system

Abstract

The system according to the embodiment aims to learn the preferences and characteristics of a user and suggest the most suitable clothes and items. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, an application unit, and a learning unit. The input unit inputs user information. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests appropriate clothing based on the analysis results obtained by the analysis unit. The application unit applies the suggestions to other items as well. The learning unit learns the user's preferences.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest optimal clothing and items based on a user's preferences and characteristics, and there is room for improvement.

[0005] The system according to the embodiment aims to learn the preferences and characteristics of a user and suggest the most suitable clothes and items. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an application unit, and a learning unit. The input unit inputs user information. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests appropriate clothing based on the analysis results obtained by the analysis unit. The application unit applies the suggestions to other items. The learning unit learns the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can learn the preferences and characteristics of a user and suggest the most suitable clothes and items. [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 system according to an embodiment of the present invention utilizes AI to recommend clothes that users will want to wear for a long time. This system uses AI to analyze a user's anatomy, color analysis, and preferences, and then recommends the most suitable clothes. This allows users to obtain clothes that fit their personal preferences and reduce clothing waste. This system can also be applied to other items, allowing users to obtain original items that reflect their preferences. For example, a user inputs information such as their body type and skin color. Specifically, the user inputs information such as their height, weight, shoulder width, waist size, and skin color. This allows the AI ​​to perform a diagnosis of the user's anatomy and color. Next, the AI ​​analyzes the user's preferences. The user inputs information such as their favorite style, color, and design. For example, the user may input information such as "I like casual styles," "I like blue," or "I like simple designs." This allows the AI ​​to analyze the user's preferences. The AI ​​comprehensively analyzes the user's anatomy, color analysis, and preferences, and then recommends the most suitable clothes. For example, it can recommend clothes with a silhouette that matches the user's body type, a color that matches their skin color, and a design that matches their preferences. This allows the user to obtain clothes that fit their preferences. Furthermore, this system can be applied to other items, not just apparel. For example, it can be applied to shoes, accessories, furniture, etc. Users can obtain original items that reflect their own wishes. This system allows users to obtain clothes and items that they want to use for a long time, thereby reducing waste. For example, by obtaining clothes that suit their tastes, users can wear them for a long time without getting bored of them. Also, by obtaining original items, they can have items that are unique to them and that no one else has. This reduces waste and contributes to the realization of a sustainable society. In this way, the AI-based system can suggest clothes and items that users want to use for a long time, thereby reducing waste.

[0029] The suggestion system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an application unit, and a learning unit. The input unit inputs user information. The user information includes, but is not limited to, age, gender, body type, and preferences. The input unit can input, for example, information such as the user's body type and skin color. For example, information such as height, weight, shoulder width, waist size, and skin color can be input. The input unit can also input user preferences. For example, information such as a preference for casual styles, blue, and simple designs can be input. The analysis unit analyzes the information input by the input unit. The analysis unit can, for example, perform a bone structure and color diagnosis of the user. For example, the analysis can be performed using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. The suggestion unit suggests optimal clothing based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest clothing with a silhouette that matches the user's body type, a color that matches the user's skin color, and a design that matches the user's preferences. For example, suggestions can be made based on design, such as silhouettes (e.g., A-line or H-line), colors (e.g., hue, brightness, saturation), trends, and user preferences. The application unit can also be applied to other items. The application unit can also be applied to other items, such as shoes, accessories, and furniture. For example, the application unit can be applied to items such as sneakers, boots, necklaces, earrings, sofas, and tables. The learning unit learns the user's preferences and can respond even if the preferences change. The learning unit learns the user's preferences based on, for example, the user's past selection history and survey results, and can respond even if the preferences change. As a result, the suggestion system according to the embodiment can suggest optimal clothes and items based on user information and reduce waste.

[0030] The input unit can input information including the user's body shape and skin color. The input unit can input, for example, information such as the user's body shape and skin color. For example, information such as height, weight, shoulder width, waist size, and skin color can be input. This makes it possible to suggest optimal clothing based on the user's body shape and skin color. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's body shape and skin color to AI, and the AI ​​can perform analysis based on that information.

[0031] The analysis unit can perform a bone structure and color diagnosis of the user. The analysis unit can perform a bone structure and color diagnosis of the user, for example. For example, the analysis can be performed using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. This makes it possible to suggest optimal clothing based on the user's bone structure and color diagnosis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information on the user's bone structure and color diagnosis into AI, and the AI ​​can perform analysis based on that information.

