System

Abstract

An object of a system according to an embodiment is to recommend optimal clothes according to a user's preference, trend, and season.SOLUTION: A system includes a preference analysis part, a trend analysis part, a season analysis part, and a recommendation part. The preference analysis unit analyzes the user's preference and past purchase history. The trend analysis unit analyzes a current fashion trend. The season analysis unit selects clothes according to the season. The recommendation unit recommends optimal clothes to the user in comprehensive consideration of information obtained by the preference analysis unit, the trend analysis unit, and the season analysis unit.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 the problem that it is difficult to select the most suitable clothes based on the user's preferences, trends, and season.

[0005] The system according to the embodiment aims to recommend optimal clothing according to the user's preferences, trends, and seasons. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a trend analysis unit, a seasonal analysis unit, and a recommendation unit. The preference analysis unit analyzes a user's preferences and past purchase history. The trend analysis unit analyzes current fashion trends. The seasonal analysis unit selects clothing appropriate for the season. The recommendation unit comprehensively considers the information obtained by the preference analysis unit, trend analysis unit, and seasonal analysis unit to recommend clothing that is most suitable for the user. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable clothes according to the user's preferences, trends, and seasons. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The clothing recommendation system according to an embodiment of the present invention is a system in which AI selects and recommends optimal clothing according to a user's preferences, trends, and seasons. This allows the clothing recommendation system to suggest clothing that matches the user's preferences, trends, and seasons.

[0029] The clothing recommendation system according to the embodiment includes a preference analysis unit, a trend analysis unit, a seasonal analysis unit, and a recommendation unit. The preference analysis unit analyzes a user's preferences and past purchase history. For example, the preference analysis unit identifies the user's preferences based on data on clothing purchased by the user in the past. The preference analysis unit identifies the user's preferred styles, colors, and brands, and selects clothing based on the identified styles, colors, and brands. The trend analysis unit analyzes current fashion trends. For example, the trend analysis unit collects data from fashion magazines, social media, and online shops to identify current trends. The trend analysis unit selects clothing that matches the latest trends. The seasonal analysis unit selects clothing according to the season. For example, the system recommends clothing made of cool materials in summer and clothing made of warm materials in winter. The seasonal analysis unit provides the user with clothing appropriate for the season. The recommendation unit comprehensively considers the information obtained by the preference analysis unit, trend analysis unit, and seasonal analysis unit to recommend clothing that is optimal for the user. For example, the system recommends clothing that matches the current trend based on the user's preferred styles, colors, and brands. The system also recommends clothing made of materials and designs appropriate for the season. As a result, the clothing recommendation system according to the embodiment can recommend optimal clothing according to the user's preferences, trends, and seasons.

[0030] The preference analysis unit can analyze a user's social media posts and blog articles to identify changes in preferences and new interests. The preference analysis unit, for example, analyzes a user's social media posts and identifies changes in preferences from the content of past posts. For example, if there is an increase in posts about a particular brand or style, it can determine that interest in that brand or style is growing. The preference analysis unit can also analyze a user's blog articles to identify new interests. For example, if a user mentions a new brand or style, clothing suggestions can be made taking that interest into consideration. This makes it possible to identify changes in a user's preferences and new interests.

[0031] The preference analysis unit can take into account the user's life events and suggest clothes that correspond to them. For example, the preference analysis unit collects information about the user's life events and suggests clothes that match important events such as marriage or job changes. For example, when a user receives a wedding invitation, the unit suggests formal dresses or suits. Also, when a user changes jobs, the unit suggests clothes that match the dress code of the new workplace. This makes it possible to suggest clothes that correspond to the user's life events.

[0032] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family and friends and make group coordination suggestions. The preference analysis unit, for example, analyzes the purchase history and preferences of the user's family and friends and makes group coordination suggestions. For example, it may suggest that all family members wear clothes from the same brand. It may also suggest coordination that matches an event for a group of friends. For example, it may suggest clothes that are suitable for a trip for a group of friends. This makes it possible to make group coordination suggestions that take into account the preferences of family and friends.

[0033] When analyzing the user's preferences, the preference analysis unit takes into consideration data on past travel destinations and events visited, and can suggest clothes suitable for specific locations or events. For example, the preference analysis unit analyzes data on the user's past travel destinations and suggests clothes suitable for those locations. For example, based on data from trips to beach resorts, it suggests swimsuits and resort wear suitable for the next trip. It also analyzes data on events the user has attended and suggests clothes suitable for those events. For example, based on data from when the user attended a music festival, it suggests casual clothes suitable for the next festival. In this way, it is possible to suggest clothes suitable for specific locations or events.

