system

Abstract

An object of a system according to an embodiment is to automatically propose fashion items that match conditions and preferences of a user.SOLUTION: A system includes a condition setting unit, an information collection unit, and a proposal unit. The condition setting unit sets conditions and preferences of the user. The information collection unit automatically checks the new content information of the EC site or the brand based on the condition set by the condition setting unit. The proposing unit analyzes the information collected by the information collecting unit and automatically proposes fashion items that exactly match the user's needs.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 technologies have had the problem that users have to spend a lot of time and effort to find fashion items that meet their requirements and preferences.

[0005] The system according to the embodiment aims to automatically suggest fashion items that match the user's conditions and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a condition setting unit, an information collecting unit, and a suggestion unit. The condition setting unit sets the user's conditions and preferences. The information collecting unit automatically checks information on new products from e-commerce sites and brands based on the conditions set by the condition setting unit. The suggestion unit analyzes the information collected by the information collecting unit and automatically suggests fashion items that perfectly match the user's needs. [Effects of the Invention]

[0007] The system according to the embodiment can automatically suggest fashion items that match the user's conditions 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 fashion suggestion system according to an embodiment of the present invention is a system in which a user simply sets specific conditions and preferences, and the AI ​​generation automatically checks new product information from various e-commerce sites and brands, and automatically suggests fashion items that perfectly match the user's needs. This allows the fashion suggestion system to provide all fashion-related information, from new release information to hidden gems on sale, on a single platform.

[0029] A fashion suggestion system according to an embodiment includes a condition setting unit, an information collecting unit, and a suggestion unit. The condition setting unit sets user conditions and preferences. For example, the user can set conditions such as a specific brand, color, size, price range, and style. The information collecting unit automatically checks new product information from e-commerce sites and brands based on the conditions set by the condition setting unit. For example, the generation AI automatically checks new product information from various e-commerce sites and brands based on the conditions and preferences set by the user. The generation AI collects information based on prompts including the conditions and preferences set by the user. The suggestion unit analyzes the information collected by the information collecting unit and automatically suggests fashion items that perfectly match the user's needs. For example, the generation AI analyzes the collected information and automatically suggests fashion items that perfectly match the user's needs. This allows the fashion suggestion system to automatically suggest optimal fashion items based on the user's conditions and preferences.

[0030] The condition setting unit can analyze a user's past purchase history and browsing history and automatically estimate and set preferences and conditions. The condition setting unit, for example, analyzes a user's past purchase history and extracts trends in frequently purchased brands and items. For example, if a user has purchased dresses from a specific brand multiple times, the brand is automatically set as a preference. The condition setting unit also analyzes a user's browsing history and extracts trends in items of interest. For example, if a user frequently browses items of a specific style, the style is automatically set as a preference. The condition setting unit also combines the user's past purchase history and browsing history to more accurately estimate preferences and conditions. For example, the user's preferences are identified based on the degree of agreement between the purchase history and the browsing history. This makes it possible to automatically set preferences and conditions based on the user's past behavior.

[0031] The condition setting unit allows the AI ​​to automatically collect opinions from related fashion blogs and influencers based on conditions set by the user and suggest them to the user. For example, the condition setting unit automatically searches related fashion blogs based on conditions set by the user to collect the latest trends and styles. For example, it suggests articles related to specific brands and styles. The condition setting unit also collects opinions from influencers and suggests them to the user. For example, it suggests items recommended by specific influencers. The condition setting unit also combines opinions from fashion blogs and influencers to provide the user with the most suitable information. For example, it suggests the most suitable items to the user based on blog articles and influencer opinions. This makes it possible to automatically collect and suggest opinions from related fashion blogs and influencers based on the user's conditions.

[0032] The condition setting unit can share the conditions set by the user within the community and receive feedback from other users. The condition setting unit, for example, shares the conditions set by the user within the community and receives feedback from other users. For example, conditions are shared between users who like the same brand or style. The condition setting unit also adjusts the conditions based on feedback within the community. For example, conditions that are highly rated by other users are given priority. The condition setting unit also suggests popular conditions based on rankings within the community. For example, it suggests top-ranked conditions to the user. This allows the conditions set by the user to be shared with other users and receive feedback.

[0033] The condition setting unit allows the user to set conditions using a new interface such as voice input or gesture input. The condition setting unit allows the user to set conditions using, for example, voice input. For example, a condition is set by simply giving a voice command such as "Find a red dress." The condition setting unit also allows the user to set conditions using gesture input. For example, a color or style can be selected using a specific gesture. The condition setting unit also combines voice input and gesture input to enable more intuitive condition setting. For example, a color can be specified by voice and a style can be selected by gesture. This allows the user to set conditions using a new interface such as voice input or gesture input.

[0034] When the AI ​​checks new product information, the information collection unit uses image recognition technology to analyze the design and color of the product and collect information that matches the user's preferences. For example, the information collection unit uses image recognition technology to analyze the design and color of new products from e-commerce sites and brands. For example, it automatically detects items with specific colors or patterns. The information collection unit also uses image recognition technology to analyze the characteristics of the product and collect information that matches the user's preferences. For example, it prioritizes collecting items with specific designs or styles. The information collection unit also uses image recognition technology to analyze detailed product information and provide it to the user. For example, it collects information such as the material, size, and color variations of the product. This allows the image recognition technology to analyze the design and color of the product and collect information that matches the user's preferences.

