System

Abstract

An object of a system according to an embodiment is to perform matching with clothes that a user has in consideration of a preference of the user.SOLUTION: A system includes a preference analysis unit, a matching unit, and a proposal unit. The preference analyzer includes a generation AI. The matching unit performs matching with clothes that the user has on the basis of the preference of the user analyzed by the preference analysis unit. The proposal unit displays the proposal of the clothes matched by the matching unit on the digital signage.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to match clothes with the user's existing clothes or to find clothes that suit individual preferences.

[0005] The system according to the embodiment aims to take into consideration the user's preferences and match them with the clothes the user already owns. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a matching unit, and a suggestion unit. The preference analysis unit is equipped with a generation AI. The matching unit matches clothes owned by the user based on the user's preferences analyzed by the preference analysis unit. The suggestion unit displays the clothing suggestions matched by the matching unit on digital signage. [Effects of the Invention]

[0007] The system according to the embodiment can take into consideration the user's preferences and match them with the clothes the user already owns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital signage system according to an embodiment of the present invention utilizes generative AI to suggest clothing that takes into account individual preferences. This system analyzes a user's preferences, matches them with clothing they already own, and displays suggestions on the digital signage. This allows the digital signage system to suggest the most suitable clothing for the user.

[0029] A digital signage system according to an embodiment includes a preference analysis unit, a matching unit, and a suggestion unit. The preference analysis unit is equipped with a generation AI and analyzes a user's preferences. For example, the generation AI collects information such as the user's past purchase history, browsing history, and "likes" and comments on social media, and analyzes this data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to understand the user's preferences. The matching unit matches the user's existing clothes based on the user's preferences analyzed by the preference analysis unit. For example, the user takes a photo of the clothes they own and inputs the data into the generation AI. The generation AI analyzes the data and matches the colors and styles of the clothes they own with new clothes. The suggestion unit displays the clothing suggestions matched by the matching unit on the digital signage. For example, when a user approaches a digital signage installed in a shopping mall or store, the system displays the optimal clothing coordination for the user based on the information analyzed by the generation AI. This allows the digital signage system according to an embodiment to suggest optimal clothing to the user. For example, the user can try on or purchase the suggested clothes on the spot.

[0030] The preference analysis unit collects lifestyle data about the user, and the generation AI analyzes that data to suggest clothes that are suitable for that lifestyle. For example, the preference analysis unit collects data about the user's exercise habits, and the generation AI analyzes that data to suggest clothes that are suitable for exercise. For example, breathable clothes are suggested for a user who runs. The preference analysis unit also collects data about the user's eating habits, and the generation AI analyzes that data to suggest clothes that are suitable for mealtimes. For example, casual clothes are suggested for a user who eats out often. The preference analysis unit also collects information about the user's overall lifestyle (for example, work style and hobbies), and the generation AI analyzes that data to suggest clothes that are suitable for everyday life. This makes it possible to suggest clothes based on the user's lifestyle.

[0031] The preference analysis unit can analyze changes in a user's preferences by season and develop an algorithm for making different suggestions for each season. The preference analysis unit, for example, analyzes the user's past purchase history by season to extract changes in preferences by season. For example, if there is a tendency to prefer bright-colored clothes in the summer, suggestions are made based on that information. The preference analysis unit also analyzes the user's social media posts by season to extract changes in preferences by season. For example, if there is a tendency to prefer clothes made of warm materials in the winter, suggestions are made based on that information. The preference analysis unit also analyzes the user's browsing history by season to extract changes in preferences by season. For example, if there is a tendency to prefer floral-patterned clothes in the spring, suggestions are made based on that information. This makes it possible to make different suggestions for each season.

[0032] The preference analysis unit collects the user's music and movie preferences, as well as other preference data besides fashion, and the generation AI can make comprehensive suggestions based on that data. For example, the preference analysis unit collects the user's music preferences, and the generation AI analyzes that data to suggest clothes that match the music genre. For example, casual clothes are suggested for a user who likes rock. The preference analysis unit also collects the user's movie preferences, and the generation AI analyzes that data to suggest clothes that match the movie genre. For example, elegant clothes are suggested for a user who likes romantic movies. The preference analysis unit also collects the user's reading preferences, and the generation AI analyzes that data to suggest clothes that match the reading genre. For example, chic clothes are suggested for a user who likes mystery novels. This makes it possible to make comprehensive suggestions based on preference data other than fashion.

