System

Abstract

An object of a system according to an embodiment is to provide feedback that makes a user confident about clothing selected by the user.SOLUTION: A system includes a reception unit, a generation unit, and a display unit. The reception unit receives information on the clothes selected by the user. The generation unit generates words associated with the features of the color, type, material, and design of the clothing on the basis of the information received by the reception unit. The display unit displays the words generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to receive feedback that would give them confidence in the clothes they choose.

[0005] The system according to the embodiment aims to provide feedback that will help users feel confident about the clothes they have chosen. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives information about clothes selected by a user. The generation unit generates words associated with the color, type, material, and design characteristics of the clothes based on the information received by the reception unit. The display unit displays the words generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide feedback that allows the user to feel confident about the clothes they have chosen. [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 accepts information about clothes selected by a user, and a generation AI generates associated words based on that information and displays them to the user. This system accepts information about clothes selected by a user, and a generation AI generates associated words based on that information and displays them to the user, thereby helping the user feel confident about the clothes they have chosen. For example, the user inputs the color, type, material, and design features of the clothes they have selected. The generation AI then analyzes the input information and generates associated words based on the color, type, material, and design features of the clothes. For example, it generates words such as "passionate and elegant" for a "red silk dress," and words such as "refreshing and intelligent" for a "blue cotton shirt." The generated words are then displayed to the user. For example, a message such as "You are passionate and elegant because you chose the red silk dress" is displayed. This allows the user to feel confident about the clothes they have chosen. The system thus allows the user to feel confident about the clothes they have chosen. For example, by receiving positive feedback about the clothes they have chosen, the user can feel more esteemed. This allows the user to feel confident about the clothes they have chosen.

[0029] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives information about clothes selected by a user. The information about the clothes selected by the user includes, but is not limited to, the color, type, material, and design features of the clothes. The reception unit provides an interface for inputting, for example, the color, type, material, and design features of the clothes selected by the user. The reception unit can also store the information about the clothes selected by the user in a database. The generation unit uses a generation AI to generate words associated with the color, type, material, and design features of the clothes based on the information received by the reception unit. For example, the generation AI analyzes the color, type, material, and design features of the clothes and generates associated words based on the analysis. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and may generate, for example, words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. The display unit displays the words generated by the generation unit to the user. The display unit provides an interface for displaying, for example, the words generated for the clothes selected by the user. For example, a message such as "You are passionate and elegant because you chose a red silk dress" is displayed. This allows the system according to the embodiment to help the user feel confident about the clothes they have chosen. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs information received by the reception unit into the generation AI, and the generation AI generates words associated with the information. This allows the generation unit to generate appropriate compliments for the clothes the user has chosen.

[0030] The reception unit can receive the color, type, material, and design features of the clothes selected by the user. The reception unit provides, for example, an interface for inputting the color, type, material, and design features of the clothes selected by the user. For example, the user can input information such as "red silk dress" or "blue cotton shirt." The reception unit can also store information about the clothes selected by the user in a database. This makes it possible to receive detailed information about the clothes selected by the user. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input information about the clothes selected by the user to AI, which can then analyze the information.

[0031] The generation unit can generate words associated with the color, type, material, and design characteristics of the clothing. For example, the generation unit uses a generation AI to analyze the color, type, material, and design characteristics of the clothing and generate associated words based on the analysis. For example, it generates words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. This makes it possible to generate appropriate compliments for the clothing selected by the user. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the information received by the reception unit into the generation AI, which then generates associated words. This makes it possible for the generation unit to generate appropriate compliments for the clothing selected by the user.

[0032] The display unit can display the generated words to the user. The display unit provides an interface for displaying the generated words for, for example, clothes selected by the user. For example, the display unit may display a message such as, "You are passionate and elegant because you chose a red silk dress." This allows the user to receive positive feedback for the clothes they selected. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the words generated by the generation unit into AI, and the AI ​​displays the words to the user. This allows the display unit to receive positive feedback for the clothes selected by the user.

[0033] The generation unit can generate feedback for the clothes selected by the user. For example, the generation unit generates positive feedback for the clothes selected by the user using the generation AI. For example, for a "red silk dress," the generation unit generates feedback such as "passionate and elegant." The generation unit can also store the generated feedback in a database. This allows the user to have confidence in the clothes they have selected. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the information received by the reception unit into the generation AI, and the generation AI generates feedback. This allows the generation unit to generate appropriate feedback for the clothes selected by the user.

[0034] The reception unit can analyze the user's past clothing selection history and select the reception method. For example, the reception unit analyzes the user's past clothing selection history and selects the optimal reception method. For example, the reception unit can preferentially receive information about similar clothing based on the color and type of clothing selected by the user in the past. The reception unit can also suggest information about related clothing based on the material and design characteristics preferred by the user in the past. The reception unit can also receive information about clothing suitable for a specific season or event from the user's past selection history. This makes it possible to provide the optimal reception method based on the user's past selection history. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's past clothing selection history into AI, which analyzes the information and selects the optimal reception method. This makes it possible for the reception unit to provide the optimal reception method based on the user's past selection history.

