system

Abstract

The system according to the embodiment aims to provide highly accurate personal color diagnosis and specific item suggestions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes a photo based on the information received by the reception unit. The identification unit identifies a personal color based on the analysis results obtained by the analysis unit. The suggestion unit suggests items based on the personal color identified by the identification unit. The provision unit provides a shopping list or a shopping list for the items suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of low accuracy in personal color diagnosis and a lack of specific item suggestions based on the diagnosis results.

[0005] The system according to the embodiment aims to provide highly accurate personal color diagnosis and specific item suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes a photo based on the information received by the reception unit. The identification unit identifies a personal color based on the analysis result by the analysis unit. The suggestion unit suggests items based on the personal color identified by the identification unit. The provision unit provides a shopping list or a shopping list of the items suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide highly accurate personal color diagnosis and specific item suggestions. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A personal color diagnosis system according to an embodiment of the present invention uses a chat generation AI to perform a personal color diagnosis. In this personal color diagnosis system, a user interactively answers questions about their characteristics and hobbies and uploads photos, and the generation AI performs a real-time diagnosis. Based on the diagnosis results, the system suggests not only colors that suit the user but also specific items (e.g., clothing, accessories, and cosmetics). It also provides a summary of recommended purchases and shopping lists. For example, a user interactively answers questions about their characteristics and hobbies and preferences. For example, they input their skin color, hair color, eye color, favorite colors and styles. This information is input into the generation AI. Next, the user uploads a photo. The generation AI analyzes the uploaded photo and diagnoses the user's skin color, hair color, eye color, and other characteristics in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. The generation AI then suggests colors that suit the user based on the information obtained through the interactive process and the results of the photo analysis. For example, it suggests clothing colors that match the user's skin color and accessory colors that match their hair color. Furthermore, the generation AI suggests specific items (e.g., clothing, accessories, and cosmetics). For example, it suggests clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. Finally, the generative AI provides a list of recommended purchases and shops. For example, it provides online shops where users can purchase items that suit them, as well as store information for specific brands. This system allows users to easily find out what colors and items suit them without having to undergo a professional diagnosis. In addition, by providing specific purchase items and shop lists, users can enjoy shopping easily. As a result, the personal color diagnosis system allows users to easily find out what colors and items suit them, allowing them to enjoy shopping easily.

[0029] A personal color diagnosis system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, the user's profile information, photos, hobbies, and preferences. The reception unit interactively receives, for example, the user's characteristics and hobbies. For example, the user interactively answers questions about their own characteristics and hobbies. For example, the user inputs information such as skin color, hair color, eye color, favorite colors and styles, etc. This information is input to the generation AI. The analysis unit analyzes photos based on the information received by the reception unit. Photo analysis includes, but is not limited to, image processing algorithms and analysis accuracy. The analysis unit analyzes uploaded photos and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. The identification unit identifies the personal color based on the results of the analysis by the analysis unit. Identifying a personal color includes, but is not limited to, criteria such as hue, saturation, and brightness. The identification unit identifies colors that suit the user based on, for example, interactive information and the results of photo analysis. For example, it identifies clothing colors that match the user's skin color and accessory colors that match the user's hair color. The suggestion unit suggests specific items based on the personal color identified by the identification unit. The suggested items include, but are not limited to, clothing, accessories, and cosmetics. For example, the suggestion unit suggests clothing, accessories, and cosmetics from a specific brand based on the user's personal color. The providing unit provides a shopping list and shopping recommendations for the items suggested by the suggestion unit. The provided shopping list and shopping recommendations include, but are not limited to, links to online shops and store information. For example, the providing unit provides a summary of recommended shopping lists and shopping recommendations. For example, it provides online shops where items that suit the user can be purchased and store information for specific brands.As a result, the personal color diagnosis system according to the embodiment allows the user to easily find out the colors and items that suit them, and to enjoy shopping easily.

[0030] The reception unit can interactively receive the user's characteristics, hobbies, and preferences. For example, the reception unit allows the user to interactively answer questions about their own characteristics, hobbies, and preferences. For example, the user inputs skin color, hair color, eye color, favorite colors and styles, etc. This information is input to the generation AI. By interactively receiving the user's characteristics, hobbies, and preferences, more detailed information can be collected. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit may provide an interactive interface for inputting the user's characteristics, hobbies, and preferences, and the generation AI may analyze the information and generate the next question.

[0031] The analysis unit can analyze uploaded photos and diagnose the user's skin color, hair color, and eye color. The analysis unit, for example, analyzes uploaded photos and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. In this way, by analyzing the photo, the user's skin color, hair color, eye color, etc. can be diagnosed in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input uploaded photos into the generation AI, which can analyze the photo and diagnose the user's skin color, hair color, and eye color.