[0032] The suggestion unit can suggest clothes with a silhouette that suits the user's body type, a color that suits the user's skin color, and a design that suits the user's preferences. The suggestion unit can suggest clothes with a silhouette that suits the user's body type, a color that suits the user's skin color, and a design that suits the user's preferences, for example. For example, suggestions can be made based on silhouettes such as A-line or H-line, colors such as hue, brightness, and saturation, and designs such as trends and the user's preferences. This makes it possible to suggest clothes that suit the user's body type and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's body type and preferences into AI, and the AI ​​can make suggestions based on that information.

[0033] The application unit can also be applied to other items such as shoes, accessories, and furniture. The application unit can also be applied to other items such as shoes, accessories, and furniture. For example, it can be applied to items such as sneakers, boots, necklaces, earrings, sofas, and tables. This allows it to be applied to items other than clothing. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about other items into the AI, and the AI ​​can perform application based on that information.

[0034] The learning unit learns the user's preferences and can respond when the preferences change. The learning unit learns the preferences based on, for example, the user's past selection history or survey results, and can respond when the preferences change. This makes it possible to respond when the user's preferences change. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information about the user's preferences into AI, and the AI ​​can learn based on that information.

[0035] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display information about body type and skin color that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's past input history to AI, and the AI ​​can suggest the optimal input method based on that information.

[0036] The input unit can filter the input content based on the user's current activity status and environment at the time of input. For example, when the user is out, the input unit can prioritize and provide a simple input method. Furthermore, when the user is at home, the input unit can prioritize and provide detailed input options. Furthermore, when the user is exercising, the input unit can prioritize and provide voice input. This allows the input content to be filtered according to the user's activity status and environment. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's activity status and environment to AI, and the AI ​​can filter the input content based on that information.

[0037] During input, the input unit can prioritize input of highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize input of information related to that area. Furthermore, when the user is traveling, the input unit can prioritize input of information related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize input of information related to the user's home. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, and the AI ​​can prioritize input of highly relevant information based on that information.

[0038] The input unit can analyze the user's social media activity at the time of input and input relevant information. The input unit can suggest relevant input content based on, for example, information shared by the user on social media. The input unit can also suggest input content based on brands or styles the user follows on social media. The input unit can also suggest input content based on posts the user has "liked" on social media. This makes it possible to input relevant information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's social media activity to AI, and the AI ​​can input relevant information based on that information.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit can improve the accuracy of the analysis by referring to the user's past purchase history, for example. The analysis unit can also improve the accuracy of the analysis by referring to the user's past style selection history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past color diagnosis results. This allows the accuracy of the analysis to be improved based on the user's past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can improve the accuracy of the analysis based on that information.

[0040] During analysis, the analysis unit can perform analysis based on the user's lifestyle habits and behavioral patterns. For example, the analysis unit can analyze optimal clothing based on the user's daily activities. The analysis unit can also analyze optimal clothing based on the user's weekend habits. The analysis unit can also analyze optimal clothing based on the user's work style. This allows analysis to be performed based on the user's lifestyle habits and behavioral patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the user's lifestyle habits and behavioral patterns into AI, and the AI ​​can perform analysis based on that information.

[0041] During analysis, the analysis unit can perform analysis based on the geographical distribution of the user. For example, the analysis unit can analyze optimal clothing based on the climate of the area where the user lives. The analysis unit can also analyze optimal clothing based on the culture of areas the user frequently visits. The analysis unit can also analyze optimal clothing based on the climate of destinations where the user travels. This allows analysis to be performed based on the geographical distribution of the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the geographical distribution of the user into AI, and the AI ​​can perform analysis based on that information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and data related to the user. For example, the analysis unit can improve the accuracy of the analysis by referring to articles in fashion magazines that the user has read in the past. The analysis unit can also improve the accuracy of the analysis by referring to data on fashion events that the user has previously attended. The analysis unit can also improve the accuracy of the analysis by referring to data on online fashion stores that the user has previously viewed. This makes it possible to improve the accuracy of the analysis based on the literature and data related to the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's literature and data related to the user into AI, and the AI ​​can improve the accuracy of the analysis based on that information.

[0043] When making a suggestion, the suggestion unit can make the optimal suggestion by referring to the user's past selection history. The suggestion unit can make the optimal suggestion, for example, based on styles selected by the user in the past. The suggestion unit can also make the optimal suggestion by referring to the user's past purchase history. The suggestion unit can also make the optimal suggestion by referring to the user's past color diagnosis results. This allows the optimal suggestion to be made based on the user's past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's past selection history into AI, and the AI ​​can make the optimal suggestion based on that information.

