System

Abstract

An object of a system according to an embodiment is to propose optimal coordination on the basis of a user's physique and preference.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a learning unit, a schedule learning unit, and a product proposal unit. The collection unit collects physique data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes coordination on the basis of an analysis result obtained by the analysis unit. The learning unit learns the user's preferences and purchase history. The schedule learning unit learns a meeting schedule of a user. A commodity proposal part picks up a commodity from an electronic commerce site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately proposed optimal outfits based on a user's physique and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal outfits based on the user's build and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a learning unit, a schedule learning unit, and a product suggestion unit. The collection unit collects physique data of the user. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The learning unit learns the user's preferences and purchase history. The schedule learning unit learns the user's planned meetings. The product suggestion unit picks out products from an e-commerce site. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable outfits based on the user's build and preferences. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The outfit suggestion system according to an embodiment of the present invention uses AI to suggest optimal outfits tailored to a user's physique, body type, and appearance. The outfit suggestion system collects data on the user's physique, body type, and appearance, and then analyzes the data using AI to suggest optimal outfits. This process also takes into account current trends, the user's preferences, and the user's usual price range and purchase frequency. The outfit suggestion system also learns when and who the user met and makes suggestions to help users avoid the mistake of wearing the same clothes as the last time they met. Furthermore, the outfit suggestion system selects optimal products from e-commerce sites and recommends them to the user. For example, the outfit suggestion system collects information provided by the user, such as photos, height, weight, and measurements. For example, the user uploads a photo of themselves to an app and enters their height, weight, and measurements. This information is then input into AI. The outfit suggestion system then analyzes the collected data using AI. The AI ​​then suggests optimal outfits based on the user's physique, body type, and appearance. For example, the AI ​​analyzes the user's photos and suggests clothing styles that suit them, taking into account factors such as face shape, skin color, and hairstyle. The system also takes into account current trends, the user's preferences, the price range and frequency of purchases that they usually make. This allows it to suggest the perfect outfit for the user. Furthermore, the outfit suggestion system learns when and with whom the user meets. For example, the user enters their schedule into the app and records who they are meeting. The AI ​​learns this information and makes suggestions to help users avoid the mistake of "wearing the same clothes as the last time they met." For example, the AI ​​checks the user's schedule and suggests a different outfit from the last time they met. Finally, the outfit suggestion system selects the best products from an e-commerce site and recommends them to the user. For example, the AI ​​considers the user's preferences, usual price range, and purchase frequency to select the best products from an e-commerce site and suggest them to the user. This allows users to easily find the perfect outfit for themselves.This allows the outfit suggestion system to suggest the best outfits to suit the user's build, shape, and appearance, and to suggest the best products based on their purchase history and plans. For example, users can easily find the best outfits to suit their build, shape, and appearance. The system also takes into account current trends, the user's preferences, and the price range and frequency of purchases that users usually make, making suggestions that are optimal for the user. Furthermore, the system learns when and with whom the user met, and makes suggestions to help users avoid the mistake of "wearing the same clothes as the last time they met." This not only allows users to easily find the best outfits for themselves, but also helps them avoid making outfit mistakes.

[0029] The outfit suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a learning unit, a schedule learning unit, and a product suggestion unit. The collection unit collects physique data of a user. The physique data of a user includes, but is not limited to, height, weight, and three sizes. The collection unit collects, for example, photos provided by the user, as well as information on height, weight, and three sizes. The collection unit also allows the user to upload their own photos to the app and input their height, weight, and three sizes. For example, the collection unit collects full-body photos and facial photos provided by the user and inputs them into AI. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's photos and suggests clothing styles that suit them, taking into consideration their face shape, skin color, hairstyle, and other factors. For example, the analysis unit uses image analysis technology to analyze the user's face shape and classify them as round, oval, etc. The analysis unit can also analyze skin color and classify them based on hue and brightness. The analysis unit can also analyze hairstyles and classify them based on length and style. The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal clothing styles based on, for example, the user's face shape, skin color, and hairstyle. The suggestion unit, for example, selects color combinations and styles and suggests them to the user. The suggestion unit can also take into account current trends, the user's preferences, the user's usual price range, and purchase frequency. The learning unit learns the user's preferences and purchase history. The learning unit, for example, learns based on the user's past purchase history and ratings. The learning unit, for example, collects data on products the user has purchased in the past and inputs it into the AI. The learning unit can also collect user rating data and input it into the AI. The schedule learning unit learns who the user has met. For example, the schedule learning unit learns the schedule entered by the user into the app and suggests outfits that are different from the last time they met. For example, the schedule learning unit records the user's schedule entered into the app and who they will be meeting with. The schedule learning unit also learns this information and makes suggestions to avoid the mistake of "wearing the same clothes as the last time they met." The product proposal department selects the most suitable products from e-commerce sites.The product suggestion unit selects optimal products from an e-commerce site and suggests them to the user, taking into consideration, for example, the user's preferences, usual price range, and purchase frequency. The product suggestion unit, for example, uses AI to select optimal products based on the user's preferences and purchase history. The product suggestion unit can also suggest products at optimal times, taking into consideration the user's purchase frequency. As a result, the outfit suggestion system according to the embodiment can suggest optimal clothing coordinations that match the user's physique, figure, and appearance, and suggest optimal products based on the user's purchase history and plans.

[0030] The collection unit can collect information such as a photo, height, weight, and three sizes provided by the user. The collection unit, for example, collects full-body photos and face photos provided by the user. For example, the collection unit allows the user to upload their own photo to the app and input their height, weight, and three sizes. The collection unit can also collect information such as height, weight, and three sizes provided by the user. For example, the collection unit inputs the height, weight, and three sizes data input by the user into AI. This allows for detailed collection of the user's physique data, making it possible to propose more accurate outfits. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input information such as a photo, height, weight, and three sizes provided by the user into AI, and the AI ​​can analyze the data.

[0031] The analysis unit can analyze a user's photo and suggest a clothing style that suits the user based on their face shape, skin color, and hairstyle. For example, the analysis unit can analyze a user's photo and suggest a clothing style that suits the user based on their face shape, skin color, hairstyle, etc. For example, the analysis unit can analyze the user's face shape using image analysis technology and classify the face shape as round, oval, etc. The analysis unit can also analyze skin color and classify the face shape based on hue and brightness. Furthermore, the analysis unit can analyze hairstyle and classify the face shape based on length and style. This makes it possible to suggest an optimal clothing style based on the user's appearance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input a user's photo into AI, which can analyze the face shape, skin color, and hairstyle to suggest an optimal clothing style.

[0032] The learning unit can learn based on the user's past purchase history and ratings. The learning unit learns, for example, based on the user's past purchase history and ratings. For example, the learning unit collects data on products purchased by the user in the past and inputs it into the AI. The learning unit can also collect user rating data and input it into the AI. This enables more appropriate suggestions to be made by learning based on the user's purchase history and ratings. Some or all of the above-described processing in the learning unit may be performed using, or without, AI, for example. For example, the learning unit can input the user's past purchase history and rating data into the AI, which then analyzes the data and learns.