[0032] The identification unit can identify a color that suits the user based on the information obtained interactively and the results of photo analysis. The identification unit, for example, identifies a color that suits the user based on the information obtained interactively and the results of photo analysis. For example, it identifies the color of clothes that matches the user's skin color or the color of accessories that matches the user's hair color. In this way, it is possible to identify a color that suits the user based on the information obtained interactively and the results of photo analysis. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit can input the information obtained interactively and the results of photo analysis into the generation AI, which can then identify a color that suits the user.

[0033] The suggestion unit can suggest items (clothes, accessories, cosmetics) based on the user's personal color. For example, the suggestion unit can suggest clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. This makes it possible to suggest specific items based on the user's personal color. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's personal color into the generation AI, which can then suggest items that suit the user.

[0034] The providing unit can provide a collection of recommended purchases and shopping lists. The providing unit, for example, provides a collection of recommended purchases and shopping lists. For example, it provides online shops where items that suit the user can be purchased, or store information for specific brands. By providing a collection of recommended purchases and shopping lists, the user can easily enjoy shopping. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input items that suit the user into a generation AI, which then generates recommended purchases and shopping lists.

[0035] The reception unit can analyze the user's past answer history and automatically generate optimal questions. The reception unit, for example, analyzes the user's past answer history and automatically generates optimal questions. For example, the reception unit automatically generates related questions based on the content of the user's past answers. The reception unit can also automatically generate questions that are likely to interest the user from the user's past answer history. The reception unit can also analyze the user's past answer history and automatically generate new questions that do not overlap. This enables efficient information collection by generating optimal questions based on the user's past answer history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past answer history into a generation AI, which can then automatically generate optimal questions.

[0036] The reception unit can generate follow-up questions and collect information based on the user's answers. The reception unit generates follow-up questions and collects information based on the user's answers, for example. For example, if the user mentions a specific color, detailed questions related to the color can be generated in real time. Also, if the user answers that they like a specific style, questions related to the style can be generated in real time. Also, if the user answers that they like a specific brand, questions related to the brand can be generated in real time. In this way, detailed information can be collected by generating follow-up questions in real time based on the user's answers. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's answers to a generation AI, which can generate follow-up questions.

[0037] The reception unit can add questions about regional fashion trends based on the user's geographical location information. The reception unit adds questions about regional fashion trends based on the user's geographical location information, for example. For example, if the user lives in a specific region, the reception unit adds questions about the fashion trends of that region. Also, if the user is traveling, questions about the fashion trends of the region the user is visiting can be added. Also, if the user is planning to move, questions about the fashion trends of the new region can be added. In this way, questions about regional fashion trends can be added by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then generate questions about regional fashion trends.

[0038] The reception unit can analyze the user's social media activity and generate related questions. The reception unit, for example, analyzes the user's social media activity and generates related questions. For example, the reception unit can analyze photos shared by the user on social media and generate questions related to the photos. Questions can also be generated based on brands and influencers the user follows on social media. Questions can also be generated based on posts the user has "liked" on social media. In this way, related questions can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity into the generation AI, which then generates related questions.

[0039] The analysis unit can improve the accuracy of analysis by taking into account different lighting conditions and backgrounds when analyzing photos. For example, if a photo is dark, the analysis unit can adjust the brightness using image processing technology to improve the accuracy of analysis. Furthermore, if the background of the photo is complex, the analysis unit can automatically remove the background to improve the accuracy of analysis. Furthermore, if the photo is backlit, backlight correction can be performed to improve the accuracy of analysis. This improves the accuracy of photo analysis by taking into account different lighting conditions and backgrounds. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the lighting conditions and background of the photo into the generation AI, which then performs processing to improve the accuracy of analysis.

[0040] When analyzing a photo, the analysis unit can correct the analysis results based on the user's facial expression and posture. For example, if the user is smiling, the analysis unit corrects the analysis results taking into account the user's facial expression. Furthermore, if the user is facing at an angle, the analysis unit can correct the analysis results taking into account their posture. Furthermore, if the user has their eyes closed, the analysis results can be corrected taking into account whether their eyes are open or closed. This improves the accuracy of the analysis results by taking into account the user's facial expression and posture. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's facial expression and posture into the generation AI, which can correct the analysis results.

[0041] When analyzing photos, the analysis unit can reflect region-specific skin color and hair color based on the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can reflect the skin color and hair color of that region in the analysis. Furthermore, if the user is traveling, the analysis unit can reflect the skin color and hair color of the region the user is visiting in the analysis. Furthermore, if the user is planning to move, the analysis unit can reflect the skin color and hair color of the new region in the analysis. This enables analysis that reflects region-specific skin color and hair color by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then reflect the region-specific skin color and hair color.

