System

Abstract

An object of a system according to an embodiment is to enable a user to have confidence in clothes selected by the user.SOLUTION: A system includes a clothing analysis part, a reward generation part, an expression conversion part, and a feedback part. The clothes analyzer analyzes the clothes selected by the user. The reward generation unit generates a reward based on the color and type of the clothes analyzed by the clothes analysis unit. The expression conversion unit converts the reward generated by the reward generation unit into a human-like expression. The feedback unit feeds back the reward converted by the expression conversion 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 techniques have had the problem of making it difficult for users to feel confident about the clothes they choose.

[0005] The system according to the embodiment aims to enable users to feel confident about the clothes they choose. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing analysis unit, a compliment generation unit, an expression conversion unit, and a feedback unit. The clothing analysis unit analyzes clothing selected by a user. The compliment generation unit generates compliments based on the color and type of clothing analyzed by the clothing analysis unit. The expression conversion unit converts the compliments generated by the compliment generation unit into human-like expressions. The feedback unit feeds back the compliments converted by the expression conversion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to feel confident about the clothes they choose. [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) The OMOTENASHI system according to an embodiment of the present invention is a system that helps users to feel confident when choosing clothes that suit them. This system uses words that can be associated with the color and type of the clothes selected by the user to give compliments in a human-like manner, rather than a mechanical one. In this way, the OMOTENASHI system can help users to feel confident when choosing clothes that suit them.

[0029] The OMOTENASHI system according to the embodiment includes a clothing analysis unit, a compliment generation unit, an expression conversion unit, and a feedback unit. The clothing analysis unit analyzes the clothing selected by the user. For example, the clothing analysis unit receives as input image and text information of the clothing selected by the user, and the generation AI analyzes the color and type of the clothing based on that information. The compliment generation unit generates compliments based on the color and type of the clothing analyzed by the clothing analysis unit. For example, if the user selects a blue shirt, the generation AI generates a compliment such as, "That blue shirt is very refreshing and brings out your charm." The expression conversion unit converts the compliment generated by the compliment generation unit into a more human-like expression. For example, the compliment "That blue shirt is very refreshing and brings out your charm" can be expressed as, "That blue shirt is really refreshing and lovely! It really brings out your charm." The feedback unit feeds back the compliment converted by the expression conversion unit to the user. For example, in response to an outfit chosen by a user, the system may provide feedback such as, "That blue shirt is really refreshing and lovely! It really brings out your charm." In this way, the OMOTENASHI system according to the embodiment can help the user to feel more confident when choosing clothes that suit them. For example, the user can feel more confident about the outfit they have chosen, which makes choosing clothes more enjoyable. Furthermore, the user can respect themselves and improve their self-esteem.

[0030] The clothing analysis unit analyzes the user's past clothing selection history and learns preference trends, thereby improving analysis accuracy. In the clothing analysis unit, for example, the generation AI collects the user's past clothing selection history and analyzes color and style trends. For example, it learns the colors and designs of clothing the user has chosen in the past and reflects this in the next analysis. In addition, the clothing analysis unit identifies preference trends based on the user's past clothing selection history and improves analysis accuracy. For example, it prioritizes analysis of colors and styles that the user frequently chooses. In addition, the generation AI analyzes the user's past clothing selection history and tracks changes in preferences. For example, it learns the user's preferences, which change depending on the season and trends, and reflects this in the analysis. In this way, it is possible to learn the user's preference trends and improve analysis accuracy.

[0031] The clothing analysis unit can take into account the user's body shape and skin color and suggest the most suitable color and type of clothing. For example, the generation AI collects the user's body shape data and suggests the most suitable color and type of clothing based on that. For example, it analyzes silhouettes and designs that suit the body shape. The clothing analysis unit also analyzes the user's skin color and suggests clothing colors that match it. For example, it selects colors that suit skin tones. The generation AI also comprehensively analyzes the user's body shape and skin color and suggests the most suitable clothing combination. For example, it combines a silhouette that flatters the body shape with colors that match the skin color. This makes it possible to suggest the most suitable clothing based on the user's body shape and skin color.

[0032] The compliment generation unit can learn the user's past history of compliments and generate more personalized compliments. For example, the compliment generation unit uses a generation AI to collect the user's past history of compliments and generate personalized compliments based on that data. For example, it uses compliments that were well received in the past as a reference. The compliment generation unit also analyzes the user's past history of compliments and the generation AI learns their trends. For example, it identifies a tendency to prefer specific expressions and wording. The compliment generation unit also uses a generation AI to generate more individualized compliments based on the user's past history of compliments. For example, it uses expressions that match the user's preferences. This allows the compliment generation unit to learn the user's past history of compliments and generate more personalized compliments.