[0033] The schedule learning unit learns the schedule entered by the user into the app and can suggest outfits that are different from the outfits worn the last time the user met. For example, the schedule learning unit learns the schedule entered by the user into the app and suggests outfits that are different from the outfits worn the last time the user met. For example, the schedule learning unit records the user's schedule entered into the app and who the user will be meeting. The schedule learning unit also allows the AI ​​to learn this information and make suggestions to avoid the user making the mistake of "wearing the same clothes as the last time the user met." This makes it possible to avoid the user making the mistake of wearing the same clothes as the last time the user met. Some or all of the above-described processing in the schedule learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule learning unit can input the schedule entered by the user into the app into AI, and the AI ​​can analyze the data and learn.

[0034] The product proposal unit can select products from an e-commerce site based on the user's preferences, usual price range, and purchase frequency, and propose them to the user. The product proposal unit selects optimal products from the e-commerce site, taking into account the user's preferences, usual price range, and purchase frequency, and proposes them to the user. For example, the product proposal unit uses AI to select optimal products based on the user's preferences and purchase history. The product proposal unit can also propose products at optimal times, taking into account the user's purchase frequency. This makes it possible to propose optimal products based on the user's preferences and purchase history. Some or all of the above-described processing in the product proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the product proposal unit can input the user's preferences and purchase history into AI, which analyzes the data and selects optimal products.

[0035] The collection unit can analyze the user's past data provision history and select a collection method. The collection unit, for example, analyzes the user's past data provision history and selects the optimal collection method. For example, the collection unit selects the optimal collection method based on the format of data previously provided by the user (photos, text, etc.). The collection unit can also analyze the frequency of data previously provided by the user and collect data at an appropriate time. The collection unit can also determine whether detailed data collection is necessary based on the accuracy of data previously provided by the user. This enables efficient data collection by selecting the optimal collection method based on the user's past data provision history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past data provision history into AI, which then selects the optimal collection method.

[0036] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit filters data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit collects appropriate data based on the user's current living situation (work, vacation, etc.). The collection unit can also prioritize collecting relevant data based on the user's areas of interest (fashion, sports, etc.). The collection unit can also adjust the timing of data collection to match the user's lifestyle (morning type, night owl, etc.). This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation and areas of interest into AI, which then filters the data.

[0037] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit prioritizes voice data collection. Furthermore, if the user prefers text input, the collection unit can also collect text data. Furthermore, if the user prefers image input, the collection unit can also collect data using photos or images. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into AI, which can select the optimal collection means.

[0038] The collection unit can collect highly relevant data taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data taking into account the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home. This enables more appropriate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can collect highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect physique data based on photos the user shared on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables more appropriate data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into AI, which can collect related data.

[0040] The collection unit can adjust the collection method based on the user's past feedback when collecting data. For example, the collection unit improves the collection method based on the user's past feedback when collecting data. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also adjust the timing and method of data collection by reflecting the user's feedback. This enables more appropriate data collection by customizing the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI, which can adjust the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the data. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. The analysis unit can also perform a particularly detailed analysis on data that is of great interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms based on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a body type analysis algorithm to physique data. The analysis unit can also apply a face recognition algorithm to appearance data. The analysis unit can also apply a purchasing behavior analysis algorithm to purchase history data. In this way, by applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can apply different analysis algorithms.

[0043] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust parameters for improving the accuracy of the analysis based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority during analysis, taking into account the time of data submission. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also prioritize analysis of data from a time specified by the user. The analysis unit can also determine the analysis priority by referring to past data. In this way, by determining the analysis priority based on the time of data submission, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis during analysis, taking into account the relevance of the data. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of data that is of great interest to the user. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis during analysis, taking into account the user's level of expertise. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI, which can then adjust the use of technical terms in the analysis.

[0047] The suggestion unit can adjust the level of detail of the suggestion taking into account the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important product. The suggestion unit can also make a basic suggestion for a general product. The suggestion unit can also make a particularly detailed suggestion for a product in which the user is highly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, it is possible to provide a more appropriate suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the importance of the product to AI, which can then adjust the level of detail of the suggestion.

[0048] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit can apply a fashion suggestion algorithm to fashion products. The suggestion unit can also apply an electronic device suggestion algorithm to electronic devices. The suggestion unit can also apply a food suggestion algorithm to food. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the product category into AI, and the AI ​​can apply different suggestion algorithms.

[0049] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also adjust parameters for improving the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into AI, which can improve the accuracy of the suggestion.

[0050] The suggestion unit can determine the priority of suggestions taking into account the time of product submission when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes the most recent products. The suggestion unit can also prioritize products from a time specified by the user. The suggestion unit can also determine the priority of suggestions by referring to past suggestion history. In this way, more appropriate suggestions can be provided by determining the priority of suggestions based on the time of product submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of product submission into AI, which can then determine the priority of suggestions.

[0051] The suggestion unit can adjust the order of suggestions taking into account the relevance of the products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also prioritize suggesting products that the user is highly interested in. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. In this way, by adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products into AI, which can adjust the order of suggestions.

[0052] The suggestion unit may adjust the use of technical terminology in the proposal, taking into account the user's level of expertise. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit may make a concise and easy-to-understand proposal. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise into AI, which may then adjust the use of technical terminology in the proposal.

[0053] The learning unit can optimize the learning algorithm based on past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also adjust parameters for improving learning accuracy from the user's past learning data. The learning unit can also improve learning accuracy by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, which can optimize the learning algorithm.

[0054] The learning unit can analyze fluctuations in the user's purchase history during learning and adjust the update frequency of the learning data. For example, the learning unit can analyze fluctuations in the user's purchase history during learning and adjust the update frequency of the learning data. For example, if the user's purchase history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's purchase history is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuations in the user's purchase history and determine an optimal update frequency. In this way, by analyzing fluctuations in the user's purchase history, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input fluctuations in the user's purchase history into AI, which can adjust the update frequency of the learning data.

[0055] The learning unit can weight the learning data during learning, taking into account the time when the purchase history was submitted. For example, the learning unit weights the learning data based on the time when the purchase history was submitted during learning. For example, the learning unit weights the learning data by prioritizing the most recent purchase history. The learning unit can also weight the learning data by prioritizing purchase history from a time specified by the user. The learning unit can also weight the learning data by referring to past purchase history. In this way, weighting the learning data based on the time when the purchase history was submitted enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the purchase history was submitted into AI, and the AI ​​can weight the learning data.

[0056] The learning unit can adjust the learning algorithm based on user feedback during learning. For example, the learning unit adjusts the learning algorithm based on user feedback during learning. For example, the learning unit optimizes the learning algorithm based on user feedback. The learning unit can also adjust parameters for improving learning accuracy based on user feedback. The learning unit can also improve learning accuracy by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be optimized and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input user feedback into AI, which can adjust the learning algorithm.

[0057] The scheduled learning unit can select an optimal learning method based on the user's past schedule history during scheduled learning. For example, during scheduled learning, the scheduled learning unit selects an optimal learning method by referring to the user's past schedule history. For example, the scheduled learning unit selects an optimal learning method based on the user's past schedule history. The scheduled learning unit can also adjust parameters for improving learning accuracy based on the user's past schedule history. The scheduled learning unit can also improve learning accuracy by referring to the user's past schedule history. In this way, by referring to the user's past schedule history, an optimal learning method can be selected and learning accuracy can be improved. Some or all of the above-described processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's past schedule history into AI, which can select an optimal learning method.

