal collection method based on geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0065] The feedback unit analyzes the user's social media activity and collects relevant feedback when collecting feedback. For example, the feedback unit analyzes the user's preferences from their social media activity and collects relevant feedback. For example, the feedback unit collects relevant feedback based on the user's social media friendships. The feedback unit also collects relevant feedback based on information shared on the user's social media. This enables the feedback unit to collect relevant feedback based on social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The suggestion function can analyze a user's past clothing history to understand their seasonal clothing trends. For example, it can suggest summer outfits based on the clothes a user has previously preferred to wear in the summer. It can also suggest winter outfits based on the clothes a user has preferred to wear in the winter. Furthermore, it can suggest outfits appropriate for spring and autumn. This allows the suggestion function to make suggestions based on seasonal clothing trends.

[0068] The suggestion department can analyze users' clothing trends and make suggestions based on the latest fashion. For example, it can acquire data from the latest fashion magazines and online shops to suggest outfits based on trends. It can also suggest outfits that are in line with current trends, taking into account the user's preferences and past clothing history. Furthermore, it can make suggestions that reflect trends according to the season and events. This allows the suggestion department to make suggestions based on the latest fashion trends.

[0069] The proposal department can also make suggestions that take into account the durability of the user's clothing. For example, the proposal department can evaluate the durability of clothing that the user frequently wears and suggest long-lasting clothing. Furthermore, the proposal department can suggest clothing made from highly durable materials depending on the user's activity level and usage environment. In addition, the proposal department can suggest cost-effective clothing according to the user's budget. This allows the proposal department to make suggestions that take clothing durability into consideration.

[0070] The proposal department can also make suggestions that take into account the user's clothing maintenance. For example, the proposal department can suggest clothing that is easy for the user to clean and iron. Furthermore, the proposal department can suggest clothing that requires less maintenance, depending on the user's lifestyle. In addition, the proposal department can suggest clothing with low maintenance costs, depending on the user's budget. This allows the proposal department to make suggestions that consider clothing maintenance.

[0071] The proposal department can also make suggestions that take into account the eco-friendliness of the user's clothing. For example, the proposal department can suggest clothing made from environmentally friendly materials. Furthermore, the proposal department can suggest clothing made from recyclable materials. In addition, the proposal department can suggest eco-friendly clothing tailored to the user's lifestyle. This allows the proposal department to make suggestions that consider eco-friendliness.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk enters the user's basic information. This information includes, for example, name, age, gender, and address. The reception desk can also save the user's entered information to a database and update it in real time. Step 2: The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The learning unit analyzes the types and frequency of clothes the user has worn in the past to identify preferred colors, styles, brands, and designs. Step 3: The suggestion unit proposes the most suitable clothing based on weather forecasts and schedules, using information learned by the learning unit. The suggestion unit acquires weather forecast data, proposes clothing that matches the user's schedule, and makes suggestions considering the user's preferences and past clothing history. Step 4: The provision team provides the user with the clothing suggested by the proposal team. The provision team provides interactive advice using chat and avatars, and provides real-time feedback to the user.

[0074] (Example of form 2) The personal fashion advisor AI system according to an embodiment of the present invention is a system that supports busy business people in choosing their clothes. This system takes the user's basic information (age, gender, occupation, residential area, etc.) as input, learns the user's preferences and past clothing history, and provides personalized advice. Furthermore, it suggests the optimal outfit in real time based on weather forecasts and schedules. For example, it suggests waterproof clothing on rainy days and formal attire on days with important business meetings. It also suggests outfits that take into account how easily clothes get dirty, reducing the burden of dry cleaning costs. Moreover, it enhances user affinity by providing interactive advice using chat and avatars. This mechanism allows users to efficiently choose their clothes even on busy mornings, achieving smart attire appropriate for the occasion. For example, based on the basic information entered by the user, the system learns the user's preferences and past clothing history and provides personalized advice. For example, based on weather forecasts and schedules, the system suggests the optimal outfit in real time. For example, it suggests waterproof clothing on rainy days and formal attire on days with important business meetings. Furthermore, the system suggests outfits that take into account how easily clothes get dirty, reducing the burden of dry cleaning costs. Furthermore, the system enhances user engagement by providing interactive advice through chat and avatars. This allows users to efficiently choose their outfits even on busy mornings, enabling them to achieve smart attire appropriate for the occasion. Based on the user's basic information, the AI ​​personal fashion advisor system can then provide personalized clothing suggestions.