[0033] The compliment generation unit generates compliments that correspond to different cultures and languages, making it possible to accommodate international users. For example, the generation AI generates compliments that correspond to different cultures and languages. For example, compliments are generated in multiple languages, such as English, French, and Chinese. The compliment generation unit also learns the expressions and nuances of compliments from different cultures, and the generation AI generates compliments based on that. For example, the compliment generation unit reflects the characteristics of compliments from each culture. The compliment generation unit also builds a database of different languages ​​and cultures, and generates compliments based on that, so that the generation AI can accommodate international users. This makes it possible to generate compliments that correspond to different cultures and languages, making it possible to accommodate international users.

[0034] The expression conversion unit can use natural language processing technology to generate more natural and fluent compliments. For example, the generation AI in the expression conversion unit uses natural language processing technology to generate more natural and fluent compliments. For example, it optimizes the selection of grammar and vocabulary. The expression conversion unit also uses natural language processing technology to allow the generation AI to understand the context of the compliment and generate more natural expressions. For example, it selects appropriate wording according to the context. The expression conversion unit also uses natural language processing technology to adjust the tone and rhythm of the compliment. For example, it generates compliments with more friendly expressions and rhythm. This allows the generation of more natural and fluent compliments.

[0035] The expression conversion unit can learn the user's speaking style and language usage and generate compliments that match it. For example, the generation AI of the expression conversion unit collects the user's speaking style and language usage and generates compliments based on that data. For example, it incorporates phrases and expressions that the user frequently uses. The expression conversion unit also analyzes the user's speaking style and language usage and the generation AI learns these tendencies. For example, it generates compliments that match a user who prefers formal language. The expression conversion unit also builds a system in which the generation AI generates more personalized compliments based on the user's speaking style and language usage. For example, it generates compliments with a tone and rhythm that matches the user's language usage. This makes it possible to generate compliments that match the user's speaking style and language usage.

[0036] The feedback unit can learn from the user's feedback history and provide more effective feedback. For example, the feedback unit uses a generation AI to collect the user's past feedback history and provide effective feedback based on that data. For example, it refers to feedback that was well received in the past. The feedback unit also analyzes the user's feedback history and the generation AI learns its trends. For example, it identifies a tendency to prefer certain expressions and wording. The feedback unit also builds a system in which the generation AI provides more personalized feedback based on the user's feedback history. For example, it uses expressions that match the user's preferences. This allows the feedback unit to learn from the user's feedback history and provide more effective feedback.

[0037] The feedback unit can collect and refer to feedback from other users regarding the clothing selected by the user. For example, the generation AI in the feedback unit collects feedback from other users regarding the clothing selected by the user and provides feedback based on that data. For example, it refers to feedback that other users have given high ratings. The feedback unit also analyzes other users' feedback, allowing the generation AI to learn their tendencies. For example, it identifies tendencies toward preferring certain expressions and wording. The feedback unit also builds a system in which the generation AI provides more diverse feedback based on the feedback of other users. For example, it combines multiple pieces of feedback to provide new feedback. This allows the generation AI to provide more diverse feedback by referring to the feedback of other users.

[0038] The feedback unit can provide visual and audio feedback related to the clothing selected by the user. For example, the generation AI analyzes visual data related to the clothing selected by the user and provides feedback based on that. For example, feedback based on the design or color of the clothing can be provided. The feedback unit can also analyze audio data related to the clothing selected by the user and the generation AI can provide feedback based on that. For example, feedback that matches the tone or rhythm of the audio can be provided. The feedback unit can also build a system in which the generation AI provides more multi-sensory feedback based on visual and audio data. For example, feedback that appeals to the visual and auditory senses can be provided. This makes it possible to provide visual and audio feedback.

[0039] The feedback unit learns from past data on the user's confidence improvement and can provide more effective support. For example, the generation AI collects past data on the user's confidence improvement and provides effective support based on that data. For example, it refers to support methods that have been well-received in the past. The feedback unit also analyzes past data on the user's confidence improvement and the generation AI learns the trends. For example, it identifies tendencies to prefer specific expressions and wording. The feedback unit also builds a system in which the generation AI provides more personalized support based on past data on the user's confidence improvement. For example, it uses expressions that match the user's preferences. In this way, the generation AI can learn from past data on the user's confidence improvement and provide more effective support.

[0040] The feedback unit can set goals for improving the user's self-confidence and monitor the degree of achievement. For example, the feedback unit constructs a system in which the generation AI sets goals for improving the user's self-confidence and monitors the degree of achievement. For example, the feedback unit tracks progress toward goals set by the user in real time. The feedback unit also sets goals for improving the user's self-confidence and the generation AI evaluates the degree of achievement. For example, the feedback unit provides feedback for goals achieved by the user. The feedback unit also maintains the user's motivation by having the generation AI set goals for improving the user's self-confidence and monitors the degree of achievement. For example, the feedback unit provides encouraging messages toward goal achievement. This makes it possible to set goals for improving the user's self-confidence and monitor the degree of achievement.