[0034] The trend analysis unit analyzes video of a fashion show and can incorporate the latest designs and styles in real time. The trend analysis unit, for example, analyzes video of a fashion show and incorporates the latest designs and styles in real time. For example, it analyzes the designs and colors of the clothes worn by models in the video and identifies trends. The trend analysis unit also analyzes video of a fashion show and incorporates the latest designs and styles in real time. For example, it identifies the latest trends based on the designs and styles in the video and suggests them to the user. This allows the latest designs and styles to be incorporated in real time.

[0035] The trend analysis unit can consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, the trend analysis unit can consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, it can prioritize suggesting new products from brands that the user likes. The trend analysis unit can also consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, it can prioritize suggesting new products from brands that the user likes. This makes it possible to suggest brands that suit the user.

[0036] The trend analysis unit takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, the trend analysis unit takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, it can propose clothes that incorporate trends from Asia and Europe. The trend analysis unit also takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, it can propose clothes that incorporate trends from Asia and Europe. This makes it possible to make suggestions from a global perspective.

[0037] The trend analysis unit can reflect the results of the trend analysis in customized proposals according to the user's occupation and lifestyle. For example, the trend analysis unit reflects the results of the trend analysis in customized proposals according to the user's occupation. For example, it may suggest office casual clothing to a business person. The trend analysis unit also reflects the results of the trend analysis in customized proposals according to the user's lifestyle. For example, it may suggest sportswear to a user with an active lifestyle. This makes it possible to make customized proposals according to the user's occupation and lifestyle.

[0038] The seasonal analysis unit can analyze weather data for each season and suggest clothes that are best suited to the climate of a specific region. The seasonal analysis unit, for example, analyzes weather data for each season and suggests clothes that are best suited to the climate of a specific region. For example, clothes made of breathable materials are suggested for hot and humid regions in the summer, and clothes made of highly insulating materials are suggested for cold regions in the winter. In this way, it is possible to suggest clothes that are best suited to the climate of a specific region.

[0039] The seasonal analysis unit can take seasonal events into consideration and suggest appropriate clothing for each event. For example, the seasonal analysis unit collects seasonal event data and suggests appropriate clothing for each event. For example, casual, easy-to-move-in clothing is suggested for summer music festivals, while formal party dresses and suits are suggested for the winter holiday season. This allows the unit to suggest clothing appropriate for seasonal events.

[0040] The season analysis unit can take the user's health condition into consideration when proposing clothes appropriate for the season. The season analysis unit, for example, takes the user's health condition into consideration and proposes clothes appropriate for the season. For example, clothes made of hypoallergenic materials are proposed to a user with allergies. Also, clothes with temperature regulation functions are proposed to a user who has difficulty regulating their body temperature. This makes it possible to propose clothes that take the user's health condition into consideration.

[0041] The season analysis unit can take the user's activity level into consideration when proposing clothes appropriate for the season. The season analysis unit, for example, takes the user's activity level into consideration and proposes clothes appropriate for the season. For example, it proposes clothes made of materials that are easy to move in to a user who plays sports. It also proposes clothes made of durable materials to a user who enjoys outdoor activities. This makes it possible to propose clothes that take the user's activity level into consideration.

[0042] The recommendation unit can take into consideration the optimal fit for the user's body type and size when making recommendations. The recommendation unit, for example, suggests clothes with the optimal fit based on the user's body type data. For example, the recommendation unit selects clothes taking into consideration the user's height, weight, and body type characteristics. The recommendation unit also suggests clothes with the optimal fit based on the user's size data. For example, the recommendation unit selects clothes taking into consideration the user's bust, waist, and hip sizes. This makes it possible to suggest clothes that take into consideration the optimal fit for the user's body type and size.

[0043] When making recommendations, the recommendation unit can suggest clothes with high cost performance, taking into consideration the user's budget and price range. The recommendation unit can suggest clothes with high cost performance, for example, based on the user's budget data. For example, it can select the most suitable clothes within the budget set by the user. The recommendation unit can also suggest clothes with high cost performance, based on the user's price range data. For example, it can select the most suitable clothes within the price range preferred by the user. This makes it possible to suggest clothes with high cost performance, taking into consideration the user's budget and price range.

[0044] The recommendation unit can make recommendations that incorporate the opinions of the user's friends and family. The recommendation unit, for example, collects the opinions of the user's friends and family and suggests clothes based on them. For example, it takes into account the brands and styles recommended by the friends and family. The recommendation unit also collects the opinions of the user's friends and family and suggests clothes based on them. For example, it takes into account the brands and styles recommended by the friends and family. This makes it possible to make suggestions that incorporate the opinions of the user's friends and family.

