system

Abstract

The system according to the embodiment aims to utilize information on the user's clothing and accessories to suggest optimal outfits based on the weather and trends. [Solution] The system according to the embodiment includes a registration unit, an acquisition unit, a suggestion unit, a feedback unit, and a linkage unit. The registration unit registers the user's clothing and accessories. The acquisition unit acquires external information on weather, temperature, and fashion trends. The suggestion unit suggests outfits based on the information obtained by the registration and acquisition units. The feedback unit accumulates user feedback. The linkage unit automatically registers purchased products in conjunction with an e-commerce site.

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 do not adequately manage a user's clothing and accessories, or suggest outfits based on weather and trends, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize information on the user's clothing and accessories to suggest optimal outfits based on the weather and trends. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an acquisition unit, a suggestion unit, a feedback unit, and a linkage unit. The registration unit registers the user's clothing and accessories. The acquisition unit acquires external information such as weather, temperature, and fashion trends. The suggestion unit suggests outfits based on the information acquired by the registration unit and acquisition unit. The feedback unit accumulates user feedback. The linkage unit automatically registers purchased products in conjunction with an e-commerce site. [Effects of the Invention]

[0007] The system according to the embodiment can utilize information about a user's clothing and accessories to suggest optimal outfits based on the weather and trends. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI app according to an embodiment of the present invention is a system that, when a user registers their clothing and accessories, suggests outfits to wear based on the day's weather, temperature, and fashion trends. This generative AI app allows users to register their clothing and accessories, and the generative AI makes suggestions based on that information. Suggestions are made in conjunction with external information such as weather, temperature, and fashion trends. Users can also provide feedback, such as "likes," on suggested outfits, and the generative AI accumulates that feedback and evolves to make suggestions tailored to the user's preferences. Furthermore, as a recommendation function, the app can be linked to e-commerce sites to automatically register purchased items. For example, users register their clothing and accessories in the app. For example, they register items such as shirts, pants, skirts, jackets, necklaces, and earrings using photos and text. This information is input into the generative AI. The generative AI then suggests optimal outfits for the user based on the registered information and in conjunction with external information such as the day's weather, temperature, and fashion trends. For example, it suggests light clothing for sunny days, warm clothing for cold days, and clothing incorporating trendy colors and designs. In addition, users can provide feedback on the suggested outfits, such as "like" or "not so good." The generative AI accumulates this feedback and evolves to make suggestions tailored to the user's preferences. For example, it will prioritize suggested styles and colors that users have given many "likes." The recommendation function also works in conjunction with e-commerce sites, allowing purchased items to be automatically registered. For example, if a user purchases a new shirt on an e-commerce site, that information is automatically registered in the app, and the generative AI can incorporate that shirt into its recommendations. This system makes it easier for users to choose their daily outfits and enjoy wearing the perfect outfit that matches their preferences and trends. This allows the generative AI app to register the user's clothing and accessories, obtain external information, suggest outfits, accumulate feedback, and automatically register purchased items.

[0029] A generative AI app according to an embodiment includes a registration unit, an acquisition unit, a suggestion unit, a feedback unit, and an interlocking unit. The registration unit registers a user's clothing and accessories. Examples of the user's clothing and accessories include, but are not limited to, shirts, pants, skirts, jackets, necklaces, and earrings. The registration unit registers the clothing and accessories by, for example, having the user take photos of the clothing and accessories and upload them to the app. The user can also input item information in text format. For example, the user may input information such as the item's brand name, color, size, and material. The acquisition unit acquires external information such as weather, temperature, and fashion trends. The acquisition unit acquires weather and temperature information from, for example, a weather data provision service. Fashion trend information can also be acquired based on trend data from fashion magazines and social media. For example, the acquisition unit analyzes the latest fashion magazine articles and extracts trending colors and designs. The suggestion unit suggests outfits based on the information obtained by the registration unit and the acquisition unit. The suggestion unit uses the generative AI to suggest optimal outfits for the user. For example, the suggestion unit suggests light clothing for sunny days, warm clothing for cold days, clothing incorporating trendy colors and designs, etc. The suggestion unit allows the generation AI to analyze the user's registered information and external information to find the optimal combination. The feedback unit accumulates user feedback. For example, the feedback unit allows the user to rate the suggested clothing, such as "like" or "not so good." The feedback unit accumulates the ratings and evolves the generation AI to make suggestions tailored to the user's preferences. For example, the feedback unit adjusts the generation AI to prioritize suggesting styles and colors that users have given many "likes." The interlocking unit automatically registers purchased products in conjunction with an e-commerce site. For example, when a user purchases a new shirt on an e-commerce site, the interlocking unit automatically registers that information in the app. The interlocking unit acquires the purchase history from the e-commerce site and reflects it in the app. As a result, the generation AI app according to the embodiment can register the user's clothing and accessories, acquire external information, suggest outfits, accumulate feedback, and automatically register purchased products.

[0030] The suggestion unit can suggest outfits based on the weather, temperature, and fashion trends. For example, the suggestion unit can suggest lighter outfits on sunny days. For example, the suggestion unit can suggest a thin shirt or a light jacket on a sunny day. The suggestion unit can also suggest warmer outfits on cold days. For example, the suggestion unit can suggest a thick coat or sweater on a cold day. The suggestion unit can also suggest outfits that incorporate trendy colors and designs. For example, the suggestion unit can suggest outfits that incorporate the trendy colors red and blue. This makes it possible to suggest outfits based on the weather, temperature, and fashion trends. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the weather, temperature, and fashion trends into the generation AI, which can then suggest optimal outfits.

[0031] The feedback unit accumulates user feedback such as "like" or "not so good," and the generation AI can evolve to make suggestions tailored to the user's preferences. For example, the feedback unit can allow the user to rate proposed clothing as "like" or "not so good." For example, the feedback unit accumulates the styles and colors of clothing that the user has "liked." The feedback unit can also accumulate the styles and colors of clothing that the user has rated as "not so good." The feedback unit evolves to allow the generation AI to make suggestions tailored to the user's preferences based on the ratings. For example, the feedback unit can adjust the generation AI to prioritize proposing styles and colors that the user has "liked." This allows the generation AI to accumulate user feedback and evolve to make suggestions tailored to the user's preferences. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the feedback unit can input user rating data into the generation AI, and the generation AI can evolve to make suggestions tailored to the user's preferences.

[0032] The interlocking unit can automatically register information about products purchased on an e-commerce site in the app. For example, when a user purchases a new shirt on an e-commerce site, the interlocking unit automatically registers that information in the app. For example, the interlocking unit obtains the purchase history of the e-commerce site and reflects it in the app. The interlocking unit can also use an API for automatically registering information about purchased products in the app. For example, the interlocking unit obtains information about purchased products using the API of the e-commerce site and registers it in the app. This makes it possible to automatically register information about products purchased on an e-commerce site in the app. Some or all of the above-described processing in the interlocking unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interlocking unit can input purchase history data into a generation AI, which then automatically registers the data in the app.