[0035] When accepting information about clothing, the reception unit can accept the information about clothing based on the user's current fashion trends and the season. For example, when accepting information about clothing, the reception unit filters the information based on the user's current fashion trends and the season. For example, the reception unit prioritizes accepting information about clothing suitable for the current season. The reception unit can also analyze the user's current fashion trends and filter and accept information about related clothing. The reception unit can also accept information about related clothing based on the style of clothing recently purchased by the user. This makes it possible to accept information about clothing suitable for the user's current fashion trends and the season. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs information about the user's current fashion trends and the season into AI, which analyzes the information and accepts information about optimal clothing. This makes it possible for the reception unit to accept information about clothing suitable for the user's current fashion trends and the season.

[0036] When accepting clothing information, the acceptance unit can select an acceptance means according to the user's input method. For example, when accepting clothing information, the acceptance unit selects the optimal acceptance means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the acceptance unit can accept the clothing information using voice recognition technology. Alternatively, if the user selects text input, a simple input form can be provided to accept the clothing information. Alternatively, if the user selects image input, the acceptance unit can accept the clothing information using image analysis technology. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit inputs to AI according to the user's input method, and the AI ​​selects the optimal acceptance means. This makes it possible for the acceptance unit to provide the optimal acceptance means according to the user's input method.

[0037] When accepting clothing information, the reception unit can prioritize accepting highly relevant information based on the user's geographical location information. For example, when accepting clothing information, the reception unit prioritizes accepting highly relevant information taking the user's geographical location information into consideration. For example, the reception unit prioritizes accepting clothing information from stores close to the user's current location. Furthermore, based on the user's geographical location, the reception unit can also accept clothing information suitable for the local climate. Furthermore, based on the user's geographical location, the reception unit can also accept clothing information that matches local fashion trends. This makes it possible to accept optimal clothing information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information to AI, which analyzes the information and accepts optimal clothing information. This makes it possible for the reception unit to accept optimal clothing information based on the user's geographical location information.

[0038] When accepting clothing information, the reception unit can analyze the user's social media activity and accept related information. For example, when accepting clothing information, the reception unit analyzes the user's social media activity and accepts related information. For example, the reception unit may preferentially accept clothing information from brands the user follows on social media. The reception unit can also analyze the content of the user's social media posts and accept related clothing information. The reception unit can also accept related clothing information based on the activity of the user's friends on social media. This makes it possible to accept optimal clothing information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's social media activity into AI, which analyzes the information and accepts optimal clothing information. This makes it possible for the reception unit to accept optimal clothing information based on the user's social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information about clothing. For example, when receiving information about clothing, the reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit may preferentially receive information about clothing that the user has previously rated highly. The reception unit may also filter out information about clothing that the user has previously rated poorly so as not to receive it. The reception method can also be customized based on the user's past feedback to provide optimal information. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback into AI, which analyzes the information and customizes the optimal reception method. This makes it possible for the reception unit to provide an optimal reception method based on the user's past feedback.

[0040] When generating words, the generation unit can generate compliments with a level of detail based on the importance of the clothes. For example, when the generation AI generates words, the generation unit adjusts the level of detail based on the importance of the clothes. For example, detailed and specific compliments can be generated for clothes for special events. Simple and easy-to-understand compliments can also be generated for everyday clothes. Emotional compliments can also be generated for clothes that the user particularly likes. This makes it possible to generate compliments with an optimal level of detail depending on the importance of the clothes. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the importance of the clothes, and the generation AI analyzes the information and generates compliments with an optimal level of detail. This makes it possible for the generation unit to generate compliments with an optimal level of detail depending on the importance of the clothes.

[0041] When generating words, the generation unit can apply a generation algorithm depending on the clothing category. For example, when the generation AI generates words, the generation unit applies different generation algorithms depending on the clothing category. For example, a generation algorithm that uses elegant and sophisticated expressions can be applied to formal clothing. A generation algorithm that uses friendly and light expressions can be applied to casual clothing. A generation algorithm that uses expressions that convey a sense of vitality and movement can be applied to sportswear. This makes it possible to apply the optimal generation algorithm depending on the clothing category. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the clothing category, and the generation AI analyzes the information and applies the optimal generation algorithm. This makes it possible for the generation unit to apply the optimal generation algorithm depending on the clothing category.

[0042] When generating words, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, when the generation AI generates words, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can generate similar compliments by referring to patterns of compliments that the user has previously rated highly. The generation unit can also improve the accuracy of generation by avoiding patterns of compliments that the user has previously rated poorly. The generation unit can also analyze the user's past generation results and improve the algorithm for generating optimal compliments. This makes it possible to generate optimal compliments based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past generation results into the generation AI, which then analyzes the information and generates optimal compliments. This makes it possible for the generation unit to generate optimal compliments based on the user's past generation results.