[0045] When making recommendations, the recommendation unit takes into account the user's past reviews and ratings and can suggest highly reliable clothing. The recommendation unit, for example, analyzes the user's past reviews and ratings and suggests highly reliable clothing. For example, it prioritizes the suggestions of brands and styles that the user has given high ratings. The recommendation unit also analyzes the user's past reviews and ratings and suggests highly reliable clothing. For example, it prioritizes the suggestions of brands and styles that the user has given high ratings. This makes it possible to suggest highly reliable clothing that takes into account the user's past reviews and ratings.

[0046] When providing feedback, the recommendation unit can analyze the user's usage status after purchase and reflect it in the next recommendation. The recommendation unit, for example, analyzes the user's usage status after purchase and reflects it in the next recommendation. For example, the next suggestion is adjusted based on the frequency of use and satisfaction level of the purchased clothes. The recommendation unit also analyzes the user's usage status after purchase and reflects it in the next recommendation. For example, the next suggestion is adjusted based on the frequency of use and satisfaction level of the purchased clothes. In this way, the user's usage status after purchase can be analyzed and reflected in the next recommendation.

[0047] When providing feedback, the recommendation unit can take changes in the user's lifestyle into consideration and reflect them in the next recommendation. The recommendation unit, for example, takes changes in the user's lifestyle into consideration and reflects them in the next recommendation. For example, it can suggest clothes that match the climate and culture of the new location. The recommendation unit can also take changes in the user's lifestyle into consideration and reflect them in the next recommendation. For example, it can suggest clothes that match the dress code of a new workplace after changing jobs. This makes it possible to make the next recommendation taking changes in the user's lifestyle into consideration.

[0048] When providing feedback, the recommendation unit can also collect the opinions of the user's friends and family and reflect them in the next recommendation. The recommendation unit, for example, collects the opinions of the user's friends and family and optimizes the next recommendation based on them. For example, it takes into account the brands and styles recommended by friends and family. The recommendation unit also collects the opinions of the user's friends and family and optimizes the next recommendation based on them. For example, it takes into account the brands and styles recommended by friends and family. This allows the opinions of the user's friends and family to be reflected in the next recommendation.

[0049] When providing feedback, the recommendation unit takes into account the user's past reviews and ratings, and can improve the accuracy of the next recommendation. The recommendation unit, for example, analyzes the user's past reviews and ratings, and improves the accuracy of the next recommendation. For example, the features of clothes that the user has given a high rating are reflected in the next suggestion. The recommendation unit also analyzes the user's past reviews and ratings, and improves the accuracy of the next recommendation. For example, the features of clothes that the user has given a high rating are reflected in the next suggestion. This makes it possible to reflect the user's past reviews and ratings in improving the accuracy of the next recommendation.

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

[0051] The preference analysis unit can also suggest clothes based on specific ingredients or colors, taking into account the user's food preferences and allergy information. For example, if the user is allergic to a specific ingredient, the unit will suggest clothes that avoid the color of that ingredient. Also, if the user likes a specific ingredient, the unit will suggest clothes that incorporate the color or design of that ingredient. This makes it possible to suggest clothes that take into account the user's food preferences and allergy information.

[0052] The trend analysis unit can analyze costume designs from movies and TV dramas and suggest clothes incorporating the styles of popular characters. For example, it can suggest designs based on the costumes of popular characters from the latest movies. It can also suggest clothes incorporating the styles of popular characters from TV dramas. This makes it possible to suggest clothes that incorporate trends influenced by movies and TV dramas.

[0053] The seasonal analysis unit can also suggest clothes that coordinate with the user's pet, taking into account the type and coat color of the pet. For example, it can suggest clothes that match the coat color of the user's pet. It can also suggest clothes made of materials and designs that are suitable for spending time with pets. This makes it possible to suggest clothes that coordinate with the user's pet.

[0054] The recommendation unit can also take into account the user's hobbies and special skills and suggest clothes with designs and functions related to those. For example, if the user's hobby is sports, it will suggest clothes made of materials and with designs that are easy to move in. Also, if the user's hobby is music, it will suggest clothes with music-related designs and functions. This makes it possible to suggest clothes that take into account the user's hobbies and special skills.