[0075] The personal fashion advisor AI system according to this embodiment comprises a reception unit, a learning unit, a suggestion unit, and a provision unit. The reception unit inputs the user's basic information. The user's basic information includes, but is not limited to, name, age, gender, and address. The reception unit, for example, stores the basic information entered by the user in a database. The reception unit can also update the user's basic information in real time. For example, if the user enters new information, the reception unit immediately reflects that information. The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The learning unit, for example, analyzes the types and frequency of clothes the user has worn in the past. The learning unit can also learn the user's preferred colors and styles. For example, the learning unit identifies the brands and designs the user likes. The suggestion unit suggests the most suitable outfit based on the weather forecast and schedule, based on the information learned by the learning unit. The suggestion unit, for example, obtains weather forecast data and suggests an outfit that matches the user's schedule. The suggestion unit can also make suggestions considering the user's preferences and past clothing history. For example, the suggestion unit proposes clothing based on the user's preferred colors and styles. The delivery unit provides the clothing suggested by the suggestion unit to the user. The delivery unit provides interactive advice, for example, using chat or an avatar. The delivery unit can also provide real-time feedback to the user. For example, the delivery unit provides an interface in which the user can input their opinion on the suggested clothing. As a result, the personal fashion advisor AI system according to this embodiment can make personalized clothing suggestions based on the user's basic information.

[0076] The reception desk inputs the user's basic information. This basic information includes, but is not limited to, name, age, gender, and address. The reception desk stores the basic information entered by the user in a database. The reception desk can also update the user's basic information in real time. For example, if a user enters new information, the reception desk immediately reflects that information. Specifically, the reception desk provides a form for entering information through the user interface and has a function to automatically save the data entered by the user to the database. When a user updates information, an algorithm is implemented that compares the existing data with the new data and updates the database as necessary. Furthermore, the reception desk is equipped with security features that encrypt data and control access to protect user privacy. For example, the user's personal information is encrypted and can only be accessed by authenticated users. The reception desk also has a function to validate the input content to prevent user input errors. For example, it checks the format of email addresses and confirms the input of required fields to support the entry of accurate information. In this way, the reception desk can manage user basic information accurately and securely, improving the reliability of the entire system.

[0077] The learning unit learns the user's preferences and past clothing history based on information entered by the reception unit. For example, the learning unit analyzes the types and frequency of clothes the user has worn in the past. The learning unit can also learn the user's preferred colors and styles. For example, the learning unit can identify the brands and designs the user likes. Specifically, the learning unit uses machine learning algorithms to analyze the user's past choices and behavioral patterns and model the user's preferences. For example, it collects data on clothes the user has purchased in the past and extracts trends in the user's preferences based on that data. The learning unit can also collect information from external data sources such as the user's social media posts and online shopping history to perform more accurate learning. Furthermore, the learning unit continuously improves its learning model by incorporating user feedback. For example, when a user evaluates suggested outfits, that evaluation is incorporated as learning data and reflected in future suggestions. This allows the learning unit to provide personalized suggestions that match the user's preferences and needs.

[0078] The suggestion unit proposes the most suitable clothing based on weather forecasts and schedules, using information learned by the learning unit. For example, the suggestion unit can acquire weather forecast data and propose clothing that matches the user's schedule. The suggestion unit can also make suggestions considering the user's preferences and past clothing history. For example, it can suggest clothing based on the colors and styles the user prefers. Specifically, the suggestion unit uses a weather forecast API to acquire the latest weather information and selects appropriate clothing based on that information. It can also link with the user's calendar app and suggest clothing according to scheduled events and activities. Furthermore, the suggestion unit has an algorithm that selects clothing that the user is likely to like, based on the user's past clothing history and preference data. For example, it considers the colors and styles the user has preferred to wear in the past and proposes clothing with similar characteristics. The suggestion unit can also make suggestions that take seasons and trends into account. For example, it can suggest clothing that incorporates the latest fashion trends or clothing made of materials and designs appropriate for the season. In this way, the suggestion unit can provide optimal clothing suggestions that match the user's needs and preferences, thereby increasing user satisfaction.