[0058] The scheduled learning unit can adjust the learning means during scheduled learning, taking into account the user's current living situation. The scheduled learning unit, for example, customizes the learning means based on the user's current living situation during scheduled learning. For example, if the user is at work, the scheduled learning unit can provide work-related learning means. Also, if the user is on vacation, the scheduled learning unit can provide relaxing learning means. The scheduled learning unit can also customize the optimal learning means according to the user's living situation. This enables more appropriate learning by customizing the learning means based on the user's current living situation. Some or all of the above-described processing in the scheduled learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's current living situation into AI, which can then adjust the learning means.

[0059] The scheduled learning unit can improve the learning method based on user feedback during scheduled learning. The scheduled learning unit improves the learning method based on user feedback during scheduled learning, for example. For example, the scheduled learning unit optimizes the learning method based on user feedback. The scheduled learning unit can also adjust parameters for improving learning accuracy based on user feedback. The scheduled learning unit can also improve learning accuracy by referring to user feedback. In this way, by reflecting user feedback, the learning method can be improved and learning accuracy can be improved. Some or all of the above-mentioned processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input user feedback into AI, which can improve the learning method.

[0060] The scheduled learning unit can select the optimal study method during scheduled learning by taking into account the user's geographical location information. For example, during scheduled learning, the scheduled learning unit selects the optimal study method by taking into account the user's geographical location information. For example, if the user is in a specific area, the scheduled learning unit selects a study method related to that area. Furthermore, if the user is traveling, the scheduled learning unit can select a study method related to the travel destination. Furthermore, if the user is at home, the scheduled learning unit can select a study method related to the user's home. In this way, selecting the optimal study method based on the user's geographical location information enables more appropriate study. Some or all of the above-described processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's geographical location information into AI, which can select the optimal study method.

[0061] The scheduled learning unit can analyze the user's social media activities and suggest study methods during scheduled learning. For example, the scheduled learning unit can analyze the user's social media activities and suggest related study methods during scheduled learning. For example, the scheduled learning unit can suggest study methods based on information shared by the user on social media. The scheduled learning unit can also analyze the content of the user's social media posts and suggest related study methods. The scheduled learning unit can also suggest related study methods by referring to the activities of the user's friends on social media. This enables more appropriate study by suggesting study methods based on the user's social media activities. Some or all of the above-mentioned processing in the scheduled learning unit can be performed, for example, using AI, or can be performed without using AI. For example, the scheduled learning unit can input the user's social media activities into AI, which can then suggest study methods.

[0062] The scheduled learning unit can adjust the learning method based on the user's past feedback during scheduled learning. The scheduled learning unit, for example, improves the learning method based on the user's past feedback during scheduled learning. For example, the scheduled learning unit improves the learning method based on the user's past feedback. The scheduled learning unit can also select an optimal learning method based on the user's past feedback. The scheduled learning unit can also customize the learning method by reflecting the user's feedback. This enables more appropriate learning by customizing the learning method based on the user's past feedback. Some or all of the above-mentioned processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's past feedback into AI, which can adjust the learning method.

[0063] The product proposal unit can select an optimal proposal method based on the user's past consumption behavior when proposing a product. For example, when proposing a product, the product proposal unit analyzes the user's past consumption behavior and selects an optimal proposal method. For example, the product proposal unit selects an optimal proposal method based on the user's past consumption behavior. The product proposal unit can also adjust parameters to improve the accuracy of proposals based on the user's past consumption behavior. The product proposal unit can also improve the accuracy of proposals by referring to the user's past consumption behavior. In this way, by analyzing the user's past consumption behavior, an optimal proposal method can be selected and the accuracy of proposals can be improved. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's past consumption behavior into AI, which can select an optimal proposal method.

[0064] The product proposal unit can adjust the proposal means taking into account the user's current living situation when proposing a product. For example, the product proposal unit customizes the proposal means based on the user's current living situation when proposing a product. For example, if the user is at work, the product proposal unit can propose work-related products. Also, if the user is on vacation, the product proposal unit can propose relaxing products. The product proposal unit can also customize optimal product proposals according to the user's living situation. This enables more appropriate proposals to be made by customizing the proposal means based on the user's current living situation. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's current living situation into AI, which can then adjust the proposal means.

[0065] The product proposal unit can improve the proposal method based on user feedback when proposing a product. For example, the product proposal unit improves the proposal method based on user feedback when proposing a product. For example, the product proposal unit optimizes the proposal method based on user feedback. The product proposal unit can also adjust parameters for improving the accuracy of proposals based on user feedback. The product proposal unit can also improve the accuracy of proposals by referring to user feedback. In this way, by reflecting user feedback, the proposal method can be improved and the accuracy of proposals can be improved. Some or all of the above-mentioned processing in the product proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the product proposal unit can input user feedback into AI, which can improve the proposal method.

[0066] The product proposal unit can select the optimal proposal method by taking into consideration the user's geographical location information when proposing products. For example, the product proposal unit selects the optimal proposal method by taking into consideration the user's geographical location information when proposing products. For example, when the user is in a specific area, the product proposal unit can make product proposals related to that area. Furthermore, when the user is traveling, the product proposal unit can make product proposals related to the travel destination. Furthermore, when the user is at home, the product proposal unit can make product proposals related to the user's home. This enables more appropriate proposals by selecting the optimal proposal method based on the user's geographical location information. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's geographical location information into AI, which can select the optimal proposal method.

[0067] The product proposal unit can analyze the user's social media activity and propose proposal means when proposing a product. For example, the product proposal unit analyzes the user's social media activity and proposes related proposal means when proposing a product. For example, the product proposal unit makes product proposals based on information shared by the user on social media. The product proposal unit can also analyze the content of the user's social media posts and propose related products. The product proposal unit can also make related product proposals by referring to the activities of the user's friends on social media. This enables more appropriate proposals by proposing proposal means based on the user's social media activity. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's social media activity into AI, which then proposes proposal means.

[0068] The product proposal unit can adjust the proposal method based on the user's past feedback when proposing a product. For example, the product proposal unit improves the proposal method based on the user's past feedback when proposing a product. For example, the product proposal unit improves the proposal method based on the user's past feedback. The product proposal unit can also select an optimal proposal method based on the user's past feedback. The product proposal unit can also customize the proposal method by reflecting the user's feedback. In this way, customizing the proposal method based on the user's past feedback enables more appropriate proposals. Some or all of the above-described processing in the product proposal unit may be performed using, or without, AI, for example. For example, the product proposal unit can input the user's past feedback into AI, which can then adjust the proposal method.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The outfit suggestion system can also monitor the user's health condition and suggest outfits based on that condition. For example, if the user is not getting enough exercise, it can suggest clothes that are easy to exercise in. If the user has a specific allergy, it can suggest clothes made from materials that are suitable for that allergy. Furthermore, if the user has set a specific health goal, it can suggest outfits that match that goal. This makes it possible to suggest optimal outfits based on the user's health condition.