[0043] When generating words, the generation unit can prioritize generating compliments based on when the clothes were selected. For example, when the generation AI generates words, the generation unit determines the generation priority based on when the clothes were selected. For example, for clothes selected at the change of seasons, the generation unit can prioritize generating compliments that reflect the seasonal feel. Also, for clothes selected before a specific event, the generation unit can prioritize generating compliments appropriate for the event. Also, for clothes frequently selected by the user, the generation unit can prioritize generating everyday compliments. This makes it possible to prioritize generating the most appropriate compliments depending on when the clothes were selected. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information into the generation AI based on when the clothes were selected, and the generation AI analyzes the information and prioritizes generating the most appropriate compliments. This makes it possible for the generation unit to prioritize generating the most appropriate compliments depending on when the clothes were selected.

[0044] When generating words, the generation unit can generate compliments in an order based on the relevance of the clothes. For example, when the generation AI generates words, the generation unit adjusts the order of generation based on the relevance of the clothes. For example, if the color or design of the clothes is particularly eye-catching, the generation unit can generate words that praise those features first. Also, if the material or texture of the clothes is important, the generation unit can generate words that praise those features first. Also, if the overall style of the clothes is important, the generation unit can generate words that praise those features first. This makes it possible to generate compliments in an optimal order based on the relevance of the clothes. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs information to the generation AI based on the relevance of the clothes, and the generation AI analyzes the information and generates compliments in an optimal order. This makes it possible for the generation unit to generate compliments in an optimal order based on the relevance of the clothes.

[0045] When generating words, the generation unit can generate compliments according to the user's level of expertise. For example, when the generation AI generates words, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can generate compliments that use a lot of technical terminology for a user who is knowledgeable about fashion. For a user who is not knowledgeable about fashion, the generation unit can also generate simple and easy-to-understand compliments. Furthermore, the generation unit can generate compliments that use technical terminology in an appropriate balance according to the user's level of expertise. This allows the generation of optimal compliments according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the user's level of expertise, and the generation AI analyzes the information and generates optimal compliments. This allows the generation unit to generate optimal compliments according to the user's level of expertise.

[0046] The display unit can select a display method by referring to the user's past operation history when displaying. For example, the display unit can select an optimal display method by referring to the user's past operation history when displaying. For example, the display unit can select an optimal display method based on a display style that the user has previously preferred. The display unit can also select a display method with high visibility based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the most effective display method. This makes it possible to provide an optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's past operation history into AI, which analyzes the information and selects an optimal display method. This makes it possible for the display unit to provide an optimal display method based on the user's past operation history.

[0047] The display unit can customize the display content according to the user's current task when displaying the content. For example, when displaying the content, the display unit customizes the display content according to the user's current task. For example, if the user is in the middle of selecting clothes, information related to the selected clothes can be displayed preferentially. Also, if the user is considering a purchase, information related to the purchase can be displayed preferentially. Also, if the user is seeking feedback, information related to compliments and reviews can be displayed preferentially. This makes it possible to provide optimal display content according to the user's current task. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's current task into AI, which analyzes the information and customizes optimal display content. This makes it possible for the display unit to provide optimal display content according to the user's current task.

[0048] The display unit can select a display method taking into account the user's device information when displaying. For example, the display unit selects the optimal display method taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a desktop, a display method optimized for a wide screen can be provided. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, which analyzes the information and selects the optimal display method. This makes it possible for the display unit to provide the optimal display method based on the user's device information.

[0049] The display unit can select a display method taking into account the user's device information when displaying. For example, the display unit selects the optimal display method taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a desktop, a display method optimized for a wide screen can be provided. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, which analyzes the information and selects the optimal display method. This makes it possible for the display unit to provide the optimal display method based on the user's device information.

[0050] The display unit can provide display content based on the user's language setting when displaying. For example, the display unit can make the display content multilingual according to the user's language setting when displaying. For example, the display content can be automatically set based on the language setting of the user's device. Furthermore, if the user uses multiple languages, a language switching function can be provided. Furthermore, if the user selects a specific language, the display content can be provided in that language. This makes it possible to provide optimal display content based on the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's language setting into AI, which analyzes the information and provides optimal display content. This makes it possible for the display unit to provide optimal display content based on the user's language setting.

[0051] The display unit can analyze the user's social media activity and provide information when displaying the information. For example, the display unit can analyze the user's social media activity and provide related information when displaying the information. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activity of the user's friends on social media. This makes it possible to provide optimal information based on the user's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's social media activity into AI, which analyzes the information and provides optimal information. This makes it possible for the display unit to provide optimal information based on the user's social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When accepting information about clothes selected by a user, the accepting unit can preferentially accept information about clothes that the user tends to like based on the user's past selection history. For example, the accepting unit can analyze the color, type, material, and design characteristics of clothes selected by the user in the past and preferentially accept information about similar clothes. It can also preferentially accept information about clothes that the user has previously given high ratings to. This makes it possible to accept more personalized clothing information based on the user's past selection history.