[0033] The preference analysis unit refers to the fashion styles of the user's friends and family, and the generation AI can make suggestions based on that data. For example, the preference analysis unit collects the fashion styles of the user's friends and family, and the generation AI analyzes that data to suggest clothes that suit the user. For example, it refers to the styles preferred by friends. In addition, it collects the fashion styles of the user's family, and the generation AI analyzes that data to suggest clothes that suit the family's preferences. For example, if all family members prefer casual styles, suggestions are made based on that information. In addition, it collects the fashion styles of the user's friends, and the generation AI analyzes that data to suggest clothes with a similar style to the friends. For example, if a friend prefers a formal style, suggestions are made based on that information. This makes it possible to make suggestions based on the fashion styles of friends and family.

[0034] The matching unit can analyze the material and texture of the clothes you own and perform matching that takes into account their physical compatibility with new clothes. For example, the matching unit collects material data of the clothes you own, and the generation AI analyzes that data to perform matching that takes into account their physical compatibility with new clothes. For example, it can suggest clothes made of the same material. It can also collect texture data of the clothes you own, and the generation AI analyzes that data to perform matching that takes into account their texture compatibility with new clothes. For example, it can suggest clothes with a soft texture. It can also analyze the material and texture of the clothes you own, and the generation AI can evaluate their compatibility with new clothes based on that data. For example, it can suggest new silk clothes for silk clothes. This makes it possible to match new clothes based on the material and texture of the clothes you own.

[0035] The matching unit analyzes how often you use the clothes you own, and can prioritize suggesting combinations of frequently used clothes with new clothes. For example, the matching unit collects data on how often you use the clothes you own, and the generation AI analyzes that data to suggest combinations of frequently used clothes with new clothes. For example, it might suggest tops that go well with jeans you often wear. It also analyzes how often you use the clothes you own, and the generation AI evaluates how they would go with new clothes based on that data. For example, it might suggest jackets that go well with frequently used shirts. It also prioritizes suggesting combinations with new clothes based on the data on how often you use the clothes you own. For example, it might suggest blouses that go well with skirts you often wear. This makes it possible to prioritize suggesting combinations of frequently used clothes with new clothes.

[0036] When analyzing data on the clothes you own, the matching unit can suggest total coordinations that include accessories, shoes, and other small items. For example, the matching unit collects data on accessories and shoes in addition to data on the clothes you own, and the generation AI analyzes that data to suggest total coordinations. For example, it can suggest a dress that matches a necklace you own. Furthermore, when analyzing data on the clothes you own, the generation AI can also take into account data on accessories and shoes to suggest total coordinations. For example, it can suggest pants that match shoes you own. Furthermore, a system can be built that suggests total coordinations that include accessories and shoes based on data on the clothes you own. For example, it can suggest a top that matches earrings you own. This makes it possible to suggest total coordinations that include accessories, shoes, and other small items.

[0037] The matching unit can share data about clothes owned by users with other users and suggest matching with clothes owned by other users. For example, the matching unit shares data about clothes owned by users with other users, and the generation AI analyzes that data and suggests matching with clothes owned by other users. For example, combining one's own clothes with clothes owned by friends. Also, a platform for sharing data about clothes owned by users can be built, and the generation AI can use that data to suggest matching with other users' clothes. For example, combining one's own clothes with clothes owned by family members. Also, data about clothes owned by users can be shared with other users, and the generation AI can analyze that data and suggest coordination for the entire group. For example, combining clothes owned by everyone in a group of friends. This makes it possible to match with clothes owned by other users.

[0038] The suggestion unit can customize the suggestions displayed on the digital signage based on the user's mood and physical condition on that day. The suggestion unit, for example, analyzes the user's mood on that day and customizes the suggestions displayed on the digital signage. For example, when the user is in a relaxed mood, casual clothes are suggested. The suggestion unit also collects the user's physical condition data and customizes the suggestions displayed on the digital signage. For example, when the user is tired, comfortable clothes are suggested. Furthermore, a system is constructed that dynamically changes the suggestions displayed on the digital signage based on the user's mood and physical condition. For example, when the user is in good spirits, bright colored clothes are suggested. This makes it possible to customize the suggestions based on the user's mood and physical condition on that day.

