System

Abstract

An object of the system according to the embodiment is to provide a wearing image when the user actually wears the clothes based on weather forecast data and body shape data of the user.SOLUTION: A system includes a weather forecast data acquisition part, a body shape data acquisition part, and a wearing image generation part. The weather forecast data acquisition unit acquires and analyzes weather forecast data. The physique data acquisition unit acquires physique data of the user. A wearing image generation part generates a wearing image when the user actually wears the clothes on the basis of the weather forecast data acquired by the weather forecast data acquisition part and the figure data acquired by the figure data acquisition part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to select appropriate clothing based on the weather forecast, and there is a lack of means for users to check how the clothing will actually look when worn.

[0005] The system according to the embodiment aims to provide an image of how the user will actually wear the clothes, based on weather forecast data and the user's body type data. [Means for solving the problem]

[0006] The system according to the embodiment includes a weather forecast data acquisition unit, a body type data acquisition unit, and a wearing image generation unit. The weather forecast data acquisition unit acquires and analyzes weather forecast data. The body type data acquisition unit acquires body type data of a user. The wearing image generation unit generates an image of how the user would look wearing the clothing, based on the weather forecast data acquired by the weather forecast data acquisition unit and the body type data acquired by the body type data acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an image of how the user will actually wear the clothes based on weather forecast data and the user's body type data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A fashion app according to an embodiment of the present invention is a system that suggests appropriate clothing based on weather forecast data and allows users to decide on an outfit while checking how they will look when wearing it. This allows the fashion app to allow users to select clothing appropriate for the weather and check outfits that suit their own style.

[0029] A fashion app according to an embodiment includes a weather forecast data acquisition unit, a body type data acquisition unit, and a wearing image generation unit. The weather forecast data acquisition unit acquires and analyzes weather forecast data. For example, based on data such as temperature, precipitation probability, and wind speed, the app suggests clothing appropriate for the day's weather. The body type data acquisition unit acquires a user's body type data. For example, the body type data acquisition unit acquires the user's height, weight, and measurements of each body part while wearing the ZOZO Suit. The wearing image generation unit generates an image of how the user would actually wear the clothing based on the weather forecast data acquired by the weather forecast data acquisition unit and the body type data acquired by the body type data acquisition unit. For example, the generation AI generates a 3D model of the user wearing the clothing selected by the user based on the user's body type data and the suggested clothing data, and provides the image to the user. This allows the fashion app according to an embodiment to allow the user to select clothing appropriate for the weather and check outfits that suit their style.

[0030] The weather forecast data acquisition unit can analyze past weather data in addition to weather forecast data and make clothing suggestions that reflect seasonal trends. The weather forecast data acquisition unit collects weather data from the past 10 years, for example, and analyzes seasonal trends in temperature and precipitation. This allows it to suggest clothing suitable for specific seasons. For example, it can suggest clothing made of light materials in spring and clothing that is highly resistant to the cold in winter. This makes it possible to suggest clothing that reflects seasonal trends.

[0031] The weather forecast data acquisition unit analyzes the user's past clothing selection history along with the weather forecast data, and can make suggestions tailored to the preferences of each individual user. For example, the weather forecast data acquisition unit collects the user's clothing selection history from the past year and analyzes it against the weather forecast data. This allows the unit to identify the user's preferred styles and colors and make personalized clothing suggestions. This makes it possible to make clothing suggestions tailored to the preferences of each individual user.

[0032] The weather forecast data acquisition unit can suggest appropriate clothing for an event based on event information in addition to weather forecast data. For example, the weather forecast data acquisition unit collects local event information along with weather forecast data and suggests appropriate clothing for the event. For example, casual clothing is suggested for a music festival, and comfortable clothing is suggested for a sporting event. This makes it possible to suggest appropriate clothing for an event.