[0054] When displaying the generated words to the user, the display unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. This makes it possible to provide the optimal display method based on the user's device information.

[0055] The generator can adjust the words generated for the clothes selected by the user based on when the clothes were selected. For example, for clothes selected at the change of seasons, the generator can generate compliments that reflect the seasonal feeling. Also, for clothes selected before a specific event, the generator can generate compliments that are appropriate for the event. This makes it possible to generate optimal compliments depending on when the clothes were selected.

[0056] The reception unit can customize the reception method by reflecting the user's past feedback. For example, it can preferentially receive information about clothes that the user has previously rated highly. It can also filter out information about clothes that the user has previously rated poorly so as not to receive it. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0057] The reception unit can analyze the user's social media activity and receive related information. For example, it can prioritize receiving clothing information from brands the user follows on social media. It can also analyze the content of the user's social media posts and receive related clothing information. This allows the reception of optimal clothing information based on the user's social media activity.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives information about the clothes selected by the user. The information about the clothes selected by the user includes color, type, material, and design features. The reception unit provides an interface for inputting information about the clothes selected by the user, and can also store the information in a database. Step 2: The generation unit uses a generation AI to generate words associated with the color, type, material, and design characteristics of the clothing based on the information received by the reception unit. The generation AI may be a text generation AI or a multimodal generation AI, and may generate words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. Step 3: The display unit displays the words generated by the generation unit to the user. The display unit provides an interface for displaying the words generated for the clothes selected by the user. For example, the display unit displays a message such as "You are passionate and elegant because you chose a red silk dress."

[0060] (Example 2) A system according to an embodiment of the present invention accepts information about clothes selected by a user, and a generation AI generates associated words based on that information and displays them to the user. This system accepts information about clothes selected by a user, and a generation AI generates associated words based on that information and displays them to the user, thereby helping the user feel confident about the clothes they have chosen. For example, the user inputs the color, type, material, and design features of the clothes they have selected. The generation AI then analyzes the input information and generates associated words based on the color, type, material, and design features of the clothes. For example, it generates words such as "passionate and elegant" for a "red silk dress," and words such as "refreshing and intelligent" for a "blue cotton shirt." The generated words are then displayed to the user. For example, a message such as "You are passionate and elegant because you chose the red silk dress" is displayed. This allows the user to feel confident about the clothes they have chosen. The system thus allows the user to feel confident about the clothes they have chosen. For example, by receiving positive feedback about the clothes they have chosen, the user can feel more esteemed. This allows the user to feel confident about the clothes they have chosen.

[0061] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives information about clothes selected by a user. The information about the clothes selected by the user includes, but is not limited to, the color, type, material, and design features of the clothes. The reception unit provides an interface for inputting, for example, the color, type, material, and design features of the clothes selected by the user. The reception unit can also store the information about the clothes selected by the user in a database. The generation unit uses a generation AI to generate words associated with the color, type, material, and design features of the clothes based on the information received by the reception unit. For example, the generation AI analyzes the color, type, material, and design features of the clothes and generates associated words based on the analysis. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and may generate, for example, words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. The display unit displays the words generated by the generation unit to the user. The display unit provides an interface for displaying, for example, the words generated for the clothes selected by the user. For example, a message such as "You are passionate and elegant because you chose a red silk dress" is displayed. This allows the system according to the embodiment to help the user feel confident about the clothes they have chosen. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs information received by the reception unit into the generation AI, and the generation AI generates words associated with the information. This allows the generation unit to generate appropriate compliments for the clothes the user has chosen.

[0062] The reception unit can receive the color, type, material, and design features of the clothes selected by the user. The reception unit provides, for example, an interface for inputting the color, type, material, and design features of the clothes selected by the user. For example, the user can input information such as "red silk dress" or "blue cotton shirt." The reception unit can also store information about the clothes selected by the user in a database. This makes it possible to receive detailed information about the clothes selected by the user. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input information about the clothes selected by the user to AI, which can then analyze the information.

[0063] The generation unit can generate words associated with the color, type, material, and design characteristics of the clothing. For example, the generation unit uses a generation AI to analyze the color, type, material, and design characteristics of the clothing and generate associated words based on the analysis. For example, it generates words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. This makes it possible to generate appropriate compliments for the clothing selected by the user. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the information received by the reception unit into the generation AI, which then generates associated words. This makes it possible for the generation unit to generate appropriate compliments for the clothing selected by the user.

[0064] The display unit can display the generated words to the user. The display unit provides an interface for displaying the generated words for, for example, clothes selected by the user. For example, the display unit may display a message such as, "You are passionate and elegant because you chose a red silk dress." This allows the user to receive positive feedback for the clothes they selected. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the words generated by the generation unit into AI, and the AI ​​displays the words to the user. This allows the display unit to receive positive feedback for the clothes selected by the user.