[0035] When the AI ​​checks new product information, the information collection unit can simultaneously collect product reviews and ratings and provide them to the user. For example, when the AI ​​checks new product information, the information collection unit simultaneously collects product reviews and ratings. For example, items with high user ratings are displayed preferentially. The information collection unit also collects information from review sites and rating sites and provides it to the user. For example, based on the reviews and ratings of a specific product, it suggests the most suitable item for the user. The information collection unit also analyzes the reviews and ratings and provides information that matches the user's preferences. For example, it suggests highly rated items preferentially. This allows product reviews and ratings to be simultaneously collected and provided to the user.

[0036] The information collecting unit checks new product information in accordance with specific time periods or events, and can provide the information to the user at the optimal timing. For example, the information collecting unit checks new product information in accordance with specific time periods and provides the information to the user at the optimal timing. For example, the information is notified during the time period when the user is most active. The information collecting unit also checks new product information in accordance with specific events and provides the information to the user. For example, it collects information in accordance with sales events or new product presentations. The information collecting unit also analyzes the user's behavioral patterns and provides the information at the optimal timing. For example, it notifies the user of the information during the time period when the user frequently accesses the site. In this way, new product information can be checked in accordance with specific time periods or events, and the information can be provided at the optimal timing.

[0037] The information gathering unit can expand its checks of new product information to include e-commerce sites and brands in different regions and countries, thereby collecting information from a global perspective. For example, the information gathering unit can collect information from a global perspective by expanding its checks of new product information to include e-commerce sites and brands in different regions and countries. For example, it can collect information about new products from popular overseas brands. The information gathering unit can also collect information from e-commerce sites in different regions and countries and provide it to users. For example, it can suggest items that are popular in a specific region. The information gathering unit can also analyze information from a global perspective and provide users with the most suitable information. For example, it can compare trends in different regions and make suggestions to users. This allows it to expand its checks of new product information to include e-commerce sites and brands in different regions and countries, thereby collecting information from a global perspective.

[0038] The suggestion unit can analyze the user's past feedback and improve the accuracy of suggestions. The suggestion unit, for example, analyzes the user's past feedback and improves the accuracy of suggestions. For example, it extracts features of items that the user has given high ratings and reflects them in the next suggestion. The suggestion unit also adjusts the suggestion algorithm based on the user's feedback. For example, it excludes items that the user has given low ratings. The suggestion unit also analyzes the user's feedback in real time and updates the suggestion content. For example, it adjusts the suggestion content every time the user enters feedback. In this way, the user's past feedback can be analyzed and the accuracy of suggestions can be improved.

[0039] The suggestion unit can increase the reliability of the suggestions by referring to the ratings and reviews of other users. The suggestion unit, for example, analyzes the ratings and reviews of other users to increase the reliability of the suggestions. For example, it prioritizes suggesting highly rated items. The suggestion unit also collects information from review sites and rating sites and reflects this information in the suggestions. For example, it suggests the most suitable item for the user based on the reviews and ratings of a specific product. The suggestion unit also analyzes the ratings and reviews of other users in real time and updates the suggestions. For example, it adjusts the suggestions based on the latest reviews. This allows the suggestion unit to increase the reliability of the suggestions by referring to the ratings and reviews of other users.

[0040] The suggestion unit can add a function to share suggested items with the user's friends and family and to refer to other people's opinions. The suggestion unit adds, for example, a function to share suggested items with the user's friends and family. For example, the items are shared via social networking sites or messaging apps. The suggestion unit also collects the opinions of friends and family about the shared items and provides them to the user. For example, the suggestion unit suggests items that are most suitable for the user based on the ratings of friends and family. The suggestion unit also analyzes feedback about the shared items and adjusts the suggestion content. For example, it prioritizes suggesting items that are highly rated by friends and family. This allows the user to share the suggested items with friends and family and refer to other people's opinions.

[0041] The suggestion unit may enable the user to try on the suggested items using a virtual try-on function. For example, the suggestion unit may enable the user to try on the suggested items using the virtual try-on function. For example, the suggestion unit may upload a photo of the user and virtually try on the items. The suggestion unit may also enable the user to check the fit and style of the items using the virtual try-on function. For example, the suggestion unit may use 3D modeling technology to display detailed fit of the items. The suggestion unit may also enable the user to compare multiple items using the virtual try-on function. For example, the suggestion unit may try on and compare items of different colors or designs. This allows the user to try on the suggested items using the virtual try-on function.

[0042] When providing new product release information, the information collecting unit can also provide detailed product specifications and background stories. For example, when providing new product release information, the information collecting unit also provides detailed product specifications. For example, it displays information such as material, size, and color variations. The information collecting unit also provides background stories for the products to pique the user's interest. For example, it provides information on the product's design concept and manufacturing process. The information collecting unit also provides detailed product specifications and background stories in combination. For example, it displays the product's features and the background behind them together. This makes it possible to simultaneously provide detailed product specifications and background stories when providing new product release information.

[0043] When providing new product release information, the information collecting unit can prioritize providing information that is likely to be of particular interest to the user based on the user's past purchase history and preferences. The information collecting unit provides new product release information based on the user's past purchase history and preferences, for example. For example, new product information for a brand that the user has previously purchased may be displayed preferentially. The information collecting unit also customizes new product release information based on the user's preferences. For example, new product information in a particular color or style may be provided preferentially. The information collecting unit also identifies information that is likely to be of particular interest to the user by combining the user's past purchase history and preferences. For example, new product information for items that the user has previously rated may be provided preferentially. This allows information that is likely to be of particular interest to be provided preferentially based on the user's past purchase history and preferences.