[0055] The trend analysis department can also propose clothes that incorporate trends from different industries. For example, it can propose clothes with futuristic designs that incorporate trends from the technology industry. It can also propose clothes with original designs that incorporate trends from the art industry. This makes it possible to propose clothes that incorporate trends from different industries.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The preference analysis unit analyzes the user's preferences and past purchase history. For example, it identifies the user's preferences based on data on the clothes the user has purchased in the past. It also identifies the user's preferred styles, colors, and brands, and selects clothes based on that. Step 2: The trend analysis department analyzes current fashion trends. For example, it collects data from fashion magazines, social media, and online shops to understand current trends. It also selects clothes that match the latest trends. Step 3: The seasonal analysis unit selects clothes according to the season. For example, it suggests clothes made of cool materials in summer and clothes made of warm materials in winter. It also provides the user with clothes appropriate for the season. Step 4: The recommendation unit comprehensively considers the information obtained by the preference analysis unit, trend analysis unit, and seasonal analysis unit to recommend the most suitable clothing for the user. For example, it suggests clothing that matches the current trend based on the user's preferred style, color, and brand. It also suggests clothing made of appropriate materials and designs according to the season.

[0058] (Example 2) The clothing recommendation system according to an embodiment of the present invention is a system in which AI selects and recommends optimal clothing according to a user's preferences, trends, and seasons. This allows the clothing recommendation system to suggest clothing that matches the user's preferences, trends, and seasons.

[0059] The clothing recommendation system according to the embodiment includes a preference analysis unit, a trend analysis unit, a seasonal analysis unit, and a recommendation unit. The preference analysis unit analyzes a user's preferences and past purchase history. For example, the preference analysis unit identifies the user's preferences based on data on clothing purchased by the user in the past. The preference analysis unit identifies the user's preferred styles, colors, and brands, and selects clothing based on the identified styles, colors, and brands. The trend analysis unit analyzes current fashion trends. For example, the trend analysis unit collects data from fashion magazines, social media, and online shops to identify current trends. The trend analysis unit selects clothing that matches the latest trends. The seasonal analysis unit selects clothing according to the season. For example, the system recommends clothing made of cool materials in summer and clothing made of warm materials in winter. The seasonal analysis unit provides the user with clothing appropriate for the season. The recommendation unit comprehensively considers the information obtained by the preference analysis unit, trend analysis unit, and seasonal analysis unit to recommend clothing that is optimal for the user. For example, the system recommends clothing that matches the current trend based on the user's preferred styles, colors, and brands. The system also recommends clothing made of materials and designs appropriate for the season. As a result, the clothing recommendation system according to the embodiment can recommend optimal clothing according to the user's preferences, trends, and seasons.

[0060] The preference analysis unit can analyze a user's social media posts and blog articles to identify changes in preferences and new interests. The preference analysis unit, for example, analyzes a user's social media posts and identifies changes in preferences from the content of past posts. For example, if there is an increase in posts about a particular brand or style, it can determine that interest in that brand or style is growing. The preference analysis unit can also analyze a user's blog articles to identify new interests. For example, if a user mentions a new brand or style, clothing suggestions can be made taking that interest into consideration. This makes it possible to identify changes in a user's preferences and new interests.

[0061] The preference analysis unit can take into account the user's life events and suggest clothes that correspond to them. For example, the preference analysis unit collects information about the user's life events and suggests clothes that match important events such as marriage or job changes. For example, when a user receives a wedding invitation, the unit suggests formal dresses or suits. Also, when a user changes jobs, the unit suggests clothes that match the dress code of the new workplace. This makes it possible to suggest clothes that correspond to the user's life events.

[0062] The preference analysis unit uses the emotion estimation function to analyze the user's emotions regarding clothes previously purchased and can identify clothes that elicit positive emotions. The preference analysis unit, for example, analyzes reviews and comments regarding clothes previously purchased by the user and calculates an emotion score. For example, it identifies styles and brands of clothes with many positive reviews. The emotion estimation function also analyzes the user's emotions regarding clothes previously purchased and identifies clothes that elicit positive emotions. For example, it preferentially suggests clothes that bring joy to the user. This makes it possible to identify clothes that elicit positive emotions in the user.

[0063] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family and friends and make group coordination suggestions. The preference analysis unit, for example, analyzes the purchase history and preferences of the user's family and friends and makes group coordination suggestions. For example, it may suggest that all family members wear clothes from the same brand. It may also suggest coordination that matches an event for a group of friends. For example, it may suggest clothes that are suitable for a trip for a group of friends. This makes it possible to make group coordination suggestions that take into account the preferences of family and friends.

[0064] When analyzing the user's preferences, the preference analysis unit takes into consideration data on past travel destinations and events visited, and can suggest clothes suitable for specific locations or events. For example, the preference analysis unit analyzes data on the user's past travel destinations and suggests clothes suitable for those locations. For example, based on data from trips to beach resorts, it suggests swimsuits and resort wear suitable for the next trip. It also analyzes data on events the user has attended and suggests clothes suitable for those events. For example, based on data from when the user attended a music festival, it suggests casual clothes suitable for the next festival. In this way, it is possible to suggest clothes suitable for specific locations or events.