[0039] The suggestion unit can personalize the suggestions displayed on the digital signage based on the user's past purchase history and browsing history. The suggestion unit, for example, analyzes the user's past purchase history and personalizes the suggestions displayed on the digital signage. For example, it suggests new products from brands that the user has previously purchased. It also analyzes the user's browsing history and personalizes the suggestions displayed on the digital signage. For example, it suggests clothing styles that the user frequently browses. It also builds a system that customizes the suggestions displayed on the digital signage based on the user's purchase history and browsing history. For example, it suggests clothing in colors and designs that the user has shown interest in in the past. This makes it possible to personalize suggestions based on the user's past purchase history and browsing history.

[0040] The suggestion unit can provide a function for sharing suggestions displayed on the digital signage with the user's friends and family, and enjoying coordinating outfits together. The suggestion unit, for example, provides a function for sharing suggestions displayed on the digital signage with the user's friends and family. For example, it generates a sharing link for enjoying coordinating outfits together with friends. It also provides a function for sharing suggestions displayed on the digital signage with family members, and enjoying coordinating outfits together. For example, it makes suggestions for combining clothes for all family members. It also builds a system for sharing suggestions displayed on the digital signage with friends and family in real time, and enjoying coordinating outfits together. For example, it allows trying on suggested clothes together with friends. This provides a function for enjoying coordinating outfits together with friends and family.

[0041] The suggestion unit can link the suggestions displayed on the digital signage with specific event or sale information at stores or shopping malls visited by the user. For example, the suggestion unit links the suggestions displayed on the digital signage with sale information at stores visited by the user. For example, it prioritizes suggestions for items on sale. Furthermore, it links the suggestions displayed on the digital signage with specific event information at shopping malls. For example, it makes suggestions based on information about upcoming fashion shows. Furthermore, a system is constructed that links the suggestions displayed on the digital signage with event information at stores or shopping malls visited by the user. For example, it suggests outfits that match the event. This makes it possible to link suggestions with specific event or sale information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The digital signage system can also collect the user's health data, which the generation AI analyzes to suggest clothing appropriate for the user's health condition. For example, the system can collect the user's heart rate and sleep data, analyze it, and suggest clothing that is relaxing. The system can also collect the user's exercise data, analyze it, and suggest clothing appropriate for after exercise. For example, it can suggest post-exercise recovery wear. The system can also analyze the user's stress level, and based on that data, the generation AI can suggest clothing that reduces stress. This makes it possible to suggest clothing based on the user's health condition.

[0044] The digital signage system can also collect the user's travel data, and the generation AI can analyze that data to suggest clothing appropriate for the travel destination. For example, if the user is traveling to a beach resort, the generation AI can analyze that data and suggest clothing appropriate for the beach. If the user is traveling to a cold region, the generation AI can analyze that data and suggest clothing appropriate for cold regions, for example, suggesting warm coats and sweaters. The generation AI can also analyze the user's purpose of travel (for example, business trip or leisure trip), and based on that data, suggest clothing appropriate for the purpose of the trip. This makes it possible to suggest clothing based on the user's travel destination and purpose.

[0045] The digital signage system can also make suggestions based on the user's hobbies and interests. For example, if a user likes outdoor activities, the generation AI will analyze that data and suggest clothes suitable for outdoor activities. Also, if the user is interested in art and music, the generation AI will analyze that data and suggest clothes suitable for art events and concerts. For example, it will suggest elegant clothes for going to an art gallery. Based on the user's hobbies and interests, the generation AI will also use that data to suggest clothes suitable for specific events and activities. This makes it possible to suggest clothes based on the user's hobbies and interests.

[0046] The digital signage system can also collect the user's occupational data, and the generation AI can analyze that data to suggest clothing appropriate for the occupation. For example, if the user is a businessperson, the generation AI can analyze that data to suggest clothing appropriate for business situations. Also, if the user is engaged in a creative occupation, the generation AI can analyze that data to suggest clothing appropriate for creative situations. For example, it can suggest unique clothing for designers. Furthermore, based on the user's occupation, the generation AI can suggest clothing appropriate for the occupation. This makes it possible to suggest clothing based on the user's occupation.