[0044] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation and environment. For example, if the user is at work, the suggestion unit can make a business casual suggestion. Furthermore, if the user is on holiday, the suggestion unit can make a casual suggestion. Furthermore, if the user is traveling, the suggestion unit can make a suggestion that matches the climate of the travel destination. This allows the proposal content to be customized based on the user's current living situation and environment. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's living situation and environment into AI, and the AI ​​can customize the proposal content based on that information.

[0045] When making a suggestion, the suggestion unit can make a highly relevant suggestion by taking into account the user's geographical location information. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to that area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion related to the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion related to the user's home. This makes it possible to make a highly relevant suggestion based on the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's geographical location information into AI, and the AI ​​can make a highly relevant suggestion based on that information.

[0046] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. For example, the suggestion unit can make relevant suggestions based on information shared by the user on social media. The suggestion unit can also make suggestions based on brands and styles the user follows on social media. The suggestion unit can also make suggestions based on posts the user has "liked" on social media. This allows relevant suggestions to be made based on the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's social media activity into AI, and the AI ​​can make relevant suggestions based on that information.

[0047] At the time of application, the application unit can select the optimal application method by referring to the user's past usage history. The application unit can select the optimal application method, for example, based on items used by the user in the past. The application unit can also narrow the application scope to frequently used items based on the user's past usage history. The application unit can also analyze the user's past usage history and select the most efficient application method. This makes it possible to select the optimal application method based on the user's past usage history. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's past usage history into AI, and the AI ​​can select the optimal application method based on that information.

[0048] The application unit can customize the application content based on the user's current living situation and environment at the time of application. For example, when the user is at work, the application unit can apply business-related items. Furthermore, when the user is on holiday, the application unit can apply casual items. Furthermore, when the user is traveling, the application unit can apply items that suit the climate and culture of the travel destination. This allows the application content to be customized based on the user's current living situation and environment. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's living situation and environment into AI, and the AI ​​can customize the application content based on that information.

[0049] The application unit can perform highly relevant application by taking into account the user's geographical location information when applying the application. For example, when the user is in a specific area, the application unit can apply to items related to that area. Furthermore, when the user is traveling, the application unit can apply to items related to the travel destination. Furthermore, when the user is at home, the application unit can apply to items related to the home. This allows highly relevant application to be performed based on the user's geographical location information. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input the user's geographical location information into AI, and the AI ​​can perform highly relevant application based on that information.

[0050] At the time of application, the application unit can analyze the user's social media activity and make a relevant application. For example, the application unit can apply relevant items based on information shared by the user on social media. The application unit can also apply based on brands or styles the user follows on social media. The application unit can also apply based on posts the user has "liked" on social media. In this way, a relevant application can be made based on the user's social media activity. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's social media activity into AI, and the AI ​​can make a relevant application based on that information.

[0051] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can optimize the learning algorithm by referring to, for example, the user's past selection history. The learning unit can also optimize the learning algorithm by referring to the user's past purchase history. The learning unit can also optimize the learning algorithm by referring to the user's past color diagnosis results. This makes it possible to optimize the learning algorithm based on past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm based on that information.

[0052] During learning, the learning unit can weight the learning data based on the time when the user submitted the input data. For example, the learning unit can weight data recently input by the user while learning. The learning unit can also weight data previously input by the user while learning. The learning unit can also adjust the weighting of the learning data based on the time when the user submitted the input data. This allows the learning data to be weighted based on the time when the user submitted the input data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information about the time when the user submitted the input data to AI, and the AI ​​can weight the learning data based on that information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The input unit can analyze the user's past purchase history and suggest the optimal input method. For example, it can automatically display information about items that the user has frequently purchased in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest an input method to be used during a specific time period based on the user's past purchase history. This makes it possible to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input information about the user's past purchase history into AI, and the AI ​​can suggest the optimal input method based on that information.

[0055] The suggestion unit can make highly relevant suggestions by taking into account the user's geographical location information. For example, if the user is in a specific area, suggestions related to that area can be made. Furthermore, if the user is traveling, suggestions related to the travel destination can be made. Furthermore, if the user is at home, suggestions related to the home can be made. This makes it possible to make highly relevant suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, and the AI ​​can make highly relevant suggestions based on that information.