[0033] The acquisition unit can acquire external information such as weather, temperature, and fashion trends. The acquisition unit acquires weather and temperature information from, for example, a weather data provision service. For example, the acquisition unit acquires the latest weather and temperature information using the API of the weather data provision service. The acquisition unit can also acquire fashion trend information based on trend data from fashion magazines and social media. For example, the acquisition unit analyzes articles in fashion magazines to extract trendy colors and designs. The acquisition unit can also analyze trend data on social media to acquire the latest fashion information. For example, the acquisition unit analyzes posts on social media to extract trending keywords and hashtags. This makes it possible to acquire external information such as weather, temperature, and fashion trends. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input weather data and fashion trend data into the generation AI, which then acquires the external information.

[0034] The registration unit can register the user's clothing and accessories using photos or text. The registration unit performs registration by, for example, having the user take photos of the clothing and accessories and upload them to the app. For example, the registration unit allows the user to take photos of the clothing and accessories using a smartphone camera and upload the photos to the app. The registration unit can also allow the user to input item information in text format. For example, the registration unit provides a form for inputting information such as the brand name, color, size, and material of the item. This makes it possible to register the user's clothing and accessories using photos or text. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input data entered by the user into the generation AI, which then analyzes and registers the item information.

[0035] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit can prioritize displaying categories of items that the user has frequently registered in the past. For example, the registration unit can prioritize displaying categories such as shirts and pants that the user has frequently registered in the past. The registration unit can also prioritize suggesting registration methods (e.g., photos, text) that the user has used in the past. For example, if the user has frequently used photos in the past to register, the registration unit can prioritize suggesting a registration method using photos. The registration unit can also analyze the user's past registration history to determine whether they tend to register at specific times of the day and suggest the optimal timing. For example, if the user has frequently registered at night in the past, the registration unit can send a notification encouraging the user to register at night. This makes it possible to analyze the user's past registration history and select the optimal registration method. Some or all of the above-described processing in the registration unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the registration unit can input the user's past registration data into the generation AI, which can select the optimal registration method.

[0036] When registering clothing and accessories, the registration unit can filter based on the user's current fashion style and areas of interest. For example, if the user prefers a casual style, the registration unit can prioritize displaying casual items. For example, the registration unit can prioritize displaying casual shirts and pants. Furthermore, if the user is interested in a particular brand, the registration unit can prioritize displaying items from that brand. For example, if the user has registered many items from a particular brand, the registration unit can prioritize displaying new items from that brand. Furthermore, if the user prefers a particular color or design, the registration unit can prioritize displaying items of that color or design. For example, if the user has registered many blue items, the registration unit can prioritize displaying new blue items. This enables filtering based on the user's current fashion style and areas of interest. Some or all of the above-described processing in the registration unit can be performed using, or without, a generation AI. For example, the registration unit can input data on the user's fashion style and areas of interest into the generation AI, which can then filter out the most suitable items.

[0037] When registering clothing and accessories, the registration unit can prioritize registering highly relevant items taking into account the user's geographical location information. For example, if the user lives in a cold region, the registration unit prioritizes registering warm clothing. For example, the registration unit prioritizes registering warm items such as coats and sweaters for a user living in a cold region. The registration unit can also prioritize registering items with an urban design for a user living in an urban area. For example, the registration unit prioritizes registering items with an urban design such as suits and dresses for a user living in an urban area. The registration unit can also prioritize registering beachwear and casual items for a user living by the sea. For example, the registration unit prioritizes registering beachwear such as swimsuits and sandals for a user living by the sea. This makes it possible to prioritize registering highly relevant items taking into account the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the user's geographical location information into the generation AI, which can then prioritize registering highly relevant items.

[0038] The registration unit can analyze the user's social media activity and register related items when registering clothing or accessories. For example, the registration unit analyzes the style that the user frequently posts on social media and registers related items. For example, if the user frequently posts casual styles on social media, the registration unit can prioritize registering casual items. The registration unit can also register items based on the style of a fashion influencer the user follows. For example, the registration unit can prioritize registering items recommended by the influencer the user follows. The registration unit can also prioritize registering items that the user has given many "likes" or comments on. For example, the registration unit can prioritize displaying items that the user has given many "likes" to encourage registration. This makes it possible to analyze the user's social media activity and register related items. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the registration unit can input the user's social media data into the generation AI, which can then register related items.

[0039] The acquisition unit can analyze past weather and temperature data and select an appropriate acquisition method. The acquisition unit, for example, prioritizes acquisition of information tailored to a specific season or weather based on past weather data. For example, the acquisition unit analyzes past weather data and prioritizes acquisition of information related to weather that frequently occurs in a specific season. The acquisition unit can also prioritize acquisition of information tailored to a specific temperature range based on past temperature data. For example, the acquisition unit analyzes past temperature data and prioritizes acquisition of information related to a specific temperature range. The acquisition unit can also analyze past weather and temperature data and acquire information based on predicted weather. For example, the acquisition unit predicts future weather based on past weather and temperature data and acquires information based on the prediction. This makes it possible to analyze past weather and temperature data and select an optimal acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input past weather and temperature data into a generation AI, which selects an optimal acquisition method.

[0040] When acquiring external information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user prefers outdoor activities, the acquisition unit prioritizes acquiring weather information related to outdoor activities. For example, the acquisition unit prioritizes acquiring weather information useful for the user when engaging in outdoor activities. Furthermore, if the user is a businessperson, the acquisition unit can prioritize acquiring weather information related to business. For example, the acquisition unit prioritizes acquiring weather information useful for business meetings and business trips. Furthermore, if the user is interested in fashion, the acquisition unit can prioritize acquiring information related to fashion trends. For example, the acquisition unit prioritizes acquiring information about fashion trends in which the user is interested. This enables filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the optimal external information.

[0041] When acquiring external information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in a cold region, the acquisition unit prioritizes acquiring weather information related to the cold region. For example, the acquisition unit prioritizes acquiring weather information for the cold region for a user living in a cold region. Furthermore, if the user lives in an urban area, the acquisition unit can also prioritize acquiring weather information related to the urban area. For example, the acquisition unit prioritizes acquiring weather information for the urban area for a user living in an urban area. Furthermore, if the user lives by the sea, the acquisition unit can also prioritize acquiring weather information related to the seaside. For example, the acquisition unit prioritizes acquiring weather information for the seaside for a user living by the sea. This makes it possible to prioritize acquiring highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI, which can then prioritize acquiring highly relevant information.

[0042] When acquiring external information, the acquisition unit can analyze the user's social media activities and acquire related information. For example, the acquisition unit prioritizes acquiring information related to topics to which the user frequently posts on social media. For example, the acquisition unit prioritizes acquiring weather information and fashion trend information related to topics to which the user frequently posts on social media. The acquisition unit can also prioritize acquiring information related to the content posted by influencers followed by the user. For example, the acquisition unit prioritizes acquiring information related to topics posted by influencers followed by the user. The acquisition unit can also prioritize acquiring information related to topics to which the user has given many "likes" and comments. For example, the acquisition unit prioritizes acquiring information related to topics to which the user has given many "likes." This makes it possible to analyze the user's social media activities and acquire related information. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media data into the generation AI, which then acquires the related information.