[0044] The information collection unit can link new product release information with the user's calendar and reminders to notify the user at the optimal timing. For example, the information collection unit links new product release information with the user's calendar to notify the user at the optimal timing. For example, a reminder can be set the day before the release date. The information collection unit also links with the user's reminders to ensure that important information is not missed. For example, the release date of a new product or the start date of a sale can be added to the reminder. The information collection unit also analyzes the user's behavioral patterns to notify the user at the optimal timing. For example, the information can be notified during the time period when the user is most active. In this way, new product release information can be linked with the user's calendar and reminders to notify the user at the optimal timing.

[0045] The information collection unit can link new product release information with social media, allowing users to share information. The information collection unit, for example, links new product release information with social media, allowing users to share information. For example, it provides a function for sharing new product information on SNS. The information collection unit also enables users to share new product information with friends and followers through social media. For example, it shares information about new products from a specific brand. The information collection unit also provides optimal information to users based on feedback from social media. For example, it analyzes reactions to shared information and suggests information relevant to the user. In this way, new product release information can be linked with social media, allowing users to share information.

[0046] The information collecting unit can provide product reviews and ratings at the same time as providing sale information, thereby enabling users to make better choices. For example, the information collecting unit can provide product reviews and ratings at the same time as providing sale information. For example, items that have received high user ratings are displayed preferentially. The information collecting unit can also collect information from review sites and rating sites and provide it to users. For example, based on reviews and ratings of a specific product, it can suggest the most suitable item for the user. The information collecting unit can also analyze reviews and ratings to provide information that matches the user's preferences. For example, it can suggest highly rated items preferentially. This allows the information collecting unit to provide product reviews and ratings at the same time as providing sale information, thereby enabling users to make better choices.

[0047] When providing sale information, the information collecting unit can prioritize providing information that is likely to be of particular interest to the user based on the user's past purchase history and preferences. The information collecting unit provides sale information based on the user's past purchase history and preferences, for example. For example, sale information for brands that the user has purchased in the past may be displayed preferentially. The information collecting unit also customizes sale information based on the user's preferences. For example, sale information for specific colors or styles may be provided preferentially. The information collecting unit also combines the user's past purchase history and preferences to identify information that is likely to be of particular interest to the user. For example, sale information for items that the user has given high ratings in the past may be provided preferentially. This allows sale information that is likely to be of particular interest to be provided preferentially based on the user's past purchase history and preferences.

[0048] The information collection unit can link sale information with the user's calendar and reminders to notify the user at the optimal timing. For example, the information collection unit links sale information with the user's calendar to notify the user at the optimal timing. For example, a reminder can be set the day before the sale starts. The information collection unit also links with the user's reminders to ensure that important information is not missed. For example, the start and end dates of the sale can be added to the reminder. The information collection unit also analyzes the user's behavioral patterns to notify the user at the optimal timing. For example, the information can be notified during the time period when the user is most active. In this way, sale information can be linked with the user's calendar and reminders to notify the user at the optimal timing.

[0049] The information collection unit can link sales information with social media, allowing users to share information. The information collection unit, for example, links sales information with social media, allowing users to share information. For example, it provides a function for sharing sales information on SNS. The information collection unit also allows users to share sales information with friends and followers through social media. For example, it shares sales information for a specific brand. The information collection unit also provides optimal information to users based on feedback from social media. For example, it analyzes reactions to shared information and suggests information relevant to the user. In this way, sales information can be linked with social media, allowing users to share information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The condition setting unit can analyze the user's lifestyle and activity patterns and suggest optimal fashion items based on them. For example, if the user frequently goes to the gym, sportswear and activewear can be suggested first. If the user has many business meetings, formal business wear can be suggested. Furthermore, if the user is planning a trip, casual wear and accessories suitable for travel can be suggested. This allows the system to suggest fashion items that match the user's lifestyle.

[0052] The condition setting unit can analyze the user's health and fitness data and suggest optimal fashion items based on that data. For example, it can suggest well-fitting clothing based on the user's weight and body fat percentage. It can also suggest appropriate sportswear and shoes based on the user's exercise habits. It can also suggest fashion items that motivate the user and match their health goals. This allows it to suggest fashion items that match the user's health condition.

[0053] The condition setting unit can analyze a user's social media activity and suggest optimal fashion items based on that analysis. For example, it can refer to the styles of fashion influencers the user follows on Instagram. It can also extract preferred styles from photos and comments posted by the user and make suggestions based on those. It can also analyze information about events and parties the user will be attending and suggest fashion items that are suitable for those events. This allows it to suggest fashion items based on the user's social media activity.

[0054] The information gathering unit can collect information on new brands and designers that the user may be interested in and suggest them to the user. For example, if the user has a preference for a particular style or design, new brands that fit that style can be suggested. Also, if the user is interested in eco-friendly fashion, information on sustainable brands and designers can be provided. Furthermore, if the user is interested in a particular region or culture, information on brands and designers related to that region or culture can be suggested. This allows the user to be provided with information on new brands and designers.

[0055] The information collection unit can suggest fashion items suitable for specific seasons or events based on the user's purchasing history and preferences. For example, the information collection unit can analyze the trends of items the user has purchased in the past and suggest summer beachwear or winter coats. If the user plans to attend a specific event, the information collection unit can suggest dresses and accessories suitable for that event. Furthermore, if the user prefers different styles for each season, the information collection unit can suggest fashion items suitable for that season. This makes it possible to suggest fashion items suitable for specific seasons or events.