[0056] The learning unit can optimize the learning algorithm by referring to the user's past selection history. For example, the learning algorithm can be optimized by referring to the user's past purchase history. The learning algorithm can also be optimized by referring to the user's past style selection history. Furthermore, the learning algorithm can be optimized by referring to the user's past color diagnosis results. This allows the learning algorithm to be optimized based on past data. Some or all of the above-mentioned processing in the learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit can input past data into AI, and the AI ​​can optimize the learning algorithm based on that information.

[0057] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. For example, relevant suggestions can be made based on information shared by the user on social media. Suggestions can also be made based on brands and styles the user follows on social media. Furthermore, suggestions can be made based on posts the user has "liked" on social media. This allows relevant suggestions to be made based on the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input information about the user's social media activity into AI, which can then make relevant suggestions based on that information.

[0058] When applying, the application unit can perform highly relevant application taking into account the user's geographical location information. For example, if the user is in a specific area, the application can be to items related to that area. Furthermore, if the user is traveling, the application can be to items related to the travel destination. Furthermore, if the user is at home, the application can be to items related to the home. This allows highly relevant application to be performed based on the user's geographical location information. Some or all of the above-mentioned processing in the application unit may be performed using AI, or may be performed without using AI. For example, the application unit can input the user's geographical location information into AI, and the AI ​​can perform highly relevant application based on that information.

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

[0060] Step 1: The input unit inputs user information. The user information includes age, gender, body type, preferences, etc. For example, information such as height, weight, shoulder width, waist size, skin color, preference for casual style, preference for the color blue, preference for simple designs, etc. can be input. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit performs a bone structure and color diagnosis of the user, and performs analysis using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. Step 3: The suggestion unit suggests optimal clothing based on the analysis results obtained by the analysis unit. For example, it suggests clothing with a silhouette that suits the user's body type, a color that matches their skin tone, and a design that suits their preferences. Step 4: The application unit can be applied to other items, such as shoes, accessories, furniture, etc. Step 5: The learning unit learns the user's preferences and responds even if they change. For example, it can learn the user's preferences based on the user's past selection history and survey results, and respond even if the preferences change.

[0061] (Example 2) A system according to an embodiment of the present invention utilizes AI to recommend clothes that users will want to wear for a long time. This system uses AI to analyze a user's anatomy, color analysis, and preferences, and then recommends the most suitable clothes. This allows users to obtain clothes that fit their personal preferences and reduce clothing waste. This system can also be applied to other items, allowing users to obtain original items that reflect their preferences. For example, a user inputs information such as their body type and skin color. Specifically, the user inputs information such as their height, weight, shoulder width, waist size, and skin color. This allows the AI ​​to perform a diagnosis of the user's anatomy and color. Next, the AI ​​analyzes the user's preferences. The user inputs information such as their favorite style, color, and design. For example, the user may input information such as "I like casual styles," "I like blue," or "I like simple designs." This allows the AI ​​to analyze the user's preferences. The AI ​​comprehensively analyzes the user's anatomy, color analysis, and preferences, and then recommends the most suitable clothes. For example, it can recommend clothes with a silhouette that matches the user's body type, a color that matches their skin color, and a design that matches their preferences. This allows the user to obtain clothes that fit their preferences. Furthermore, this system can be applied to other items, not just apparel. For example, it can be applied to shoes, accessories, furniture, etc. Users can obtain original items that reflect their own wishes. This system allows users to obtain clothes and items that they want to use for a long time, thereby reducing waste. For example, by obtaining clothes that suit their tastes, users can wear them for a long time without getting bored of them. Also, by obtaining original items, they can have items that are unique to them and that no one else has. This reduces waste and contributes to the realization of a sustainable society. In this way, the AI-based system can suggest clothes and items that users want to use for a long time, thereby reducing waste.

[0062] The suggestion system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an application unit, and a learning unit. The input unit inputs user information. The user information includes, but is not limited to, age, gender, body type, and preferences. The input unit can input, for example, information such as the user's body type and skin color. For example, information such as height, weight, shoulder width, waist size, and skin color can be input. The input unit can also input user preferences. For example, information such as a preference for casual styles, blue, and simple designs can be input. The analysis unit analyzes the information input by the input unit. The analysis unit can, for example, perform a bone structure and color diagnosis of the user. For example, the analysis can be performed using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. The suggestion unit suggests optimal clothing based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest clothing with a silhouette that matches the user's body type, a color that matches the user's skin color, and a design that matches the user's preferences. For example, suggestions can be made based on design, such as silhouettes (e.g., A-line or H-line), colors (e.g., hue, brightness, saturation), trends, and user preferences. The application unit can also be applied to other items. The application unit can also be applied to other items, such as shoes, accessories, and furniture. For example, the application unit can be applied to items such as sneakers, boots, necklaces, earrings, sofas, and tables. The learning unit learns the user's preferences and can respond even if the preferences change. The learning unit learns the user's preferences based on, for example, the user's past selection history and survey results, and can respond even if the preferences change. As a result, the suggestion system according to the embodiment can suggest optimal clothes and items based on user information and reduce waste.