[0041] The feedback unit can collect and refer to feedback from other users regarding the confidence improvement of the user's selected outfit. For example, the feedback unit allows the generation AI to collect feedback from other users regarding the confidence improvement of the user's selected outfit and provide assistance based on that data. For example, it refers to feedback that other users have given high ratings. The feedback unit also analyzes other users' feedback regarding the confidence improvement of the user, allowing the generation AI to learn the trends. For example, it identifies the tendency to prefer certain expressions and wording. The feedback unit also builds a system that provides more diverse assistance based on the generation AI's feedback regarding the confidence improvement of other users. For example, it combines multiple pieces of feedback to provide new assistance. In this way, more diverse assistance can be provided by referring to other users' feedback regarding the confidence improvement of other users.

[0042] The feedback unit can provide self-confidence-boosting support using visuals and audio related to the clothing selected by the user. For example, the generation AI analyzes visual data related to the clothing selected by the user and provides self-confidence-boosting support based on that data. For example, it provides support based on the design and color of the clothing. The feedback unit can also analyze audio data related to the clothing selected by the user and provide self-confidence-boosting support based on that data. For example, it can provide support that matches the tone and rhythm of the audio. The feedback unit can also build a system in which the generation AI provides more multi-sensory self-confidence-boosting support based on visual and audio data. For example, it can provide support that appeals to the visual and auditory senses. This makes it possible to provide self-confidence-boosting support using visuals and audio.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The OMOTENASHI system may further include an advice unit that provides advice to the user regarding clothing selection. The advice unit provides advice based on the season and weather for the clothing selected by the user. For example, for clothing selected on a hot summer day, the advice unit may provide advice such as, "This shirt is cool and perfect for summer." The advice unit may also provide advice appropriate for a specific event or situation for the clothing selected by the user. For example, for clothing selected for a business meeting, the advice unit may provide advice such as, "This suit gives a professional impression." The advice unit may also provide advice based on the latest trends and fashion information for the clothing selected by the user. For example, the advice unit may provide advice such as, "This design is trending this season." This allows the user to select more appropriate clothing.

[0045] The OMOTENASHI system can further include a history display unit that displays the history of the user's clothing selections. The history display unit, for example, visually displays the history of clothing the user has selected in the past. For example, it may display past outfits in calendar format, allowing the user to see which outfits were chosen on which days. The history display unit also displays ratings and feedback on outfits the user has selected in the past. For example, it may display outfits that have received high ratings or compliments in the past, allowing the user to refer to them. The history display unit also analyzes and visually displays the user's clothing selection trends and patterns. For example, it may display a graph showing the frequency of selection of a particular color or style. This allows the user to understand their clothing selection trends and make better choices.

[0046] The OMOTENASHI system may further include a community section that provides a community function related to users' clothing choices. The community section, for example, provides a platform for users to exchange opinions and advice about clothing with other users. For example, users can post their chosen outfits and receive comments and feedback from other users. The community section also provides a forum for users to share outfit ideas related to specific themes or events. For example, users can share outfit ideas suitable for weddings or parties. The community section also provides a ranking function that allows users to refer to other users' outfit choices. For example, the outfits that have received the most "likes" are displayed in a ranked format. This allows users to refer to the opinions and ideas of other users and make better outfit choices.

[0047] The OMOTENASHI system may further include a reminder unit that provides a reminder function for the user regarding clothing selection. The reminder unit may, for example, set a reminder for the user to select clothing appropriate for a specific event or situation. For example, the user may set a reminder the day before a business meeting and receive a notification to select appropriate clothing. The reminder unit may also provide reminders for the user to select clothing appropriate for the season or weather. For example, the reminder may be provided the day before a cold day, such as "It's going to be cold tomorrow, so select warm clothing." The reminder unit may also provide reminders for the user to select clothing appropriate for a specific trend or fashion event. For example, the reminder may be provided such as "It's fashion week this weekend, so select clothing that matches the trends." This allows the user to select appropriate clothing appropriate for important events or situations.

[0048] The OMOTENASHI system can further include a shopping support unit that provides shopping support functions related to the user's clothing selection. The shopping support unit, for example, suggests items related to the clothing selected by the user. For example, it suggests pants and accessories that go well with the shirt selected by the user. The shopping support unit also suggests items that can be purchased on an online shopping site based on the clothing selected by the user. For example, it suggests pants that go well with the shirt selected by the user from the online shopping site. The shopping support unit also provides discount and sale information related to the clothing selected by the user. For example, it notifies the user that the shirt selected by the user is on sale. This allows the user to more efficiently select and purchase clothing.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The clothing analysis unit analyzes the clothing selected by the user. For example, the clothing analysis unit receives images and text information of the clothing selected by the user as input, and the generation AI analyzes the color and type of the clothing based on that information. Step 2: The compliment generator generates compliments based on the color and type of clothing analyzed by the clothing analyzer. For example, if a user chooses a blue shirt, the generator AI might generate a compliment such as, "That blue shirt is very refreshing and brings out your charm." Step 3: The expression conversion unit converts the compliments generated by the compliment generation unit into human-like expressions. For example, the compliment "That blue shirt is very refreshing and brings out your charm" is expressed as "That blue shirt is really refreshing and lovely! It brings out your charm even more." Step 4: The feedback unit provides the user with the compliment converted by the expression conversion unit. For example, in response to the user's chosen outfit, the feedback unit may say, "That blue shirt is really refreshing and lovely! It really brings out your charm."