[0033] The weather forecast data acquisition unit can suggest clothing based on the culture and customs of each region along with the weather forecast data. For example, the weather forecast data acquisition unit analyzes the weather forecast data and the culture and customs of each region to suggest clothing appropriate for that region. For example, it can suggest traditional clothing in certain regions and modern clothing in other regions. This makes it possible to suggest clothing that takes into account the culture and customs of each region.

[0034] The body shape data acquisition unit can analyze the user's posture and walking style in addition to the ZOZO Suit data and make clothing suggestions based on that. For example, the body shape data acquisition unit can collect the user's posture data in addition to the ZOZO Suit data and make clothing suggestions based on the posture. For example, for a user with a hunched back, it can suggest clothing that has the effect of straightening the back. This makes it possible to suggest clothing based on the user's posture and walking style.

[0035] The body type data acquisition unit can suggest clothes with materials and designs that are optimal for a specific body type based on the user's body type data. The body type data acquisition unit, for example, suggests materials that are optimal for a specific body type based on the user's body type data. For example, it suggests moisture-wicking and quick-drying materials to a user who sweats easily. This makes it possible to suggest clothes with materials and designs that are optimal for a specific body type.

[0036] The body type data acquisition unit can make clothing suggestions that take into account the user's skin color and hairstyle in addition to the ZOZO Suit data. For example, the body type data acquisition unit can analyze the user's skin color in addition to the ZOZO Suit data and suggest clothing that suits that skin color. For example, pastel-colored clothing can be suggested to a user with light skin. This makes it possible to suggest clothing based on the user's skin color and hairstyle.

[0037] The body type data acquisition unit can suggest accessories and shoes suitable for a specific body type based on the user's body type data. For example, the body type data acquisition unit can suggest accessories suitable for a specific body type based on the user's body type data. For example, a long necklace can be suggested for a user with a long neck. This makes it possible to suggest accessories and shoes suitable for a specific body type.

[0038] The wearing image generation unit uses a generation AI to generate 3D models of the clothing selected by the user from different angles, allowing for a more detailed wearing image. For example, the wearing image generation unit uses a generation AI to generate 3D models of the clothing selected by the user from different angles, such as the front, back, and side. This allows the user to see the overall image of the clothing. This allows for a more detailed wearing image of the clothing selected by the user.

[0039] The wearing image generation unit can use generation AI to simulate how the clothing selected by the user looks under different lighting conditions. The wearing image generation unit, for example, uses generation AI to simulate how the clothing selected by the user looks under different lighting conditions. For example, natural light during the day, artificial lighting at night, etc. This makes it possible to simulate how the clothing looks under different lighting conditions.

[0040] The wearing image generation unit can use generation AI to simulate how the clothes selected by the user will look against different backgrounds. For example, the wearing image generation unit uses generation AI to simulate how the clothes selected by the user will look against an office background. For example, how they will look against the background of a desk or conference room. This makes it possible to simulate how the clothes will look against different backgrounds.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Fashion apps can also suggest clothing that takes into account the user's activity level. For example, if a user exercises daily, they can suggest sportswear that is easy to move in. For a user who does a lot of desk work, they can suggest clothing that is comfortable even when sitting for long periods of time. Furthermore, for a user who goes out a lot, they can suggest clothing that takes into account comfort while traveling. This makes it possible to suggest clothing that matches the user's lifestyle.

[0043] Fashion apps can suggest clothing that takes into account the user's health condition. For example, for a user with allergies, clothing made from materials that avoid allergens can be suggested. For a user who is prone to feeling cold, clothing with high thermal insulation properties can be suggested. Furthermore, for a user with sensitive skin, clothing made from materials that are gentle on the skin can be suggested. This makes it possible to suggest clothing that takes into account the user's health condition.

[0044] Fashion apps can suggest clothing based on a user's occupation. For example, formal attire suitable for the office can be suggested to business people. Individual and free-style clothing can be suggested to users in creative professions. Furthermore, clothing that is easy to move in and has a clean appearance can be suggested to users in the service industry. This makes it possible to suggest clothing that suits the user's occupation.