[0079] The service provider provides users with clothing suggestions proposed by the suggestion provider. The service provider offers interactive advice, for example, using chat or avatars. The service provider can also provide real-time feedback to users. For example, the service provider provides an interface where users can input their opinions on the suggested clothing. Specifically, the service provider provides a chatbot where users can input questions and comments about the suggested clothing, and responds immediately to user inquiries and requests. In addition, by providing visual advice using avatars, users can visually confirm the suggested clothing. Furthermore, the service provider can collect user feedback and use that information to improve the suggestions. For example, by having users rate the suggested clothing, this rating is incorporated as training data and reflected in future suggestions. The service provider can also provide customized advice tailored to the user's preferences and needs. For example, it can suggest clothing suitable for specific events or situations, or provide advice tailored to the user's body type and style. In this way, the service provider can provide personalized advice to users and increase user satisfaction.

[0080] The suggestion unit includes an acquisition unit that obtains weather forecasts and schedule information. For example, the suggestion unit can acquire weather forecast data and suggest clothing that matches the user's schedule. The suggestion unit can acquire weather forecast data in real time, for example, by using a weather forecast API. The suggestion unit can also acquire schedule information from the user's calendar app or schedule management tool. For example, the suggestion unit can acquire schedule information from the user's calendar app and suggest the most appropriate clothing based on that information. In this way, the suggestion unit can suggest the most appropriate clothing based on weather forecasts and schedules.

[0081] The proposal department will suggest outfits that take into account how easily clothes get dirty. For example, the proposal department will suggest clothing that is less likely to get dirty, taking into account the material, color, and environment in which it will be worn. For example, the proposal department can suggest clothing made of stain-resistant materials. The proposal department can also suggest clothing in colors that do not show dirt easily. For example, the proposal department can suggest dark-colored clothing that does not show dirt easily. Furthermore, the proposal department can suggest clothing that is less likely to get dirty depending on the environment in which it will be worn. For example, the proposal department can suggest stain-resistant clothing to users who spend a lot of time outdoors. This allows the proposal department to make suggestions that take into account how easily clothes get dirty.

[0082] The service provider includes a dialogue unit that provides advice interactively using chat and avatars. For example, the service provider can interact with users using a chatbot and provide clothing advice. For example, when a user enters a question into the chatbot, the chatbot will provide appropriate clothing advice. The service provider can also provide advice interactively using avatars. For example, the service provider can provide an interface in which an avatar gives clothing advice to the user. This enables the service provider to provide interactive advice to users.

[0083] The learning unit includes a feedback unit that collects user feedback and improves the accuracy of suggestions. The learning unit provides, for example, an interface where users can input their opinions on suggested clothing. When a user inputs feedback on suggested clothing, the learning unit collects this information and improves the accuracy of suggestions. Furthermore, the learning unit can also improve the suggestion algorithm based on user feedback. For example, the learning unit analyzes user feedback and adjusts the suggestion algorithm. This allows the learning unit to improve the accuracy of suggestions based on user feedback.

[0084] The reception desk estimates the user's emotions and adjusts the input method for basic information based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, for example, the reception desk provides detailed input options and suggests a customizable input method. Also, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of basic information. This allows the reception desk to adjust the input method 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.

[0085] The reception desk analyzes the user's past basic information input history and selects the optimal input method. For example, the reception desk automatically displays basic information that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk predicts and suggests basic information to be used during specific time periods based on the user's past input history. This allows the reception desk to select the optimal input method 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.