[0043] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on fluctuations in weather and temperature. For example, when the weather changes suddenly, the suggestion unit makes a proposal including detailed weather information. For example, when the weather changes suddenly, the suggestion unit makes a proposal for clothing including detailed weather information. Furthermore, when the temperature fluctuates significantly, the suggestion unit can also make a detailed proposal for clothing according to the temperature. For example, when the temperature fluctuates significantly, the suggestion unit makes a detailed proposal for clothing according to the temperature. Furthermore, the suggestion unit can also make a simple proposal when the weather and temperature are stable. For example, when the weather and temperature are stable, the suggestion unit makes a simple proposal for clothing. This makes it possible to adjust the level of detail of the proposal based on fluctuations in weather and temperature. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input weather and temperature data into the generation AI, which can adjust the level of detail of the proposal.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the fashion trend. The suggestion unit, for example, suggests color combinations based on trendy colors. For example, the suggestion unit suggests color combinations based on trendy colors such as red and blue. The suggestion unit can also suggest design combinations based on trendy designs. For example, the suggestion unit suggests design combinations based on trendy designs such as stripes and checks. The suggestion unit can also suggest brand combinations based on trendy brands. For example, the suggestion unit suggests brand combinations based on specific trendy brands. This makes it possible to apply an optimal suggestion algorithm depending on the category of the fashion trend. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on fashion trends into a generation AI, which can then apply different suggestion algorithms.

[0045] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the forecast of weather and temperature. For example, when rain is predicted, the suggestion unit prioritizes suggesting rain gear and waterproof clothing. For example, when rain is predicted, the suggestion unit prioritizes suggesting a raincoat and waterproof shoes. The suggestion unit can also prioritize suggesting cool clothing when the temperature is high. For example, when the temperature is high, the suggestion unit prioritizes suggesting a thin shirt and shorts. The suggestion unit can also prioritize suggesting warm clothing when the temperature is low. For example, when the temperature is low, the suggestion unit prioritizes suggesting a thick coat and sweater. This makes it possible to determine the priority of the suggestions based on the forecast of weather and temperature. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input forecast data of weather and temperature into the generation AI, which can then determine the priority of the suggestions.

[0046] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of fashion trends. For example, the suggestion unit preferentially suggests color combinations based on trendy colors. For example, the suggestion unit preferentially suggests color combinations based on trendy colors such as red and blue. The suggestion unit can also preferentially suggest design combinations based on trendy designs. For example, the suggestion unit preferentially suggests design combinations based on trendy designs such as stripes and checks. The suggestion unit can also preferentially suggest brand combinations based on trendy brands. For example, the suggestion unit preferentially suggests brand combinations based on specific trendy brands. This makes it possible to adjust the order of suggestions based on the relevance of fashion trends. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on fashion trends into the generation AI, which can then adjust the order of suggestions.

[0047] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, if the user has provided detailed feedback in the past, the feedback unit requests detailed feedback. For example, when the user has provided detailed feedback in the past, the feedback unit asks a similar detailed question to request specific feedback. Furthermore, if the user has provided brief feedback in the past, the feedback unit can also request brief feedback. For example, when the user has provided brief feedback in the past, the feedback unit asks a similar brief question to collect feedback in a short period of time. Furthermore, the feedback unit can also request feedback for a specific time period based on the user's past feedback history. For example, if the user has provided feedback in a specific time period in the past, the feedback unit sends a notification requesting feedback for that time period. This makes it possible to select an optimal collection method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which can then select the optimal collection method.

[0048] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides a feedback collection method tailored to the screen size. For example, the feedback unit asks simple questions optimized for the small screen of a smartphone to collect feedback. Furthermore, if the user is using a tablet, the feedback unit can also provide a feedback collection method optimized for a large screen. For example, the feedback unit asks detailed questions optimized for the large screen of a tablet to collect feedback. Furthermore, if the user is using a smartwatch, the feedback unit can also provide a concise and highly visible feedback collection method. For example, the feedback unit asks simple questions optimized for the small screen of a smartwatch to collect feedback. This makes it possible to select the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's device information into the generation AI, which can then select the optimal collection method.

[0049] During automatic registration, the interlocking unit can select an appropriate registration method by referring to the user's past purchase history. For example, the interlocking unit prioritizes registration of categories of items the user has purchased in the past. For example, the interlocking unit prioritizes registration of categories such as shirts and pants that the user has purchased frequently in the past. The interlocking unit can also prioritize suggesting registration methods (e.g., photos, text) that the user has used in the past. For example, if the user has used photos frequently in the past to register, the interlocking unit prioritizes suggesting a registration method using photos. The interlocking unit can also analyze the user's tendency to register at specific time periods based on the user's past purchase history and suggest the optimal timing. For example, if the user has made many purchases at night in the past, the interlocking unit can send a notification encouraging registration at night. This makes it possible to select the optimal registration method by referring to the user's past purchase history. Some or all of the above-described processing in the interlocking unit may be performed using, or without, a generation AI. For example, the interlocking unit can input the user's past purchase data into the generation AI, which can then select the optimal registration method.

[0050] During automatic registration, the interlocking unit can select an appropriate registration method taking into account the user's geographical location information. For example, if the user lives in a cold region, the interlocking unit prioritizes registering warm clothing. For example, the interlocking unit prioritizes registering warm items such as coats and sweaters for a user living in a cold region. Furthermore, the interlocking unit can also prioritize registering items with an urban design for a user living in an urban area. For example, the interlocking unit prioritizes registering items with an urban design such as suits and dresses for a user living in an urban area. Furthermore, the interlocking unit can also prioritize registering beachwear and casual items for a user living by the sea. For example, the interlocking unit prioritizes registering beachwear such as swimsuits and sandals for a user living by the sea. This makes it possible to select an optimal registration method taking into account the user's geographical location information. Some or all of the above-described processing by the interlocking unit may be performed using, or without, a generation AI. For example, the interlocking unit can input the user's geographical location information into the generation AI, which can then select the optimal registration method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The suggestion unit can analyze the user's past feedback history to improve the accuracy of suggestions. For example, the suggestion unit can extract characteristics of items that the user has given many "likes" in the past and suggest new items with similar characteristics. The suggestion unit can also adjust the suggestions to avoid characteristics of items that the user has rated as "not so good." Furthermore, the suggestion unit can make suggestions tailored to specific seasons or events based on the user's feedback history. This makes it possible to make more accurate suggestions by utilizing the user's past feedback history.

[0053] The acquisition unit can analyze the user's social media activity and acquire related external information. For example, the acquisition unit can analyze the content of posts by fashion influencers the user follows on social media to acquire the latest fashion trends. The acquisition unit can also prioritize acquisition of information related to posts that the user has received many likes and comments on. Furthermore, the acquisition unit can analyze the time periods during which the user is active on social media and provide the latest information tailored to those time periods. This makes it possible to acquire more relevant external information by utilizing the user's social media activity.

[0054] The linking unit can automatically register highly relevant products by taking into account the user's geographical location information. For example, if the user lives in a cold region, warm clothing can be registered with priority. Also, if the user lives in an urban area, items with an urban design can be registered with priority. Furthermore, if the user lives by the sea, beachwear and casual items can be registered with priority. This makes it possible to automatically register more relevant products by utilizing the user's geographical location information.