[0065] The preference analysis unit uses the emotion estimation function to analyze the emotions of the user when selecting clothes in real time and make suggestions to reduce stress. The preference analysis unit, for example, uses the emotion estimation function to analyze the emotions of the user when selecting clothes in real time and make suggestions to reduce stress. For example, if the user is feeling stressed, the preference analysis unit suggests clothes made of materials and designs that will help the user relax. The preference analysis unit also uses the emotion estimation function to analyze the emotions of the user when selecting clothes and make suggestions to reduce stress. For example, the preference analysis unit suggests clothes in colors and styles that will help the user relax. This makes it possible to make suggestions to reduce the user's stress.

[0066] The trend analysis unit analyzes video of a fashion show and can incorporate the latest designs and styles in real time. The trend analysis unit, for example, analyzes video of a fashion show and incorporates the latest designs and styles in real time. For example, it analyzes the designs and colors of the clothes worn by models in the video and identifies trends. The trend analysis unit also analyzes video of a fashion show and incorporates the latest designs and styles in real time. For example, it identifies the latest trends based on the designs and styles in the video and suggests them to the user. This allows the latest designs and styles to be incorporated in real time.

[0067] The trend analysis unit can consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, the trend analysis unit can consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, it can prioritize suggesting new products from brands that the user likes. The trend analysis unit can also consider the influence of specific brands and designers when analyzing trends and suggest brands that suit the user. For example, it can prioritize suggesting new products from brands that the user likes. This makes it possible to suggest brands that suit the user.

[0068] The trend analysis unit can use the emotion estimation function to analyze the user's emotional response to trends and prioritize trends that elicit a positive response. The trend analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to trends and prioritize trends that elicit a positive response. For example, it prioritizes suggesting trends that make the user feel happy. The emotion estimation function can also be used to analyze the user's emotional response to trends and prioritize trends that elicit a positive response. For example, it prioritizes suggesting trends that make the user feel happy. This makes it possible to prioritize trends that elicit a positive response.

[0069] The trend analysis unit takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, the trend analysis unit takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, it can propose clothes that incorporate trends from Asia and Europe. The trend analysis unit also takes into account trends from different cultural spheres and regions when analyzing trends, and can make suggestions from a global perspective. For example, it can propose clothes that incorporate trends from Asia and Europe. This makes it possible to make suggestions from a global perspective.

[0070] The trend analysis unit can reflect the results of the trend analysis in customized proposals according to the user's occupation and lifestyle. For example, the trend analysis unit reflects the results of the trend analysis in customized proposals according to the user's occupation. For example, it may suggest office casual clothing to a business person. The trend analysis unit also reflects the results of the trend analysis in customized proposals according to the user's lifestyle. For example, it may suggest sportswear to a user with an active lifestyle. This makes it possible to make customized proposals according to the user's occupation and lifestyle.

[0071] The trend analysis unit uses the emotion estimation function to analyze the user's emotion toward a trend in real time, and can respond quickly to changes in the trend. The trend analysis unit, for example, uses the emotion estimation function to analyze the user's emotion toward a trend in real time, and can respond quickly to changes in the trend. For example, trends with low user emotion scores are eliminated. The emotion estimation function also analyzes the user's emotion toward a trend in real time, and can respond quickly to changes in the trend. For example, trends with low user emotion scores are eliminated. This allows for quick responses to changes in the trend.

[0072] The seasonal analysis unit can analyze weather data for each season and suggest clothes that are best suited to the climate of a specific region. The seasonal analysis unit, for example, analyzes weather data for each season and suggests clothes that are best suited to the climate of a specific region. For example, clothes made of breathable materials are suggested for hot and humid regions in the summer, and clothes made of highly insulating materials are suggested for cold regions in the winter. In this way, it is possible to suggest clothes that are best suited to the climate of a specific region.

[0073] The seasonal analysis unit can take seasonal events into consideration and suggest appropriate clothing for each event. For example, the seasonal analysis unit collects seasonal event data and suggests appropriate clothing for each event. For example, casual, easy-to-move-in clothing is suggested for summer music festivals, while formal party dresses and suits are suggested for the winter holiday season. This allows the unit to suggest clothing appropriate for seasonal events.

[0074] The season analysis unit uses the emotion estimation function to analyze the user's emotion for each season and identify clothes that will elicit positive emotions according to the season. The season analysis unit, for example, uses the emotion estimation function to analyze the user's emotion for each season and identify clothes that will elicit positive emotions. For example, bright colored clothes are suggested for spring. The emotion estimation function is also used to analyze the user's emotion for each season and identify clothes that will elicit positive emotions. For example, warm colored clothes are suggested for autumn. In this way, it is possible to identify clothes that will elicit positive emotions according to the season.