[0047] The digital signage system can also make suggestions based on the user's cultural background and religious beliefs. For example, if a user has a particular cultural background, the generation AI analyzes that data to suggest clothing appropriate for that culture. Also, if a user has particular religious beliefs, the generation AI analyzes that data to suggest clothing appropriate for those beliefs. For example, when attending a particular religious event, the generation AI suggests clothing appropriate for that event. Furthermore, based on the user's cultural background and religious beliefs, the generation AI uses that data to suggest clothing appropriate for specific events or occasions. This makes it possible to suggest clothing based on the user's cultural background and religious beliefs.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The preference analysis unit is equipped with a generation AI and analyzes the user's preferences. The generation AI collects information such as the user's past purchase history, browsing history, and "likes" and comments on social media, and analyzes this data. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to understand the user's preferences. Step 2: The matching unit matches the user's existing clothes based on the user's preferences analyzed by the preference analysis unit. The user takes a photo of the clothes they own and inputs that data into the generation AI. The generation AI analyzes the data and matches the colors and styles of the clothes they own with the new clothes. Step 3: The suggestion unit displays the clothing suggestions matched by the matching unit on digital signage. When a user approaches a digital signage installed in a shopping mall or store, the system displays the optimal clothing coordination for the user based on the information analyzed by the generation AI.

[0050] (Example 2) A digital signage system according to an embodiment of the present invention utilizes generative AI to suggest clothing that takes into account individual preferences. This system analyzes a user's preferences, matches them with clothing they already own, and displays suggestions on the digital signage. This allows the digital signage system to suggest the most suitable clothing for the user.

[0051] A digital signage system according to an embodiment includes a preference analysis unit, a matching unit, and a suggestion unit. The preference analysis unit is equipped with a generation AI and analyzes a user's preferences. For example, the generation AI collects information such as the user's past purchase history, browsing history, and "likes" and comments on social media, and analyzes this data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to understand the user's preferences. The matching unit matches the user's existing clothes based on the user's preferences analyzed by the preference analysis unit. For example, the user takes a photo of the clothes they own and inputs the data into the generation AI. The generation AI analyzes the data and matches the colors and styles of the clothes they own with new clothes. The suggestion unit displays the clothing suggestions matched by the matching unit on the digital signage. For example, when a user approaches a digital signage installed in a shopping mall or store, the system displays the optimal clothing coordination for the user based on the information analyzed by the generation AI. This allows the digital signage system according to an embodiment to suggest optimal clothing to the user. For example, the user can try on or purchase the suggested clothes on the spot.

[0052] The preference analysis unit may be equipped with an emotion estimation function that analyzes a user's emotional responses to their past purchase history and browsing history and extracts preference trends based on their emotions. The preference analysis unit uses the emotion estimation function, for example, to analyze a user's emotional responses to their past purchase history and browsing history. For example, if a user expresses positive emotions toward purchased clothes, this tendency is extracted. The emotion estimation function also analyzes the user's emotional responses to the clothes they browsed in real time and extracts the styles and colors of clothes for which they expressed positive emotions as preference trends. The emotion estimation function also analyzes the user's emotional responses to "likes" and comments on social media and extracts preference trends based on the content of posts for which they expressed positive emotions. In this way, preference trends can be extracted more accurately by analyzing the user's emotional responses.

[0053] The preference analysis unit collects lifestyle data about the user, and the generation AI analyzes that data to suggest clothes that are suitable for that lifestyle. For example, the preference analysis unit collects data about the user's exercise habits, and the generation AI analyzes that data to suggest clothes that are suitable for exercise. For example, breathable clothes are suggested for a user who runs. The preference analysis unit also collects data about the user's eating habits, and the generation AI analyzes that data to suggest clothes that are suitable for mealtimes. For example, casual clothes are suggested for a user who eats out often. The preference analysis unit also collects information about the user's overall lifestyle (for example, work style and hobbies), and the generation AI analyzes that data to suggest clothes that are suitable for everyday life. This makes it possible to suggest clothes based on the user's lifestyle.

[0054] The preference analysis unit can analyze changes in a user's preferences by season and develop an algorithm for making different suggestions for each season. The preference analysis unit, for example, analyzes the user's past purchase history by season to extract changes in preferences by season. For example, if there is a tendency to prefer bright-colored clothes in the summer, suggestions are made based on that information. The preference analysis unit also analyzes the user's social media posts by season to extract changes in preferences by season. For example, if there is a tendency to prefer clothes made of warm materials in the winter, suggestions are made based on that information. The preference analysis unit also analyzes the user's browsing history by season to extract changes in preferences by season. For example, if there is a tendency to prefer floral-patterned clothes in the spring, suggestions are made based on that information. This makes it possible to make different suggestions for each season.