[0056] The information collection unit can suggest fashion items related to a specific theme or collection based on the user's purchasing history and preferences. For example, it can analyze the trends of items the user has purchased in the past and suggest a new collection from a specific designer. Also, if the user is interested in a specific theme, it can suggest items related to that theme. Furthermore, if the user is looking for a collection tailored to a specific event or season, it can also suggest items related to that collection. This makes it possible to suggest fashion items related to a specific theme or collection.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The condition setting section sets the user's conditions and preferences. For example, the user can set conditions such as a specific brand, color, size, price range, style, etc. Step 2: The information gathering unit automatically checks new product information from e-commerce sites and brands based on the conditions set by the condition setting unit. For example, the generation AI automatically checks new product information from various e-commerce sites and brands based on the conditions and preferences set by the user. The generation AI collects information based on prompts that include the conditions and preferences set by the user. Step 3: The suggestion unit analyzes the information collected by the information collection unit and automatically suggests fashion items that perfectly match the user's needs. For example, the generation AI analyzes the collected information and automatically suggests fashion items that perfectly match the user's needs.

[0059] (Example 2) The fashion suggestion system according to an embodiment of the present invention is a system in which a user simply sets specific conditions and preferences, and the AI ​​generation automatically checks new product information from various e-commerce sites and brands, and automatically suggests fashion items that perfectly match the user's needs. This allows the fashion suggestion system to provide all fashion-related information, from new release information to hidden gems on sale, on a single platform.

[0060] A fashion suggestion system according to an embodiment includes a condition setting unit, an information collecting unit, and a suggestion unit. The condition setting unit sets user conditions and preferences. For example, the user can set conditions such as a specific brand, color, size, price range, and style. The information collecting unit automatically checks new product information from e-commerce sites and brands based on the conditions set by the condition setting unit. For example, the generation AI automatically checks new product information from various e-commerce sites and brands based on the conditions and preferences set by the user. The generation AI collects information based on prompts including the conditions and preferences set by the user. The suggestion unit analyzes the information collected by the information collecting unit and automatically suggests fashion items that perfectly match the user's needs. For example, the generation AI analyzes the collected information and automatically suggests fashion items that perfectly match the user's needs. This allows the fashion suggestion system to automatically suggest optimal fashion items based on the user's conditions and preferences.

[0061] The condition setting unit can analyze a user's past purchase history and browsing history and automatically estimate and set preferences and conditions. The condition setting unit, for example, analyzes a user's past purchase history and extracts trends in frequently purchased brands and items. For example, if a user has purchased dresses from a specific brand multiple times, the brand is automatically set as a preference. The condition setting unit also analyzes a user's browsing history and extracts trends in items of interest. For example, if a user frequently browses items of a specific style, the style is automatically set as a preference. The condition setting unit also combines the user's past purchase history and browsing history to more accurately estimate preferences and conditions. For example, the user's preferences are identified based on the degree of agreement between the purchase history and the browsing history. This makes it possible to automatically set preferences and conditions based on the user's past behavior.

[0062] The condition setting unit allows the AI ​​to automatically collect opinions from related fashion blogs and influencers based on conditions set by the user and suggest them to the user. For example, the condition setting unit automatically searches related fashion blogs based on conditions set by the user to collect the latest trends and styles. For example, it suggests articles related to specific brands and styles. The condition setting unit also collects opinions from influencers and suggests them to the user. For example, it suggests items recommended by specific influencers. The condition setting unit also combines opinions from fashion blogs and influencers to provide the user with the most suitable information. For example, it suggests the most suitable items to the user based on blog articles and influencer opinions. This makes it possible to automatically collect and suggest opinions from related fashion blogs and influencers based on the user's conditions.

[0063] The condition setting unit can use the emotion estimation function to analyze the emotion of the user when inputting in real time and suggest condition settings that will elicit positive emotions. For example, when the user inputs conditions, the condition setting unit analyzes facial expressions and voice tones and calculates an emotion score. For example, it prioritizes suggesting conditions that are associated with strong positive emotions. The condition setting unit also suggests conditions that will elicit positive emotions based on the user's input. For example, it prioritizes suggesting colors and designs that the user likes. The condition setting unit also monitors the user's emotions in real time and adjusts the conditions according to changes in emotion. For example, if the user expresses negative emotions, it suggests different conditions. This makes it possible to suggest positive condition settings based on the user's emotions.

[0064] The condition setting unit can share the conditions set by the user within the community and receive feedback from other users. The condition setting unit, for example, shares the conditions set by the user within the community and receives feedback from other users. For example, conditions are shared between users who like the same brand or style. The condition setting unit also adjusts the conditions based on feedback within the community. For example, conditions that are highly rated by other users are given priority. The condition setting unit also suggests popular conditions based on rankings within the community. For example, it suggests top-ranked conditions to the user. This allows the conditions set by the user to be shared with other users and receive feedback.

[0065] The condition setting unit allows the user to set conditions using a new interface such as voice input or gesture input. The condition setting unit allows the user to set conditions using, for example, voice input. For example, a condition is set by simply giving a voice command such as "Find a red dress." The condition setting unit also allows the user to set conditions using gesture input. For example, a color or style can be selected using a specific gesture. The condition setting unit also combines voice input and gesture input to enable more intuitive condition setting. For example, a color can be specified by voice and a style can be selected by gesture. This allows the user to set conditions using a new interface such as voice input or gesture input.