[0086] The reception desk customizes input fields based on the user's current lifestyle and areas of interest when basic information is entered. For example, if the user is busy, the reception desk will display only the minimum necessary input fields. If the user has a particular area of ​​interest, the reception desk will add input fields related to that area. The reception desk will also adjust the order of the input fields according to the user's lifestyle. This allows the reception desk to customize input fields according to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0087] The reception desk estimates the user's emotions and determines the priority of the basic information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize the input of important basic information. For example, if the user is relaxed, the reception desk will prompt for detailed basic information. Furthermore, if the user is in a hurry, the reception desk will prompt for only the most important basic information. This allows the reception desk to determine the priority of basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk prioritizes inputting highly relevant information when users enter basic information, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk prioritizes inputting basic information related to that region. For example, if a user is traveling, the reception desk prioritizes inputting basic information related to their travel destination. Furthermore, if a user is planning to move, the reception desk prioritizes inputting basic information related to their new place of residence. This enables the reception desk to input highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0089] The reception desk analyzes the user's social media activity and inputs relevant information when basic information is entered. For example, the reception desk automatically inputs basic information based on information the user has shared on social media. For example, the reception desk prompts the user to input basic information related to their areas of interest from their social media activity. The reception desk also prompts the user to input relevant basic information based on their social media friendships. This enables the reception desk to input relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0090] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit selects detailed training data. If the user is in a hurry, the learning unit selects concise training data. If the user is excited, the learning unit selects visually stimulating training data. This allows the learning unit to select training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit adjusts parameters to improve learning accuracy based on past learning data. The learning unit also analyzes past learning data to identify areas for improvement in the learning algorithm. This enables the learning unit to optimize the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0092] The learning unit adjusts the accuracy of its learning based on the level of detail of the user's clothing history during the learning process. For example, the learning unit increases the accuracy of learning when the user's clothing history is detailed. For example, the learning unit adjusts the accuracy of learning when the user's clothing history is concise. The learning unit also adjusts the parameters of the learning algorithm according to the level of detail of the user's clothing history. This allows the learning unit to adjust the accuracy of learning according to the level of detail of the clothing history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0093] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, the learning unit increases the learning frequency when the user is relaxed. For example, it decreases the learning frequency when the user is in a hurry. It also adjusts the learning frequency when the user is excited. This allows the learning unit to adjust the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The learning unit weights the training data based on the user's lifestyle during training. For example, if the user is a business person, the learning unit increases the weight of training data related to business scenes. For example, if the user has a casual lifestyle, the learning unit increases the weight of training data related to casual clothing. The learning unit also adjusts the weighting of the training data according to the user's lifestyle. This allows the learning unit to weight the training data according to the user's lifestyle. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0095] The learning unit improves the accuracy of its learning by referring to the user's purchase history during the learning process. For example, the learning unit improves the accuracy of its learning based on the user's purchase history. For example, the learning unit improves the accuracy of its learning by analyzing the user's preferences from their purchase history. The learning unit also adjusts the parameters of its learning algorithm by referring to the user's purchase history. This allows the learning unit to improve the accuracy of its learning based on the purchase history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0096] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is excited, the suggestion unit will provide visually stimulating suggestions. This allows the suggestion unit to adjust the way it presents 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The proposal unit adjusts the accuracy of its proposals based on the level of detail of the weather forecast and schedule. For example, the proposal unit increases the accuracy of its proposals when the weather forecast is detailed. The proposal unit adjusts the accuracy of its proposals according to the level of detail of the schedule. The proposal unit also improves the accuracy of its proposals based on the level of detail of the weather forecast and schedule. This allows the proposal unit to adjust the accuracy of its proposals according to the level of detail of the weather forecast and schedule. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0098] The suggestion unit determines the priority of suggestions based on the user's past clothing history when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the user's past clothing history. For example, the suggestion unit analyzes the user's preferences from their past clothing history and determines the priority of suggestions. The suggestion unit also adjusts the priority of suggestions by referring to the user's past clothing history. This enables the suggestion unit to determine the priority of suggestions based on past clothing history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0099] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is excited, the suggestion unit will provide visually stimulating suggestions. This allows the suggestion unit to adjust the length 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The suggestion unit, when making a suggestion, takes into account the user's geographical location information to propose the most suitable clothing. For example, if the user lives in a specific region, the suggestion unit will suggest clothing appropriate for that region. For example, if the user is traveling, the suggestion unit will suggest clothing appropriate for the travel destination. Furthermore, if the user is planning