[0065] The generation unit can generate feedback for the clothes selected by the user. For example, the generation unit generates positive feedback for the clothes selected by the user using the generation AI. For example, for a "red silk dress," the generation unit generates feedback such as "passionate and elegant." The generation unit can also store the generated feedback in a database. This allows the user to have confidence in the clothes they have selected. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the information received by the reception unit into the generation AI, and the generation AI generates feedback. This allows the generation unit to generate appropriate feedback for the clothes selected by the user.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving clothing information based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of receiving clothing information based on the estimated user emotions. For example, if the user is relaxed, the timing of receiving clothing information can be delayed to encourage input at a slower pace. Also, if the user is in a hurry, a simplified input form can be provided to quickly receive clothing information. Also, if the user is feeling stressed, the input can be divided and received in stages to reduce the burden. This allows clothing information to be received at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI, or may be performed without AI. For example, the reception unit inputs the user's emotion data into a generation AI, which then estimates the emotion and adjusts the timing of receiving the emotion based on the result. This allows the reception unit to receive information about clothes at the optimal timing according to the user's emotions.

[0067] The reception unit can analyze the user's past clothing selection history and select the reception method. For example, the reception unit analyzes the user's past clothing selection history and selects the optimal reception method. For example, the reception unit can preferentially receive information about similar clothing based on the color and type of clothing selected by the user in the past. The reception unit can also suggest information about related clothing based on the material and design characteristics preferred by the user in the past. The reception unit can also receive information about clothing suitable for a specific season or event from the user's past selection history. This makes it possible to provide the optimal reception method based on the user's past selection history. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's past clothing selection history into AI, which analyzes the information and selects the optimal reception method. This makes it possible for the reception unit to provide the optimal reception method based on the user's past selection history.

[0068] When accepting information about clothing, the reception unit can accept the information about clothing based on the user's current fashion trends and the season. For example, when accepting information about clothing, the reception unit filters the information based on the user's current fashion trends and the season. For example, the reception unit prioritizes accepting information about clothing suitable for the current season. The reception unit can also analyze the user's current fashion trends and filter and accept information about related clothing. The reception unit can also accept information about related clothing based on the style of clothing recently purchased by the user. This makes it possible to accept information about clothing suitable for the user's current fashion trends and the season. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs information about the user's current fashion trends and the season into AI, which analyzes the information and accepts information about optimal clothing. This makes it possible for the reception unit to accept information about clothing suitable for the user's current fashion trends and the season.

[0069] When accepting clothing information, the acceptance unit can select an acceptance means according to the user's input method. For example, when accepting clothing information, the acceptance unit selects the optimal acceptance means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the acceptance unit can accept the clothing information using voice recognition technology. Alternatively, if the user selects text input, a simple input form can be provided to accept the clothing information. Alternatively, if the user selects image input, the acceptance unit can accept the clothing information using image analysis technology. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit inputs to AI according to the user's input method, and the AI ​​selects the optimal acceptance means. This makes it possible for the acceptance unit to provide the optimal acceptance means according to the user's input method.

[0070] The reception unit can estimate the user's emotions and determine the priority of clothing information to be accepted based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of clothing information to be accepted based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize clothing information with bright colors and designs. Also, if the user is relaxed, the reception unit can prioritize clothing information with subdued colors and materials. Also, if the user is stressed, the reception unit can prioritize clothing information that is simple and visually less stressful. This allows the reception of clothing information that is optimal for 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 reception unit may be performed using AI or without AI. For example, the reception unit inputs the user's emotion data into the generation AI, which then estimates the emotion and determines the priority of clothing information to be accepted based on the result. This allows the reception unit to preferentially receive information on clothes that are most suitable for the user's emotions.

[0071] When accepting clothing information, the reception unit can prioritize accepting highly relevant information based on the user's geographical location information. For example, when accepting clothing information, the reception unit prioritizes accepting highly relevant information taking the user's geographical location information into consideration. For example, the reception unit prioritizes accepting clothing information from stores close to the user's current location. Furthermore, based on the user's geographical location, the reception unit can also accept clothing information suitable for the local climate. Furthermore, based on the user's geographical location, the reception unit can also accept clothing information that matches local fashion trends. This makes it possible to accept optimal clothing information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information to AI, which analyzes the information and accepts optimal clothing information. This makes it possible for the reception unit to accept optimal clothing information based on the user's geographical location information.

[0072] When accepting clothing information, the reception unit can analyze the user's social media activity and accept related information. For example, when accepting clothing information, the reception unit analyzes the user's social media activity and accepts related information. For example, the reception unit may preferentially accept clothing information from brands the user follows on social media. The reception unit can also analyze the content of the user's social media posts and accept related clothing information. The reception unit can also accept related clothing information based on the activity of the user's friends on social media. This makes it possible to accept optimal clothing information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's social media activity into AI, which analyzes the information and accepts optimal clothing information. This makes it possible for the reception unit to accept optimal clothing information based on the user's social media activity.