[0066] The condition setting unit uses the emotion estimation function to set conditions based on the user's emotions and can automatically suggest conditions that the user will be most satisfied with. The condition setting unit, for example, uses the emotion estimation function to automatically suggest conditions that the user will be most satisfied with. For example, conditions entered by the user with a smile are set with priority. The condition setting unit also monitors the user's emotions in real time and adjusts the conditions according to changes in emotion. For example, if the user expresses negative emotions, different conditions are suggested. The condition setting unit also identifies the most satisfying conditions based on the user's past emotion data. For example, conditions that have been highly rated in the past are suggested with priority. This makes it possible to automatically suggest the most satisfying conditions based on the user's emotions.

[0067] When the AI ​​checks new product information, the information collection unit uses image recognition technology to analyze the design and color of the product and collect information that matches the user's preferences. For example, the information collection unit uses image recognition technology to analyze the design and color of new products from e-commerce sites and brands. For example, it automatically detects items with specific colors or patterns. The information collection unit also uses image recognition technology to analyze the characteristics of the product and collect information that matches the user's preferences. For example, it prioritizes collecting items with specific designs or styles. The information collection unit also uses image recognition technology to analyze detailed product information and provide it to the user. For example, it collects information such as the material, size, and color variations of the product. This allows the image recognition technology to analyze the design and color of the product and collect information that matches the user's preferences.

[0068] When the AI ​​checks new product information, the information collection unit can simultaneously collect product reviews and ratings and provide them to the user. For example, when the AI ​​checks new product information, the information collection unit simultaneously collects product reviews and ratings. For example, items with high user ratings are displayed preferentially. The information collection unit also collects information from review sites and rating sites and provides it to the user. For example, based on the reviews and ratings of a specific product, it suggests the most suitable item for the user. The information collection unit also analyzes the reviews and ratings and provides information that matches the user's preferences. For example, it suggests highly rated items preferentially. This allows product reviews and ratings to be simultaneously collected and provided to the user.

[0069] The information collection unit uses the emotion estimation function to analyze the emotion of the user when viewing new release information, and can provide information that elicits positive emotions preferentially. The information collection unit, for example, uses the emotion estimation function to analyze the emotion of the user when viewing new release information. For example, if the user is excited, that information is preferentially displayed. The information collection unit also monitors the user's emotions in real time and provides information according to changes in emotion. For example, if the user shows positive emotions, that information is preferentially displayed. The information collection unit also identifies information that elicits positive emotions based on the user's past emotion data. For example, information that has been highly rated in the past is preferentially provided. This makes it possible to provide positive information preferentially based on the user's emotions.

[0070] The information collecting unit checks new product information in accordance with specific time periods or events, and can provide the information to the user at the optimal timing. For example, the information collecting unit checks new product information in accordance with specific time periods and provides the information to the user at the optimal timing. For example, the information is notified during the time period when the user is most active. The information collecting unit also checks new product information in accordance with specific events and provides the information to the user. For example, it collects information in accordance with sales events or new product presentations. The information collecting unit also analyzes the user's behavioral patterns and provides the information at the optimal timing. For example, it notifies the user of the information during the time period when the user frequently accesses the site. In this way, new product information can be checked in accordance with specific time periods or events, and the information can be provided at the optimal timing.

[0071] The information gathering unit can expand its checks of new product information to include e-commerce sites and brands in different regions and countries, thereby collecting information from a global perspective. For example, the information gathering unit can collect information from a global perspective by expanding its checks of new product information to include e-commerce sites and brands in different regions and countries. For example, it can collect information about new products from popular overseas brands. The information gathering unit can also collect information from e-commerce sites in different regions and countries and provide it to users. For example, it can suggest items that are popular in a specific region. The information gathering unit can also analyze information from a global perspective and provide users with the most suitable information. For example, it can compare trends in different regions and make suggestions to users. This allows it to expand its checks of new product information to include e-commerce sites and brands in different regions and countries, thereby collecting information from a global perspective.

[0072] The information collecting unit can use the emotion estimation function to identify new release information that the user is most interested in and provide that information preferentially. The information collecting unit, for example, uses the emotion estimation function to identify new release information that the user is most interested in. For example, it preferentially displays information with a high emotion score. The information collecting unit also monitors the user's emotions in real time and provides information according to changes in emotion. For example, it preferentially displays information that the user is excited about. The information collecting unit also identifies the information that the user is most interested in based on the user's past emotion data. For example, it preferentially provides information that the user has given a high rating in the past. In this way, it is possible to identify new release information that the user is most interested in and provide that information preferentially.

[0073] The suggestion unit can analyze the user's past feedback and improve the accuracy of suggestions. The suggestion unit, for example, analyzes the user's past feedback and improves the accuracy of suggestions. For example, it extracts features of items that the user has given high ratings and reflects them in the next suggestion. The suggestion unit also adjusts the suggestion algorithm based on the user's feedback. For example, it excludes items that the user has given low ratings. The suggestion unit also analyzes the user's feedback in real time and updates the suggestion content. For example, it adjusts the suggestion content every time the user enters feedback. In this way, the user's past feedback can be analyzed and the accuracy of suggestions can be improved.