to move, the suggestion unit will suggest clothing appropriate for the new place of residence. This enables the suggestion unit to propose the most suitable clothing based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0101] The suggestion unit analyzes the user's social media activity and suggests relevant clothing when making suggestions. For example, the suggestion unit analyzes the user's preferences from their social media activity and suggests relevant clothing. For example, the suggestion unit suggests relevant clothing based on the user's social media friendships. The suggestion unit also suggests relevant clothing based on information shared on the user's social media. This enables the suggestion unit to suggest relevant clothing based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0102] The service provider estimates the user's emotions and adjusts the way it displays clothing based on those emotions. For example, if the user is relaxed, it provides a detailed display. If the user is in a hurry, it provides a concise display. If the user is excited, it provides a visually stimulating display. This allows the service provider to adjust the way it displays clothing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The delivery unit selects the optimal delivery method by referring to the user's past clothing delivery history at the time of delivery. For example, the delivery unit selects the optimal delivery method based on the clothing delivery methods the user has preferred in the past. For example, the delivery unit analyzes the user's preference trends from their past clothing delivery history and selects the optimal delivery method. The delivery unit also adjusts the parameters of the delivery method by referring to the user's past clothing delivery history. This enables the delivery unit to select the optimal delivery method based on past clothing delivery history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0104] The service provider customizes the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider selects a simple delivery method. For example, if the user is relaxed, the service provider selects a detailed delivery method. The service provider also adjusts the parameters of the delivery method according to the user's living situation. This allows the service provider to customize the delivery method according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0105] The service provider estimates the user's emotions and determines the priority of clothing to offer based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed clothing. If the user is in a hurry, the service provider will prioritize simple clothing. If the user is excited, the service provider will prioritize visually stimulating clothing. This allows the service provider to determine clothing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The service provider, at the time of delivery, provides the most suitable clothing considering the user's geographical location. For example, if the user lives in a specific region, the service provider provides clothing suitable for that region. For example, if the user is traveling, the service provider provides clothing suitable for the travel destination. Furthermore, if the user is planning to move, the service provider provides clothing suitable for the new place of residence. This enables the service provider to provide the most suitable clothing based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0107] The service provider analyzes the user's social media activity at the time of service provision and provides relevant clothing. For example, the service provider analyzes the user's preferences from their social media activity and provides relevant clothing. For example, the service provider provides relevant clothing based on the user's social media friendships. The service provider also provides relevant clothing based on information shared on the user's social media. This enables the service provider to provide relevant clothing based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0108] The acquisition unit estimates the user's emotions and adjusts the timing of acquiring weather forecasts and schedules based on the estimated emotions. For example, if the user is relaxed, the acquisition unit acquires detailed weather forecasts and schedules. If the user is in a hurry, for example, the acquisition unit acquires concise weather forecasts and schedules. Also, if the user is excited, the acquisition unit acquires visually stimulating weather forecasts and schedules. This allows the acquisition unit to adjust the timing of acquiring weather forecasts and schedules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The acquisition unit analyzes the user's past acquisition history when acquiring weather forecasts or schedules and selects the optimal acquisition method. For example, the acquisition unit selects the optimal acquisition method based on the weather forecast or schedule acquisition methods the user has preferred in the past. For example, the acquisition unit analyzes the user's preferred trends from their past acquisition history and selects the optimal acquisition method. The acquisition unit also adjusts the parameters of the acquisition method by referring to the user's past acquisition history. This enables the acquisition unit to select the optimal acquisition method based on past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0110] The acquisition unit filters weather forecasts and schedules based on the user's current lifestyle and areas of interest. For example, if the user is busy, the acquisition unit will acquire only the minimum necessary weather forecasts and schedules. If the user has a specific area of ​​interest, the acquisition unit will acquire weather forecasts and schedules related to that area. The acquisition unit also adjusts the order in which it acquires weather forecasts and schedules according to the user's lifestyle. This allows the acquisition unit to filter weather forecasts and schedules according to the user's lifestyle and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0111] The information acquisition unit estimates the user's emotions and determines the priority of information to acquire based on the estimated emotions. For example, if the user is relaxed, the acquisition unit prioritizes acquiring detailed information. If the user is in a hurry, the acquisition unit prioritizes acquiring concise information. If the user is excited, the acquisition unit prioritizes acquiring visually stimulating information. This allows the acquisition unit to determine the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The data acquisition unit prioritizes acquiring highly relevant information, taking into account the user's geographical location, when acquiring weather forecasts and schedules. For example, if the user lives in a specific region, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to that region. For example, if the user is traveling, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to the travel destination. Furthermore, if the user is planning to move, the data acquisition unit prioritizes acquiring weather forecasts and schedules related to the new place of residence. This enables the data acquisition unit to acquire highly relevant information based on geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI.