[0071] The outfit suggestion system can also suggest outfits based on the user's lifestyle. For example, if the user likes outdoor activities, it can suggest clothes suitable for outdoor activities. Also, if the user wishes to wear the equipment in a business setting, it can suggest clothes suitable for business. Furthermore, if the user has a particular hobby, it can suggest outfits that match that hobby. This makes it possible to suggest optimal outfits based on the user's lifestyle.

[0072] The coordination suggestion system can analyze the user's past coordination history and suggest new coordinations based on past successes and failures. For example, new suggestions can be made based on coordinations that the user has received high ratings for in the past. It can also suggest coordinations that the user has not received favorable reviews for in the past. Furthermore, it can analyze trends based on the user's past coordination history and make suggestions that match the latest trends. This makes it possible to suggest optimal coordinations based on the user's past history.

[0073] The outfit suggestion system can also suggest outfits that match the local climate and culture, taking into account the user's geographical location information. For example, if the user lives in a cold region, it can suggest warm clothing. If the user lives in a tropical region, it can suggest cool clothing. Furthermore, if the user is attending a specific cultural event, it can suggest outfits that match that culture. This makes it possible to suggest optimal outfits based on the user's geographical location information.

[0074] The outfit suggestion system can analyze a user's social media activity and suggest outfits based on the influencers and brands the user follows. For example, if a user follows a specific influencer, the system can suggest outfits based on that influencer's style. Also, if a user likes a specific brand, the system can suggest outfits using products from that brand. Furthermore, the system can analyze a user's social media activity and suggest outfits that match the latest trends. This makes it possible to suggest optimal outfits based on the user's social media activity.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects the user's physique data. The user's physique data includes, for example, height, weight, and three sizes. The collection unit collects photos provided by the user and information on height, weight, and three sizes. Users can also upload their own photos to the app and enter their height, weight, and three sizes. For example, the collection unit collects full-body photos and face photos provided by the user and inputs them into the AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes a photo of the user and suggests clothing styles that suit them, taking into account factors such as face shape, skin color, and hairstyle. Image analysis technology is used to analyze the user's face shape and classify it as round, oval, etc. Skin color can also be analyzed and classified based on hue and brightness. Hairstyles can also be analyzed and classified based on length and style. Step 3: The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal clothing styles based on the user's face shape, skin color, and hairstyle, for example. It selects color combinations and styles and suggests them to the user. It can also take into account current trends, the user's preferences, the price range and frequency of purchases that they usually make. Step 4: The learning unit learns the user's preferences and purchase history. For example, the learning unit learns based on the user's past purchase history and ratings. Data on products purchased by the user in the past and evaluation data are collected and input into the AI. Step 5: The schedule learning unit learns who the user has met. For example, the schedule learning unit learns the schedule the user has entered into the app and suggests outfits that are different from the last time they met. The user enters their schedule into the app and records who they will be meeting. The AI ​​learns this information and makes suggestions to avoid the mistake of "wearing the same clothes as the last time they met." Step 6: The product suggestion unit selects the most suitable products from the e-commerce site. The product suggestion unit selects the most suitable products from the e-commerce site and suggests them to the user, taking into account, for example, the user's preferences, usual price range, and purchase frequency. AI selects the most suitable products based on the user's preferences and purchase history. It can also suggest products at the optimal time, taking into account the user's purchase frequency.

[0077] (Example 2) The outfit suggestion system according to an embodiment of the present invention uses AI to suggest optimal outfits tailored to a user's physique, body type, and appearance. The outfit suggestion system collects data on the user's physique, body type, and appearance, and then analyzes the data using AI to suggest optimal outfits. This process also takes into account current trends, the user's preferences, and the user's usual price range and purchase frequency. The outfit suggestion system also learns when and who the user met and makes suggestions to help users avoid the mistake of wearing the same clothes as the last time they met. Furthermore, the outfit suggestion system selects optimal products from e-commerce sites and recommends them to the user. For example, the outfit suggestion system collects information provided by the user, such as photos, height, weight, and measurements. For example, the user uploads a photo of themselves to an app and enters their height, weight, and measurements. This information is then input into AI. The outfit suggestion system then analyzes the collected data using AI. The AI ​​then suggests optimal outfits based on the user's physique, body type, and appearance. For example, the AI ​​analyzes the user's photos and suggests clothing styles that suit them, taking into account factors such as face shape, skin color, and hairstyle. The system also takes into account current trends, the user's preferences, the price range and frequency of purchases that they usually make. This allows it to suggest the perfect outfit for the user. Furthermore, the outfit suggestion system learns when and with whom the user meets. For example, the user enters their schedule into the app and records who they are meeting. The AI ​​learns this information and makes suggestions to help users avoid the mistake of "wearing the same clothes as the last time they met." For example, the AI ​​checks the user's schedule and suggests a different outfit from the last time they met. Finally, the outfit suggestion system selects the best products from an e-commerce site and recommends them to the user. For example, the AI ​​considers the user's preferences, usual price range, and purchase frequency to select the best products from an e-commerce site and suggest them to the user. This allows users to easily find the perfect outfit for themselves.This allows the outfit suggestion system to suggest the best outfits to suit the user's build, shape, and appearance, and to suggest the best products based on their purchase history and plans. For example, users can easily find the best outfits to suit their build, shape, and appearance. The system also takes into account current trends, the user's preferences, and the price range and frequency of purchases that users usually make, making suggestions that are optimal for the user. Furthermore, the system learns when and with whom the user met, and makes suggestions to help users avoid the mistake of "wearing the same clothes as the last time they met." This not only allows users to easily find the best outfits for themselves, but also helps them avoid making outfit mistakes.

[0078] The outfit suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a learning unit, a schedule learning unit, and a product suggestion unit. The collection unit collects physique data of a user. The physique data of a user includes, but is not limited to, height, weight, and three sizes. The collection unit collects, for example, photos provided by the user, as well as information on height, weight, and three sizes. The collection unit also allows the user to upload their own photos to the app and input their height, weight, and three sizes. For example, the collection unit collects full-body photos and facial photos provided by the user and inputs them into AI. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's photos and suggests clothing styles that suit them, taking into consideration their face shape, skin color, hairstyle, and other factors. For example, the analysis unit uses image analysis technology to analyze the user's face shape and classify them as round, oval, etc. The analysis unit can also analyze skin color and classify them based on hue and brightness. The analysis unit can also analyze hairstyles and classify them based on length and style. The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal clothing styles based on, for example, the user's face shape, skin color, and hairstyle. The suggestion unit, for example, selects color combinations and styles and suggests them to the user. The suggestion unit can also take into account current trends, the user's preferences, the user's usual price range, and purchase frequency. The learning unit learns the user's preferences and purchase history. The learning unit, for example, learns based on the user's past purchase history and ratings. The learning unit, for example, collects data on products the user has purchased in the past and inputs it into the AI. The learning unit can also collect user rating data and input it into the AI. The schedule learning unit learns who the user has met. For example, the schedule learning unit learns the schedule entered by the user into the app and suggests outfits that are different from the last time they met. For example, the schedule learning unit records the user's schedule entered into the app and who they will be meeting with. The schedule learning unit also learns this information and makes suggestions to avoid the mistake of "wearing the same clothes as the last time they met." The product proposal department selects the most suitable products from e-commerce sites.The product suggestion unit selects optimal products from an e-commerce site and suggests them to the user, taking into consideration, for example, the user's preferences, usual price range, and purchase frequency. The product suggestion unit, for example, uses AI to select optimal products based on the user's preferences and purchase history. The product suggestion unit can also suggest products at optimal times, taking into consideration the user's purchase frequency. As a result, the outfit suggestion system according to the embodiment can suggest optimal clothing coordinations that match the user's physique, figure, and appearance, and suggest optimal products based on the user's purchase history and plans.