[0075] The season analysis unit can take the user's health condition into consideration when proposing clothes appropriate for the season. The season analysis unit, for example, takes the user's health condition into consideration and proposes clothes appropriate for the season. For example, clothes made of hypoallergenic materials are proposed to a user with allergies. Also, clothes with temperature regulation functions are proposed to a user who has difficulty regulating their body temperature. This makes it possible to propose clothes that take the user's health condition into consideration.

[0076] The season analysis unit can take the user's activity level into consideration when proposing clothes appropriate for the season. The season analysis unit, for example, takes the user's activity level into consideration and proposes clothes appropriate for the season. For example, it proposes clothes made of materials that are easy to move in to a user who plays sports. It also proposes clothes made of durable materials to a user who enjoys outdoor activities. This makes it possible to propose clothes that take the user's activity level into consideration.

[0077] The season analysis unit can use the emotion estimation function to analyze the user's emotion for each season in real time and suggest clothes suitable for the change of seasons. The season analysis unit, for example, uses the emotion estimation function to analyze the user's emotion for each season in real time and suggest clothes suitable for the change of seasons. For example, clothes made of light materials are suggested during the transition from spring to summer. The emotion estimation function can also be used to analyze the user's emotion for each season in real time and suggest clothes suitable for the change of seasons. For example, clothes made of warm materials are suggested during the transition from autumn to winter. This makes it possible to suggest clothes suitable for the change of seasons.

[0078] The recommendation unit can take into consideration the optimal fit for the user's body type and size when making recommendations. The recommendation unit, for example, suggests clothes with the optimal fit based on the user's body type data. For example, the recommendation unit selects clothes taking into consideration the user's height, weight, and body type characteristics. The recommendation unit also suggests clothes with the optimal fit based on the user's size data. For example, the recommendation unit selects clothes taking into consideration the user's bust, waist, and hip sizes. This makes it possible to suggest clothes that take into consideration the optimal fit for the user's body type and size.

[0079] When making recommendations, the recommendation unit can suggest clothes with high cost performance, taking into consideration the user's budget and price range. The recommendation unit can suggest clothes with high cost performance, for example, based on the user's budget data. For example, it can select the most suitable clothes within the budget set by the user. The recommendation unit can also suggest clothes with high cost performance, based on the user's price range data. For example, it can select the most suitable clothes within the price range preferred by the user. This makes it possible to suggest clothes with high cost performance, taking into consideration the user's budget and price range.

[0080] The recommendation unit can use the emotion estimation function to analyze the user's emotion toward the recommended clothes and prioritize clothes that elicit positive emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotion toward the recommended clothes and prioritize clothes that elicit positive emotions. For example, it prioritizes suggesting clothes that bring joy to the user. Also, it uses the emotion estimation function to analyze the user's emotion toward the recommended clothes and prioritizes clothes that elicit positive emotions. For example, it prioritizes suggesting clothes that give the user high satisfaction. This makes it possible to prioritize suggesting clothes that elicit positive emotions.

[0081] The recommendation unit can make recommendations that incorporate the opinions of the user's friends and family. The recommendation unit, for example, collects the opinions of the user's friends and family and suggests clothes based on them. For example, it takes into account the brands and styles recommended by the friends and family. The recommendation unit also collects the opinions of the user's friends and family and suggests clothes based on them. For example, it takes into account the brands and styles recommended by the friends and family. This makes it possible to make suggestions that incorporate the opinions of the user's friends and family.

[0082] When making recommendations, the recommendation unit takes into account the user's past reviews and ratings and can suggest highly reliable clothing. The recommendation unit, for example, analyzes the user's past reviews and ratings and suggests highly reliable clothing. For example, it prioritizes the suggestions of brands and styles that the user has given high ratings. The recommendation unit also analyzes the user's past reviews and ratings and suggests highly reliable clothing. For example, it prioritizes the suggestions of brands and styles that the user has given high ratings. This makes it possible to suggest highly reliable clothing that takes into account the user's past reviews and ratings.

[0083] The recommendation unit uses the emotion estimation function to analyze the user's emotion regarding the recommended clothes in real time and continuously make optimal suggestions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the recommended clothes in real time and continuously make optimal suggestions. For example, it prioritizes suggesting clothes with a high user emotion score. The recommendation unit also uses the emotion estimation function to analyze the user's emotion regarding the recommended clothes in real time and continuously make optimal suggestions. For example, it prioritizes suggesting clothes with a high user emotion score. This allows the recommendation unit to analyze the user's emotion in real time and continuously make optimal suggestions.