[0045] Fashion apps can suggest clothing based on the climate and culture of a user's travel destination. For example, a user traveling to a tropical resort can be suggested light and cool clothing. A user traveling to a cold climate can be suggested clothing that provides warmth. Furthermore, clothing that takes into consideration specific cultures and religions can be suggested. This makes it possible to suggest clothing that is appropriate for each travel destination.

[0046] A fashion app can suggest clothes that fit a specific body type based on the user's body type data. For example, for a user who is not confident about their body type, clothes designed to cover their body type can be suggested. For a muscular user, clothes that fit well and accentuate their muscles can be suggested. Furthermore, for a slim user, tight clothes that accentuate their figure can be suggested. This makes it possible to suggest clothes that fit well to a specific body type.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The weather forecast data acquisition unit acquires and analyzes weather forecast data. For example, it suggests clothing appropriate for the weather of the day based on data such as temperature, probability of precipitation, and wind speed. Step 2: The body type data acquisition unit acquires the user's body type data, such as the user's height, weight, and measurements of each body part while wearing the ZOZO Suit. Step 3: The wearing image generation unit generates an image of how the user will actually wear the clothes based on the weather forecast data acquired by the weather forecast data acquisition unit and the body shape data acquired by the body shape data acquisition unit. For example, the generation AI generates a 3D model of the user wearing the clothes selected by the user based on the user's body shape data and the suggested clothing data, and provides the image to the user.

[0049] (Example 2) A fashion app according to an embodiment of the present invention is a system that suggests appropriate clothing based on weather forecast data and allows users to decide on an outfit while checking how they will look when wearing it. This allows the fashion app to allow users to select clothing appropriate for the weather and check outfits that suit their own style.

[0050] A fashion app according to an embodiment includes a weather forecast data acquisition unit, a body type data acquisition unit, and a wearing image generation unit. The weather forecast data acquisition unit acquires and analyzes weather forecast data. For example, based on data such as temperature, precipitation probability, and wind speed, the app suggests clothing appropriate for the day's weather. The body type data acquisition unit acquires a user's body type data. For example, the body type data acquisition unit acquires the user's height, weight, and measurements of each body part while wearing the ZOZO Suit. The wearing image generation unit generates an image of how the user would actually wear the clothing based on the weather forecast data acquired by the weather forecast data acquisition unit and the body type data acquired by the body type data acquisition unit. For example, the generation AI generates a 3D model of the user wearing the clothing selected by the user based on the user's body type data and the suggested clothing data, and provides the image to the user. This allows the fashion app according to an embodiment to allow the user to select clothing appropriate for the weather and check outfits that suit their style.

[0051] The weather forecast data acquisition unit can analyze past weather data in addition to weather forecast data and make clothing suggestions that reflect seasonal trends. The weather forecast data acquisition unit collects weather data from the past 10 years, for example, and analyzes seasonal trends in temperature and precipitation. This allows it to suggest clothing suitable for specific seasons. For example, it can suggest clothing made of light materials in spring and clothing that is highly resistant to the cold in winter. This makes it possible to suggest clothing that reflects seasonal trends.

[0052] The weather forecast data acquisition unit analyzes the user's past clothing selection history along with the weather forecast data, and can make suggestions tailored to the preferences of each individual user. For example, the weather forecast data acquisition unit collects the user's clothing selection history from the past year and analyzes it against the weather forecast data. This allows the unit to identify the user's preferred styles and colors and make personalized clothing suggestions. This makes it possible to make clothing suggestions tailored to the preferences of each individual user.

[0053] The weather forecast data acquisition unit can use the emotion estimation function to estimate the user's emotion regarding the weather and suggest clothing based on that emotion. For example, the weather forecast data acquisition unit analyzes past weather data and user emotion data to estimate the user's emotion regarding the weather. For example, for a user who feels negative emotions on a rainy day, the unit can suggest bright-colored clothing that will lift their spirits. This makes it possible to suggest clothing based on the user's emotions.