[0055] The acquisition unit can analyze past weather and temperature data and select an appropriate acquisition method. For example, it can prioritize acquisition of information tailored to a specific season or weather based on past weather data. It can also prioritize acquisition of information tailored to a specific temperature range based on past temperature data. It can also analyze past weather and temperature data and acquire information based on predicted weather. This makes it possible to acquire more accurate external information by utilizing past data.

[0056] The feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, if the user has provided detailed feedback in the past, a question requesting detailed feedback can be asked. Also, if the user has provided brief feedback in the past, a question requesting brief feedback can be asked. Furthermore, feedback can be requested at a specific time period based on the user's past feedback history. This makes it possible to provide an optimal collection method by utilizing the user's past feedback history.

[0057] When making suggestions, the suggestion unit can determine the priority of the suggestions based on predicted weather and temperature. For example, if rain is predicted, rain gear and waterproof clothing can be suggested with priority. Also, if the temperature is high, cool clothing can be suggested with priority. Furthermore, if the temperature is low, warm clothing can be suggested with priority. This makes it possible to make more appropriate suggestions based on predicted weather and temperature.

[0058] When acquiring external information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user likes outdoor activities, weather information related to outdoor activities can be acquired preferentially. Also, if the user is a business person, weather information related to business can be acquired preferentially. Furthermore, if the user is interested in fashion, information related to fashion trends can be acquired preferentially. This makes it possible to acquire more relevant external information based on the user's current living situation and areas of interest.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The registration section registers the user's clothing and accessories. Users can register clothing and accessories by taking a photo of the clothing or accessories and uploading it to the app. They can also enter item information (brand name, color, size, material, etc.) in text format. Step 2: The acquisition unit acquires external information such as weather, temperature, fashion trends, etc. The acquisition unit acquires weather and temperature information from a weather data provider service, and acquires fashion trend information based on trend data from fashion magazines and social media. Step 3: The suggestion unit suggests outfits based on the information obtained by the registration unit and acquisition unit. Using a generation AI, the suggestion unit suggests optimal outfits to the user based on the weather and fashion trends. Step 4: The feedback section accumulates user feedback. Users rate the proposed outfits, and as these ratings accumulate, the generation AI evolves to make suggestions that match the user's preferences. Step 5: The linking unit automatically registers the purchased products in conjunction with the e-commerce site. Information about the products purchased by the user on the e-commerce site is automatically registered in the app.

[0061] (Example 2) A generative AI app according to an embodiment of the present invention is a system that, when a user registers their clothing and accessories, suggests outfits to wear based on the day's weather, temperature, and fashion trends. This generative AI app allows users to register their clothing and accessories, and the generative AI makes suggestions based on that information. Suggestions are made in conjunction with external information such as weather, temperature, and fashion trends. Users can also provide feedback, such as "likes," on suggested outfits, and the generative AI accumulates that feedback and evolves to make suggestions tailored to the user's preferences. Furthermore, as a recommendation function, the app can be linked to e-commerce sites to automatically register purchased items. For example, users register their clothing and accessories in the app. For example, they register items such as shirts, pants, skirts, jackets, necklaces, and earrings using photos and text. This information is input into the generative AI. The generative AI then suggests optimal outfits for the user based on the registered information and in conjunction with external information such as the day's weather, temperature, and fashion trends. For example, it suggests light clothing for sunny days, warm clothing for cold days, and clothing incorporating trendy colors and designs. In addition, users can provide feedback on the suggested outfits, such as "like" or "not so good." The generative AI accumulates this feedback and evolves to make suggestions tailored to the user's preferences. For example, it will prioritize suggested styles and colors that users have given many "likes." The recommendation function also works in conjunction with e-commerce sites, allowing purchased items to be automatically registered. For example, if a user purchases a new shirt on an e-commerce site, that information is automatically registered in the app, and the generative AI can incorporate that shirt into its recommendations. This system makes it easier for users to choose their daily outfits and enjoy wearing the perfect outfit that matches their preferences and trends. This allows the generative AI app to register the user's clothing and accessories, obtain external information, suggest outfits, accumulate feedback, and automatically register purchased items.

[0062] A generative AI app according to an embodiment includes a registration unit, an acquisition unit, a suggestion unit, a feedback unit, and an interlocking unit. The registration unit registers a user's clothing and accessories. Examples of the user's clothing and accessories include, but are not limited to, shirts, pants, skirts, jackets, necklaces, and earrings. The registration unit registers the clothing and accessories by, for example, having the user take photos of the clothing and accessories and upload them to the app. The user can also input item information in text format. For example, the user may input information such as the item's brand name, color, size, and material. The acquisition unit acquires external information such as weather, temperature, and fashion trends. The acquisition unit acquires weather and temperature information from, for example, a weather data provision service. Fashion trend information can also be acquired based on trend data from fashion magazines and social media. For example, the acquisition unit analyzes the latest fashion magazine articles and extracts trending colors and designs. The suggestion unit suggests outfits based on the information obtained by the registration unit and the acquisition unit. The suggestion unit uses the generative AI to suggest optimal outfits for the user. For example, the suggestion unit suggests light clothing for sunny days, warm clothing for cold days, clothing incorporating trendy colors and designs, etc. The suggestion unit allows the generation AI to analyze the user's registered information and external information to find the optimal combination. The feedback unit accumulates user feedback. For example, the feedback unit allows the user to rate the suggested clothing, such as "like" or "not so good." The feedback unit accumulates the ratings and evolves the generation AI to make suggestions tailored to the user's preferences. For example, the feedback unit adjusts the generation AI to prioritize suggesting styles and colors that users have given many "likes." The interlocking unit automatically registers purchased products in conjunction with an e-commerce site. For example, when a user purchases a new shirt on an e-commerce site, the interlocking unit automatically registers that information in the app. The interlocking unit acquires the purchase history from the e-commerce site and reflects it in the app. As a result, the generation AI app according to the embodiment can register the user's clothing and accessories, acquire external information, suggest outfits, accumulate feedback, and automatically register purchased products.

[0063] The suggestion unit can suggest outfits based on the weather, temperature, and fashion trends. For example, the suggestion unit can suggest lighter outfits on sunny days. For example, the suggestion unit can suggest a thin shirt or a light jacket on a sunny day. The suggestion unit can also suggest warmer outfits on cold days. For example, the suggestion unit can suggest a thick coat or sweater on a cold day. The suggestion unit can also suggest outfits that incorporate trendy colors and designs. For example, the suggestion unit can suggest outfits that incorporate the trendy colors red and blue. This makes it possible to suggest outfits based on the weather, temperature, and fashion trends. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the weather, temperature, and fashion trends into the generation AI, which can then suggest optimal outfits.

[0064] The feedback unit accumulates user feedback such as "like" or "not so good," and the generation AI can evolve to make suggestions tailored to the user's preferences. For example, the feedback unit can allow the user to rate proposed clothing as "like" or "not so good." For example, the feedback unit accumulates the styles and colors of clothing that the user has "liked." The feedback unit can also accumulate the styles and colors of clothing that the user has rated as "not so good." The feedback unit evolves to allow the generation AI to make suggestions tailored to the user's preferences based on the ratings. For example, the feedback unit can adjust the generation AI to prioritize proposing styles and colors that the user has "liked." This allows the generation AI to accumulate user feedback and evolve to make suggestions tailored to the user's preferences. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the feedback unit can input user rating data into the generation AI, and the generation AI can evolve to make suggestions tailored to the user's preferences.