[0113] The acquisition unit analyzes the user's social media activity and obtains relevant information when acquiring weather forecasts and schedules. For example, the acquisition unit analyzes the user's preferences from their social media activity and obtains relevant weather forecasts and schedules. For example, the acquisition unit obtains relevant weather forecasts and schedules based on the user's social media friendships. The acquisition unit also obtains relevant weather forecasts and schedules based on information shared on the user's social media. This enables the acquisition unit to acquire relevant information based on social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0114] The dialogue unit estimates the user's emotions and adjusts the way it expresses the dialogue based on those emotions. For example, if the user is relaxed, the dialogue unit will engage in detailed conversation. If the user is in a hurry, the dialogue unit will engage in concise conversation. If the user is excited, the dialogue unit will engage in visually stimulating conversation. This allows the dialogue unit to adjust the way it expresses the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The dialogue unit selects the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit selects the optimal dialogue method based on the dialogue method the user has preferred in the past. For example, the dialogue unit analyzes the user's preferences from their past dialogue history and selects the optimal dialogue method. The dialogue unit also adjusts the parameters of the dialogue method by referring to the user's past dialogue history. This enables the dialogue unit to select the optimal dialogue method based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0116] The dialogue unit customizes the means of dialogue based on the user's current life situation during the conversation. For example, if the user is busy, the dialogue unit selects a concise dialogue method. For example, if the user is relaxed, the dialogue unit selects a detailed dialogue method. The dialogue unit also adjusts the parameters of the dialogue method according to the user's life situation. This allows the dialogue unit to customize the dialogue method according to the user's life situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0117] The dialogue unit estimates the user's emotions and determines the priority of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will prioritize detailed dialogue. If the user is in a hurry, the dialogue unit will prioritize concise dialogue. Also, if the user is excited, the dialogue unit will prioritize visually stimulating dialogue. This allows the dialogue unit to determine the priority of dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The dialogue unit selects the optimal dialogue method during a conversation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the dialogue unit selects a dialogue method appropriate for that region. For example, if the user is traveling, the dialogue unit selects a dialogue method appropriate for the travel destination. Furthermore, if the user is planning to move, the dialogue unit selects a dialogue method appropriate for the new place of residence. This enables the dialogue unit to select the optimal dialogue method based on geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0119] The dialogue unit analyzes the user's social media activity during a conversation and conducts relevant dialogue. For example, the dialogue unit analyzes the user's preferences from their social media activity and conducts relevant dialogue. For example, the dialogue unit conducts relevant dialogue based on the user's social media friendships. Furthermore, the dialogue unit conducts relevant dialogue based on information shared on the user's social media. This enables the dialogue unit to conduct relevant dialogue based on social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0120] The feedback unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback unit collects detailed feedback. If the user is in a hurry, the feedback unit collects concise feedback. If the user is excited, the feedback unit collects visually stimulating feedback. This allows the feedback unit to adjust the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The feedback unit selects the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit selects the optimal collection method based on the user's preferred feedback collection method in the past. For example, the feedback unit analyzes the user's preferred trends from their past feedback history and selects the optimal collection method. The feedback unit also adjusts the parameters of the collection method by referring to the user's past feedback history. This enables the feedback unit to select the optimal collection method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0122] The feedback unit customizes the means of collecting feedback based on the user's current living situation. For example, if the user is busy, the feedback unit selects a simple collection method. For example, if the user is relaxed, the feedback unit selects a detailed collection method. The feedback unit also adjusts the parameters of the collection method according to the user's living situation. This allows the feedback unit to customize the collection method according to the user's living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0123] The feedback unit estimates the user's emotions and prioritizes feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit prioritizes collecting detailed feedback. If the user is in a hurry, the feedback unit prioritizes collecting concise feedback. If the user is excited, the feedback unit prioritizes collecting visually stimulating feedback. This allows the feedback unit to prioritize feedback 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The feedback unit selects the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the feedback unit selects a feedback collection method suitable for that region. For example, if the user is traveling, the feedback unit selects a feedback collection method suitable for the travel destination. Furthermore, if the user is planning to move, the feedback unit selects a feedback collection method suitable for the new place of residence. This enables the feedback unit to select the optimal collection method based on geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0125] The feedback unit analyzes the user's social media activity and collects relevant feedback when collecting feedback. For example, the feedback unit analyzes the user's preferences from their social media activity and collects relevant feedback. For example, the feedback unit collects relevant feedback based on the user's social media friendships. The feedback unit also collects relevant feedback based on information shared on the user's social media. This enables the feedback unit to collect relevant feedback based on social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can delay suggestions until the user is relaxed. If the user is in a hurry, the suggestion unit can make suggestions quickly. Furthermore, if the user is excited, the suggestion unit can make visually stimulating suggestions. This allows the suggestion unit to adjust the timing of suggestions 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.