[0042] When analyzing photos, the analysis unit can analyze the user's social media activity and improve the analysis accuracy by referring to related images. For example, the analysis unit analyzes photos shared by the user on social media and improves the analysis accuracy by referring to the photos. The analysis unit can also improve the analysis accuracy by referring to images of brands and influencers the user follows on social media. The analysis accuracy can also improve by referring to images of posts that the user has "liked" on social media. In this way, the analysis accuracy is improved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into a generation AI, which can then improve the analysis accuracy by referring to related images.

[0043] When identifying a color, the identification unit can improve the accuracy of identification by referring to the user's past fashion history. The identification unit, for example, identifies a color by referring to the colors of items purchased by the user in the past. The identification unit can also identify a color by referring to the colors of items worn by the user in the past. The identification unit can also identify a color by referring to the colors of cosmetics that the user has favored in the past. In this way, the accuracy of color identification is improved by referring to the user's past fashion history. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit inputs the user's past fashion history into the generation AI, which can improve the accuracy of color identification.

[0044] When identifying a color, the identification unit can correct the identification result based on the user's current fashion trends. For example, the identification unit performs color identification taking into account the user's currently preferred fashion style. The identification unit can also perform color identification taking into account the colors of cosmetics the user is currently using. The identification unit can also perform color identification taking into account the colors of items the user is currently wearing. This improves the accuracy of the identification result by taking into account the user's current fashion trends. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit can input the user's current fashion trends into the generation AI, which can correct the identification result.

[0045] The identification unit can reflect region-specific color trends based on the user's geographical location information when identifying a color. For example, if the user lives in a specific region, the identification unit can identify a color by reflecting the color trends of that region. Furthermore, if the user is traveling, the identification unit can identify a color by reflecting the color trends of the region the user is visiting. Furthermore, if the user is planning to move, the identification unit can identify a color by reflecting the color trends of the new region. In this way, by taking the user's geographical location information into consideration, color identification that reflects region-specific color trends becomes possible. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input the user's geographical location information into the generation AI, which can then reflect the region-specific color trends.

[0046] When identifying a color, the identification unit can analyze the user's social media activity and refer to related information to improve the identification accuracy. For example, the identification unit can analyze photos shared by the user on social media and identify the color by referring to the photos. The identification unit can also identify the color by referring to information about brands and influencers the user follows on social media. The identification unit can also identify the color by referring to information about posts the user has "liked" on social media. In this way, the identification accuracy is improved by analyzing the user's social media activity. Some or all of the above-described processing in the identification unit can be performed using, or without, a generation AI. For example, the identification unit can input the user's social media activity into the generation AI, which can then refer to related information to improve the identification accuracy.

[0047] When making a suggestion, the suggestion unit can suggest optimal items by referring to the user's past purchase history. For example, the suggestion unit can suggest related items by referring to the color and style of items previously purchased by the user. The suggestion unit can also suggest new items of the same brand by referring to brands previously purchased by the user. The suggestion unit can also analyze the frequency of use of items previously purchased by the user and suggest optimal items. In this way, optimal items can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past purchase history into the generation AI, which then suggests optimal items.

[0048] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current fashion trends. For example, the suggestion unit can suggest related items taking into account the user's currently preferred fashion style. The suggestion unit can also suggest related items taking into account the color of cosmetics the user is currently using. The suggestion unit can also suggest related items taking into account the color and style of items the user is currently wearing. This enables more appropriate suggestions by taking into account the user's current fashion trends. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current fashion trends into the generation AI, which can then customize the suggestion content.

[0049] When making a suggestion, the suggestion unit can suggest region-specific items based on the user's geographical location information. For example, if the user lives in a specific region, the suggestion unit can suggest items that match the fashion trends of that region. Also, if the user is traveling, the suggestion unit can suggest items that match the fashion trends of the region the user is visiting. Also, if the user is planning to move, the suggestion unit can suggest items that match the fashion trends of the new region. In this way, region-specific items can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI, which can then suggest region-specific items.

[0050] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related items. For example, the suggestion unit can analyze photos shared by the user on social media and suggest items related to the photos. The suggestion unit can also suggest items based on brands or influencers the user follows on social media. The suggestion unit can also suggest items based on posts the user has "liked" on social media. In this way, related items can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's social media activity into a generation AI, which then suggests related items.

[0051] When providing the list, the providing unit can provide an optimal shopping list by referring to the user's past purchase history. For example, the providing unit can provide a related shopping list by referring to the brands of items the user has previously purchased. The providing unit can also provide a related shopping list by referring to the categories of items the user has previously purchased. The providing unit can also analyze the frequency of use of items the user has previously purchased and provide an optimal shopping list. In this way, the optimal shopping list can be provided by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past purchase history into a generation AI, which then provides an optimal shopping list.

[0052] The providing unit can customize the provided content based on the user's current fashion trends when providing the content. For example, the providing unit can provide a relevant shop list taking into account the user's current preferred fashion style. The providing unit can also provide a relevant shop list taking into account the brand of cosmetics the user is currently using. The providing unit can also provide a relevant shop list taking into account the brand of items the user is currently wearing. This makes it possible to provide a more appropriate shop list by taking into account the user's current fashion trends. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's current fashion trends into the generation AI, which can then customize the provided content.