[0063] The input unit can input information including the user's body shape and skin color. The input unit can input, for example, information such as the user's body shape and skin color. For example, information such as height, weight, shoulder width, waist size, and skin color can be input. This makes it possible to suggest optimal clothing based on the user's body shape and skin color. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's body shape and skin color to AI, and the AI ​​can perform analysis based on that information.

[0064] The analysis unit can perform a bone structure and color diagnosis of the user. The analysis unit can perform a bone structure and color diagnosis of the user, for example. For example, the analysis can be performed using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. This makes it possible to suggest optimal clothing based on the user's bone structure and color diagnosis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information on the user's bone structure and color diagnosis into AI, and the AI ​​can perform analysis based on that information.

[0065] The suggestion unit can suggest clothes with a silhouette that suits the user's body type, a color that suits the user's skin color, and a design that suits the user's preferences. The suggestion unit can suggest clothes with a silhouette that suits the user's body type, a color that suits the user's skin color, and a design that suits the user's preferences, for example. For example, suggestions can be made based on silhouettes such as A-line or H-line, colors such as hue, brightness, and saturation, and designs such as trends and the user's preferences. This makes it possible to suggest clothes that suit the user's body type and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's body type and preferences into AI, and the AI ​​can make suggestions based on that information.

[0066] The application unit can also be applied to other items such as shoes, accessories, and furniture. The application unit can also be applied to other items such as shoes, accessories, and furniture. For example, it can be applied to items such as sneakers, boots, necklaces, earrings, sofas, and tables. This allows it to be applied to items other than clothing. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about other items into the AI, and the AI ​​can perform application based on that information.

[0067] The learning unit learns the user's preferences and can respond when the preferences change. The learning unit learns the preferences based on, for example, the user's past selection history or survey results, and can respond when the preferences change. This makes it possible to respond when the user's preferences change. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information about the user's preferences into AI, and the AI ​​can learn based on that information.

[0068] The input unit can estimate a user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly input information such as body shape and skin color. This allows the input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input information about the user's emotions into an AI, and the AI ​​can adjust the display method of the interface based on the information.

[0069] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display information about body type and skin color that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's past input history to AI, and the AI ​​can suggest the optimal input method based on that information.

[0070] The input unit can filter the input content based on the user's current activity status and environment at the time of input. For example, when the user is out, the input unit can prioritize and provide a simple input method. Furthermore, when the user is at home, the input unit can prioritize and provide detailed input options. Furthermore, when the user is exercising, the input unit can prioritize and provide voice input. This allows the input content to be filtered according to the user's activity status and environment. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's activity status and environment to AI, and the AI ​​can filter the input content based on that information.

[0071] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, the input unit can prioritize input of important information when the user is stressed. Furthermore, the input unit can prioritize input of detailed information when the user is relaxed. Furthermore, the input unit can prioritize input of minimal information when the user is in a hurry. This allows the prioritization of input content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input information about the user's emotions to an AI, and the AI ​​can prioritize input content based on that information.

[0072] During input, the input unit can prioritize input of highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize input of information related to that area. Furthermore, when the user is traveling, the input unit can prioritize input of information related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize input of information related to the user's home. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, and the AI ​​can prioritize input of highly relevant information based on that information.

[0073] The input unit can analyze the user's social media activity at the time of input and input relevant information. The input unit can suggest relevant input content based on, for example, information shared by the user on social media. The input unit can also suggest input content based on brands or styles the user follows on social media. The input unit can also suggest input content based on posts the user has "liked" on social media. This makes it possible to input relevant information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the user's social media activity to AI, and the AI ​​can input relevant information based on that information.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. If the user is in a hurry, the analysis unit can use an algorithm that performs a quick analysis. If the user is stressed, the analysis unit can use an algorithm that performs a simple analysis. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input information about the user's emotions into an AI, and the AI ​​can adjust the analysis algorithm based on the information.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit can improve the accuracy of the analysis by referring to the user's past purchase history, for example. The analysis unit can also improve the accuracy of the analysis by referring to the user's past style selection history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past color diagnosis results. This allows the accuracy of the analysis to be improved based on the user's past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can improve the accuracy of the analysis based on that information.