[0079] The collection unit can collect information such as a photo, height, weight, and three sizes provided by the user. The collection unit, for example, collects full-body photos and face photos provided by the user. For example, the collection unit allows the user to upload their own photo to the app and input their height, weight, and three sizes. The collection unit can also collect information such as height, weight, and three sizes provided by the user. For example, the collection unit inputs the height, weight, and three sizes data input by the user into AI. This allows for detailed collection of the user's physique data, making it possible to propose more accurate outfits. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input information such as a photo, height, weight, and three sizes provided by the user into AI, and the AI ​​can analyze the data.

[0080] The analysis unit can analyze a user's photo and suggest a clothing style that suits the user based on their face shape, skin color, and hairstyle. For example, the analysis unit can analyze a user's photo and suggest a clothing style that suits the user based on their face shape, skin color, hairstyle, etc. For example, the analysis unit can analyze the user's face shape using image analysis technology and classify the face shape as round, oval, etc. The analysis unit can also analyze skin color and classify the face shape based on hue and brightness. Furthermore, the analysis unit can analyze hairstyle and classify the face shape based on length and style. This makes it possible to suggest an optimal clothing style based on the user's appearance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input a user's photo into AI, which can analyze the face shape, skin color, and hairstyle to suggest an optimal clothing style.

[0081] The learning unit can learn based on the user's past purchase history and ratings. The learning unit learns, for example, based on the user's past purchase history and ratings. For example, the learning unit collects data on products purchased by the user in the past and inputs it into the AI. The learning unit can also collect user rating data and input it into the AI. This enables more appropriate suggestions to be made by learning based on the user's purchase history and ratings. Some or all of the above-described processing in the learning unit may be performed using, or without, AI, for example. For example, the learning unit can input the user's past purchase history and rating data into the AI, which then analyzes the data and learns.

[0082] The schedule learning unit learns the schedule entered by the user into the app and can suggest outfits that are different from the outfits worn the last time the user met. For example, the schedule learning unit learns the schedule entered by the user into the app and suggests outfits that are different from the outfits worn the last time the user met. For example, the schedule learning unit records the user's schedule entered into the app and who the user will be meeting. The schedule learning unit also allows the AI ​​to learn this information and make suggestions to avoid the user making the mistake of "wearing the same clothes as the last time the user met." This makes it possible to avoid the user making the mistake of wearing the same clothes as the last time the user met. Some or all of the above-described processing in the schedule learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule learning unit can input the schedule entered by the user into the app into AI, and the AI ​​can analyze the data and learn.

[0083] The product proposal unit can select products from an e-commerce site based on the user's preferences, usual price range, and purchase frequency, and propose them to the user. The product proposal unit selects optimal products from the e-commerce site, taking into account the user's preferences, usual price range, and purchase frequency, and proposes them to the user. For example, the product proposal unit uses AI to select optimal products based on the user's preferences and purchase history. The product proposal unit can also propose products at optimal times, taking into account the user's purchase frequency. This makes it possible to propose optimal products based on the user's preferences and purchase history. Some or all of the above-described processing in the product proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the product proposal unit can input the user's preferences and purchase history into AI, which analyzes the data and selects optimal products.

[0084] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the user's emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user's emotions. For example, if the user is relaxed, the collection unit selects the timing to collect photos and physique data. If the user is feeling stressed, the collection unit can postpone data collection and collect data when the user is calm. If the user is in a hurry, the collection unit can quickly collect data using simple questions. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotions into an AI, which can then adjust the timing of data collection.

[0085] The collection unit can analyze the user's past data provision history and select a collection method. The collection unit, for example, analyzes the user's past data provision history and selects the optimal collection method. For example, the collection unit selects the optimal collection method based on the format of data previously provided by the user (photos, text, etc.). The collection unit can also analyze the frequency of data previously provided by the user and collect data at an appropriate time. The collection unit can also determine whether detailed data collection is necessary based on the accuracy of data previously provided by the user. This enables efficient data collection by selecting the optimal collection method based on the user's past data provision history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past data provision history into AI, which then selects the optimal collection method.

[0086] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit filters data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit collects appropriate data based on the user's current living situation (work, vacation, etc.). The collection unit can also prioritize collecting relevant data based on the user's areas of interest (fashion, sports, etc.). The collection unit can also adjust the timing of data collection to match the user's lifestyle (morning type, night owl, etc.). This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation and areas of interest into AI, which then filters the data.

[0087] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit prioritizes voice data collection. Furthermore, if the user prefers text input, the collection unit can also collect text data. Furthermore, if the user prefers image input, the collection unit can also collect data using photos or images. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into AI, which can select the optimal collection means.

[0088] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. For example, the collection unit can prioritize detailed data collection when the user is relaxed. The collection unit can also prioritize basic data collection when the user is stressed. The collection unit can also prioritize the most important data collection when the user is in a hurry. This enables more appropriate data collection by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotions into an AI, which can then determine the priority of data.

[0089] The collection unit can collect highly relevant data taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data taking into account the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home. This enables more appropriate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can collect highly relevant data.

[0090] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect physique data based on photos the user shared on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables more appropriate data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into AI, which can collect related data.

[0091] The collection unit can adjust the collection method based on the user's past feedback when collecting data. For example, the collection unit improves the collection method based on the user's past feedback when collecting data. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. The collection unit can also adjust the timing and method of data collection by reflecting the user's feedback. This enables more appropriate data collection by customizing the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI, which can adjust the collection method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. The analysis unit can also provide analysis results that focus on the main points when the user is in a hurry. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotions into an AI, which can then adjust the presentation method of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the data. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a basic analysis on general data. The analysis unit can also perform a particularly detailed analysis on data that is of great interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms based on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a body type analysis algorithm to physique data. The analysis unit can also apply a face recognition algorithm to appearance data. The analysis unit can also apply a purchasing behavior analysis algorithm to purchase history data. In this way, by applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can apply different analysis algorithms.

[0095] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust parameters for improving the accuracy of the analysis based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. The analysis unit can also perform a short analysis that focuses on the main points when the user is in a hurry. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotions into an AI, which can then adjust the length of the analysis.

[0097] The analysis unit can determine the analysis priority during analysis, taking into account the time of data submission. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also prioritize analysis of data from a time specified by the user. The analysis unit can also determine the analysis priority by referring to past data. In this way, by determining the analysis priority based on the time of data submission, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can determine the analysis priority.

[0098] The analysis unit can adjust the order of analysis during analysis, taking into account the relevance of the data. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of data that is of great interest to the user. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0099] The analysis unit can adjust the use of technical terms in the analysis during analysis, taking into account the user's level of expertise. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI, which can then adjust the use of technical terms in the analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the user's emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is stressed. Furthermore, the suggestion unit can provide suggestions that focus on the main points when the user is in a hurry. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotions into an AI, which can then adjust the way the suggestions are expressed.