[0073] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information about clothing. For example, when receiving information about clothing, the reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit may preferentially receive information about clothing that the user has previously rated highly. The reception unit may also filter out information about clothing that the user has previously rated poorly so as not to receive it. The reception method can also be customized based on the user's past feedback to provide optimal information. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback into AI, which analyzes the information and customizes the optimal reception method. This makes it possible for the reception unit to provide an optimal reception method based on the user's past feedback.

[0074] The generation unit can estimate the user's emotions and adjust the expression of the words to be generated based on the estimated user emotions. For example, the generation unit uses a generation AI to estimate the user's emotions and adjust the expression of the words to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate compliments using calm and gentle expressions. If the user is excited, the generation unit can generate compliments using energetic and powerful expressions. If the user is stressed, the generation unit can generate compliments using expressions that give a sense of security. This makes it possible to generate compliments using the optimal expression method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which then estimates the emotion and adjusts the expression of the words to be generated based on the result. This allows the generation unit to generate a compliment in an optimal expression method according to the user's feelings.

[0075] When generating words, the generation unit can generate compliments with a level of detail based on the importance of the clothes. For example, when the generation AI generates words, the generation unit adjusts the level of detail based on the importance of the clothes. For example, detailed and specific compliments can be generated for clothes for special events. Simple and easy-to-understand compliments can also be generated for everyday clothes. Emotional compliments can also be generated for clothes that the user particularly likes. This makes it possible to generate compliments with an optimal level of detail depending on the importance of the clothes. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the importance of the clothes, and the generation AI analyzes the information and generates compliments with an optimal level of detail. This makes it possible for the generation unit to generate compliments with an optimal level of detail depending on the importance of the clothes.

[0076] When generating words, the generation unit can apply a generation algorithm depending on the clothing category. For example, when the generation AI generates words, the generation unit applies different generation algorithms depending on the clothing category. For example, a generation algorithm that uses elegant and sophisticated expressions can be applied to formal clothing. A generation algorithm that uses friendly and light expressions can be applied to casual clothing. A generation algorithm that uses expressions that convey a sense of vitality and movement can be applied to sportswear. This makes it possible to apply the optimal generation algorithm depending on the clothing category. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the clothing category, and the generation AI analyzes the information and applies the optimal generation algorithm. This makes it possible for the generation unit to apply the optimal generation algorithm depending on the clothing category.

[0077] When generating words, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, when the generation AI generates words, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can generate similar compliments by referring to patterns of compliments that the user has previously rated highly. The generation unit can also improve the accuracy of generation by avoiding patterns of compliments that the user has previously rated poorly. The generation unit can also analyze the user's past generation results and improve the algorithm for generating optimal compliments. This makes it possible to generate optimal compliments based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past generation results into the generation AI, which then analyzes the information and generates optimal compliments. This makes it possible for the generation unit to generate optimal compliments based on the user's past generation results.

[0078] The generation unit can estimate the user's emotions and adjust the length of the generated words based on the estimated user emotions. For example, the generation unit uses a generation AI to estimate the user's emotions and adjust the length of the generated words based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a longer, more detailed compliment. If the user is in a hurry, the generation unit can generate a short, more to-the-point compliment. If the user is stressed, the generation unit can generate a concise, reassuring compliment. This allows compliments to be generated with an optimal length depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which then estimates the emotion and adjusts the length of the generated words based on the result. This allows the generator to generate a compliment of an optimal length according to the user's emotions.

[0079] When generating words, the generation unit can prioritize generating compliments based on when the clothes were selected. For example, when the generation AI generates words, the generation unit determines the generation priority based on when the clothes were selected. For example, for clothes selected at the change of seasons, the generation unit can prioritize generating compliments that reflect the seasonal feel. Also, for clothes selected before a specific event, the generation unit can prioritize generating compliments appropriate for the event. Also, for clothes frequently selected by the user, the generation unit can prioritize generating everyday compliments. This makes it possible to prioritize generating the most appropriate compliments depending on when the clothes were selected. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information into the generation AI based on when the clothes were selected, and the generation AI analyzes the information and prioritizes generating the most appropriate compliments. This makes it possible for the generation unit to prioritize generating the most appropriate compliments depending on when the clothes were selected.

[0080] When generating words, the generation unit can generate compliments in an order based on the relevance of the clothes. For example, when the generation AI generates words, the generation unit adjusts the order of generation based on the relevance of the clothes. For example, if the color or design of the clothes is particularly eye-catching, the generation unit can generate words that praise those features first. Also, if the material or texture of the clothes is important, the generation unit can generate words that praise those features first. Also, if the overall style of the clothes is important, the generation unit can generate words that praise those features first. This makes it possible to generate compliments in an optimal order based on the relevance of the clothes. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit inputs information to the generation AI based on the relevance of the clothes, and the generation AI analyzes the information and generates compliments in an optimal order. This makes it possible for the generation unit to generate compliments in an optimal order based on the relevance of the clothes.