[0084] When providing feedback, the recommendation unit can analyze the user's usage status after purchase and reflect it in the next recommendation. The recommendation unit, for example, analyzes the user's usage status after purchase and reflects it in the next recommendation. For example, the next suggestion is adjusted based on the frequency of use and satisfaction level of the purchased clothes. The recommendation unit also analyzes the user's usage status after purchase and reflects it in the next recommendation. For example, the next suggestion is adjusted based on the frequency of use and satisfaction level of the purchased clothes. In this way, the user's usage status after purchase can be analyzed and reflected in the next recommendation.

[0085] When providing feedback, the recommendation unit can take changes in the user's lifestyle into consideration and reflect them in the next recommendation. The recommendation unit, for example, takes changes in the user's lifestyle into consideration and reflects them in the next recommendation. For example, it can suggest clothes that match the climate and culture of the new location. The recommendation unit can also take changes in the user's lifestyle into consideration and reflect them in the next recommendation. For example, it can suggest clothes that match the dress code of a new workplace after changing jobs. This makes it possible to make the next recommendation taking changes in the user's lifestyle into consideration.

[0086] The recommendation unit can use the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. For example, the feature of clothing that the user is highly satisfied with is reflected in the next recommendation. The emotion estimation function can also be used to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions. For example, the feature of clothing that the user is highly satisfied with is reflected in the next recommendation. This makes it possible to analyze the user's emotion regarding the feedback and make improvements that draw out positive emotions.

[0087] When providing feedback, the recommendation unit can also collect the opinions of the user's friends and family and reflect them in the next recommendation. The recommendation unit, for example, collects the opinions of the user's friends and family and optimizes the next recommendation based on them. For example, it takes into account the brands and styles recommended by friends and family. The recommendation unit also collects the opinions of the user's friends and family and optimizes the next recommendation based on them. For example, it takes into account the brands and styles recommended by friends and family. This allows the opinions of the user's friends and family to be reflected in the next recommendation.

[0088] When providing feedback, the recommendation unit takes into account the user's past reviews and ratings, and can improve the accuracy of the next recommendation. The recommendation unit, for example, analyzes the user's past reviews and ratings, and improves the accuracy of the next recommendation. For example, the features of clothes that the user has given a high rating are reflected in the next suggestion. The recommendation unit also analyzes the user's past reviews and ratings, and improves the accuracy of the next recommendation. For example, the features of clothes that the user has given a high rating are reflected in the next suggestion. This makes it possible to reflect the user's past reviews and ratings in improving the accuracy of the next recommendation.

[0089] The recommendation unit uses the emotion estimation function to analyze the user's emotion regarding the feedback in real time and can quickly reflect it in the next recommendation. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the feedback in real time and can quickly reflect it in the next recommendation. For example, it preferentially suggests clothes with a high user emotion score. Also, the emotion estimation function is used to analyze the user's emotion regarding the feedback in real time and can quickly reflect it in the next recommendation. For example, it preferentially suggests clothes with a high user emotion score. This allows the user's emotion regarding the feedback to be quickly reflected in the next recommendation.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The preference analysis unit can also suggest clothes based on specific ingredients or colors, taking into account the user's food preferences and allergy information. For example, if the user is allergic to a specific ingredient, the unit will suggest clothes that avoid the color of that ingredient. Also, if the user likes a specific ingredient, the unit will suggest clothes that incorporate the color or design of that ingredient. This makes it possible to suggest clothes that take into account the user's food preferences and allergy information.

[0092] The trend analysis unit can analyze costume designs from movies and TV dramas and suggest clothes incorporating the styles of popular characters. For example, it can suggest designs based on the costumes of popular characters from the latest movies. It can also suggest clothes incorporating the styles of popular characters from TV dramas. This makes it possible to suggest clothes that incorporate trends influenced by movies and TV dramas.

[0093] The seasonal analysis unit can also suggest clothes that coordinate with the user's pet, taking into account the type and coat color of the pet. For example, it can suggest clothes that match the coat color of the user's pet. It can also suggest clothes made of materials and designs that are suitable for spending time with pets. This makes it possible to suggest clothes that coordinate with the user's pet.

[0094] The recommendation unit can also take into account the user's hobbies and special skills and suggest clothes with designs and functions related to those. For example, if the user's hobby is sports, it will suggest clothes made of materials and with designs that are easy to move in. Also, if the user's hobby is music, it will suggest clothes with music-related designs and functions. This makes it possible to suggest clothes that take into account the user's hobbies and special skills.