[0101] The suggestion unit can adjust the level of detail of the suggestion taking into account the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important product. The suggestion unit can also make a basic suggestion for a general product. The suggestion unit can also make a particularly detailed suggestion for a product in which the user is highly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, it is possible to provide a more appropriate suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the importance of the product to AI, which can then adjust the level of detail of the suggestion.

[0102] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit can apply a fashion suggestion algorithm to fashion products. The suggestion unit can also apply an electronic device suggestion algorithm to electronic devices. The suggestion unit can also apply a food suggestion algorithm to food. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the product category into AI, and the AI ​​can apply different suggestion algorithms.

[0103] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also adjust parameters for improving the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into AI, which can improve the accuracy of the suggestion.

[0104] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is stressed. Furthermore, the suggestion unit can provide short suggestions that focus on the main points when the user is in a hurry. This allows the length of the suggestion to be adjusted according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion into an AI, which can adjust the length of the suggestion.

[0105] The suggestion unit can determine the priority of suggestions taking into account the time of product submission when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes the most recent products. The suggestion unit can also prioritize products from a time specified by the user. The suggestion unit can also determine the priority of suggestions by referring to past suggestion history. In this way, more appropriate suggestions can be provided by determining the priority of suggestions based on the time of product submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of product submission into AI, which can then determine the priority of suggestions.

[0106] The suggestion unit can adjust the order of suggestions taking into account the relevance of the products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also prioritize suggesting products that the user is highly interested in. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. In this way, by adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products into AI, which can adjust the order of suggestions.

[0107] The suggestion unit may adjust the use of technical terminology in the proposal, taking into account the user's level of expertise. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit may make a concise and easy-to-understand proposal. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise into AI, which may then adjust the use of technical terminology in the proposal.

[0108] The learning unit can estimate the user's emotions and select training data based on the user's emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user's emotions. For example, the learning unit selects detailed training data when the user is relaxed. The learning unit can also select basic training data when the user is stressed. The learning unit can also select training data that focuses on the main points when the user is in a hurry. This enables more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's emotions into an AI, which then selects the training data.

[0109] The learning unit can optimize the learning algorithm based on past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also adjust parameters for improving learning accuracy from the user's past learning data. The learning unit can also improve learning accuracy by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, which can optimize the learning algorithm.

[0110] The learning unit can analyze fluctuations in the user's purchase history during learning and adjust the update frequency of the learning data. For example, the learning unit can analyze fluctuations in the user's purchase history during learning and adjust the update frequency of the learning data. For example, if the user's purchase history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's purchase history is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuations in the user's purchase history and determine an optimal update frequency. In this way, by analyzing fluctuations in the user's purchase history, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input fluctuations in the user's purchase history into AI, which can adjust the update frequency of the learning data.

[0111] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the user's emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user's emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is stressed. The learning unit can also adjust the frequency of learning when the user is in a hurry. This enables more appropriate learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's emotions into an AI, which can then adjust the frequency of learning.

[0112] The learning unit can weight the learning data during learning, taking into account the time when the purchase history was submitted. For example, the learning unit weights the learning data based on the time when the purchase history was submitted during learning. For example, the learning unit weights the learning data by prioritizing the most recent purchase history. The learning unit can also weight the learning data by prioritizing purchase history from a time specified by the user. The learning unit can also weight the learning data by referring to past purchase history. In this way, weighting the learning data based on the time when the purchase history was submitted enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the purchase history was submitted into AI, and the AI ​​can weight the learning data.

[0113] The learning unit can adjust the learning algorithm based on user feedback during learning. For example, the learning unit adjusts the learning algorithm based on user feedback during learning. For example, the learning unit optimizes the learning algorithm based on user feedback. The learning unit can also adjust parameters for improving learning accuracy based on user feedback. The learning unit can also improve learning accuracy by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be optimized and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input user feedback into AI, which can adjust the learning algorithm.

[0114] The scheduled learning unit can estimate the user's emotions and adjust the scheduled learning method based on the user's emotions. The scheduled learning unit, for example, estimates the user's emotions and adjusts the scheduled learning method based on the estimated user's emotions. For example, the scheduled learning unit can perform detailed scheduled learning when the user is relaxed. The scheduled learning unit can also perform concise scheduled learning when the user is stressed. The scheduled learning unit can also perform scheduled learning that focuses on the main points when the user is in a hurry. This allows for more appropriate learning by adjusting the scheduled learning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the scheduled learning unit can be performed using an AI, for example, or without an AI. For example, the scheduled learning unit can input the user's emotions into an AI, which can then adjust the scheduled learning method.

[0115] The scheduled learning unit can select an optimal learning method based on the user's past schedule history during scheduled learning. For example, during scheduled learning, the scheduled learning unit selects an optimal learning method by referring to the user's past schedule history. For example, the scheduled learning unit selects an optimal learning method based on the user's past schedule history. The scheduled learning unit can also adjust parameters for improving learning accuracy based on the user's past schedule history. The scheduled learning unit can also improve learning accuracy by referring to the user's past schedule history. In this way, by referring to the user's past schedule history, an optimal learning method can be selected and learning accuracy can be improved. Some or all of the above-described processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's past schedule history into AI, which can select an optimal learning method.

[0116] The scheduled learning unit can adjust the learning means during scheduled learning, taking into account the user's current living situation. The scheduled learning unit, for example, customizes the learning means based on the user's current living situation during scheduled learning. For example, if the user is at work, the scheduled learning unit can provide work-related learning means. Also, if the user is on vacation, the scheduled learning unit can provide relaxing learning means. The scheduled learning unit can also customize the optimal learning means according to the user's living situation. This enables more appropriate learning by customizing the learning means based on the user's current living situation. Some or all of the above-described processing in the scheduled learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's current living situation into AI, which can then adjust the learning means.

[0117] The scheduled learning unit can improve the learning method based on user feedback during scheduled learning. The scheduled learning unit improves the learning method based on user feedback during scheduled learning, for example. For example, the scheduled learning unit optimizes the learning method based on user feedback. The scheduled learning unit can also adjust parameters for improving learning accuracy based on user feedback. The scheduled learning unit can also improve learning accuracy by referring to user feedback. In this way, by reflecting user feedback, the learning method can be improved and learning accuracy can be improved. Some or all of the above-mentioned processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input user feedback into AI, which can improve the learning method.

[0118] The scheduled learning unit can estimate the user's emotions and determine the priority of scheduled learning based on the user's emotions. The scheduled learning unit, for example, estimates the user's emotions and determines the priority of scheduled learning based on the estimated user's emotions. For example, if the user is relaxed, the scheduled learning unit can prioritize detailed scheduled learning. Also, if the user is stressed, the scheduled learning unit can prioritize basic scheduled learning. Also, if the user is in a hurry, the scheduled learning unit can prioritize the most important scheduled learning. This enables more appropriate learning by determining the priority of scheduled learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the scheduled learning unit can be performed using an AI, for example, or without an AI. For example, the scheduled learning unit can input the user's emotions into an AI, which can then determine the priority of scheduled learning.