[0053] The providing unit can provide a region-specific shop list based on the user's geographical location information at the time of providing. For example, if the user lives in a specific region, the providing unit can provide a shop list for that region. Also, if the user is traveling, the providing unit can provide a shop list for the region the user is visiting. Also, if the user is planning to move, the providing unit can provide a shop list for the new region. In this way, a region-specific shop list can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then provide a region-specific shop list.

[0054] At the time of providing, the providing unit can analyze the user's social media activity and provide a related shop list. For example, the providing unit can analyze photos shared by the user on social media and provide a shop list related to the photos. The providing unit can also provide a shop list based on brands and influencers the user follows on social media. The providing unit can also provide a shop list based on posts the user has "liked" on social media. In this way, a related shop list can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's social media activity into a generation AI, which then provides a related shop list.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past answer history and automatically generate optimal questions. For example, it can automatically generate related questions based on the content of the user's past answers. It can also automatically generate questions that are likely to interest the user from the user's past answer history. It can also analyze the user's past answer history and automatically generate new questions that do not overlap. This allows for efficient information collection by generating optimal questions based on the user's past answer history.

[0057] When analyzing photos, the analysis unit can improve analysis accuracy by taking into account different lighting conditions and backgrounds. For example, if a photo is dark, image processing technology can be used to adjust the brightness to improve analysis accuracy. Also, if the photo has a complex background, the background can be automatically removed to improve analysis accuracy. Furthermore, if the photo is backlit, backlight correction can be performed to improve analysis accuracy. This improves the accuracy of photo analysis by taking into account different lighting conditions and backgrounds.

[0058] When identifying a color, the identification unit can improve the accuracy of identification by referring to the user's past fashion history. For example, the identification unit can identify a color by referring to the colors of items the user has previously purchased. The identification unit can also identify a color by referring to the colors of items the user has previously worn. The identification unit can also identify a color by referring to the colors of cosmetics the user has previously preferred. In this way, the accuracy of color identification can be improved by referring to the user's past fashion history.

[0059] When making a suggestion, the suggestion unit can suggest the most suitable item by referring to the user's past purchase history. For example, it can suggest related items by referring to the color and style of items the user has previously purchased. It can also suggest new items of the same brand by referring to brands the user has previously purchased. It can also analyze the frequency of use of items the user has previously purchased and suggest the most suitable item. In this way, it is possible to suggest the most suitable item by referring to the user's past purchase history.

[0060] The providing unit can provide a region-specific shop list based on the user's geographical location information at the time of providing. For example, if the user lives in a specific region, a list of shops in that region can be provided. Also, if the user is traveling, a list of shops in the region the user is visiting can be provided. Furthermore, if the user is planning to move, a list of shops in the new region can be provided. In this way, a region-specific shop list can be provided by taking the user's geographical location information into consideration.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives information from the user. The information from the user includes, for example, the user's profile information, photos, hobbies, and preferences. The reception unit interactively receives the user's characteristics, hobbies, and preferences. For example, the user interactively answers questions about their own characteristics, hobbies, and preferences, and inputs their skin color, hair color, eye color, favorite colors, and styles. Step 2: The analysis unit analyzes the photo based on the information received by the reception unit. The analysis unit analyzes the uploaded photo and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. Step 3: The identification unit identifies a personal color based on the results of the analysis by the analysis unit. The identification unit identifies colors that suit the user based on the information obtained through the interactive process and the results of the photo analysis. For example, it identifies clothing colors that match the user's skin color or accessory colors that match the user's hair color. Step 4: The suggestion unit suggests specific items based on the personal color identified by the identification unit. The suggested items include, for example, clothes, accessories, cosmetics, etc. The suggestion unit suggests clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. Step 5: The providing unit provides a shopping list or a list of items to purchase that are suggested by the suggestion unit. The provided shopping list or list of items to purchase includes, for example, links to online shops and store information. The providing unit compiles and provides a list of recommended shopping items or shops. For example, it provides online shops where items that suit the user can be purchased, store information for specific brands, etc.