[0074] The suggestion unit can increase the reliability of the suggestions by referring to the ratings and reviews of other users. The suggestion unit, for example, analyzes the ratings and reviews of other users to increase the reliability of the suggestions. For example, it prioritizes suggesting highly rated items. The suggestion unit also collects information from review sites and rating sites and reflects this information in the suggestions. For example, it suggests the most suitable item for the user based on the reviews and ratings of a specific product. The suggestion unit also analyzes the ratings and reviews of other users in real time and updates the suggestions. For example, it adjusts the suggestions based on the latest reviews. This allows the suggestion unit to increase the reliability of the suggestions by referring to the ratings and reviews of other users.

[0075] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward the suggested items and preferentially suggest items that elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user feels toward the suggested items. For example, it preferentially suggests items that the user is excited about. The suggestion unit also monitors the user's emotions in real time and adjusts the suggestion content according to changes in emotion. For example, if the user expresses negative emotions, it suggests a different item. The suggestion unit also identifies items that elicit positive emotions based on the user's past emotion data. For example, it preferentially suggests items that have been highly rated in the past. This makes it possible to preferentially suggest items that elicit positive emotions based on the user's emotions.

[0076] The suggestion unit can add a function to share suggested items with the user's friends and family and to refer to other people's opinions. The suggestion unit adds, for example, a function to share suggested items with the user's friends and family. For example, the items are shared via social networking sites or messaging apps. The suggestion unit also collects the opinions of friends and family about the shared items and provides them to the user. For example, the suggestion unit suggests items that are most suitable for the user based on the ratings of friends and family. The suggestion unit also analyzes feedback about the shared items and adjusts the suggestion content. For example, it prioritizes suggesting items that are highly rated by friends and family. This allows the user to share the suggested items with friends and family and refer to other people's opinions.

[0077] The suggestion unit may enable the user to try on the suggested items using a virtual try-on function. For example, the suggestion unit may enable the user to try on the suggested items using the virtual try-on function. For example, the suggestion unit may upload a photo of the user and virtually try on the items. The suggestion unit may also enable the user to check the fit and style of the items using the virtual try-on function. For example, the suggestion unit may use 3D modeling technology to display detailed fit of the items. The suggestion unit may also enable the user to compare multiple items using the virtual try-on function. For example, the suggestion unit may try on and compare items of different colors or designs. This allows the user to try on the suggested items using the virtual try-on function.

[0078] The suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when making a suggestion in order to make a suggestion that will most satisfy the user. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when making a suggestion. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The suggestion unit also monitors the user's emotions in real time and adjusts the suggestion content according to changes in emotion. For example, if the user expresses negative emotions, it suggests a different item. The suggestion unit also identifies the most satisfying suggestion based on the user's past emotion data. For example, it prioritizes suggesting items that the user has rated highly in the past. In this way, the user's emotions can be monitored in real time and the most satisfying suggestion can be made.

[0079] When providing new product release information, the information collecting unit can also provide detailed product specifications and background stories. For example, when providing new product release information, the information collecting unit also provides detailed product specifications. For example, it displays information such as material, size, and color variations. The information collecting unit also provides background stories for the products to pique the user's interest. For example, it provides information on the product's design concept and manufacturing process. The information collecting unit also provides detailed product specifications and background stories in combination. For example, it displays the product's features and the background behind them together. This makes it possible to simultaneously provide detailed product specifications and background stories when providing new product release information.

[0080] When providing new product release information, the information collecting unit can prioritize providing information that is likely to be of particular interest to the user based on the user's past purchase history and preferences. The information collecting unit provides new product release information based on the user's past purchase history and preferences, for example. For example, new product information for a brand that the user has previously purchased may be displayed preferentially. The information collecting unit also customizes new product release information based on the user's preferences. For example, new product information in a particular color or style may be provided preferentially. The information collecting unit also identifies information that is likely to be of particular interest to the user by combining the user's past purchase history and preferences. For example, new product information for items that the user has previously rated may be provided preferentially. This allows information that is likely to be of particular interest to be provided preferentially based on the user's past purchase history and preferences.

[0081] The information collection unit uses the emotion estimation function to analyze the emotions of the user when receiving new product information, and can provide information that elicits positive emotions preferentially. The information collection unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving new product information. For example, it preferentially displays information that the user is excited about. The information collection unit also monitors the user's emotions in real time and provides information according to changes in emotions. For example, if the user expresses positive emotions, it preferentially displays that information. The information collection unit also identifies information that elicits positive emotions based on the user's past emotion data. For example, it preferentially provides information that has been highly rated in the past. This makes it possible to provide positive information preferentially based on the user's emotions.

[0082] The information collection unit can link new product release information with the user's calendar and reminders to notify the user at the optimal timing. For example, the information collection unit links new product release information with the user's calendar to notify the user at the optimal timing. For example, a reminder can be set the day before the release date. The information collection unit also links with the user's reminders to ensure that important information is not missed. For example, the release date of a new product or the start date of a sale can be added to the reminder. The information collection unit also analyzes the user's behavioral patterns to notify the user at the optimal timing. For example, the information can be notified during the time period when the user is most active. In this way, new product release information can be linked with the user's calendar and reminders to notify the user at the optimal timing.

[0083] The information collection unit can link new product release information with social media, allowing users to share information. The information collection unit, for example, links new product release information with social media, allowing users to share information. For example, it provides a function for sharing new product information on SNS. The information collection unit also enables users to share new product information with friends and followers through social media. For example, it shares information about new products from a specific brand. The information collection unit also provides optimal information to users based on feedback from social media. For example, it analyzes reactions to shared information and suggests information relevant to the user. In this way, new product release information can be linked with social media, allowing users to share information.