[0065] The interlocking unit can automatically register information about products purchased on an e-commerce site in the app. For example, when a user purchases a new shirt on an e-commerce site, the interlocking unit automatically registers that information in the app. For example, the interlocking unit obtains the purchase history of the e-commerce site and reflects it in the app. The interlocking unit can also use an API for automatically registering information about purchased products in the app. For example, the interlocking unit obtains information about purchased products using the API of the e-commerce site and registers it in the app. This makes it possible to automatically register information about products purchased on an e-commerce site in the app. Some or all of the above-described processing in the interlocking unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interlocking unit can input purchase history data into a generation AI, which then automatically registers the data in the app.

[0066] The acquisition unit can acquire external information such as weather, temperature, and fashion trends. The acquisition unit acquires weather and temperature information from, for example, a weather data provision service. For example, the acquisition unit acquires the latest weather and temperature information using the API of the weather data provision service. The acquisition unit can also acquire fashion trend information based on trend data from fashion magazines and social media. For example, the acquisition unit analyzes articles in fashion magazines to extract trendy colors and designs. The acquisition unit can also analyze trend data on social media to acquire the latest fashion information. For example, the acquisition unit analyzes posts on social media to extract trending keywords and hashtags. This makes it possible to acquire external information such as weather, temperature, and fashion trends. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input weather data and fashion trend data into the generation AI, which then acquires the external information.

[0067] The registration unit can register the user's clothing and accessories using photos or text. The registration unit performs registration by, for example, having the user take photos of the clothing and accessories and upload them to the app. For example, the registration unit allows the user to take photos of the clothing and accessories using a smartphone camera and upload the photos to the app. The registration unit can also allow the user to input item information in text format. For example, the registration unit provides a form for inputting information such as the brand name, color, size, and material of the item. This makes it possible to register the user's clothing and accessories using photos or text. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input data entered by the user into the generation AI, which then analyzes and registers the item information.

[0068] The registration unit can estimate the user's emotions and adjust the timing of clothing and accessory registration based on the estimated user emotions. For example, when the user is relaxed, the registration unit guides the user through the registration process in detail and proceeds slowly. For example, when the user is relaxed, the registration unit carefully explains each step, allowing the user to proceed with registration with peace of mind. Furthermore, when the user is in a hurry, the registration unit can provide a simplified registration procedure to quickly complete registration. For example, when the user is in a hurry, the registration unit allows the user to enter only the minimum necessary information, completing registration in a short time. Furthermore, when the user is stressed, the registration unit can minimize the registration procedure and provide a simple interface. For example, when the user is stressed, the registration unit avoids complicated operations and completes registration with simple operations. This makes it possible to adjust the timing of clothing and accessory registration based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the registration unit may input user emotion data to the generation AI, and the generation AI may adjust the registration timing based on the user emotion.

[0069] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit can prioritize displaying categories of items that the user has frequently registered in the past. For example, the registration unit can prioritize displaying categories such as shirts and pants that the user has frequently registered in the past. The registration unit can also prioritize suggesting registration methods (e.g., photos, text) that the user has used in the past. For example, if the user has frequently used photos in the past to register, the registration unit can prioritize suggesting a registration method using photos. The registration unit can also analyze the user's past registration history to determine whether they tend to register at specific times of the day and suggest the optimal timing. For example, if the user has frequently registered at night in the past, the registration unit can send a notification encouraging the user to register at night. This makes it possible to analyze the user's past registration history and select the optimal registration method. Some or all of the above-described processing in the registration unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the registration unit can input the user's past registration data into the generation AI, which can select the optimal registration method.

[0070] When registering clothing and accessories, the registration unit can filter based on the user's current fashion style and areas of interest. For example, if the user prefers a casual style, the registration unit can prioritize displaying casual items. For example, the registration unit can prioritize displaying casual shirts and pants. Furthermore, if the user is interested in a particular brand, the registration unit can prioritize displaying items from that brand. For example, if the user has registered many items from a particular brand, the registration unit can prioritize displaying new items from that brand. Furthermore, if the user prefers a particular color or design, the registration unit can prioritize displaying items of that color or design. For example, if the user has registered many blue items, the registration unit can prioritize displaying new blue items. This enables filtering based on the user's current fashion style and areas of interest. Some or all of the above-described processing in the registration unit can be performed using, or without, a generation AI. For example, the registration unit can input data on the user's fashion style and areas of interest into the generation AI, which can then filter out the most suitable items.

[0071] The registration unit can estimate the user's emotions and prioritize the items to be registered based on the estimated user emotions. For example, when the user is relaxed, the registration unit provides detailed registration options and suggests a customizable registration method. For example, when the user is relaxed, the registration unit prompts the user to enter detailed information about the items and suggests a customizable registration method. The registration unit can also prioritize registering important items when the user is in a hurry. For example, when the user is in a hurry, the registration unit prompts the user to enter only the minimum necessary information and prioritizes registering important items. The registration unit can also provide a simple interface and prioritize registering important items when the user is stressed. For example, when the user is stressed, the registration unit allows the user to register important items with simple operations, avoiding complex operations. This makes it possible to prioritize the items to be registered based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the registration unit may input user emotion data into the generation AI, and the generation AI may determine the priority of items to be registered based on the user emotion.

[0072] When registering clothing and accessories, the registration unit can prioritize registering highly relevant items taking into account the user's geographical location information. For example, if the user lives in a cold region, the registration unit prioritizes registering warm clothing. For example, the registration unit prioritizes registering warm items such as coats and sweaters for a user living in a cold region. The registration unit can also prioritize registering items with an urban design for a user living in an urban area. For example, the registration unit prioritizes registering items with an urban design such as suits and dresses for a user living in an urban area. The registration unit can also prioritize registering beachwear and casual items for a user living by the sea. For example, the registration unit prioritizes registering beachwear such as swimsuits and sandals for a user living by the sea. This makes it possible to prioritize registering highly relevant items taking into account the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the user's geographical location information into the generation AI, which can then prioritize registering highly relevant items.

[0073] The registration unit can analyze the user's social media activity and register related items when registering clothing or accessories. For example, the registration unit analyzes the style that the user frequently posts on social media and registers related items. For example, if the user frequently posts casual styles on social media, the registration unit can prioritize registering casual items. The registration unit can also register items based on the style of a fashion influencer the user follows. For example, the registration unit can prioritize registering items recommended by the influencer the user follows. The registration unit can also prioritize registering items that the user has given many "likes" or comments on. For example, the registration unit can prioritize displaying items that the user has given many "likes" to encourage registration. This makes it possible to analyze the user's social media activity and register related items. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the registration unit can input the user's social media data into the generation AI, which can then register related items.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of external information acquisition based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit periodically acquires external information and provides the latest information. For example, when the user is relaxed, the acquisition unit acquires weather and fashion trend information at regular intervals and provides the information to the user. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire the minimum amount of external information necessary. For example, when the user is in a hurry, the acquisition unit acquires only important weather information and fashion trend information and provides it quickly. Furthermore, when the user is feeling stressed, the acquisition unit can acquire only important external information to avoid information overload. For example, when the user is feeling stressed, the acquisition unit acquires only the minimum amount of information necessary and provides it to the user. This makes it possible to adjust the timing of external information acquisition based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input user emotion data to the generation AI, and the generation AI may adjust the timing of acquiring external information.