[0119] The scheduled learning unit can select the optimal study method during scheduled learning by taking into account the user's geographical location information. For example, during scheduled learning, the scheduled learning unit selects the optimal study method by taking into account the user's geographical location information. For example, if the user is in a specific area, the scheduled learning unit selects a study method related to that area. Furthermore, if the user is traveling, the scheduled learning unit can select a study method related to the travel destination. Furthermore, if the user is at home, the scheduled learning unit can select a study method related to the user's home. In this way, selecting the optimal study method based on the user's geographical location information enables more appropriate study. Some or all of the above-described processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's geographical location information into AI, which can select the optimal study method.

[0120] The scheduled learning unit can analyze the user's social media activities and suggest study methods during scheduled learning. For example, the scheduled learning unit can analyze the user's social media activities and suggest related study methods during scheduled learning. For example, the scheduled learning unit can suggest study methods based on information shared by the user on social media. The scheduled learning unit can also analyze the content of the user's social media posts and suggest related study methods. The scheduled learning unit can also suggest related study methods by referring to the activities of the user's friends on social media. This enables more appropriate study by suggesting study methods based on the user's social media activities. Some or all of the above-mentioned processing in the scheduled learning unit can be performed, for example, using AI, or can be performed without using AI. For example, the scheduled learning unit can input the user's social media activities into AI, which can then suggest study methods.

[0121] The scheduled learning unit can adjust the learning method based on the user's past feedback during scheduled learning. The scheduled learning unit, for example, improves the learning method based on the user's past feedback during scheduled learning. For example, the scheduled learning unit improves the learning method based on the user's past feedback. The scheduled learning unit can also select an optimal learning method based on the user's past feedback. The scheduled learning unit can also customize the learning method by reflecting the user's feedback. This enables more appropriate learning by customizing the learning method based on the user's past feedback. Some or all of the above-mentioned processing in the scheduled learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scheduled learning unit can input the user's past feedback into AI, which can adjust the learning method.

[0122] The product proposal unit can estimate the user's emotions and adjust the method of product proposal based on the user's emotions. The product proposal unit, for example, estimates the user's emotions and adjusts the method of product proposal based on the estimated user's emotions. For example, the product proposal unit can make detailed product proposals when the user is relaxed. Furthermore, the product proposal unit can make concise product proposals when the user is stressed. Furthermore, the product proposal unit can make product proposals that focus on the main points when the user is in a hurry. This enables more appropriate proposals by adjusting the method of product proposal according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the product proposal unit may be performed using an AI, for example, or without an AI. For example, the product proposal unit can input the user's emotions into an AI, which can then adjust the method of product proposal.

[0123] The product proposal unit can select an optimal proposal method based on the user's past consumption behavior when proposing a product. For example, when proposing a product, the product proposal unit analyzes the user's past consumption behavior and selects an optimal proposal method. For example, the product proposal unit selects an optimal proposal method based on the user's past consumption behavior. The product proposal unit can also adjust parameters to improve the accuracy of proposals based on the user's past consumption behavior. The product proposal unit can also improve the accuracy of proposals by referring to the user's past consumption behavior. In this way, by analyzing the user's past consumption behavior, an optimal proposal method can be selected and the accuracy of proposals can be improved. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's past consumption behavior into AI, which can select an optimal proposal method.

[0124] The product proposal unit can adjust the proposal means taking into account the user's current living situation when proposing a product. For example, the product proposal unit customizes the proposal means based on the user's current living situation when proposing a product. For example, if the user is at work, the product proposal unit can propose work-related products. Also, if the user is on vacation, the product proposal unit can propose relaxing products. The product proposal unit can also customize optimal product proposals according to the user's living situation. This enables more appropriate proposals to be made by customizing the proposal means based on the user's current living situation. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's current living situation into AI, which can then adjust the proposal means.

[0125] The product proposal unit can improve the proposal method based on user feedback when proposing a product. For example, the product proposal unit improves the proposal method based on user feedback when proposing a product. For example, the product proposal unit optimizes the proposal method based on user feedback. The product proposal unit can also adjust parameters for improving the accuracy of proposals based on user feedback. The product proposal unit can also improve the accuracy of proposals by referring to user feedback. In this way, by reflecting user feedback, the proposal method can be improved and the accuracy of proposals can be improved. Some or all of the above-mentioned processing in the product proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the product proposal unit can input user feedback into AI, which can improve the proposal method.

[0126] The product proposal unit can estimate the user's emotions and prioritize product proposals based on the user's emotions. The product proposal unit, for example, estimates the user's emotions and prioritizes product proposals based on the estimated user's emotions. For example, if the user is relaxed, the product proposal unit can prioritize detailed product proposals. Also, if the user is stressed, the product proposal unit can prioritize basic product proposals. Also, if the user is in a hurry, the product proposal unit can prioritize the most important product proposals. This enables more appropriate proposals by prioritizing product proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the product proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the product proposal unit can input the user's emotions into an AI, which then prioritizes product proposals.

[0127] The product proposal unit can select the optimal proposal method by taking into consideration the user's geographical location information when proposing products. For example, the product proposal unit selects the optimal proposal method by taking into consideration the user's geographical location information when proposing products. For example, when the user is in a specific area, the product proposal unit can make product proposals related to that area. Furthermore, when the user is traveling, the product proposal unit can make product proposals related to the travel destination. Furthermore, when the user is at home, the product proposal unit can make product proposals related to the user's home. This enables more appropriate proposals by selecting the optimal proposal method based on the user's geographical location information. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's geographical location information into AI, which can select the optimal proposal method.

[0128] The product proposal unit can analyze the user's social media activity and propose proposal means when proposing a product. For example, the product proposal unit analyzes the user's social media activity and proposes related proposal means when proposing a product. For example, the product proposal unit makes product proposals based on information shared by the user on social media. The product proposal unit can also analyze the content of the user's social media posts and propose related products. The product proposal unit can also make related product proposals by referring to the activities of the user's friends on social media. This enables more appropriate proposals by proposing proposal means based on the user's social media activity. Some or all of the above-described processing in the product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the product proposal unit can input the user's social media activity into AI, which then proposes proposal means.

[0129] The product proposal unit can adjust the proposal method based on the user's past feedback when proposing a product. For example, the product proposal unit improves the proposal method based on the user's past feedback when proposing a product. For example, the product proposal unit improves the proposal method based on the user's past feedback. The product proposal unit can also select an optimal proposal method based on the user's past feedback. The product proposal unit can also customize the proposal method by reflecting the user's feedback. In this way, customizing the proposal method based on the user's past feedback enables more appropriate proposals. Some or all of the above-described processing in the product proposal unit may be performed using, or without, AI, for example. For example, the product proposal unit can input the user's past feedback into AI, which can then adjust the proposal method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, schedule learning unit, and product suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the user's physique data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests outfits based on the analysis results. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and purchase history. The schedule learning unit is realized, for example, by the control unit 46A of the smart device 14 and learns the user's schedule. The product suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products from an e-commerce site. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, schedule learning unit, and product suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the user's physique data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests outfits based on the analysis results. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and purchase history. The schedule learning unit is realized, for example, by the control unit 46A of the smart glasses 214 and learns the user's schedule. The product suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products from an e-commerce site. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, schedule learning unit, and product suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect the user's physique data using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and suggests outfits based on the analysis results. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and purchase history. The schedule learning unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and learns the user's schedule. The product suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products from an e-commerce site. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, schedule learning unit, and product suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's physique data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests outfits based on the analysis results. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and purchase history. The schedule learning unit is realized, for example, by the control unit 46A of the robot 414 and learns the user's schedule. The product suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products from an e-commerce site. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the timing of data collection.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The outfit suggestion system can also monitor the user's health condition and suggest outfits based on that condition. For example, if the user is not getting enough exercise, it can suggest clothes that are easy to exercise in. If the user has a specific allergy, it can suggest clothes made from materials that are suitable for that allergy. Furthermore, if the user has set a specific health goal, it can suggest outfits that match that goal. This makes it possible to suggest optimal outfits based on the user's health condition.