[0054] The weather forecast data acquisition unit can suggest appropriate clothing for an event based on event information in addition to weather forecast data. For example, the weather forecast data acquisition unit collects local event information along with weather forecast data and suggests appropriate clothing for the event. For example, casual clothing is suggested for a music festival, and comfortable clothing is suggested for a sporting event. This makes it possible to suggest appropriate clothing for an event.

[0055] The weather forecast data acquisition unit can suggest clothing based on the culture and customs of each region along with the weather forecast data. For example, the weather forecast data acquisition unit analyzes the weather forecast data and the culture and customs of each region to suggest clothing appropriate for that region. For example, it can suggest traditional clothing in certain regions and modern clothing in other regions. This makes it possible to suggest clothing that takes into account the culture and customs of each region.

[0056] The weather forecast data acquisition unit can use the emotion estimation function to analyze what emotions a user will feel under specific weather conditions and suggest clothing based on those emotions. For example, the weather forecast data acquisition unit can use the emotion estimation function to analyze what emotions a user will feel under specific weather conditions. For example, for a user who feels negative emotions on a rainy day, the unit can suggest bright-colored clothing that will lift their spirits. This makes it possible to suggest clothing based on the user's emotions under specific weather conditions.

[0057] The body shape data acquisition unit can analyze the user's posture and walking style in addition to the ZOZO Suit data and make clothing suggestions based on that. For example, the body shape data acquisition unit can collect the user's posture data in addition to the ZOZO Suit data and make clothing suggestions based on the posture. For example, for a user with a hunched back, it can suggest clothing that has the effect of straightening the back. This makes it possible to suggest clothing based on the user's posture and walking style.

[0058] The body type data acquisition unit can suggest clothes with materials and designs that are optimal for a specific body type based on the user's body type data. The body type data acquisition unit, for example, suggests materials that are optimal for a specific body type based on the user's body type data. For example, it suggests moisture-wicking and quick-drying materials to a user who sweats easily. This makes it possible to suggest clothes with materials and designs that are optimal for a specific body type.

[0059] The body shape data acquisition unit uses the emotion estimation function to analyze the emotions a user has about their own body shape and can make clothing suggestions based on those emotions. The body shape data acquisition unit, for example, uses the emotion estimation function to analyze the emotions a user has about their own body shape. For example, it suggests clothing that will make a user feel confident about their body shape. This makes it possible to make clothing suggestions based on the user's emotions about their body shape.

[0060] The body type data acquisition unit can make clothing suggestions that take into account the user's skin color and hairstyle in addition to the ZOZO Suit data. For example, the body type data acquisition unit can analyze the user's skin color in addition to the ZOZO Suit data and suggest clothing that suits that skin color. For example, pastel-colored clothing can be suggested to a user with light skin. This makes it possible to suggest clothing based on the user's skin color and hairstyle.

[0061] The body type data acquisition unit can suggest accessories and shoes suitable for a specific body type based on the user's body type data. For example, the body type data acquisition unit can suggest accessories suitable for a specific body type based on the user's body type data. For example, a long necklace can be suggested for a user with a long neck. This makes it possible to suggest accessories and shoes suitable for a specific body type.

[0062] The body shape data acquisition unit can use the emotion estimation function to analyze the emotion a user feels toward a specific outfit and suggest accessories and shoes based on that emotion. The body shape data acquisition unit, for example, uses the emotion estimation function to analyze the emotion a user feels toward a specific outfit. For example, for a user who feels positive toward a specific outfit, accessories that match the outfit can be suggested. This makes it possible to suggest accessories and shoes based on the emotion a user feels toward a specific outfit.

[0063] The wearing image generation unit uses a generation AI to generate 3D models of the clothing selected by the user from different angles, allowing for a more detailed wearing image. For example, the wearing image generation unit uses a generation AI to generate 3D models of the clothing selected by the user from different angles, such as the front, back, and side. This allows the user to see the overall image of the clothing. This allows for a more detailed wearing image of the clothing selected by the user.