[0075] The acquisition unit can analyze past weather and temperature data and select an appropriate acquisition method. The acquisition unit, for example, prioritizes acquisition of information tailored to a specific season or weather based on past weather data. For example, the acquisition unit analyzes past weather data and prioritizes acquisition of information related to weather that frequently occurs in a specific season. The acquisition unit can also prioritize acquisition of information tailored to a specific temperature range based on past temperature data. For example, the acquisition unit analyzes past temperature data and prioritizes acquisition of information related to a specific temperature range. The acquisition unit can also analyze past weather and temperature data and acquire information based on predicted weather. For example, the acquisition unit predicts future weather based on past weather and temperature data and acquires information based on the prediction. This makes it possible to analyze past weather and temperature data and select an optimal acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input past weather and temperature data into a generation AI, which selects an optimal acquisition method.

[0076] When acquiring external information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user prefers outdoor activities, the acquisition unit prioritizes acquiring weather information related to outdoor activities. For example, the acquisition unit prioritizes acquiring weather information useful for the user when engaging in outdoor activities. Furthermore, if the user is a businessperson, the acquisition unit can prioritize acquiring weather information related to business. For example, the acquisition unit prioritizes acquiring weather information useful for business meetings and business trips. Furthermore, if the user is interested in fashion, the acquisition unit can prioritize acquiring information related to fashion trends. For example, the acquisition unit prioritizes acquiring information about fashion trends in which the user is interested. This enables filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the optimal external information.

[0077] The acquisition unit can estimate the user's emotions and determine the priority of external information to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit prioritizes acquiring detailed external information. For example, when the user is relaxed, the acquisition unit prioritizes acquiring detailed weather information and fashion trend information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring only important external information. For example, when the user is in a hurry, the acquisition unit prioritizes acquiring only important weather information and fashion trend information. Furthermore, when the user is feeling stressed, the acquisition unit can prioritize acquiring external information that is useful to the user. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring only information that is useful to the user. This makes it possible to determine the priority of external information to be acquired based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input user emotion data into the generation AI, and the generation AI may determine the priority of the external information.

[0078] When acquiring external information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in a cold region, the acquisition unit prioritizes acquiring weather information related to the cold region. For example, the acquisition unit prioritizes acquiring weather information for the cold region for a user living in a cold region. Furthermore, if the user lives in an urban area, the acquisition unit can also prioritize acquiring weather information related to the urban area. For example, the acquisition unit prioritizes acquiring weather information for the urban area for a user living in an urban area. Furthermore, if the user lives by the sea, the acquisition unit can also prioritize acquiring weather information related to the seaside. For example, the acquisition unit prioritizes acquiring weather information for the seaside for a user living by the sea. This makes it possible to prioritize acquiring highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI, which can then prioritize acquiring highly relevant information.

[0079] When acquiring external information, the acquisition unit can analyze the user's social media activities and acquire related information. For example, the acquisition unit prioritizes acquiring information related to topics to which the user frequently posts on social media. For example, the acquisition unit prioritizes acquiring weather information and fashion trend information related to topics to which the user frequently posts on social media. The acquisition unit can also prioritize acquiring information related to the content posted by influencers followed by the user. For example, the acquisition unit prioritizes acquiring information related to topics posted by influencers followed by the user. The acquisition unit can also prioritize acquiring information related to topics to which the user has given many "likes" and comments. For example, the acquisition unit prioritizes acquiring information related to topics to which the user has given many "likes." This makes it possible to analyze the user's social media activities and acquire related information. Some or all of the above-described processing by the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media data into the generation AI, which then acquires the related information.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit makes detailed suggestions and provides many options. For example, when the user is relaxed, the suggestion unit makes detailed clothing suggestions and provides multiple options. Furthermore, when the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. For example, when the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. Furthermore, when the user is feeling stressed, the suggestion unit can make simple and highly visible suggestions. For example, when the user is feeling stressed, the suggestion unit can make simple and highly visible suggestions. This makes it possible to adjust the way suggestions are expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then adjust the way the suggestion is expressed.

[0081] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on fluctuations in weather and temperature. For example, when the weather changes suddenly, the suggestion unit makes a proposal including detailed weather information. For example, when the weather changes suddenly, the suggestion unit makes a proposal for clothing including detailed weather information. Furthermore, when the temperature fluctuates significantly, the suggestion unit can also make a detailed proposal for clothing according to the temperature. For example, when the temperature fluctuates significantly, the suggestion unit makes a detailed proposal for clothing according to the temperature. Furthermore, the suggestion unit can also make a simple proposal when the weather and temperature are stable. For example, when the weather and temperature are stable, the suggestion unit makes a simple proposal for clothing. This makes it possible to adjust the level of detail of the proposal based on fluctuations in weather and temperature. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input weather and temperature data into the generation AI, which can adjust the level of detail of the proposal.

[0082] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the fashion trend. The suggestion unit, for example, suggests color combinations based on trendy colors. For example, the suggestion unit suggests color combinations based on trendy colors such as red and blue. The suggestion unit can also suggest design combinations based on trendy designs. For example, the suggestion unit suggests design combinations based on trendy designs such as stripes and checks. The suggestion unit can also suggest brand combinations based on trendy brands. For example, the suggestion unit suggests brand combinations based on specific trendy brands. This makes it possible to apply an optimal suggestion algorithm depending on the category of the fashion trend. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on fashion trends into a generation AI, which can then apply different suggestion algorithms.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit makes detailed suggestions and provides many options. For example, when the user is relaxed, the suggestion unit makes detailed clothing suggestions and provides multiple options. Furthermore, when the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. For example, when the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. Furthermore, when the user is feeling stressed, the suggestion unit can make simple and highly visible suggestions. For example, when the user is feeling stressed, the suggestion unit can make simple and highly visible suggestions. This makes it possible to adjust the length of the suggestions based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then adjust the length of the suggestion.

[0084] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the forecast of weather and temperature. For example, when rain is predicted, the suggestion unit prioritizes suggesting rain gear and waterproof clothing. For example, when rain is predicted, the suggestion unit prioritizes suggesting a raincoat and waterproof shoes. The suggestion unit can also prioritize suggesting cool clothing when the temperature is high. For example, when the temperature is high, the suggestion unit prioritizes suggesting a thin shirt and shorts. The suggestion unit can also prioritize suggesting warm clothing when the temperature is low. For example, when the temperature is low, the suggestion unit prioritizes suggesting a thick coat and sweater. This makes it possible to determine the priority of the suggestions based on the forecast of weather and temperature. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input forecast data of weather and temperature into the generation AI, which can then determine the priority of the suggestions.