[0132] The outfit suggestion system can also estimate the user's emotions and adjust the color and style of the outfit based on the estimated emotions. For example, if the user is relaxed, it can suggest outfits with muted colors. If the user is feeling stressed, it can also suggest outfits with bright colors. Furthermore, if the user is attending a specific event, it can suggest outfits based on the emotions associated with that event. This makes it possible to suggest the optimal outfit according to the user's emotions.

[0133] The outfit suggestion system can also suggest outfits based on the user's lifestyle. For example, if the user likes outdoor activities, it can suggest clothes suitable for outdoor activities. Also, if the user wishes to wear the equipment in a business setting, it can suggest clothes suitable for business. Furthermore, if the user has a particular hobby, it can suggest outfits that match that hobby. This makes it possible to suggest optimal outfits based on the user's lifestyle.

[0134] The outfit suggestion system can also estimate the user's emotions and suggest accessories for the outfit based on the estimated emotions. For example, if the user is relaxed, simple accessories can be suggested. If the user is feeling stressed, flashy accessories can be suggested. Furthermore, if the user is attending a specific event, accessories can be suggested based on the emotions associated with that event. This makes it possible to suggest the most suitable accessories according to the user's emotions.

[0135] The coordination suggestion system can analyze the user's past coordination history and suggest new coordinations based on past successes and failures. For example, new suggestions can be made based on coordinations that the user has received high ratings for in the past. It can also suggest coordinations that the user has not received favorable reviews for in the past. Furthermore, it can analyze trends based on the user's past coordination history and make suggestions that match the latest trends. This makes it possible to suggest optimal coordinations based on the user's past history.

[0136] The outfit suggestion system can also estimate the user's emotions and select the materials for the outfit based on the estimated emotions. For example, if the user is relaxed, it can suggest clothes made of soft materials. If the user is feeling stressed, it can also suggest clothes made of breathable materials. Furthermore, if the user is participating in a specific event, it can select materials based on the emotions associated with that event. This makes it possible to suggest clothes made of the most suitable materials according to the user's emotions.

[0137] The outfit suggestion system can also suggest outfits that match the local climate and culture, taking into account the user's geographical location information. For example, if the user lives in a cold region, it can suggest warm clothing. If the user lives in a tropical region, it can suggest cool clothing. Furthermore, if the user is attending a specific cultural event, it can suggest outfits that match that culture. This makes it possible to suggest optimal outfits based on the user's geographical location information.

[0138] The outfit suggestion system can also estimate the user's emotions and suggest outfit layering based on the estimated emotions. For example, if the user is relaxed, simple layering can be suggested. If the user is stressed, complex layering can be suggested. Furthermore, if the user is attending a specific event, layering can be suggested based on the emotions associated with that event. This makes it possible to suggest optimal layering according to the user's emotions.

[0139] The outfit suggestion system can analyze a user's social media activity and suggest outfits based on the influencers and brands the user follows. For example, if a user follows a specific influencer, the system can suggest outfits based on that influencer's style. Also, if a user likes a specific brand, the system can suggest outfits using products from that brand. Furthermore, the system can analyze a user's social media activity and suggest outfits that match the latest trends. This makes it possible to suggest optimal outfits based on the user's social media activity.

[0140] The outfit suggestion system can also estimate the user's emotions and adjust the fit of the outfit based on the estimated emotions. For example, if the user is relaxed, loose-fitting clothes can be suggested. On the other hand, if the user is feeling stressed, tight-fitting clothes can be suggested. Furthermore, if the user is participating in a specific event, the fit can be adjusted based on the emotions associated with the event. This makes it possible to suggest clothes with the optimal fit according to the user's emotions.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The collection unit collects the user's physique data. The user's physique data includes, for example, height, weight, and three sizes. The collection unit collects photos provided by the user and information on height, weight, and three sizes. Users can also upload their own photos to the app and enter their height, weight, and three sizes. For example, the collection unit collects full-body photos and face photos provided by the user and inputs them into the AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes a photo of the user and suggests clothing styles that suit them, taking into account factors such as face shape, skin color, and hairstyle. Image analysis technology is used to analyze the user's face shape and classify it as round, oval, etc. Skin color can also be analyzed and classified based on hue and brightness. Hairstyles can also be analyzed and classified based on length and style. Step 3: The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal clothing styles based on the user's face shape, skin color, and hairstyle, for example. It selects color combinations and styles and suggests them to the user. It can also take into account current trends, the user's preferences, the price range and frequency of purchases that they usually make. Step 4: The learning unit learns the user's preferences and purchase history. For example, the learning unit learns based on the user's past purchase history and ratings. Data on products purchased by the user in the past and evaluation data are collected and input into the AI. Step 5: The schedule learning unit learns who the user has met. For example, the schedule learning unit learns the schedule the user has entered into the app and suggests outfits that are different from the last time they met. The user enters their schedule into the app and records who they will be meeting. The AI ​​learns this information and makes suggestions to avoid the mistake of "wearing the same clothes as the last time they met." Step 6: The product suggestion unit selects the most suitable products from the e-commerce site. The product suggestion unit selects the most suitable products from the e-commerce site and suggests them to the user, taking into account, for example, the user's preferences, usual price range, and purchase frequency. AI selects the most suitable products based on the user's preferences and purchase history. It can also suggest products at the optimal time, taking into account the user's purchase frequency.

[0143] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0146] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a 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.

[0181] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0196] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] 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.

[0206] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects physique data of a user; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests outfits based on the analysis results obtained by the analysis unit; a learning unit that learns user preferences and purchase history; a schedule learning unit that learns the user's meeting schedule; a product suggestion unit that picks up products from an e-commerce site; A system characterized by:

2. The collecting unit Collecting user-provided information such as photos, height, weight, and measurements 2. The system of claim 1.

3. The analysis unit Analyzes the user's photo and suggests clothing styles that suit them based on their face shape, skin color, and hairstyle.

2. The system of claim 1.

4. The learning unit Learns based on users' past purchases and ratings 2. The system of claim 1.

5. The scheduled learning unit The app learns the schedule entered by the user and suggests different outfits from the last time they met.

2. The system of claim 1.

6. The product proposal unit Select and recommend products from e-commerce sites based on the user's preferences, usual price range, and purchase frequency 2. The system of claim 1.

7. The collecting unit Infer user emotions and adjust data collection timing based on user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past data provision history and select collection methods 2. The system of claim 1.

Images