[0051] (Example 2) The OMOTENASHI system according to an embodiment of the present invention is a system that helps users to feel confident when choosing clothes that suit them. This system uses words that can be associated with the color and type of the clothes selected by the user to give compliments in a human-like manner, rather than a mechanical one. In this way, the OMOTENASHI system can help users to feel confident when choosing clothes that suit them.

[0052] The OMOTENASHI system according to the embodiment includes a clothing analysis unit, a compliment generation unit, an expression conversion unit, and a feedback unit. The clothing analysis unit analyzes the clothing selected by the user. For example, the clothing analysis unit receives as input image and text information of the clothing selected by the user, and the generation AI analyzes the color and type of the clothing based on that information. The compliment generation unit generates compliments based on the color and type of the clothing analyzed by the clothing analysis unit. For example, if the user selects a blue shirt, the generation AI generates a compliment such as, "That blue shirt is very refreshing and brings out your charm." The expression conversion unit converts the compliment generated by the compliment generation unit into a more human-like expression. For example, the compliment "That blue shirt is very refreshing and brings out your charm" can be expressed as, "That blue shirt is really refreshing and lovely! It really brings out your charm." The feedback unit feeds back the compliment converted by the expression conversion unit to the user. For example, in response to an outfit chosen by a user, the system may provide feedback such as, "That blue shirt is really refreshing and lovely! It really brings out your charm." In this way, the OMOTENASHI system according to the embodiment can help the user to feel more confident when choosing clothes that suit them. For example, the user can feel more confident about the outfit they have chosen, which makes choosing clothes more enjoyable. Furthermore, the user can respect themselves and improve their self-esteem.

[0053] The clothing analysis unit analyzes the user's past clothing selection history and learns preference trends, thereby improving analysis accuracy. In the clothing analysis unit, for example, the generation AI collects the user's past clothing selection history and analyzes color and style trends. For example, it learns the colors and designs of clothing the user has chosen in the past and reflects this in the next analysis. In addition, the clothing analysis unit identifies preference trends based on the user's past clothing selection history and improves analysis accuracy. For example, it prioritizes analysis of colors and styles that the user frequently chooses. In addition, the generation AI analyzes the user's past clothing selection history and tracks changes in preferences. For example, it learns the user's preferences, which change depending on the season and trends, and reflects this in the analysis. In this way, it is possible to learn the user's preference trends and improve analysis accuracy.

[0054] The clothing analysis unit can take into account the user's body shape and skin color and suggest the most suitable color and type of clothing. For example, the generation AI collects the user's body shape data and suggests the most suitable color and type of clothing based on that. For example, it analyzes silhouettes and designs that suit the body shape. The clothing analysis unit also analyzes the user's skin color and suggests clothing colors that match it. For example, it selects colors that suit skin tones. The generation AI also comprehensively analyzes the user's body shape and skin color and suggests the most suitable clothing combination. For example, it combines a silhouette that flatters the body shape with colors that match the skin color. This makes it possible to suggest the most suitable clothing based on the user's body shape and skin color.

[0055] The clothing analysis unit uses the emotion estimation function to analyze the user's emotions regarding the clothing selected and can adjust the analysis results based on those emotions. The clothing analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the clothing selected in real time. For example, it estimates emotions from the user's facial expressions and voice. The clothing analysis unit also adjusts the analysis results using a generation AI based on the user's emotion data. For example, it prioritizes analysis of clothing that evokes strong positive emotions. The clothing analysis unit also uses the emotion estimation function to track changes in the user's emotions regarding the clothing selected and reflects them in the analysis results. For example, it updates the analysis results if the user's emotions regarding the selected clothing change. This allows the analysis results to be adjusted based on the user's emotions.

[0056] The compliment generation unit can learn the user's past history of compliments and generate more personalized compliments. For example, the compliment generation unit uses a generation AI to collect the user's past history of compliments and generate personalized compliments based on that data. For example, it uses compliments that were well received in the past as a reference. The compliment generation unit also analyzes the user's past history of compliments and the generation AI learns their trends. For example, it identifies a tendency to prefer specific expressions and wording. The compliment generation unit also uses a generation AI to generate more individualized compliments based on the user's past history of compliments. For example, it uses expressions that match the user's preferences. This allows the compliment generation unit to learn the user's past history of compliments and generate more personalized compliments.