[0095] The preference analysis unit can also use the user's emotion estimation function to analyze the emotions the user feels toward specific colors or designs, and suggest clothes that elicit positive emotions. For example, if a user has positive emotions toward a specific color, the unit will suggest clothes that incorporate that color. Also, if a user has positive emotions toward a specific design, the unit will suggest clothes that incorporate that design. This makes it possible to suggest clothes that take the user's emotions into consideration.

[0096] The trend analysis unit can also use the emotion estimation function to analyze the user's emotions toward clothes they have purchased in the past, and prioritize trends that evoke positive emotions. For example, if the user has positive emotions toward clothes they have purchased in the past, the trend analysis unit can suggest clothes that incorporate that trend. On the other hand, if the user has negative emotions toward clothes they have purchased in the past, the trend analysis unit can suggest clothes that avoid that trend. This makes it possible to suggest trends that take the user's emotions into consideration.

[0097] The season analysis unit can also use the emotion estimation function to analyze the user's emotions for each season and suggest clothes that will bring out positive emotions. For example, if the user has positive emotions about spring, bright colored clothes that are suitable for spring will be suggested. Also, if the user has positive emotions about autumn, warm colored clothes that are suitable for autumn will be suggested. This makes it possible to suggest clothes that take into account the user's emotions for each season.

[0098] The recommendation unit can also use the emotion estimation function to analyze the user's emotions toward clothes they have purchased in the past and suggest clothes that evoke positive emotions. For example, if the user has positive emotions toward clothes they have purchased in the past, the recommendation unit can suggest clothes that incorporate the style and design of those clothes. On the other hand, if the user has negative emotions toward clothes they have purchased in the past, the recommendation unit can suggest clothes that avoid those styles and designs. This makes it possible to suggest clothes that take the user's emotions into consideration.

[0099] The recommendation unit can also use the emotion estimation function to analyze the user's emotions in real time when choosing clothes and make suggestions to reduce stress. For example, if the user feels stressed when choosing clothes, the recommendation unit can suggest clothes made of materials and with designs that will relax them. Also, if the user has positive emotions when choosing clothes, the recommendation unit can suggest clothes that bring out those emotions. This makes it possible to suggest clothes that take the user's emotions into consideration.

[0100] The trend analysis department can also propose clothes that incorporate trends from different industries. For example, it can propose clothes with futuristic designs that incorporate trends from the technology industry. It can also propose clothes with original designs that incorporate trends from the art industry. This makes it possible to propose clothes that incorporate trends from different industries.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The preference analysis unit analyzes the user's preferences and past purchase history. For example, it identifies the user's preferences based on data on the clothes the user has purchased in the past. It also identifies the user's preferred styles, colors, and brands, and selects clothes based on that. Step 2: The trend analysis department analyzes current fashion trends. For example, it collects data from fashion magazines, social media, and online shops to understand current trends. It also selects clothes that match the latest trends. Step 3: The seasonal analysis unit selects clothes according to the season. For example, it suggests clothes made of cool materials in summer and clothes made of warm materials in winter. It also provides the user with clothes appropriate for the season. Step 4: The recommendation unit comprehensively considers the information obtained by the preference analysis unit, trend analysis unit, and seasonal analysis unit to recommend the most suitable clothing for the user. For example, it suggests clothing that matches the current trend based on the user's preferred style, color, and brand. It also suggests clothing made of appropriate materials and designs according to the season.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 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 preference analysis unit that analyzes user preferences and past purchase history; A trend analysis department that analyzes current fashion trends, A seasonal analysis section that selects clothes according to the season, a recommendation unit that recommends optimal clothing to a user by comprehensively considering the information obtained by the preference analysis unit, the trend analysis unit, and the season analysis unit. A system characterized by:

2. The preference analysis unit Using an emotion estimation function, the emotions of the user regarding the clothes purchased in the past are analyzed, and clothes that evoke positive emotions are identified.

2. The system of claim 1.

3. The trend analysis unit Analyzing fashion show footage and incorporating the latest designs and styles in real time 2. The system of claim 1.

4. The seasonal analysis unit Analyzes seasonal weather data and suggests clothing that is best suited to the climate of a specific region 2. The system of claim 1.

5. The recommendation unit When making recommendations, consider the best fit for the user's body type and size.

2. The system of claim 1.

6. The trend analysis unit Using a sentiment estimation function to analyze the user's emotional response to trends and prioritize the trends that elicit a positive response.

2. The system of claim 1.

7. The seasonal analysis unit Using an emotion estimation function, the emotions of the user for each season are analyzed, and the clothing that elicits positive emotions according to the season is identified.

2. The system of claim 1.

8. The recommendation unit Using an emotion estimation function, the user's emotions regarding the recommended clothes are analyzed, and the clothes that evoke positive emotions are prioritized.

2. The system of claim 1.

Images