[0084] The information collecting unit can use the emotion estimation function to identify new release information that the user is most interested in and provide that information preferentially. The information collecting unit, for example, uses the emotion estimation function to identify new release information that the user is most interested in. For example, it preferentially displays information with a high emotion score. The information collecting unit also monitors the user's emotions in real time and provides information according to changes in emotion. For example, it preferentially displays information that the user is excited about. The information collecting unit also identifies the information that the user is most interested in based on the user's past emotion data. For example, it preferentially provides information that the user has given a high rating in the past. In this way, it is possible to identify new release information that the user is most interested in and provide that information preferentially.

[0085] The information collecting unit can provide product reviews and ratings at the same time as providing sale information, thereby enabling users to make better choices. For example, the information collecting unit can provide product reviews and ratings at the same time as providing sale information. For example, items that have received high user ratings are displayed preferentially. The information collecting unit can also collect information from review sites and rating sites and provide it to users. For example, based on reviews and ratings of a specific product, it can suggest the most suitable item for the user. The information collecting unit can also analyze reviews and ratings to provide information that matches the user's preferences. For example, it can suggest highly rated items preferentially. This allows the information collecting unit to provide product reviews and ratings at the same time as providing sale information, thereby enabling users to make better choices.

[0086] When providing sale information, the information collecting unit can prioritize providing information that is likely to be of particular interest to the user based on the user's past purchase history and preferences. The information collecting unit provides sale information based on the user's past purchase history and preferences, for example. For example, sale information for brands that the user has purchased in the past may be displayed preferentially. The information collecting unit also customizes sale information based on the user's preferences. For example, sale information for specific colors or styles may be provided preferentially. The information collecting unit also combines the user's past purchase history and preferences to identify information that is likely to be of particular interest to the user. For example, sale information for items that the user has given high ratings in the past may be provided preferentially. This allows sale information that is likely to be of particular interest to be provided preferentially based on the user's past purchase history and preferences.

[0087] The information collection unit uses the emotion estimation function to analyze the emotions of the user when receiving sale information and can prioritize providing information that elicits positive emotions. The information collection unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving sale information. For example, it prioritizes displaying information that the user is excited about. The information collection unit also monitors the user's emotions in real time and provides information according to changes in emotions. For example, if the user expresses positive emotions, it prioritizes displaying that information. The information collection unit also identifies information that elicits positive emotions based on the user's past emotion data. For example, it prioritizes providing information that has been highly rated in the past. This makes it possible to prioritize providing positive sale information based on the user's emotions.

[0088] The information collection unit can link sale information with the user's calendar and reminders to notify the user at the optimal timing. For example, the information collection unit links sale information with the user's calendar to notify the user at the optimal timing. For example, a reminder can be set the day before the sale starts. The information collection unit also links with the user's reminders to ensure that important information is not missed. For example, the start and end dates of the sale can be added to the reminder. The information collection unit also analyzes the user's behavioral patterns to notify the user at the optimal timing. For example, the information can be notified during the time period when the user is most active. In this way, sale information can be linked with the user's calendar and reminders to notify the user at the optimal timing.

[0089] The information collection unit can link sales information with social media, allowing users to share information. The information collection unit, for example, links sales information with social media, allowing users to share information. For example, it provides a function for sharing sales information on SNS. The information collection unit also allows users to share sales information with friends and followers through social media. For example, it shares sales information for a specific brand. The information collection unit also provides optimal information to users based on feedback from social media. For example, it analyzes reactions to shared information and suggests information relevant to the user. In this way, sales information can be linked with social media, allowing users to share information.

[0090] The information collection unit can use the emotion estimation function to identify sales information that the user is most interested in and provide that information preferentially. The information collection unit, for example, uses the emotion estimation function to identify sales information that the user is most interested in. For example, information with a high emotion score is preferentially displayed. The information collection unit also monitors the user's emotions in real time and provides information according to changes in emotion. For example, information that the user is excited about is preferentially displayed. The information collection unit also identifies information that the user is most interested in based on the user's past emotion data. For example, information that has been highly rated in the past is preferentially provided. This allows the information collection unit to identify sales information that the user is most interested in and provide that information preferentially.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The condition setting unit can analyze the user's lifestyle and activity patterns and suggest optimal fashion items based on them. For example, if the user frequently goes to the gym, sportswear and activewear can be suggested first. If the user has many business meetings, formal business wear can be suggested. Furthermore, if the user is planning a trip, casual wear and accessories suitable for travel can be suggested. This allows the system to suggest fashion items that match the user's lifestyle.

[0093] The condition setting unit can analyze the user's health and fitness data and suggest optimal fashion items based on that data. For example, it can suggest well-fitting clothing based on the user's weight and body fat percentage. It can also suggest appropriate sportswear and shoes based on the user's exercise habits. It can also suggest fashion items that motivate the user and match their health goals. This allows it to suggest fashion items that match the user's health condition.

[0094] The condition setting unit can analyze a user's social media activity and suggest optimal fashion items based on that analysis. For example, it can refer to the styles of fashion influencers the user follows on Instagram. It can also extract preferred styles from photos and comments posted by the user and make suggestions based on those. It can also analyze information about events and parties the user will be attending and suggest fashion items that are suitable for those events. This allows it to suggest fashion items based on the user's social media activity.