[0081] When generating words, the generation unit can generate compliments according to the user's level of expertise. For example, when the generation AI generates words, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can generate compliments that use a lot of technical terminology for a user who is knowledgeable about fashion. For a user who is not knowledgeable about fashion, the generation unit can also generate simple and easy-to-understand compliments. Furthermore, the generation unit can generate compliments that use technical terminology in an appropriate balance according to the user's level of expertise. This allows the generation of optimal compliments according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs information to the generation AI based on the user's level of expertise, and the generation AI analyzes the information and generates optimal compliments. This allows the generation unit to generate optimal compliments according to the user's level of expertise.

[0082] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, the display unit estimates the user's emotion and adjusts the display method based on the estimated user's emotion. For example, if the user is relaxed, the display can be made using calm colors and fonts. If the user is excited, the display can be made using bright colors and large fonts. If the user is stressed, the display can be made using subdued colors and simple fonts. This makes it possible to provide an optimal display method depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, such as 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 display unit can be performed using AI or without AI. For example, the display unit inputs the user's emotion data into the generation AI, which then estimates the emotion and adjusts the display method based on the result. This makes it possible for the display unit to provide an optimal display method depending on the user's emotion.

[0083] The display unit can select a display method by referring to the user's past operation history when displaying. For example, the display unit can select an optimal display method by referring to the user's past operation history when displaying. For example, the display unit can select an optimal display method based on a display style that the user has previously preferred. The display unit can also select a display method with high visibility based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the most effective display method. This makes it possible to provide an optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's past operation history into AI, which analyzes the information and selects an optimal display method. This makes it possible for the display unit to provide an optimal display method based on the user's past operation history.

[0084] The display unit can customize the display content according to the user's current task when displaying the content. For example, when displaying the content, the display unit customizes the display content according to the user's current task. For example, if the user is in the middle of selecting clothes, information related to the selected clothes can be displayed preferentially. Also, if the user is considering a purchase, information related to the purchase can be displayed preferentially. Also, if the user is seeking feedback, information related to compliments and reviews can be displayed preferentially. This makes it possible to provide optimal display content according to the user's current task. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's current task into AI, which analyzes the information and customizes optimal display content. This makes it possible for the display unit to provide optimal display content according to the user's current task.

[0085] The display unit can select a display method taking into account the user's device information when displaying. For example, the display unit selects the optimal display method taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a desktop, a display method optimized for a wide screen can be provided. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, which analyzes the information and selects the optimal display method. This makes it possible for the display unit to provide the optimal display method based on the user's device information.

[0086] The display unit can estimate the user's emotions and determine the priority of words to display based on the estimated user emotions. For example, the display unit estimates the user's emotions and determines the priority of words to display based on the estimated user emotions. For example, if the user is relaxed, calm words can be preferentially displayed. Also, if the user is excited, energetic words can be preferentially displayed. Also, if the user is stressed, words that give a sense of security can be preferentially displayed. This allows the optimal words to be preferentially displayed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI. For example, the display unit inputs the user's emotion data into the generation AI, which then estimates the emotion and determines the priority of words to display based on the result. This allows the display unit to preferentially display the optimal words according to the user's emotions.

[0087] The display unit can select a display method taking into account the user's device information when displaying. For example, the display unit selects the optimal display method taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a desktop, a display method optimized for a wide screen can be provided. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's device information into AI, which analyzes the information and selects the optimal display method. This makes it possible for the display unit to provide the optimal display method based on the user's device information.

[0088] The display unit can provide display content based on the user's language setting when displaying. For example, the display unit can make the display content multilingual according to the user's language setting when displaying. For example, the display content can be automatically set based on the language setting of the user's device. Furthermore, if the user uses multiple languages, a language switching function can be provided. Furthermore, if the user selects a specific language, the display content can be provided in that language. This makes it possible to provide optimal display content based on the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's language setting into AI, which analyzes the information and provides optimal display content. This makes it possible for the display unit to provide optimal display content based on the user's language setting.

[0089] The display unit can analyze the user's social media activity and provide information when displaying the information. For example, the display unit can analyze the user's social media activity and provide related information when displaying the information. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activity of the user's friends on social media. This makes it possible to provide optimal information based on the user's social media activity. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit inputs the user's social media activity into AI, which analyzes the information and provides optimal information. This makes it possible for the display unit to provide optimal information based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to input information about the clothes selected by the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates associated words based on the information about the clothes using a generation AI. For example, the display unit is realized by the output device 40 of the smart device 14 and displays the generated words to the user. This allows the user to feel confident about the clothes they have selected. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input information about the clothes they have selected by voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates associated words based on the clothes information using a generation AI. For example, the display unit is realized by the speaker 240 of the smart glasses 214 and provides the generated words to the user by voice. This allows the user to feel confident about the clothes they have selected. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to input information about the clothes they have selected by voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates associated words based on the clothes information using a generation AI. For example, the display unit is realized by the display 343 of the headset-type terminal 314 and displays the generated words to the user. This allows the user to feel confident about the clothes they have selected. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to input information about the clothes selected by voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates associated words based on the clothes information using a generation AI. For example, the display unit is realized by the speaker 240 of the robot 414 and provides the generated words to the user by voice. This allows the user to feel confident about the clothes they have selected.