[0057] The compliment generation unit generates compliments that correspond to different cultures and languages, making it possible to accommodate international users. For example, the generation AI generates compliments that correspond to different cultures and languages. For example, compliments are generated in multiple languages, such as English, French, and Chinese. The compliment generation unit also learns the expressions and nuances of compliments from different cultures, and the generation AI generates compliments based on that. For example, the compliment generation unit reflects the characteristics of compliments from each culture. The compliment generation unit also builds a database of different languages ​​and cultures, and generates compliments based on that, so that the generation AI can accommodate international users. This makes it possible to generate compliments that correspond to different cultures and languages, making it possible to accommodate international users.

[0058] The compliment generation unit can use the emotion estimation function to generate compliments that match the user's current emotional state. For example, the compliment generation unit uses the emotion estimation function to analyze the user's current emotional state and generate compliments that match it. For example, if the user is happy, it generates more positive compliments. The compliment generation unit also analyzes the user's emotional state in real time, and the generation AI adjusts the compliments based on the results. For example, if the user is feeling down, it generates encouraging words. The compliment generation unit also uses the emotion estimation function to build a system that generates compliments that match the user's emotional state. For example, it changes the tone and content of the compliments based on the emotion score. This makes it possible to generate compliments that match the user's current emotional state.

[0059] The expression conversion unit can use natural language processing technology to generate more natural and fluent compliments. For example, the generation AI in the expression conversion unit uses natural language processing technology to generate more natural and fluent compliments. For example, it optimizes the selection of grammar and vocabulary. The expression conversion unit also uses natural language processing technology to allow the generation AI to understand the context of the compliment and generate more natural expressions. For example, it selects appropriate wording according to the context. The expression conversion unit also uses natural language processing technology to adjust the tone and rhythm of the compliment. For example, it generates compliments with more friendly expressions and rhythm. This allows the generation of more natural and fluent compliments.

[0060] The expression conversion unit can learn the user's speaking style and language usage and generate compliments that match it. For example, the generation AI of the expression conversion unit collects the user's speaking style and language usage and generates compliments based on that data. For example, it incorporates phrases and expressions that the user frequently uses. The expression conversion unit also analyzes the user's speaking style and language usage and the generation AI learns these tendencies. For example, it generates compliments that match a user who prefers formal language. The expression conversion unit also builds a system in which the generation AI generates more personalized compliments based on the user's speaking style and language usage. For example, it generates compliments with a tone and rhythm that matches the user's language usage. This makes it possible to generate compliments that match the user's speaking style and language usage.

[0061] The expression conversion unit can use the emotion estimation function to generate expressions that are in line with the user's emotions. For example, the expression conversion unit uses the emotion estimation function to analyze the user's emotional state and generate compliments that are in line with that state. For example, if the user is happy, it uses more positive expressions. The expression conversion unit also uses the user's emotion data to have the generation AI adjust the tone and content of the compliments. For example, if the user is feeling down, it generates words of encouragement. The expression conversion unit also uses the emotion estimation function to build a system that generates compliments that are in line with the user's emotions. For example, it changes the tone and content of the compliments based on the emotion score. This makes it possible to generate expressions that are in line with the user's emotions.

[0062] The feedback unit can learn from the user's feedback history and provide more effective feedback. For example, the feedback unit uses a generation AI to collect the user's past feedback history and provide effective feedback based on that data. For example, it refers to feedback that was well received in the past. The feedback unit also analyzes the user's feedback history and the generation AI learns its trends. For example, it identifies a tendency to prefer certain expressions and wording. The feedback unit also builds a system in which the generation AI provides more personalized feedback based on the user's feedback history. For example, it uses expressions that match the user's preferences. This allows the feedback unit to learn from the user's feedback history and provide more effective feedback.

[0063] The feedback unit can analyze the user's reaction to feedback in real time and adjust the feedback content. For example, the feedback unit uses a generation AI to analyze the user's reaction to feedback in real time and adjust the feedback content based on the results. For example, if the user has a positive reaction, the feedback unit can provide even more positive feedback. The feedback unit also collects the user's reaction to feedback in real time, and builds a system in which the generation AI adjusts the feedback content based on that data. For example, the feedback is updated every time the user's reaction changes. The feedback unit also uses a generation AI to analyze the user's reaction to feedback and optimize the feedback content based on the results. For example, the feedback can be provided to match the user's emotional state. This allows the feedback unit to analyze the user's reaction to feedback in real time and adjust the feedback content.

[0064] The feedback unit can use the emotion estimation function to provide feedback that matches the user's emotions. For example, the feedback unit uses the emotion estimation function to analyze the user's emotional state and provide feedback that matches it. For example, if the user is happy, it provides more positive feedback. Furthermore, the feedback unit uses the generation AI to adjust the feedback content based on the user's emotion data. For example, if the user is depressed, it provides encouraging feedback. Furthermore, the feedback unit uses the emotion estimation function to build a system that provides feedback that matches the user's emotions. For example, it changes the tone and content of the feedback based on the emotion score. This makes it possible to provide feedback that matches the user's emotions.