[0076] During analysis, the analysis unit can perform analysis based on the user's lifestyle habits and behavioral patterns. For example, the analysis unit can analyze optimal clothing based on the user's daily activities. The analysis unit can also analyze optimal clothing based on the user's weekend habits. The analysis unit can also analyze optimal clothing based on the user's work style. This allows analysis to be performed based on the user's lifestyle habits and behavioral patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the user's lifestyle habits and behavioral patterns into AI, and the AI ​​can perform analysis based on that information.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input information about the user's emotions into an AI, and the AI ​​can adjust the display method of the analysis results based on that information.

[0078] During analysis, the analysis unit can perform analysis based on the geographical distribution of the user. For example, the analysis unit can analyze optimal clothing based on the climate of the area where the user lives. The analysis unit can also analyze optimal clothing based on the culture of areas the user frequently visits. The analysis unit can also analyze optimal clothing based on the climate of destinations where the user travels. This allows analysis to be performed based on the geographical distribution of the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the geographical distribution of the user into AI, and the AI ​​can perform analysis based on that information.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and data related to the user. For example, the analysis unit can improve the accuracy of the analysis by referring to articles in fashion magazines that the user has read in the past. The analysis unit can also improve the accuracy of the analysis by referring to data on fashion events that the user has previously attended. The analysis unit can also improve the accuracy of the analysis by referring to data on online fashion stores that the user has previously viewed. This makes it possible to improve the accuracy of the analysis based on the literature and data related to the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's literature and data related to the user into AI, and the AI ​​can improve the accuracy of the analysis based on that information.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This allows the way suggestions are expressed to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input information about the user's emotions into an AI, and the AI ​​can adjust the way suggestions are expressed based on that information.

[0081] When making a suggestion, the suggestion unit can make the optimal suggestion by referring to the user's past selection history. The suggestion unit can make the optimal suggestion, for example, based on styles selected by the user in the past. The suggestion unit can also make the optimal suggestion by referring to the user's past purchase history. The suggestion unit can also make the optimal suggestion by referring to the user's past color diagnosis results. This allows the optimal suggestion to be made based on the user's past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's past selection history into AI, and the AI ​​can make the optimal suggestion based on that information.

[0082] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation and environment. For example, if the user is at work, the suggestion unit can make a business casual suggestion. Furthermore, if the user is on holiday, the suggestion unit can make a casual suggestion. Furthermore, if the user is traveling, the suggestion unit can make a suggestion that matches the climate of the travel destination. This allows the proposal content to be customized based on the user's current living situation and environment. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's living situation and environment into AI, and the AI ​​can customize the proposal content based on that information.

[0083] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize important suggestions. If the user is relaxed, the suggestion unit can prioritize detailed suggestions. If the user is in a hurry, the suggestion unit can prioritize minimal suggestions. This allows the priority of suggestions to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input information about the user's emotions into an AI, and the AI ​​can determine the priority of suggestions based on the information.

[0084] When making a suggestion, the suggestion unit can make a highly relevant suggestion by taking into account the user's geographical location information. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to that area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion related to the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion related to the user's home. This makes it possible to make a highly relevant suggestion based on the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's geographical location information into AI, and the AI ​​can make a highly relevant suggestion based on that information.

[0085] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. For example, the suggestion unit can make relevant suggestions based on information shared by the user on social media. The suggestion unit can also make suggestions based on brands and styles the user follows on social media. The suggestion unit can also make suggestions based on posts the user has "liked" on social media. This allows relevant suggestions to be made based on the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the user's social media activity into AI, and the AI ​​can make relevant suggestions based on that information.

[0086] The application unit can estimate the user's emotions and adjust the application range based on the estimated user's emotions. For example, if the user is relaxed, the application unit can expand the application range to a wider range of items. Furthermore, if the user is in a hurry, the application unit can narrow the application range to important items. Furthermore, if the user is stressed, the application unit can narrow the application range to simple items. This allows the application range to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input information about the user's emotions into an AI, and the AI ​​can adjust the application range based on that information.

[0087] At the time of application, the application unit can select the optimal application method by referring to the user's past usage history. The application unit can select the optimal application method, for example, based on items used by the user in the past. The application unit can also narrow the application scope to frequently used items based on the user's past usage history. The application unit can also analyze the user's past usage history and select the most efficient application method. This makes it possible to select the optimal application method based on the user's past usage history. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's past usage history into AI, and the AI ​​can select the optimal application method based on that information.