[0063] (Example 2) A personal color diagnosis system according to an embodiment of the present invention uses a chat generation AI to perform a personal color diagnosis. In this personal color diagnosis system, a user interactively answers questions about their characteristics and hobbies and uploads photos, and the generation AI performs a real-time diagnosis. Based on the diagnosis results, the system suggests not only colors that suit the user but also specific items (e.g., clothing, accessories, and cosmetics). It also provides a summary of recommended purchases and shopping lists. For example, a user interactively answers questions about their characteristics and hobbies and preferences. For example, they input their skin color, hair color, eye color, favorite colors and styles. This information is input into the generation AI. Next, the user uploads a photo. The generation AI analyzes the uploaded photo and diagnoses the user's skin color, hair color, eye color, and other characteristics in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. The generation AI then suggests colors that suit the user based on the information obtained through the interactive process and the results of the photo analysis. For example, it suggests clothing colors that match the user's skin color and accessory colors that match their hair color. Furthermore, the generation AI suggests specific items (e.g., clothing, accessories, and cosmetics). For example, it suggests clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. Finally, the generative AI provides a list of recommended purchases and shops. For example, it provides online shops where users can purchase items that suit them, as well as store information for specific brands. This system allows users to easily find out what colors and items suit them without having to undergo a professional diagnosis. In addition, by providing specific purchase items and shop lists, users can enjoy shopping easily. As a result, the personal color diagnosis system allows users to easily find out what colors and items suit them, allowing them to enjoy shopping easily.

[0064] A personal color diagnosis system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, the user's profile information, photos, hobbies, and preferences. The reception unit interactively receives, for example, the user's characteristics and hobbies. For example, the user interactively answers questions about their own characteristics and hobbies. For example, the user inputs information such as skin color, hair color, eye color, favorite colors and styles, etc. This information is input to the generation AI. The analysis unit analyzes photos based on the information received by the reception unit. Photo analysis includes, but is not limited to, image processing algorithms and analysis accuracy. The analysis unit analyzes uploaded photos and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. The identification unit identifies the personal color based on the results of the analysis by the analysis unit. Identifying a personal color includes, but is not limited to, criteria such as hue, saturation, and brightness. The identification unit identifies colors that suit the user based on, for example, interactive information and the results of photo analysis. For example, it identifies clothing colors that match the user's skin color and accessory colors that match the user's hair color. The suggestion unit suggests specific items based on the personal color identified by the identification unit. The suggested items include, but are not limited to, clothing, accessories, and cosmetics. For example, the suggestion unit suggests clothing, accessories, and cosmetics from a specific brand based on the user's personal color. The providing unit provides a shopping list and shopping recommendations for the items suggested by the suggestion unit. The provided shopping list and shopping recommendations include, but are not limited to, links to online shops and store information. For example, the providing unit provides a summary of recommended shopping lists and shopping recommendations. For example, it provides online shops where items that suit the user can be purchased and store information for specific brands.As a result, the personal color diagnosis system according to the embodiment allows the user to easily find out the colors and items that suit them, and to enjoy shopping easily.

[0065] The reception unit can interactively receive the user's characteristics, hobbies, and preferences. For example, the reception unit allows the user to interactively answer questions about their own characteristics, hobbies, and preferences. For example, the user inputs skin color, hair color, eye color, favorite colors and styles, etc. This information is input to the generation AI. By interactively receiving the user's characteristics, hobbies, and preferences, more detailed information can be collected. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit may provide an interactive interface for inputting the user's characteristics, hobbies, and preferences, and the generation AI may analyze the information and generate the next question.

[0066] The analysis unit can analyze uploaded photos and diagnose the user's skin color, hair color, and eye color. The analysis unit, for example, analyzes uploaded photos and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. In this way, by analyzing the photo, the user's skin color, hair color, eye color, etc. can be diagnosed in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input uploaded photos into the generation AI, which can analyze the photo and diagnose the user's skin color, hair color, and eye color.

[0067] The identification unit can identify a color that suits the user based on the information obtained interactively and the results of photo analysis. The identification unit, for example, identifies a color that suits the user based on the information obtained interactively and the results of photo analysis. For example, it identifies the color of clothes that matches the user's skin color or the color of accessories that matches the user's hair color. In this way, it is possible to identify a color that suits the user based on the information obtained interactively and the results of photo analysis. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit can input the information obtained interactively and the results of photo analysis into the generation AI, which can then identify a color that suits the user.

[0068] The suggestion unit can suggest items (clothes, accessories, cosmetics) based on the user's personal color. For example, the suggestion unit can suggest clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. This makes it possible to suggest specific items based on the user's personal color. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's personal color into the generation AI, which can then suggest items that suit the user.

[0069] The providing unit can provide a collection of recommended purchases and shopping lists. The providing unit, for example, provides a collection of recommended purchases and shopping lists. For example, it provides online shops where items that suit the user can be purchased, or store information for specific brands. By providing a collection of recommended purchases and shopping lists, the user can easily enjoy shopping. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input items that suit the user into a generation AI, which then generates recommended purchases and shopping lists.

[0070] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, if the user is nervous, the reception unit can start with simple questions to relax the user and gradually move on to more detailed questions. Also, if the user is relaxed, detailed questions can be asked first to efficiently collect information. Also, if the user is in a hurry, important questions can be prioritized to quickly collect information. This enables more appropriate information collection by adjusting the order and content of questions based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotions into the generation AI, which can then adjust the order and content of questions.