[0065] The feedback unit can collect and refer to feedback from other users regarding the clothing selected by the user. For example, the generation AI in the feedback unit collects feedback from other users regarding the clothing selected by the user and provides feedback based on that data. For example, it refers to feedback that other users have given high ratings. The feedback unit also analyzes other users' feedback, allowing the generation AI to learn their tendencies. For example, it identifies tendencies toward preferring certain expressions and wording. The feedback unit also builds a system in which the generation AI provides more diverse feedback based on the feedback of other users. For example, it combines multiple pieces of feedback to provide new feedback. This allows the generation AI to provide more diverse feedback by referring to the feedback of other users.

[0066] The feedback unit can provide visual and audio feedback related to the clothing selected by the user. For example, the generation AI analyzes visual data related to the clothing selected by the user and provides feedback based on that. For example, feedback based on the design or color of the clothing can be provided. The feedback unit can also analyze audio data related to the clothing selected by the user and the generation AI can provide feedback based on that. For example, feedback that matches the tone or rhythm of the audio can be provided. The feedback unit can also build a system in which the generation AI provides more multi-sensory feedback based on visual and audio data. For example, feedback that appeals to the visual and auditory senses can be provided. This makes it possible to provide visual and audio feedback.

[0067] The feedback unit can use the emotion estimation function to analyze the feedback pattern that the user finds most pleasing and provide feedback based on that. The feedback unit, for example, uses the emotion estimation function to analyze the feedback pattern that the user finds most pleasing. For example, it identifies optimal feedback based on past emotional response data. The feedback unit also analyzes the user's emotional response in real time, and the generation AI adjusts the feedback based on the results. For example, if the user is pleased, it provides more positive feedback. The feedback unit also uses the emotion estimation function to analyze the feedback pattern that the user finds most pleasing and builds a system that provides feedback based on that. For example, it changes the tone and content of the feedback based on the emotion score. This makes it possible to analyze the feedback pattern that the user finds most pleasing and provide feedback based on that.

[0068] The feedback unit learns from past data on the user's confidence improvement and can provide more effective support. For example, the generation AI collects past data on the user's confidence improvement and provides effective support based on that data. For example, it refers to support methods that have been well-received in the past. The feedback unit also analyzes past data on the user's confidence improvement and the generation AI learns the trends. For example, it identifies tendencies to prefer specific expressions and wording. The feedback unit also builds a system in which the generation AI provides more personalized support based on past data on the user's confidence improvement. For example, it uses expressions that match the user's preferences. In this way, the generation AI can learn from past data on the user's confidence improvement and provide more effective support.

[0069] The feedback unit can set goals for improving the user's self-confidence and monitor the degree of achievement. For example, the feedback unit constructs a system in which the generation AI sets goals for improving the user's self-confidence and monitors the degree of achievement. For example, the feedback unit tracks progress toward goals set by the user in real time. The feedback unit also sets goals for improving the user's self-confidence and the generation AI evaluates the degree of achievement. For example, the feedback unit provides feedback for goals achieved by the user. The feedback unit also maintains the user's motivation by having the generation AI set goals for improving the user's self-confidence and monitors the degree of achievement. For example, the feedback unit provides encouraging messages toward goal achievement. This makes it possible to set goals for improving the user's self-confidence and monitor the degree of achievement.

[0070] The feedback unit can use the emotion estimation function to provide self-confidence-boosting support that is sensitive to the user's emotions. For example, the feedback unit uses the emotion estimation function to analyze the user's emotional state and provide self-confidence-boosting support that is sensitive to that state. For example, if the user is feeling down, the feedback unit provides words of encouragement. Furthermore, the feedback unit uses the generation AI to adjust the content of self-confidence-boosting support based on the user's emotion data. For example, if the user is happy, the feedback unit provides more positive support. Furthermore, the feedback unit uses the emotion estimation function to build a system that provides self-confidence-boosting support that is sensitive to the user's emotions. For example, the tone and content of the support content can be changed based on the emotion score. This makes it possible to provide self-confidence-boosting support that is sensitive to the user's emotions.

[0071] The feedback unit can collect and refer to feedback from other users regarding the confidence improvement of the user's selected outfit. For example, the feedback unit allows the generation AI to collect feedback from other users regarding the confidence improvement of the user's selected outfit and provide assistance based on that data. For example, it refers to feedback that other users have given high ratings. The feedback unit also analyzes other users' feedback regarding the confidence improvement of the user, allowing the generation AI to learn the trends. For example, it identifies the tendency to prefer certain expressions and wording. The feedback unit also builds a system that provides more diverse assistance based on the generation AI's feedback regarding the confidence improvement of other users. For example, it combines multiple pieces of feedback to provide new assistance. In this way, more diverse assistance can be provided by referring to other users' feedback regarding the confidence improvement of other users.