[0095] The condition setting unit can estimate the user's emotions and suggest fashion items that will help the user relax based on the estimated emotions. For example, if the user is feeling stressed, it can suggest casual wear or loungewear that has a relaxing effect. If the user is tired, it can also suggest pajamas or relaxation wear made of comfortable materials. Furthermore, if the user feels like they want to refresh themselves, it can also suggest items with bright colors or designs. In this way, it is possible to suggest fashion items that will help the user relax based on the user's emotions.

[0096] The condition setting unit can estimate the user's emotions and suggest fashion items that will make the user feel confident based on the estimated emotions. For example, if the user wants to feel confident, items with stylish and eye-catching designs can be suggested. Also, if the user wants to express themselves, items with unique designs or customizable items can be suggested. Furthermore, it is also possible to suggest formal wear and accessories that will make the user feel confident when attending a special event. In this way, fashion items that will make the user feel confident can be suggested based on the user's emotions.

[0097] The information gathering unit can collect information on new brands and designers that the user may be interested in and suggest them to the user. For example, if the user has a preference for a particular style or design, new brands that fit that style can be suggested. Also, if the user is interested in eco-friendly fashion, information on sustainable brands and designers can be provided. Furthermore, if the user is interested in a particular region or culture, information on brands and designers related to that region or culture can be suggested. This allows the user to be provided with information on new brands and designers.

[0098] The information collection unit can estimate the user's emotions and provide information on fashion events and shows that the user may be interested in based on the estimated emotions. For example, if the user is excited, information on fashion shows and events taking place nearby can be provided. If the user feels like relaxing, information on fashion events and workshops that the user can participate in online can be provided. Furthermore, if the user is interested in new trends, it is also possible to provide information on the latest fashion shows and trends. This makes it possible to provide information on fashion events and shows that the user may be interested in based on the user's emotions.

[0099] The information collection unit can suggest fashion items suitable for specific seasons or events based on the user's purchasing history and preferences. For example, the information collection unit can analyze the trends of items the user has purchased in the past and suggest summer beachwear or winter coats. If the user plans to attend a specific event, the information collection unit can suggest dresses and accessories suitable for that event. Furthermore, if the user prefers different styles for each season, the information collection unit can suggest fashion items suitable for that season. This makes it possible to suggest fashion items suitable for specific seasons or events.

[0100] The information collection unit can estimate the user's emotions and provide the fashion news and trend information that the user is most interested in based on the estimated emotions. For example, if the user is excited, the latest fashion news and trend information can be displayed preferentially. Also, if the user feels like relaxing, articles and information about relaxing fashion can be provided. Furthermore, if the user is interested in a new trend, detailed information and interview articles about that trend can be provided. In this way, the fashion news and trend information that the user is most interested in can be provided based on the user's emotions.

[0101] The information collection unit can suggest fashion items related to a specific theme or collection based on the user's purchasing history and preferences. For example, it can analyze the trends of items the user has purchased in the past and suggest a new collection from a specific designer. Also, if the user is interested in a specific theme, it can suggest items related to that theme. Furthermore, if the user is looking for a collection tailored to a specific event or season, it can also suggest items related to that collection. This makes it possible to suggest fashion items related to a specific theme or collection.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The condition setting section sets the user's conditions and preferences. For example, the user can set conditions such as a specific brand, color, size, price range, style, etc. Step 2: The information gathering unit automatically checks new product information from e-commerce sites and brands based on the conditions set by the condition setting unit. For example, the generation AI automatically checks new product information from various e-commerce sites and brands based on the conditions and preferences set by the user. The generation AI collects information based on prompts that include the conditions and preferences set by the user. Step 3: The suggestion unit analyzes the information collected by the information collection unit and automatically suggests fashion items that perfectly match the user's needs. For example, the generation AI analyzes the collected information and automatically suggests fashion items that perfectly match the user's needs.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0130] 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.

[0131] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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, to avoid confusion and 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.

[0170] 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. [Explanation of symbols]

[0171] 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 condition setting unit for setting user conditions and preferences; an information collection unit that automatically checks new product information from e-commerce sites and brands based on the conditions set by the condition setting unit; a suggestion unit that analyzes the information collected by the information collection unit and automatically suggests fashion items that perfectly match the user's needs. A system characterized by:

2. The condition setting unit Using emotion estimation function, the system analyzes the emotions of the user in real time as they input their input and proposes condition settings to elicit positive emotions.

2. The system of claim 1.

3. The information collecting unit When the AI ​​checks the new product information, it uses image recognition technology to analyze the design and color of the product and collects the information that matches the user's preferences.

2. The system of claim 1.

4. The proposal unit Analyzing the user's past feedback to improve the accuracy of suggestions 2. The system of claim 1.

5. The information collecting unit Using an emotion estimation function, the emotion of the user when receiving the new product information is analyzed, and the information that elicits positive emotions is preferentially provided.

2. The system of claim 1.

6. The information collecting unit Using an emotion estimation function, the emotions of the user when receiving sales information are analyzed, and the information that elicits positive emotions is preferentially provided.

2. The system of claim 1.

7. The condition setting unit The user's conditions are set using a new interface such as voice input or gesture input.

2. The system of claim 1.

8. The information collecting unit The check of new product information is performed in accordance with a specific time period or event, and the information is provided to the user at the optimal timing.

2. The system of claim 1.

Images