[0071] The reception unit can analyze the user's past answer history and automatically generate optimal questions. The reception unit, for example, analyzes the user's past answer history and automatically generates optimal questions. For example, the reception unit automatically generates related questions based on the content of the user's past answers. The reception unit can also automatically generate questions that are likely to interest the user from the user's past answer history. The reception unit can also analyze the user's past answer history and automatically generate new questions that do not overlap. This enables efficient information collection by generating optimal questions based on the user's past answer history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past answer history into a generation AI, which can then automatically generate optimal questions.

[0072] The reception unit can generate follow-up questions and collect information based on the user's answers. The reception unit generates follow-up questions and collects information based on the user's answers, for example. For example, if the user mentions a specific color, detailed questions related to the color can be generated in real time. Also, if the user answers that they like a specific style, questions related to the style can be generated in real time. Also, if the user answers that they like a specific brand, questions related to the brand can be generated in real time. In this way, detailed information can be collected by generating follow-up questions in real time based on the user's answers. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's answers to a generation AI, which can generate follow-up questions.

[0073] The reception unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the difficulty of questions based on the estimated user emotions. For example, if the user is nervous, the reception unit can start with easy questions and gradually increase the difficulty. Also, if the user is relaxed, more difficult questions can be asked first. Also, if the user is in a hurry, important questions can be prioritized and their difficulty adjusted. This allows for more appropriate information collection by adjusting the difficulty of questions based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotions into the generation AI, and the generation AI can adjust the difficulty of the questions.

[0074] The reception unit can add questions about regional fashion trends based on the user's geographical location information. The reception unit adds questions about regional fashion trends based on the user's geographical location information, for example. For example, if the user lives in a specific region, the reception unit adds questions about the fashion trends of that region. Also, if the user is traveling, questions about the fashion trends of the region the user is visiting can be added. Also, if the user is planning to move, questions about the fashion trends of the new region can be added. In this way, questions about regional fashion trends can be added by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then generate questions about regional fashion trends.

[0075] The reception unit can analyze the user's social media activity and generate related questions. The reception unit, for example, analyzes the user's social media activity and generates related questions. For example, the reception unit can analyze photos shared by the user on social media and generate questions related to the photos. Questions can also be generated based on brands and influencers the user follows on social media. Questions can also be generated based on posts the user has "liked" on social media. In this way, related questions can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity into the generation AI, which then generates related questions.

[0076] The analysis unit can estimate the user's emotions and change the accuracy of photo analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the accuracy of photo analysis based on the estimated user emotions. For example, if the user is nervous, the accuracy of photo analysis can be increased to improve reliability. Alternatively, if the user is relaxed, the accuracy of photo analysis can be maintained at normal levels. Alternatively, if the user is in a hurry, the accuracy of photo analysis can be adjusted to provide results more quickly. This allows for more reliable analysis results to be provided by adjusting the accuracy of photo analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotions into the generation AI, which can then adjust the accuracy of photo analysis.

[0077] The analysis unit can improve the accuracy of analysis by taking into account different lighting conditions and backgrounds when analyzing photos. For example, if a photo is dark, the analysis unit can adjust the brightness using image processing technology to improve the accuracy of analysis. Furthermore, if the background of the photo is complex, the analysis unit can automatically remove the background to improve the accuracy of analysis. Furthermore, if the photo is backlit, backlight correction can be performed to improve the accuracy of analysis. This improves the accuracy of photo analysis by taking into account different lighting conditions and backgrounds. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the lighting conditions and background of the photo into the generation AI, which then performs processing to improve the accuracy of analysis.

[0078] When analyzing a photo, the analysis unit can correct the analysis results based on the user's facial expression and posture. For example, if the user is smiling, the analysis unit corrects the analysis results taking into account the user's facial expression. Furthermore, if the user is facing at an angle, the analysis unit can correct the analysis results taking into account their posture. Furthermore, if the user has their eyes closed, the analysis results can be corrected taking into account whether their eyes are open or closed. This improves the accuracy of the analysis results by taking into account the user's facial expression and posture. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's facial expression and posture into the generation AI, which can correct the analysis results.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotions into the generation AI, which can then adjust the display method of the analysis results.

[0080] When analyzing photos, the analysis unit can reflect region-specific skin color and hair color based on the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can reflect the skin color and hair color of that region in the analysis. Furthermore, if the user is traveling, the analysis unit can reflect the skin color and hair color of the region the user is visiting in the analysis. Furthermore, if the user is planning to move, the analysis unit can reflect the skin color and hair color of the new region in the analysis. This enables analysis that reflects region-specific skin color and hair color by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then reflect the region-specific skin color and hair color.

[0081] When analyzing photos, the analysis unit can analyze the user's social media activity and improve the analysis accuracy by referring to related images. For example, the analysis unit analyzes photos shared by the user on social media and improves the analysis accuracy by referring to the photos. The analysis unit can also improve the analysis accuracy by referring to images of brands and influencers the user follows on social media. The analysis accuracy can also improve by referring to images of posts that the user has "liked" on social media. In this way, the analysis accuracy is improved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into a generation AI, which can then improve the analysis accuracy by referring to related images.