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

[0091] When accepting information about clothes selected by a user, the accepting unit can preferentially accept information about clothes that the user tends to like based on the user's past selection history. For example, the accepting unit can analyze the color, type, material, and design characteristics of clothes selected by the user in the past and preferentially accept information about similar clothes. It can also preferentially accept information about clothes that the user has previously given high ratings to. This makes it possible to accept more personalized clothing information based on the user's past selection history.

[0092] The generator can adjust the words generated for the clothes selected by the user based on the user's current emotions. For example, if the user is relaxed, the generator can generate compliments using calm and gentle expressions. On the other hand, if the user is excited, the generator can generate compliments using energetic and powerful expressions. This allows the generator to generate compliments using the most appropriate expression method depending on the user's emotions.

[0093] When displaying the generated words to the user, the display unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. This makes it possible to provide the optimal display method based on the user's device information.

[0094] The reception unit can estimate the user's emotions and adjust the timing of receiving clothing information based on the estimated user emotions. For example, if the user is relaxed, the timing of receiving clothing information can be delayed to encourage input at a slower pace. Also, if the user is in a hurry, a simplified input form can be provided to quickly receive clothing information. This allows clothing information to be received at the optimal timing according to the user's emotions.

[0095] The generator can adjust the words generated for the clothes selected by the user based on when the clothes were selected. For example, for clothes selected at the change of seasons, the generator can generate compliments that reflect the seasonal feeling. Also, for clothes selected before a specific event, the generator can generate compliments that are appropriate for the event. This makes it possible to generate optimal compliments depending on when the clothes were selected.

[0096] When displaying the generated words to the user, the display unit can estimate the user's emotion and adjust the display method based on the estimated user emotion. For example, if the user is relaxed, the display can be made using calm colors and fonts. On the other hand, if the user is excited, the display can be made using bright colors and large fonts. This makes it possible to provide the optimal display method according to the user's emotion.

[0097] The reception unit can customize the reception method by reflecting the user's past feedback. For example, it can preferentially receive information about clothes that the user has previously rated highly. It can also filter out information about clothes that the user has previously rated poorly so as not to receive it. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0098] The generation unit can estimate the user's emotions and adjust the length of the words to be generated based on the estimated user's emotions. For example, if the user is relaxed, a longer and more detailed compliment can be generated. On the other hand, if the user is in a hurry, a shorter and more to-the-point compliment can be generated. This makes it possible to generate compliments of an optimal length according to the user's emotions.

[0099] The reception unit can analyze the user's social media activity and receive related information. For example, it can prioritize receiving clothing information from brands the user follows on social media. It can also analyze the content of the user's social media posts and receive related clothing information. This allows the reception of optimal clothing information based on the user's social media activity.

[0100] The display unit can estimate the user's emotions and determine the priority of words to display based on the estimated user's emotions. For example, if the user is relaxed, calm words can be displayed preferentially. Also, if the user is excited, energetic words can be displayed preferentially. In this way, the most appropriate words can be displayed preferentially according to the user's emotions.

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

[0102] Step 1: The reception unit receives information about the clothes selected by the user. The information about the clothes selected by the user includes color, type, material, and design features. The reception unit provides an interface for inputting information about the clothes selected by the user, and can also store the information in a database. Step 2: The generation unit uses a generation AI to generate words associated with the color, type, material, and design characteristics of the clothing based on the information received by the reception unit. The generation AI may be a text generation AI or a multimodal generation AI, and may generate words such as "passionate and elegant" for a "red silk dress." The generation unit can also store the generated words in a database. Step 3: The display unit displays the words generated by the generation unit to the user. The display unit provides an interface for displaying the words generated for the clothes selected by the user. For example, the display unit displays a message such as "You are passionate and elegant because you chose a red silk dress."

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about clothes selected by a user; a generator that generates words associated with the color, type, material, and design characteristics of the clothing based on the information received by the receiver; a display unit that displays the words generated by the generation unit to a user. A system characterized by:

2. The reception unit Accepts the color, type, material, and design characteristics of the clothing selected by the user 2. The system of claim 1.

3. The generation unit Generate words associated with the color, type, material, and design of clothing 2. The system of claim 1.

4. The display unit Display the generated words to the user 2. The system of claim 1.

5. The generation unit Generate feedback for the clothes the user chooses 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of receiving clothing information based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past clothing selection history and select the acceptance method.

2. The system of claim 1.

8. The reception unit When accepting information on clothing, the information is accepted based on the user's current fashion trends and seasons.

2. The system of claim 1.

Images