[0072] The feedback unit can provide self-confidence-boosting support using visuals and audio related to the clothing selected by the user. For example, the generation AI analyzes visual data related to the clothing selected by the user and provides self-confidence-boosting support based on that data. For example, it provides support based on the design and color of the clothing. The feedback unit can also analyze audio data related to the clothing selected by the user and provide self-confidence-boosting support based on that data. For example, it can provide support that matches the tone and rhythm of the audio. The feedback unit can also build a system in which the generation AI provides more multi-sensory self-confidence-boosting support based on visual and audio data. For example, it can provide support that appeals to the visual and auditory senses. This makes it possible to provide self-confidence-boosting support using visuals and audio.

[0073] The feedback unit uses the emotion estimation function to analyze feedback patterns that make the user feel most confident and can provide assistance based on that. The feedback unit, for example, uses the emotion estimation function to analyze feedback patterns that make the user feel most confident. For example, it identifies optimal feedback based on past emotional response data. The feedback unit also analyzes the user's emotional responses in real time, and the generation AI adjusts the feedback based on the results. For example, if the user is confident, it provides more positive feedback. The feedback unit also uses the emotion estimation function to analyze feedback patterns that make the user feel most confident and builds a system that provides assistance based on that. For example, it changes the tone and content of the feedback based on the emotion score. This makes it possible to analyze feedback patterns that make the user feel most confident and provide assistance based on that.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The OMOTENASHI system may further include an advice unit that provides advice to the user regarding clothing selection. The advice unit provides advice based on the season and weather for the clothing selected by the user. For example, for clothing selected on a hot summer day, the advice unit may provide advice such as, "This shirt is cool and perfect for summer." The advice unit may also provide advice appropriate for a specific event or situation for the clothing selected by the user. For example, for clothing selected for a business meeting, the advice unit may provide advice such as, "This suit gives a professional impression." The advice unit may also provide advice based on the latest trends and fashion information for the clothing selected by the user. For example, the advice unit may provide advice such as, "This design is trending this season." This allows the user to select more appropriate clothing.

[0076] The OMOTENASHI system can further include a history display unit that displays the history of the user's clothing selections. The history display unit, for example, visually displays the history of clothing the user has selected in the past. For example, it may display past outfits in calendar format, allowing the user to see which outfits were chosen on which days. The history display unit also displays ratings and feedback on outfits the user has selected in the past. For example, it may display outfits that have received high ratings or compliments in the past, allowing the user to refer to them. The history display unit also analyzes and visually displays the user's clothing selection trends and patterns. For example, it may display a graph showing the frequency of selection of a particular color or style. This allows the user to understand their clothing selection trends and make better choices.

[0077] The OMOTENASHI system may further include a community section that provides a community function related to users' clothing choices. The community section, for example, provides a platform for users to exchange opinions and advice about clothing with other users. For example, users can post their chosen outfits and receive comments and feedback from other users. The community section also provides a forum for users to share outfit ideas related to specific themes or events. For example, users can share outfit ideas suitable for weddings or parties. The community section also provides a ranking function that allows users to refer to other users' outfit choices. For example, the outfits that have received the most "likes" are displayed in a ranked format. This allows users to refer to the opinions and ideas of other users and make better outfit choices.

[0078] The OMOTENASHI system may further include a reminder unit that provides a reminder function for the user regarding clothing selection. The reminder unit may, for example, set a reminder for the user to select clothing appropriate for a specific event or situation. For example, the user may set a reminder the day before a business meeting and receive a notification to select appropriate clothing. The reminder unit may also provide reminders for the user to select clothing appropriate for the season or weather. For example, the reminder may be provided the day before a cold day, such as "It's going to be cold tomorrow, so select warm clothing." The reminder unit may also provide reminders for the user to select clothing appropriate for a specific trend or fashion event. For example, the reminder may be provided such as "It's fashion week this weekend, so select clothing that matches the trends." This allows the user to select appropriate clothing appropriate for important events or situations.

[0079] The OMOTENASHI system can further include a shopping support unit that provides shopping support functions related to the user's clothing selection. The shopping support unit, for example, suggests items related to the clothing selected by the user. For example, it suggests pants and accessories that go well with the shirt selected by the user. The shopping support unit also suggests items that can be purchased on an online shopping site based on the clothing selected by the user. For example, it suggests pants that go well with the shirt selected by the user from the online shopping site. The shopping support unit also provides discount and sale information related to the clothing selected by the user. For example, it notifies the user that the shirt selected by the user is on sale. This allows the user to more efficiently select and purchase clothing.

[0080] The OMOTENASHI system can further include an emotion support unit that supports the user in selecting clothing based on their emotions. The emotion support unit, for example, analyzes the user's emotional state and makes clothing suggestions based on that. For example, if the user is feeling down, it suggests bright-colored clothing to lift their spirits. The emotion support unit also suggests clothing appropriate for a specific event or situation based on the user's emotional state. For example, if the user is feeling nervous, it suggests casual clothing that will help them relax. The emotion support unit also analyzes the user's emotional state in real time and adjusts the clothing suggestions based on that analysis. For example, it updates the suggestions every time the user's emotions change. This makes it possible to support the user in selecting the most appropriate clothing based on their emotions.