[0088] The application unit can customize the application content based on the user's current living situation and environment at the time of application. For example, when the user is at work, the application unit can apply business-related items. Furthermore, when the user is on holiday, the application unit can apply casual items. Furthermore, when the user is traveling, the application unit can apply items that suit the climate and culture of the travel destination. This allows the application content to be customized based on the user's current living situation and environment. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's living situation and environment into AI, and the AI ​​can customize the application content based on that information.

[0089] The application unit can estimate the user's emotions and determine the priority of application based on the estimated user's emotions. For example, if the user is feeling stressed, the application unit can prioritize the application of important items. Furthermore, if the user is relaxed, the application unit can prioritize the application of detailed items. Furthermore, if the user is in a hurry, the application unit can prioritize the application of minimal items. This allows the priority of application to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input information about the user's emotions into an AI, and the AI ​​can determine the priority of application based on that information.

[0090] The application unit can perform highly relevant application by taking into account the user's geographical location information when applying the application. For example, when the user is in a specific area, the application unit can apply to items related to that area. Furthermore, when the user is traveling, the application unit can apply to items related to the travel destination. Furthermore, when the user is at home, the application unit can apply to items related to the home. This allows highly relevant application to be performed based on the user's geographical location information. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input the user's geographical location information into AI, and the AI ​​can perform highly relevant application based on that information.

[0091] At the time of application, the application unit can analyze the user's social media activity and make a relevant application. For example, the application unit can apply relevant items based on information shared by the user on social media. The application unit can also apply based on brands or styles the user follows on social media. The application unit can also apply based on posts the user has "liked" on social media. In this way, a relevant application can be made based on the user's social media activity. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input information about the user's social media activity into AI, and the AI ​​can make a relevant application based on that information.

[0092] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select data that can be quickly learned. Furthermore, if the user is stressed, the learning unit can select simple training data. This allows the selection of training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input information about the user's emotions into an AI, and the AI ​​can select training data based on that information.

[0093] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can optimize the learning algorithm by referring to, for example, the user's past selection history. The learning unit can also optimize the learning algorithm by referring to the user's past purchase history. The learning unit can also optimize the learning algorithm by referring to the user's past color diagnosis results. This makes it possible to optimize the learning algorithm based on past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm based on that information.

[0094] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can perform learning more frequently when the user is relaxed. Furthermore, the learning unit can reduce the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is feeling stressed. This makes it possible to adjust the frequency of learning according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input information about the user's emotions into an AI, and the AI ​​can adjust the frequency of learning based on that information.

[0095] During learning, the learning unit can weight the learning data based on the time when the user submitted the input data. For example, the learning unit can weight data recently input by the user while learning. The learning unit can also weight data previously input by the user while learning. The learning unit can also adjust the weighting of the learning data based on the time when the user submitted the input data. This allows the learning data to be weighted based on the time when the user submitted the input data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information about the time when the user submitted the input data to AI, and the AI ​​can weight the learning data based on that information. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, application unit, and learning unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14 and inputs information about the user's body type and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a diagnosis of the user's bone structure and color. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal clothing. The application unit is realized, for example, by the control unit 46A of the smart device 14 and applies the same to other items. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and responds to changes. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, application unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and inputs information about the user's body type and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a diagnosis of the user's bone structure and color. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal clothing. The application unit is realized, for example, by the control unit 46A of the smart glasses 214 and applies the same to other items. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and responds to changes. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, application unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and inputs information about the user's body type and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a diagnosis of the user's bone structure and color. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal clothing. The application unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and applies the same to other items. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and responds to changes. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, suggestion unit, application unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs information about the user's body type and preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a diagnosis of the user's bone structure and color. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal clothing. The application unit is realized, for example, by the control unit 46A of the robot 414 and applies the same to other items. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's preferences and responds to changes.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can select the timing to make detailed suggestions. If the user is stressed, the suggestion unit can quickly make concise suggestions. If the user is in a hurry, the suggestion unit can prioritize the most important suggestions. This allows the timing of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI or without an AI. For example, the suggestion unit can input information about the user's emotions into an AI, and the AI ​​can adjust the timing of suggestions based on that information.

[0098] The input unit can analyze the user's past purchase history and suggest the optimal input method. For example, it can automatically display information about items that the user has frequently purchased in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest an input method to be used during a specific time period based on the user's past purchase history. This makes it possible to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input information about the user's past purchase history into AI, and the AI ​​can suggest the optimal input method based on that information.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input information about the user's emotions into AI, and the AI ​​can adjust the display method of the analysis results based on that information.