[0082] The identification unit can estimate the user's emotions and change the color identification criteria based on the estimated user emotions. For example, the identification unit can estimate the user's emotions and adjust the color identification criteria based on the estimated user emotions. For example, if the user is nervous, the color identification criteria can be made stricter to improve reliability. Alternatively, if the user is relaxed, the color identification criteria can be kept normal. Alternatively, if the user is in a hurry, the color identification criteria can be adjusted to provide quick results. This enables more reliable color identification by adjusting the color identification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the user's emotions into the generation AI, which can then adjust the color identification criteria.

[0083] When identifying a color, the identification unit can improve the accuracy of identification by referring to the user's past fashion history. The identification unit, for example, identifies a color by referring to the colors of items purchased by the user in the past. The identification unit can also identify a color by referring to the colors of items worn by the user in the past. The identification unit can also identify a color by referring to the colors of cosmetics that the user has favored in the past. In this way, the accuracy of color identification is improved by referring to the user's past fashion history. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit inputs the user's past fashion history into the generation AI, which can improve the accuracy of color identification.

[0084] When identifying a color, the identification unit can correct the identification result based on the user's current fashion trends. For example, the identification unit performs color identification taking into account the user's currently preferred fashion style. The identification unit can also perform color identification taking into account the colors of cosmetics the user is currently using. The identification unit can also perform color identification taking into account the colors of items the user is currently wearing. This improves the accuracy of the identification result by taking into account the user's current fashion trends. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI, for example. For example, the identification unit can input the user's current fashion trends into the generation AI, which can correct the identification result.

[0085] The identification unit can estimate the user's emotions and adjust the display method of the identified results based on the estimated user emotions. For example, the identification unit can estimate the user's emotions and adjust the display method of the identified results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the identified results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the user's emotions into the generation AI, and the generation AI can adjust the display method of the identified results.

[0086] The identification unit can reflect region-specific color trends based on the user's geographical location information when identifying a color. For example, if the user lives in a specific region, the identification unit can identify a color by reflecting the color trends of that region. Furthermore, if the user is traveling, the identification unit can identify a color by reflecting the color trends of the region the user is visiting. Furthermore, if the user is planning to move, the identification unit can identify a color by reflecting the color trends of the new region. In this way, by taking the user's geographical location information into consideration, color identification that reflects region-specific color trends becomes possible. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input the user's geographical location information into the generation AI, which can then reflect the region-specific color trends.

[0087] When identifying a color, the identification unit can analyze the user's social media activity and refer to related information to improve the identification accuracy. For example, the identification unit can analyze photos shared by the user on social media and identify the color by referring to the photos. The identification unit can also identify the color by referring to information about brands and influencers the user follows on social media. The identification unit can also identify the color by referring to information about posts the user has "liked" on social media. In this way, the identification accuracy is improved by analyzing the user's social media activity. Some or all of the above-described processing in the identification unit can be performed using, or without, a generation AI. For example, the identification unit can input the user's social media activity into the generation AI, which can then refer to related information to improve the identification accuracy.

[0088] The suggestion unit can estimate the user's emotions and change the way suggestions are expressed based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This enables more appropriate suggestions to be made by adjusting the way suggestions are expressed based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotions into the generation AI, which can then adjust the way suggestions are expressed.

[0089] When making a suggestion, the suggestion unit can suggest optimal items by referring to the user's past purchase history. For example, the suggestion unit can suggest related items by referring to the color and style of items previously purchased by the user. The suggestion unit can also suggest new items of the same brand by referring to brands previously purchased by the user. The suggestion unit can also analyze the frequency of use of items previously purchased by the user and suggest optimal items. In this way, optimal items can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past purchase history into the generation AI, which then suggests optimal items.

[0090] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current fashion trends. For example, the suggestion unit can suggest related items taking into account the user's currently preferred fashion style. The suggestion unit can also suggest related items taking into account the color of cosmetics the user is currently using. The suggestion unit can also suggest related items taking into account the color and style of items the user is currently wearing. This enables more appropriate suggestions by taking into account the user's current fashion trends. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current fashion trends into the generation AI, which can then customize the suggestion content.

[0091] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. For example, if the user is nervous, important suggestions can be prioritized. Also, if the user is relaxed, detailed suggestions can be prioritized. Also, if the user is in a hurry, suggestions that focus on the main points can be prioritized. This enables more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotions into the generation AI, which can then determine the priority of suggestions.