[0085] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of fashion trends. For example, the suggestion unit preferentially suggests color combinations based on trendy colors. For example, the suggestion unit preferentially suggests color combinations based on trendy colors such as red and blue. The suggestion unit can also preferentially suggest design combinations based on trendy designs. For example, the suggestion unit preferentially suggests design combinations based on trendy designs such as stripes and checks. The suggestion unit can also preferentially suggest brand combinations based on trendy brands. For example, the suggestion unit preferentially suggests brand combinations based on specific trendy brands. This makes it possible to adjust the order of suggestions based on the relevance of fashion trends. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on fashion trends into the generation AI, which can then adjust the order of suggestions.

[0086] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, when the user is relaxed, the feedback unit requests detailed feedback. For example, when the user is relaxed, the feedback unit asks detailed questions and requests specific feedback. The feedback unit can also request concise feedback when the user is in a hurry. For example, when the user is in a hurry, the feedback unit asks concise questions and collects feedback in a short time. The feedback unit can also request simple feedback when the user is feeling stressed. For example, when the user is feeling stressed, the feedback unit asks simple questions and collects feedback easily. This makes it possible to adjust the feedback collection method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input user emotional data into the generation AI, which can then adjust how it collects feedback.

[0087] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, if the user has provided detailed feedback in the past, the feedback unit requests detailed feedback. For example, when the user has provided detailed feedback in the past, the feedback unit asks a similar detailed question to request specific feedback. Furthermore, if the user has provided brief feedback in the past, the feedback unit can also request brief feedback. For example, when the user has provided brief feedback in the past, the feedback unit asks a similar brief question to collect feedback in a short period of time. Furthermore, the feedback unit can also request feedback for a specific time period based on the user's past feedback history. For example, if the user has provided feedback in a specific time period in the past, the feedback unit sends a notification requesting feedback for that time period. This makes it possible to select an optimal collection method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which can then select the optimal collection method.

[0088] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, when the user is relaxed, the feedback unit prioritizes collecting detailed feedback. For example, when the user is relaxed, the feedback unit asks detailed questions and prioritizes collecting specific feedback. Furthermore, when the user is in a hurry, the feedback unit can also prioritize collecting important feedback. For example, when the user is in a hurry, the feedback unit asks important questions and prioritizes collecting important feedback in a short time. Furthermore, when the user is feeling stressed, the feedback unit can also prioritize collecting feedback that is useful to the user. For example, when the user is feeling stressed, the feedback unit asks useful questions and prioritizes collecting simple feedback. This makes it possible to determine the priority of feedback based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's emotional data into the generation AI, and the generation AI can determine the priority of the feedback.

[0089] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides a feedback collection method tailored to the screen size. For example, the feedback unit asks simple questions optimized for the small screen of a smartphone to collect feedback. Furthermore, if the user is using a tablet, the feedback unit can also provide a feedback collection method optimized for a large screen. For example, the feedback unit asks detailed questions optimized for the large screen of a tablet to collect feedback. Furthermore, if the user is using a smartwatch, the feedback unit can also provide a concise and highly visible feedback collection method. For example, the feedback unit asks simple questions optimized for the small screen of a smartwatch to collect feedback. This makes it possible to select the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the user's device information into the generation AI, which can then select the optimal collection method.

[0090] The interlocking unit can estimate the user's emotions and adjust the automatic product registration method based on the estimated user emotions. For example, when the user is relaxed, the interlocking unit provides detailed registration options and suggests a customizable registration method. For example, when the user is relaxed, the interlocking unit provides detailed options, allowing the user to customize the registration method to their preferences. Furthermore, when the user is in a hurry, the interlocking unit can provide a simplified registration procedure, allowing the user to complete registration quickly. For example, when the user is in a hurry, the interlocking unit allows the user to enter only the minimum necessary information, completing registration in a short time. Furthermore, when the user is stressed, the interlocking unit can minimize the registration procedure and provide a simple interface. For example, when the user is stressed, the interlocking unit avoids complicated operations and allows the user to complete registration with simple operations. This makes it possible to adjust the automatic product registration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interlocking unit may be performed using, or without, the generation AI. For example, the interlocking unit may input user emotion data into the generation AI, which may then adjust the automatic product registration method.

[0091] During automatic registration, the interlocking unit can select an appropriate registration method by referring to the user's past purchase history. For example, the interlocking unit prioritizes registration of categories of items the user has purchased in the past. For example, the interlocking unit prioritizes registration of categories such as shirts and pants that the user has purchased frequently in the past. The interlocking unit can also prioritize suggesting registration methods (e.g., photos, text) that the user has used in the past. For example, if the user has used photos frequently in the past to register, the interlocking unit prioritizes suggesting a registration method using photos. The interlocking unit can also analyze the user's tendency to register at specific time periods based on the user's past purchase history and suggest the optimal timing. For example, if the user has made many purchases at night in the past, the interlocking unit can send a notification encouraging registration at night. This makes it possible to select the optimal registration method by referring to the user's past purchase history. Some or all of the above-described processing in the interlocking unit may be performed using, or without, a generation AI. For example, the interlocking unit can input the user's past purchase data into the generation AI, which can then select the optimal registration method.

[0092] The interlocking unit can estimate the user's emotions and determine the priority of automatic registration based on the estimated user emotions. For example, when the user is relaxed, the interlocking unit provides detailed registration options and suggests a customizable registration method. For example, when the user is relaxed, the interlocking unit provides detailed options and allows the user to customize the registration method to suit their preferences. The interlocking unit can also prioritize the registration of important items when the user is in a hurry. For example, when the user is in a hurry, the interlocking unit allows the user to enter only the minimum necessary information and prioritizes the registration of important items. The interlocking unit can also provide a simple interface and prioritize the registration of important items when the user is stressed. For example, when the user is stressed, the interlocking unit allows the user to register important items with simple operations, avoiding complex operations. This makes it possible to determine the priority of automatic registration based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interlocking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the interlocking unit may input user emotion data into the generation AI, and the generation AI may determine the priority of automatic registration.