[0055] The preference analysis unit collects the user's music and movie preferences, as well as other preference data besides fashion, and the generation AI can make comprehensive suggestions based on that data. For example, the preference analysis unit collects the user's music preferences, and the generation AI analyzes that data to suggest clothes that match the music genre. For example, casual clothes are suggested for a user who likes rock. The preference analysis unit also collects the user's movie preferences, and the generation AI analyzes that data to suggest clothes that match the movie genre. For example, elegant clothes are suggested for a user who likes romantic movies. The preference analysis unit also collects the user's reading preferences, and the generation AI analyzes that data to suggest clothes that match the reading genre. For example, chic clothes are suggested for a user who likes mystery novels. This makes it possible to make comprehensive suggestions based on preference data other than fashion.

[0056] The preference analysis unit refers to the fashion styles of the user's friends and family, and the generation AI can make suggestions based on that data. For example, the preference analysis unit collects the fashion styles of the user's friends and family, and the generation AI analyzes that data to suggest clothes that suit the user. For example, it refers to the styles preferred by friends. In addition, it collects the fashion styles of the user's family, and the generation AI analyzes that data to suggest clothes that suit the family's preferences. For example, if all family members prefer casual styles, suggestions are made based on that information. In addition, it collects the fashion styles of the user's friends, and the generation AI analyzes that data to suggest clothes with a similar style to the friends. For example, if a friend prefers a formal style, suggestions are made based on that information. This makes it possible to make suggestions based on the fashion styles of friends and family.

[0057] The preference analysis unit can use the emotion estimation function to analyze the emotions a user has toward a specific event and suggest the most suitable clothes for the event. For example, the preference analysis unit can use the emotion estimation function to analyze the emotions a user has toward a wedding and suggest the most suitable clothes based on those emotions. For example, if the user shows positive emotions, a gorgeous dress can be suggested. The emotion estimation function can also be used to analyze the emotions a user has toward a party and suggest the most suitable clothes based on those emotions. For example, if the user shows excited emotions, a casual party dress can be suggested. The emotion estimation function can also be used to analyze the emotions a user has toward a business event and suggest the most suitable clothes based on those emotions. For example, if the user shows nervousness, a formal suit can be suggested. This makes it possible to suggest the most suitable clothes for a specific event.

[0058] The matching unit can use the emotion estimation function to analyze the user's emotional response to the clothes they own and perform matching based on the emotion. For example, the matching unit uses the emotion estimation function to analyze the user's emotional response to the clothes they own and match clothes that indicate positive emotions with new clothes. For example, the matching unit makes suggestions based on clothes of the user's favorite color. Also, the emotion estimation function can be used to analyze the user's emotional response to the clothes they own in real time and match clothes that indicate positive emotions with new clothing styles. Also, the emotion estimation function can be used to analyze the user's emotional response to the clothes they own and suggest new clothes by eliminating clothes that indicate negative emotions. For example, clothes of colors that the user dislike can be excluded. This makes it possible to match clothes based on the user's emotions.

[0059] The matching unit can analyze the material and texture of the clothes you own and perform matching that takes into account their physical compatibility with new clothes. For example, the matching unit collects material data of the clothes you own, and the generation AI analyzes that data to perform matching that takes into account their physical compatibility with new clothes. For example, it can suggest clothes made of the same material. It can also collect texture data of the clothes you own, and the generation AI analyzes that data to perform matching that takes into account their texture compatibility with new clothes. For example, it can suggest clothes with a soft texture. It can also analyze the material and texture of the clothes you own, and the generation AI can evaluate their compatibility with new clothes based on that data. For example, it can suggest new silk clothes for silk clothes. This makes it possible to match new clothes based on the material and texture of the clothes you own.

[0060] The matching unit analyzes how often you use the clothes you own, and can prioritize suggesting combinations of frequently used clothes with new clothes. For example, the matching unit collects data on how often you use the clothes you own, and the generation AI analyzes that data to suggest combinations of frequently used clothes with new clothes. For example, it might suggest tops that go well with jeans you often wear. It also analyzes how often you use the clothes you own, and the generation AI evaluates how they would go with new clothes based on that data. For example, it might suggest jackets that go well with frequently used shirts. It also prioritizes suggesting combinations with new clothes based on the data on how often you use the clothes you own. For example, it might suggest blouses that go well with skirts you often wear. This makes it possible to prioritize suggesting combinations of frequently used clothes with new clothes.