[0064] The wearing image generation unit can use generation AI to simulate how the clothing selected by the user looks under different lighting conditions. The wearing image generation unit, for example, uses generation AI to simulate how the clothing selected by the user looks under different lighting conditions. For example, natural light during the day, artificial lighting at night, etc. This makes it possible to simulate how the clothing looks under different lighting conditions.

[0065] The wearing image generation unit can use the emotion estimation function to analyze the emotion the user feels toward the generated wearing image and provide feedback based on the emotion. The wearing image generation unit can, for example, use the emotion estimation function to analyze the emotion the user feels toward the generated wearing image. For example, if the user feels positive emotion, the wearing image generation unit can recommend the clothing. This makes it possible to provide feedback based on the user's emotion.

[0066] The wearing image generation unit can use generation AI to simulate how the clothes selected by the user will look against different backgrounds. For example, the wearing image generation unit uses generation AI to simulate how the clothes selected by the user will look against an office background. For example, how they will look against the background of a desk or conference room. This makes it possible to simulate how the clothes will look against different backgrounds.

[0067] The wearing image generation unit can use the emotion estimation function to monitor in real time the emotions that the user feels toward the generated wearing image and make suggestions based on those emotions. The wearing image generation unit, for example, uses the emotion estimation function to monitor in real time the emotions that the user feels toward the generated wearing image. For example, if the user feels positive emotions, the unit recommends the clothing. This makes it possible to make real-time suggestions based on the user's emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] Fashion apps can also suggest clothing that takes into account the user's activity level. For example, if a user exercises daily, they can suggest sportswear that is easy to move in. For a user who does a lot of desk work, they can suggest clothing that is comfortable even when sitting for long periods of time. Furthermore, for a user who goes out a lot, they can suggest clothing that takes into account comfort while traveling. This makes it possible to suggest clothing that matches the user's lifestyle.

[0070] Fashion apps can suggest clothing that takes into account the user's health condition. For example, for a user with allergies, clothing made from materials that avoid allergens can be suggested. For a user who is prone to feeling cold, clothing with high thermal insulation properties can be suggested. Furthermore, for a user with sensitive skin, clothing made from materials that are gentle on the skin can be suggested. This makes it possible to suggest clothing that takes into account the user's health condition.

[0071] Fashion apps can suggest clothing based on a user's occupation. For example, formal attire suitable for the office can be suggested to business people. Individual and free-style clothing can be suggested to users in creative professions. Furthermore, clothing that is easy to move in and has a clean appearance can be suggested to users in the service industry. This makes it possible to suggest clothing that suits the user's occupation.

[0072] Fashion apps can suggest clothing based on the climate and culture of a user's travel destination. For example, a user traveling to a tropical resort can be suggested light and cool clothing. A user traveling to a cold climate can be suggested clothing that provides warmth. Furthermore, clothing that takes into consideration specific cultures and religions can be suggested. This makes it possible to suggest clothing that is appropriate for each travel destination.

[0073] A fashion app can estimate a user's emotions and suggest relaxing clothing based on those emotions. For example, a user who is feeling stressed can be suggested clothing made of soft materials that have a relaxing effect. A user who is feeling tired can be suggested clothing with a comfortable and relaxing design. Furthermore, a user who wants to refresh themselves can be suggested clothing in bright colors that will lift their spirits. This makes it possible to suggest clothing with a relaxing effect based on the user's emotions.

[0074] A fashion app can estimate a user's emotions and suggest outfits for special occasions based on those emotions. For example, if a user is nervous before a special event or date, an elegant outfit that will make them feel confident can be suggested. If a user is excited before a holiday, a glamorous outfit can be suggested. Furthermore, if a user is feeling down, a casual outfit that will lift their spirits can be suggested. This makes it possible to suggest outfits based on the emotions of a special occasion.