[0093] During automatic registration, the interlocking unit can select an appropriate registration method taking into account the user's geographical location information. For example, if the user lives in a cold region, the interlocking unit prioritizes registering warm clothing. For example, the interlocking unit prioritizes registering warm items such as coats and sweaters for a user living in a cold region. Furthermore, the interlocking unit can also prioritize registering items with an urban design for a user living in an urban area. For example, the interlocking unit prioritizes registering items with an urban design such as suits and dresses for a user living in an urban area. Furthermore, the interlocking unit can also prioritize registering beachwear and casual items for a user living by the sea. For example, the interlocking unit prioritizes registering beachwear such as swimsuits and sandals for a user living by the sea. This makes it possible to select an optimal registration method taking into account the user's geographical location information. Some or all of the above-described processing by the interlocking unit may be performed using, or without, a generation AI. For example, the interlocking unit can input the user's geographical location information into the generation AI, which can then select the optimal registration method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, suggestion unit, feedback unit, and linkage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit registers the user's clothing and accessories using the camera 42 or the reception device 38 of the smart device 14. The acquisition unit acquires external information such as weather, temperature, and fashion trends using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests outfits using a generation AI using the specific processing unit 290 of the data processing device 12. The feedback unit accumulates user feedback using the reception device 38 of the smart device 14. The linkage unit links with an e-commerce site using the specific processing unit 290 of the data processing device 12 to automatically register purchased items. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, suggestion unit, feedback unit, and linkage unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit registers the user's clothing and accessories using the camera 42 and microphone 238 of the smart glasses 214. The acquisition unit acquires external information such as weather, temperature, and fashion trends using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests outfits using a generation AI using the specific processing unit 290 of the data processing device 12. The feedback unit accumulates user feedback using the microphone 238 of the smart glasses 214. The linkage unit links with an e-commerce site using the specific processing unit 290 of the data processing device 12 to automatically register purchased items. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, suggestion unit, feedback unit, and linkage unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the registration unit registers the user's clothing and accessories using the camera 42 and microphone 238 of the headset-type terminal 314. The acquisition unit acquires external information such as weather, temperature, and fashion trends using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests outfits using a generation AI using the specific processing unit 290 of the data processing device 12. The feedback unit accumulates user feedback using the microphone 238 of the headset-type terminal 314. The linkage unit links with an e-commerce site using the specific processing unit 290 of the data processing device 12 to automatically register purchased items. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, suggestion unit, feedback unit, and interlocking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit registers the user's clothing and accessories using the camera 42 and microphone 238 of the robot 414. The acquisition unit acquires external information such as weather, temperature, and fashion trends using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests outfits using a generation AI using the specific processing unit 290 of the data processing device 12. The feedback unit accumulates user feedback using the microphone 238 of the robot 414. The interlocking unit interlocks with an e-commerce site using the specific processing unit 290 of the data processing device 12 to automatically register purchased items.

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

[0095] The suggestion unit can analyze the user's past feedback history to improve the accuracy of suggestions. For example, the suggestion unit can extract characteristics of items that the user has given many "likes" in the past and suggest new items with similar characteristics. The suggestion unit can also adjust the suggestions to avoid characteristics of items that the user has rated as "not so good." Furthermore, the suggestion unit can make suggestions tailored to specific seasons or events based on the user's feedback history. This makes it possible to make more accurate suggestions by utilizing the user's past feedback history.

[0096] The acquisition unit can analyze the user's social media activity and acquire related external information. For example, the acquisition unit can analyze the content of posts by fashion influencers the user follows on social media to acquire the latest fashion trends. The acquisition unit can also prioritize acquisition of information related to posts that the user has received many likes and comments on. Furthermore, the acquisition unit can analyze the time periods during which the user is active on social media and provide the latest information tailored to those time periods. This makes it possible to acquire more relevant external information by utilizing the user's social media activity.

[0097] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, it can ask questions that request detailed feedback. If the user is in a hurry, it can ask questions that request brief feedback. Furthermore, if the user is feeling stressed, it can ask simple questions to reduce the burden on the user. This makes it possible to provide an optimal feedback collection method that corresponds to the user's emotions.

[0098] The linking unit can automatically register highly relevant products by taking into account the user's geographical location information. For example, if the user lives in a cold region, warm clothing can be registered with priority. Also, if the user lives in an urban area, items with an urban design can be registered with priority. Furthermore, if the user lives by the sea, beachwear and casual items can be registered with priority. This makes it possible to automatically register more relevant products by utilizing the user's geographical location information.

[0099] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is relaxed, detailed suggestions can be made and many options can be offered. If the user is in a hurry, concise suggestions can be made that get to the point. Furthermore, if the user is feeling stressed, simple, highly visible suggestions can be made. This makes it possible to adjust the way suggestions are expressed based on the user's emotions.

[0100] The acquisition unit can analyze past weather and temperature data and select an appropriate acquisition method. For example, it can prioritize acquisition of information tailored to a specific season or weather based on past weather data. It can also prioritize acquisition of information tailored to a specific temperature range based on past temperature data. It can also analyze past weather and temperature data and acquire information based on predicted weather. This makes it possible to acquire more accurate external information by utilizing past data.

[0101] The feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, if the user has provided detailed feedback in the past, a question requesting detailed feedback can be asked. Also, if the user has provided brief feedback in the past, a question requesting brief feedback can be asked. Furthermore, feedback can be requested at a specific time period based on the user's past feedback history. This makes it possible to provide an optimal collection method by utilizing the user's past feedback history.

[0102] When making suggestions, the suggestion unit can determine the priority of the suggestions based on predicted weather and temperature. For example, if rain is predicted, rain gear and waterproof clothing can be suggested with priority. Also, if the temperature is high, cool clothing can be suggested with priority. Furthermore, if the temperature is low, warm clothing can be suggested with priority. This makes it possible to make more appropriate suggestions based on predicted weather and temperature.

[0103] The interlocking unit can estimate the user's emotions and adjust the automatic product registration method based on the estimated emotions. For example, if the user is relaxed, detailed registration options can be provided and a customizable registration method can be suggested. If the user is in a hurry, a simplified registration procedure can be provided to quickly complete the registration. Furthermore, if the user is stressed, the registration procedure can be minimized and a simple interface can be provided. This makes it possible to adjust the automatic product registration method based on the user's emotions.

[0104] When acquiring external information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user likes outdoor activities, weather information related to outdoor activities can be acquired preferentially. Also, if the user is a business person, weather information related to business can be acquired preferentially. Furthermore, if the user is interested in fashion, information related to fashion trends can be acquired preferentially. This makes it possible to acquire more relevant external information based on the user's current living situation and areas of interest.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The registration section registers the user's clothing and accessories. Users can register clothing and accessories by taking a photo of the clothing or accessories and uploading it to the app. They can also enter item information (brand name, color, size, material, etc.) in text format. Step 2: The acquisition unit acquires external information such as weather, temperature, fashion trends, etc. The acquisition unit acquires weather and temperature information from a weather data provider service, and acquires fashion trend information based on trend data from fashion magazines and social media. Step 3: The suggestion unit suggests outfits based on the information obtained by the registration unit and acquisition unit. Using a generation AI, the suggestion unit suggests optimal outfits to the user based on the weather and fashion trends. Step 4: The feedback section accumulates user feedback. Users rate the proposed outfits, and as these ratings accumulate, the generation AI evolves to make suggestions that match the user's preferences. Step 5: The linking unit automatically registers the purchased products in conjunction with the e-commerce site. Information about the products purchased by the user on the e-commerce site is automatically registered in the app.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 registration unit for registering user clothing and accessories; an acquisition unit that acquires external information such as weather, temperature, and fashion trends; a suggestion unit that suggests outfits based on the information obtained by the registration unit and the acquisition unit; a feedback unit that accumulates user feedback; a linking unit that links with an e-commerce site and automatically registers purchased products; Equipped with A system characterized by:

2. The proposal unit Suggest outfits based on weather, temperature, and fashion trends The system of claim 1 .

3. The interlocking portion is Automatically register information about products purchased on e-commerce sites in the app The system of claim 1 .

4. The acquisition unit Obtaining external information on weather, temperature, and fashion trends The system of claim 1 .

5. The registration unit Register the user's clothing and accessories with photos and text The system of claim 1 .

6. The registration unit Estimates the user's emotions and adjusts the timing of clothing and accessory registration based on the estimated user emotions. The system of claim 1 .

7. The registration unit Analyze the user's past registration history and select the appropriate registration method The system of claim 1 .

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