[0061] When analyzing data on the clothes you own, the matching unit can suggest total coordinations that include accessories, shoes, and other small items. For example, the matching unit collects data on accessories and shoes in addition to data on the clothes you own, and the generation AI analyzes that data to suggest total coordinations. For example, it can suggest a dress that matches a necklace you own. Furthermore, when analyzing data on the clothes you own, the generation AI can also take into account data on accessories and shoes to suggest total coordinations. For example, it can suggest pants that match shoes you own. Furthermore, a system can be built that suggests total coordinations that include accessories and shoes based on data on the clothes you own. For example, it can suggest a top that matches earrings you own. This makes it possible to suggest total coordinations that include accessories, shoes, and other small items.

[0062] The matching unit can share data about clothes owned by users with other users and suggest matching with clothes owned by other users. For example, the matching unit shares data about clothes owned by users with other users, and the generation AI analyzes that data and suggests matching with clothes owned by other users. For example, combining one's own clothes with clothes owned by friends. Also, a platform for sharing data about clothes owned by users can be built, and the generation AI can use that data to suggest matching with other users' clothes. For example, combining one's own clothes with clothes owned by family members. Also, data about clothes owned by users can be shared with other users, and the generation AI can analyze that data and suggest coordination for the entire group. For example, combining clothes owned by everyone in a group of friends. This makes it possible to match with clothes owned by other users.

[0063] The matching unit can use the emotion estimation function to analyze the emotion a user has toward a specific season or weather, and suggest matching of clothes that are optimal for that season or weather. For example, the matching unit can use the emotion estimation function to analyze the emotion a user has toward summer, and suggest matching of optimal clothes based on that emotion. For example, suggesting clothes that give a feeling of coolness. Also, the emotion estimation function can be used to analyze the emotion a user has toward rainy days, and suggest matching of optimal clothes based on that emotion. For example, suggesting waterproof clothes. Also, the emotion estimation function can be used to analyze the emotion a user has toward winter, and suggest matching of optimal clothes based on that emotion. For example, suggesting clothes that give a feeling of warmth. This makes it possible to match optimal clothes for a specific season or weather.

[0064] The suggestion unit uses the emotion estimation function to analyze the user's real-time emotional response to proposals displayed on the digital signage and can dynamically change the content of the proposal. For example, the suggestion unit analyzes the user's emotional response to proposals displayed on the digital signage in real time and prioritizes displaying proposals that show positive emotions. For example, it continues to display proposals that show a smile from the user. The suggestion unit also uses the emotion estimation function to analyze the user's emotional response to proposals displayed on the digital signage and instantly changes proposals that show negative emotions. For example, it changes proposals that show a dissatisfied expression from the user. The suggestion unit also analyzes the user's emotional response to proposals displayed on the digital signage and dynamically adjusts the content of the proposal based on the user's emotions. For example, it displays proposals that the user is interested in in more detail. This makes it possible to dynamically change the content of the proposal based on the real-time emotional response.

[0065] The suggestion unit can customize the suggestions displayed on the digital signage based on the user's mood and physical condition on that day. The suggestion unit, for example, analyzes the user's mood on that day and customizes the suggestions displayed on the digital signage. For example, when the user is in a relaxed mood, casual clothes are suggested. The suggestion unit also collects the user's physical condition data and customizes the suggestions displayed on the digital signage. For example, when the user is tired, comfortable clothes are suggested. Furthermore, a system is constructed that dynamically changes the suggestions displayed on the digital signage based on the user's mood and physical condition. For example, when the user is in good spirits, bright colored clothes are suggested. This makes it possible to customize the suggestions based on the user's mood and physical condition on that day.

[0066] The suggestion unit can personalize the suggestions displayed on the digital signage based on the user's past purchase history and browsing history. The suggestion unit, for example, analyzes the user's past purchase history and personalizes the suggestions displayed on the digital signage. For example, it suggests new products from brands that the user has previously purchased. It also analyzes the user's browsing history and personalizes the suggestions displayed on the digital signage. For example, it suggests clothing styles that the user frequently browses. It also builds a system that customizes the suggestions displayed on the digital signage based on the user's purchase history and browsing history. For example, it suggests clothing in colors and designs that the user has shown interest in in the past. This makes it possible to personalize suggestions based on the user's past purchase history and browsing history.

[0067] The suggestion unit can provide a function for sharing suggestions displayed on the digital signage with the user's friends and family, and enjoying coordinating outfits together. The suggestion unit, for example, provides a function for sharing suggestions displayed on the digital signage with the user's friends and family. For example, it generates a sharing link for enjoying coordinating outfits together with friends. It also provides a function for sharing suggestions displayed on the digital signage with family members, and enjoying coordinating outfits together. For example, it makes suggestions for combining clothes for all family members. It also builds a system for sharing suggestions displayed on the digital signage with friends and family in real time, and enjoying coordinating outfits together. For example, it allows trying on suggested clothes together with friends. This provides a function for enjoying coordinating outfits together with friends and family.