[0100] The suggestion unit can make highly relevant suggestions by taking into account the user's geographical location information. For example, if the user is in a specific area, suggestions related to that area can be made. Furthermore, if the user is traveling, suggestions related to the travel destination can be made. Furthermore, if the user is at home, suggestions related to the home can be made. This makes it possible to make highly relevant suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, and the AI ​​can make highly relevant suggestions based on that information.

[0101] The application unit can estimate the user's emotions and adjust the application range based on the estimated user's emotions. For example, if the user is relaxed, the application range can be expanded to a wider range of items. If the user is in a hurry, the application range can be narrowed to important items. If the user is stressed, the application range can be narrowed to simple items. This allows the application range to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-mentioned processing in the application unit can be performed using an AI, or can be performed without an AI. For example, the application unit can input information about the user's emotions into an AI, and the AI ​​can adjust the application range based on that information.

[0102] The learning unit can optimize the learning algorithm by referring to the user's past selection history. For example, the learning algorithm can be optimized by referring to the user's past purchase history. The learning algorithm can also be optimized by referring to the user's past style selection history. Furthermore, the learning algorithm can be optimized by referring to the user's past color diagnosis results. This allows the learning algorithm to be optimized based on past data. Some or all of the above-mentioned processing in the learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit can input past data into AI, and the AI ​​can optimize the learning algorithm based on that information.

[0103] The input unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided, minimizing input steps. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing the user to quickly enter information such as body shape and skin color. This allows the input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input information about the user's emotions into AI, which can then adjust the display method of the interface based on that information.

[0104] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. For example, relevant suggestions can be made based on information shared by the user on social media. Suggestions can also be made based on brands and styles the user follows on social media. Furthermore, suggestions can be made based on posts the user has "liked" on social media. This allows relevant suggestions to be made based on the user's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input information about the user's social media activity into AI, which can then make relevant suggestions based on that information.

[0105] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, detailed training data can be selected. If the user is in a hurry, data that can be quickly learned can be selected. Furthermore, if the user is stressed, simple training data can be selected. This allows the selection of training data according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, or can be performed without an AI. For example, the learning unit can input information about the user's emotions into an AI, and the AI ​​can select training data based on that information.

[0106] When applying, the application unit can perform highly relevant application taking into account the user's geographical location information. For example, if the user is in a specific area, the application can be to items related to that area. Furthermore, if the user is traveling, the application can be to items related to the travel destination. Furthermore, if the user is at home, the application can be to items related to the home. This allows highly relevant application to be performed based on the user's geographical location information. Some or all of the above-mentioned processing in the application unit may be performed using AI, or may be performed without using AI. For example, the application unit can input the user's geographical location information into AI, and the AI ​​can perform highly relevant application based on that information.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The input unit inputs user information. The user information includes age, gender, body type, preferences, etc. For example, information such as height, weight, shoulder width, waist size, skin color, preference for casual style, preference for the color blue, preference for simple designs, etc. can be input. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit performs a bone structure and color diagnosis of the user, and performs analysis using methods such as bone structure type, bone shape, personal color diagnosis, and color psychology. Step 3: The suggestion unit suggests optimal clothing based on the analysis results obtained by the analysis unit. For example, it suggests clothing with a silhouette that suits the user's body type, a color that matches their skin tone, and a design that suits their preferences. Step 4: The application unit can be applied to other items, such as shoes, accessories, furniture, etc. Step 5: The learning unit learns the user's preferences and responds even if they change. For example, it can learn the user's preferences based on the user's past selection history and survey results, and respond even if the preferences change.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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. an input unit for inputting user information; an analysis unit that analyzes the information input by the input unit; a suggestion unit that suggests appropriate clothes based on the analysis results obtained by the analysis unit; It has an application part that can be applied to other items, A learning unit that learns user preferences. A system characterized by:

2. The input unit Enter information about the user, including their body type and skin color 2. The system of claim 1.

3. The analysis unit Diagnose the user's bone structure and color 2. The system of claim 1.

4. The proposal unit It suggests clothes with silhouettes that suit the user's body type, colors that match their skin tone, and designs that suit their preferences.

2. The system of claim 1.

5. The application unit includes: Also applies to shoes, accessories, and other items of furniture 2. The system of claim 1.

6. The learning unit Learn your preferences and adapt when they change 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The input unit As you type, filter your input based on your current activity and environment 2. The system of claim 1.

10. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

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