[0128] The suggestion function can analyze a user's past clothing history to understand their seasonal clothing trends. For example, it can suggest summer outfits based on the clothes a user has previously preferred to wear in the summer. It can also suggest winter outfits based on the clothes a user has preferred to wear in the winter. Furthermore, it can suggest outfits appropriate for spring and autumn. This allows the suggestion function to make suggestions based on seasonal clothing trends.

[0129] The suggestion unit can also estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can offer detailed suggestions. If the user is in a hurry, the suggestion unit can offer concise suggestions. Furthermore, if the user is excited, the suggestion unit can offer visually stimulating suggestions. This allows the suggestion unit to adjust the content of its suggestions 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.

[0130] The suggestion department can analyze users' clothing trends and make suggestions based on the latest fashion. For example, it can acquire data from the latest fashion magazines and online shops to suggest outfits based on trends. It can also suggest outfits that are in line with current trends, taking into account the user's preferences and past clothing history. Furthermore, it can make suggestions that reflect trends according to the season and events. This allows the suggestion department to make suggestions based on the latest fashion trends.

[0131] The suggestion function can also estimate the user's emotions and adjust the style of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can offer casual suggestions. If the user is in a hurry, the suggestion function can offer simple and functional suggestions. Furthermore, if the user is excited, the suggestion function can offer flashy and eye-catching suggestions. This allows the suggestion function to adjust the style of suggestions 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.

[0132] The proposal department can also make suggestions that take into account the durability of the user's clothing. For example, the proposal department can evaluate the durability of clothing that the user frequently wears and suggest long-lasting clothing. Furthermore, the proposal department can suggest clothing made from highly durable materials depending on the user's activity level and usage environment. In addition, the proposal department can suggest cost-effective clothing according to the user's budget. This allows the proposal department to make suggestions that take clothing durability into consideration.

[0133] The suggestion unit can also estimate the user's emotions and adjust the frequency of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can make suggestions more frequently. If the user is in a hurry, the suggestion unit can reduce the frequency of suggestions. Furthermore, if the user is excited, the suggestion unit can make visually stimulating suggestions. This allows the suggestion unit to adjust the frequency of suggestions 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.

[0134] The proposal department can also make suggestions that take into account the user's clothing maintenance. For example, the proposal department can suggest clothing that is easy for the user to clean and iron. Furthermore, the proposal department can suggest clothing that requires less maintenance, depending on the user's lifestyle. In addition, the proposal department can suggest clothing with low maintenance costs, depending on the user's budget. This allows the proposal department to make suggestions that consider clothing maintenance.

[0135] The suggestion unit can also estimate the user's emotions and adjust the level of detail in its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to adjust the level of detail in its suggestions 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.

[0136] The proposal department can also make suggestions that take into account the eco-friendliness of the user's clothing. For example, the proposal department can suggest clothing made from environmentally friendly materials. Furthermore, the proposal department can suggest clothing made from recyclable materials. In addition, the proposal department can suggest eco-friendly clothing tailored to the user's lifestyle. This allows the proposal department to make suggestions that consider eco-friendliness.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The reception desk enters the user's basic information. This information includes, for example, name, age, gender, and address. The reception desk can also save the user's entered information to a database and update it in real time. Step 2: The learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit. The learning unit analyzes the types and frequency of clothes the user has worn in the past to identify preferred colors, styles, brands, and designs. Step 3: The suggestion unit proposes the most suitable clothing based on weather forecasts and schedules, using information learned by the learning unit. The suggestion unit acquires weather forecast data, proposes clothing that matches the user's schedule, and makes suggestions considering the user's preferences and past clothing history. Step 4: The provision team provides the user with the clothing suggested by the proposal team. The provision team provides interactive advice using chat and avatars, and provides real-time feedback to the user.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, learning unit, suggestion unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the user's basic information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's preferences and past clothing history. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests the most suitable clothing based on the weather forecast and schedule. The provision unit is implemented by the output device 40 of the smart device 14, which provides interactive advice using chat or an avatar. 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] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[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 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.