[0068] The suggestion unit can link the suggestions displayed on the digital signage with specific event or sale information at stores or shopping malls visited by the user. For example, the suggestion unit links the suggestions displayed on the digital signage with sale information at stores visited by the user. For example, it prioritizes suggestions for items on sale. Furthermore, it links the suggestions displayed on the digital signage with specific event information at shopping malls. For example, it makes suggestions based on information about upcoming fashion shows. Furthermore, a system is constructed that links the suggestions displayed on the digital signage with event information at stores or shopping malls visited by the user. For example, it suggests outfits that match the event. This makes it possible to link suggestions with specific event or sale information.

[0069] The suggestion unit can use the emotion estimation function to collect other users' emotional reactions to the proposals displayed on the digital signage and prioritize displaying popular proposals. The suggestion unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the proposals displayed on the digital signage and prioritize displaying proposals that receive many positive reactions. For example, it displays popular outfits. It also collects other users' emotional reaction data and adjusts the proposals displayed on the digital signage in real time. For example, it continuously displays proposals that receive many positive reactions. It also uses the emotion estimation function to analyze other users' emotional reactions to the proposals displayed on the digital signage and builds a system that prioritizes displaying popular proposals. This makes it possible to prioritize displaying popular proposals based on the emotional reactions of other users.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The digital signage system can also collect the user's health data, which the generation AI analyzes to suggest clothing appropriate for the user's health condition. For example, the system can collect the user's heart rate and sleep data, analyze it, and suggest clothing that is relaxing. The system can also collect the user's exercise data, analyze it, and suggest clothing appropriate for after exercise. For example, it can suggest post-exercise recovery wear. The system can also analyze the user's stress level, and based on that data, the generation AI can suggest clothing that reduces stress. This makes it possible to suggest clothing based on the user's health condition.

[0072] The preference analysis unit can use the user emotion estimation function to analyze the emotions a user has toward a specific brand and make suggestions based on that brand. For example, if a user has positive emotions toward a specific brand, new products from that brand can be suggested. On the other hand, if a user has negative emotions toward a specific brand, suggestions can be made that avoid that brand. The user emotion estimation function can also be used to analyze the emotions a user has toward a specific design and make suggestions based on that design. This makes it possible to suggest brands and designs based on the user's emotions.

[0073] The digital signage system can also collect the user's travel data, and the generation AI can analyze that data to suggest clothing appropriate for the travel destination. For example, if the user is traveling to a beach resort, the generation AI can analyze that data and suggest clothing appropriate for the beach. If the user is traveling to a cold region, the generation AI can analyze that data and suggest clothing appropriate for cold regions, for example, suggesting warm coats and sweaters. The generation AI can also analyze the user's purpose of travel (for example, business trip or leisure trip), and based on that data, suggest clothing appropriate for the purpose of the trip. This makes it possible to suggest clothing based on the user's travel destination and purpose.

[0074] The preference analysis unit can use the user's emotion estimation function to analyze the emotion the user has toward a specific color and make suggestions based on that color. For example, if the user has a positive emotion toward the color blue, blue clothing can be suggested. On the other hand, if the user has a negative emotion toward red, suggestions can be made that avoid red. The user's emotion estimation function can also be used to analyze the emotion the user has toward a specific pattern (e.g., stripes or checks) and make suggestions based on that pattern. This makes it possible to suggest colors and patterns based on the user's emotions.

[0075] The digital signage system can also make suggestions based on the user's hobbies and interests. For example, if a user likes outdoor activities, the generation AI will analyze that data and suggest clothes suitable for outdoor activities. Also, if the user is interested in art and music, the generation AI will analyze that data and suggest clothes suitable for art events and concerts. For example, it will suggest elegant clothes for going to an art gallery. Based on the user's hobbies and interests, the generation AI will also use that data to suggest clothes suitable for specific events and activities. This makes it possible to suggest clothes based on the user's hobbies and interests.

[0076] The preference analysis unit can use the user emotion estimation function to analyze the emotions a user has toward a specific fashion icon or celebrity and make suggestions based on the style of that icon or celebrity. For example, if a user has positive emotions toward a specific celebrity, the unit can suggest clothes inspired by the celebrity's style. On the other hand, if a user has negative emotions toward a specific fashion icon, the unit can suggest clothes that avoid the style of that icon. The user emotion estimation function can also be used to analyze the emotions a user has toward a specific fashion magazine or blog and make suggestions based on that information. This makes it possible to suggest styles of fashion icons and celebrities based on the user's emotions.