[0081] The OMOTENASHI system may further include an emotion adjustment unit that adjusts the compliments based on the user's emotions. The emotion adjustment unit, for example, analyzes the user's emotional state and adjusts the tone and content of the compliments based on that. For example, if the user is happy, it provides a more positive compliment. The emotion adjustment unit also provides compliments appropriate for a specific situation depending on the user's emotional state. For example, if the user is nervous, it provides a gentle compliment to relax the user. The emotion adjustment unit also analyzes the user's emotional state in real time and adjusts the content of the compliments based on that analysis. For example, it updates the compliments every time the user's emotions change. This allows the system to provide optimal compliments based on the user's emotions.

[0082] The OMOTENASHI system may further include an emotional feedback unit that adjusts feedback based on the user's emotions. The emotional feedback unit, for example, analyzes the user's emotional state and adjusts the tone and content of the feedback based on the analysis. For example, if the user is feeling down, it provides encouraging feedback. The emotional feedback unit also provides feedback appropriate for a specific situation according to the user's emotional state. For example, if the user is feeling nervous, it provides feedback to help the user relax. The emotional feedback unit also analyzes the user's emotional state in real time and adjusts the content of the feedback based on the analysis. For example, it updates the feedback every time the user's emotions change. This allows optimal feedback to be provided based on the user's emotions.

[0083] The OMOTENASHI system may further include an emotion / confidence improvement unit that provides self-confidence improvement support based on the user's emotions. The emotion / confidence improvement unit, for example, analyzes the user's emotional state and adjusts the content of the self-confidence improvement support based on the analysis. For example, if the user is feeling down, it provides words of encouragement. The emotion / confidence improvement unit also provides self-confidence improvement support appropriate for a specific situation according to the user's emotional state. For example, if the user is feeling nervous, it provides support to help the user relax. The emotion / confidence improvement unit also analyzes the user's emotional state in real time and adjusts the content of the self-confidence improvement support based on the analysis. For example, it updates the content of the support each time the user's emotions change. This makes it possible to provide optimal self-confidence improvement support based on the user's emotions.

[0084] The OMOTENASHI system can further include an emotion advice unit that provides advice based on the user's emotions. The emotion advice unit, for example, analyzes the user's emotional state and adjusts the tone and content of the advice based on that. For example, if the user is happy, more positive advice is provided. The emotion advice unit also provides advice appropriate for a specific situation depending on the user's emotional state. For example, if the user is nervous, advice to help the user relax is provided. The emotion advice unit also analyzes the user's emotional state in real time and adjusts the content of the advice based on that analysis. For example, the advice is updated every time the user's emotions change. This makes it possible to provide optimal advice based on the user's emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The clothing analysis unit analyzes the clothing selected by the user. For example, the clothing analysis unit receives images and text information of the clothing selected by the user as input, and the generation AI analyzes the color and type of the clothing based on that information. Step 2: The compliment generator generates compliments based on the color and type of clothing analyzed by the clothing analyzer. For example, if a user chooses a blue shirt, the generator AI might generate a compliment such as, "That blue shirt is very refreshing and brings out your charm." Step 3: The expression conversion unit converts the compliments generated by the compliment generation unit into human-like expressions. For example, the compliment "That blue shirt is very refreshing and brings out your charm" is expressed as "That blue shirt is really refreshing and lovely! It brings out your charm even more." Step 4: The feedback unit provides the user with the compliment converted by the expression conversion unit. For example, in response to the user's chosen outfit, the feedback unit may say, "That blue shirt is really refreshing and lovely! It really brings out your charm."

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] 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.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 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 clothing analysis unit that analyzes clothing selected by a user; a compliment generation unit that generates a compliment based on the color and type of clothing analyzed by the clothing analysis unit; an expression conversion unit that converts the compliment generated by the compliment generation unit into a human-like expression; a feedback unit that feeds back the compliment converted by the expression conversion unit to the user. A system characterized by:

2. The clothing analysis unit Analyze the user's past clothing selection history, learn preferences, and improve analysis accuracy 2. The system of claim 1.

3. The clothing analysis unit Taking into consideration the user's body type and skin color, the system suggests the most suitable color and type of clothing.

2. The system of claim 1.

4. The clothing analysis unit Analyzing the user's feelings about the clothing selected by the user and adjusting the analysis results based on the feelings.

2. The system of claim 1.

5. The compliment generation unit Learns the user's past compliment history to generate more personalized compliments 2. The system of claim 1.

6. The compliment generation unit Generate compliments that are culturally and linguistically appropriate for international users 2. The system of claim 1.

Images