[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 (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).

[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] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, learning unit, suggestion unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs the user's basic information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's preferences and past clothing history. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests the most suitable clothing based on the weather forecast and schedule. The provision unit is implemented by the speaker 240 of the smart glasses 214, which provides interactive advice using chat or an avatar. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, learning unit, suggestion unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's basic information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's preferences and past clothing history. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable clothing based on the weather forecast and schedule. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides interactive advice using chat or an avatar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the reception unit, learning unit, suggestion unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs the user's basic information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's preferences and past clothing history. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests the most suitable clothing based on the weather forecast and schedule. The provision unit is implemented by the speaker 240 of the robot 414, which provides interactive advice using chat or an avatar. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) A reception area where users enter their basic information, A learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit, Based on the information learned by the aforementioned learning unit, the proposal unit suggests the most appropriate clothing based on the weather forecast and schedule, The system comprises: a provisioning unit that provides the user with the clothing proposed by the proposal unit; A system characterized by the following features. (Note 2) The aforementioned proposal section is, It is equipped with an acquisition unit that obtains weather forecasts and schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose outfits that take into account how easily clothes get dirty. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features a dialogue section that provides interactive advice using chat and avatars. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It includes a feedback unit that collects user feedback and improves the accuracy of suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past basic information input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering basic information, the input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of basic information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering basic information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering basic information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the accuracy of the learning process is adjusted based on the level of detail in the user's clothing history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the training data is weighted based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the system improves its accuracy by referencing the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the accuracy of the proposal based on the weather forecast and the level of detail of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, the system prioritizes suggestions based on the user's past clothing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration to propose the most suitable clothing. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and suggest relevant clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how clothing is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the clothing, the system will refer to the user's past clothing provision history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of clothing items to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the system will consider the user's geographical location to provide the most suitable clothing. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant clothing. The system described in Appendix 1, characterized by the features described herein. (Note 30) The acquisition unit is, It estimates the user's emotions and adjusts the timing of obtaining weather forecasts and schedules based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The acquisition unit is, When obtaining weather forecasts or schedules, the system analyzes the user's past acquisition history to select the optimal acquisition method. The system described in Appendix 2, characterized by the features described herein. (Note 32) The acquisition unit is, When retrieving weather forecasts and schedules, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 33) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The acquisition unit is, When obtaining weather forecasts or schedules, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The acquisition unit is, When obtaining weather forecasts or schedules, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned dialogue unit, During conversations, the means of communication are customized based on the user's current life situation. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and conducts relevant dialogues. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned feedback unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned feedback unit is When collecting feedback, customize the collection method based on the user's current living situation. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned feedback unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned feedback unit is When collecting feedback, analyze users' social media activity and gather relevant feedback. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their basic information, A learning unit learns the user's preferences and past clothing history based on the information entered by the reception unit, Based on the information learned by the aforementioned learning unit, the proposal unit suggests the most appropriate clothing based on the weather forecast and schedule, The system comprises: a provisioning unit that provides the user with the clothing proposed by the proposal unit; A system characterized by the following features.

2. The aforementioned proposal section is, It is equipped with an acquisition unit that obtains weather forecasts and schedule information. The system according to feature 1.

3. The aforementioned proposal section is, We propose outfits that take into account how easily clothes get dirty. The system according to feature 1.

4. The aforementioned supply unit is, It features a dialogue section that provides interactive advice using chat and avatars. The system according to feature 1.

5. The aforementioned learning unit, It includes a feedback unit that collects user feedback and improves the accuracy of suggestions. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past basic information input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering basic information, the input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of basic information to be entered based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When entering basic information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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