[0077] The digital signage system can also collect the user's occupational data, and the generation AI can analyze that data to suggest clothing appropriate for the occupation. For example, if the user is a businessperson, the generation AI can analyze that data to suggest clothing appropriate for business situations. Also, if the user is engaged in a creative occupation, the generation AI can analyze that data to suggest clothing appropriate for creative situations. For example, it can suggest unique clothing for designers. Furthermore, based on the user's occupation, the generation AI can suggest clothing appropriate for the occupation. This makes it possible to suggest clothing based on the user's occupation.

[0078] The preference analysis unit can use the user's emotion estimation function to analyze the user's emotions toward specific seasons and weather, and make suggestions based on those seasons and weather. For example, if the user has positive emotions toward summer, the preference analysis unit can suggest clothes suitable for summer. If the user has negative emotions toward rainy days, the preference analysis unit can suggest clothes that avoid rainy days. The preference analysis unit can also use the user's emotion estimation function to analyze the user's emotions toward specific weather conditions (for example, snow or wind), and make suggestions based on those weather conditions. This makes it possible to suggest seasons and weather based on the user's emotions.

[0079] The digital signage system can also make suggestions based on the user's cultural background and religious beliefs. For example, if a user has a particular cultural background, the generation AI analyzes that data to suggest clothing appropriate for that culture. Also, if a user has particular religious beliefs, the generation AI analyzes that data to suggest clothing appropriate for those beliefs. For example, when attending a particular religious event, the generation AI suggests clothing appropriate for that event. Furthermore, based on the user's cultural background and religious beliefs, the generation AI uses that data to suggest clothing appropriate for specific events or occasions. This makes it possible to suggest clothing based on the user's cultural background and religious beliefs.

[0080] The suggestion unit can use the emotion estimation function to analyze the user's real-time emotional response to proposals displayed on the digital signage and dynamically change the content of the proposal. For example, the suggestion unit can analyze the user's emotional response to proposals displayed on the digital signage in real time and prioritize displaying proposals that show positive emotions. For example, it can continue to display proposals that show a smile from the user. The suggestion unit can also use the emotion estimation function to analyze the user's emotional response to proposals displayed on the digital signage and instantly change proposals that show negative emotions. For example, it can change proposals that show a dissatisfied expression from the user. The suggestion unit can also analyze the user's emotional response to proposals displayed on the digital signage and dynamically adjust the content of the proposal based on the user's emotions. For example, it can display proposals that the user is interested in in more detail. This allows the content of the proposals to be dynamically changed based on the real-time emotional response.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The preference analysis unit is equipped with a generation AI and analyzes the user's preferences. The generation AI collects information such as the user's past purchase history, browsing history, and "likes" and comments on social media, and analyzes this data. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to understand the user's preferences. Step 2: The matching unit matches the user's existing clothes based on the user's preferences analyzed by the preference analysis unit. The user takes a photo of the clothes they own and inputs that data into the generation AI. The generation AI analyzes the data and matches the colors and styles of the clothes they own with the new clothes. Step 3: The suggestion unit displays the clothing suggestions matched by the matching unit on digital signage. When a user approaches a digital signage installed in a shopping mall or store, the system displays the optimal clothing coordination for the user based on the information analyzed by the generation AI.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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 equipped with generative AI, a matching unit that matches clothes owned by the user based on the user's preferences analyzed by the preference analysis unit; a suggestion unit that displays the clothes suggestions matched by the matching unit on a digital signage. A system characterized by:

2. The preference analysis unit Equipped with an emotion estimation function that analyzes the user's emotional response to their past purchase history and browsing history and extracts preference trends based on their emotions.

2. The system of claim 1.

3. The preference analysis unit The user's lifestyle data is collected, and the generation AI analyzes the data to suggest clothes suitable for the lifestyle.

2. The system of claim 1.

4. The preference analysis unit Analyze changes in the user's preferences with each season and develop an algorithm to make different suggestions for each season.

2. The system of claim 1.

5. The preference analysis unit The AI ​​also collects data on the user's tastes in music, movies, and other aspects of fashion, and makes comprehensive suggestions based on that data.

2. The system of claim 1.

Images