[0075] Fashion apps can estimate a user's emotions and suggest seasonal clothing based on those emotions. For example, a user who feels negative about the cold of winter can be suggested warm and comfortable clothing. A user who feels positive about the heat of summer can be suggested cool and light clothing. Furthermore, a user who feels anxious about the change of seasons, such as spring or autumn, can be suggested layered clothing that can adapt to the change of seasons. This makes it possible to suggest clothing based on seasonal emotions.

[0076] A fashion app can estimate a user's emotions and suggest clothing that utilizes color psychology based on those emotions. For example, a user who is feeling stressed can be suggested blue or green clothing, which has a relaxing effect. A user who wants to feel energized can be suggested red or orange clothing, which gives an invigorating effect. Furthermore, a user who wants to improve their concentration can be suggested clothing in calming colors. This makes it possible to suggest clothing that matches emotions based on color psychology.

[0077] A fashion app can estimate a user's emotions and suggest appropriate clothing for a specific situation based on those emotions. For example, if a user is nervous before a presentation, a fashion app can suggest formal clothing that will make them feel confident. On the other hand, if a user wants to relax on the weekend, a fashion app can suggest casual and comfortable clothing. Furthermore, if a user is excited before a get-together with friends, a fashion app can suggest colorful clothing that will enhance their joyful mood. This makes it possible to suggest clothing based on emotions according to specific situations.

[0078] A fashion app can suggest clothes that fit a specific body type based on the user's body type data. For example, for a user who is not confident about their body type, clothes designed to cover their body type can be suggested. For a muscular user, clothes that fit well and accentuate their muscles can be suggested. Furthermore, for a slim user, tight clothes that accentuate their figure can be suggested. This makes it possible to suggest clothes that fit well to a specific body type.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The weather forecast data acquisition unit acquires and analyzes weather forecast data. For example, it suggests clothing appropriate for the weather of the day based on data such as temperature, probability of precipitation, and wind speed. Step 2: The body type data acquisition unit acquires the user's body type data, such as the user's height, weight, and measurements of each body part while wearing the ZOZO Suit. Step 3: The wearing image generation unit generates an image of how the user will actually wear the clothes based on the weather forecast data acquired by the weather forecast data acquisition unit and the body shape data acquired by the body shape data acquisition unit. For example, the generation AI generates a 3D model of the user wearing the clothes selected by the user based on the user's body shape data and the suggested clothing data, and provides the image to the user.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 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 weather forecast data acquisition unit that acquires and analyzes weather forecast data; a body type data acquisition unit that acquires body type data of a user; a wearing image generating unit that generates a wearing image of the clothing when the user actually wears the clothing based on the weather forecast data acquired by the weather forecast data acquiring unit and the body type data acquired by the body type data acquiring unit. A system characterized by:

2. The weather forecast data acquisition unit In addition to the weather forecast data, past weather data is analyzed to suggest clothing that reflects seasonal trends.

2. The system of claim 1.

3. The weather forecast data acquisition unit In addition to the weather forecast data, the system suggests appropriate clothing for the event based on event information.

2. The system of claim 1.

4. The body type data acquisition unit In addition to the data from the ZOZO Suit, the company analyzes the user's posture and walking style and makes clothing suggestions based on that.

2. The system of claim 1.

5. The wearing image generation unit The generation AI is used to generate the 3D model from different angles of the clothing selected by the user, providing a more detailed image of how it will look when worn.

2. The system of claim 1.

6. The weather forecast data acquisition unit Estimate the user's feelings about the weather and suggest clothing based on those feelings.

2. The system of claim 1.

7. The body type data acquisition unit Analyze the user's feelings about their own body shape and suggest clothing based on those feelings 2. The system of claim 1.

8. The wearing image generation unit Analyzing the emotions felt by the user regarding the generated wearing image and providing feedback based on the emotions.

2. The system of claim 1.

Images