[0092] When making a suggestion, the suggestion unit can suggest region-specific items based on the user's geographical location information. For example, if the user lives in a specific region, the suggestion unit can suggest items that match the fashion trends of that region. Also, if the user is traveling, the suggestion unit can suggest items that match the fashion trends of the region the user is visiting. Also, if the user is planning to move, the suggestion unit can suggest items that match the fashion trends of the new region. In this way, region-specific items can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI, which can then suggest region-specific items.

[0093] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related items. For example, the suggestion unit can analyze photos shared by the user on social media and suggest items related to the photos. The suggestion unit can also suggest items based on brands or influencers the user follows on social media. The suggestion unit can also suggest items based on posts the user has "liked" on social media. In this way, related items can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's social media activity into a generation AI, which then suggests related items.

[0094] The providing unit can estimate the user's emotions and change the display method of the information to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the information to be provided based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotions into the generation AI and adjust the display method of the information provided by the generation AI.

[0095] When providing the list, the providing unit can provide an optimal shopping list by referring to the user's past purchase history. For example, the providing unit can provide a related shopping list by referring to the brands of items the user has previously purchased. The providing unit can also provide a related shopping list by referring to the categories of items the user has previously purchased. The providing unit can also analyze the frequency of use of items the user has previously purchased and provide an optimal shopping list. In this way, the optimal shopping list can be provided by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past purchase history into a generation AI, which then provides an optimal shopping list.

[0096] The providing unit can customize the provided content based on the user's current fashion trends when providing the content. For example, the providing unit can provide a relevant shop list taking into account the user's current preferred fashion style. The providing unit can also provide a relevant shop list taking into account the brand of cosmetics the user is currently using. The providing unit can also provide a relevant shop list taking into account the brand of items the user is currently wearing. This makes it possible to provide a more appropriate shop list by taking into account the user's current fashion trends. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's current fashion trends into the generation AI, which can then customize the provided content.

[0097] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information to be provided based on the estimated user emotions. For example, if the user is nervous, important information can be provided preferentially. Also, if the user is relaxed, detailed information can be provided preferentially. Also, if the user is in a hurry, information that covers the main points can be provided preferentially. This enables more appropriate information to be provided by determining the priority of information to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotions into the generation AI and determine the priority of information to be provided by the generation AI.

[0098] The providing unit can provide a region-specific shop list based on the user's geographical location information at the time of providing. For example, if the user lives in a specific region, the providing unit can provide a shop list for that region. Also, if the user is traveling, the providing unit can provide a shop list for the region the user is visiting. Also, if the user is planning to move, the providing unit can provide a shop list for the new region. In this way, a region-specific shop list can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then provide a region-specific shop list.

[0099] At the time of providing, the providing unit can analyze the user's social media activity and provide a related shop list. For example, the providing unit can analyze photos shared by the user on social media and provide a shop list related to the photos. The providing unit can also provide a shop list based on brands and influencers the user follows on social media. The providing unit can also provide a shop list based on posts the user has "liked" on social media. In this way, a related shop list can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's social media activity into a generation AI, which then provides a related shop list. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the user's profile information, photos, and hobbies and preferences. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes uploaded photos to diagnose the user's skin color, hair color, eye color, etc. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies colors that suit the user based on information obtained interactively and the results of photo analysis. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and suggests specific items based on the user's personal color. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides a shopping list and shopping items for the suggested items. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's profile information, photos, and hobbies and preferences. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes uploaded photos to diagnose the user's skin color, hair color, eye color, etc. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies colors that suit the user based on information obtained interactively and the results of photo analysis. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and suggests specific items based on the user's personal color. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a shopping list and shopping items for the suggested items. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's profile information, photos, and hobbies and preferences. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes uploaded photos to diagnose the user's skin color, hair color, eye color, etc. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies colors that suit the user based on information obtained interactively and the results of photo analysis. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and suggests specific items based on the user's personal color. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides a shopping list and shopping items for the suggested items. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts the user's profile information, photos, and hobbies and preferences. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes uploaded photos to diagnose the user's skin color, hair color, eye color, etc. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies colors that suit the user based on information obtained interactively and the results of photo analysis. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and suggests specific items based on the user's personal color. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides a shopping list and shopping items for the suggested items.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated emotions. For example, if the user is nervous, the reception unit can start with simple questions to relax the user and gradually move on to more detailed questions. Also, if the user is relaxed, the reception unit can ask detailed questions from the beginning to efficiently collect information. Furthermore, if the user is in a hurry, the reception unit can prioritize important questions to quickly collect information. In this way, by adjusting the order and content of questions based on the user's emotions, more appropriate information collection is possible.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of photo analysis based on the estimated emotions. For example, if the user is nervous, the accuracy of photo analysis can be increased to improve reliability. Alternatively, if the user is relaxed, the accuracy of photo analysis can be maintained at normal levels. Furthermore, if the user is in a hurry, the accuracy of photo analysis can be adjusted to provide results quickly. In this way, by adjusting the accuracy of photo analysis based on the user's emotions, more reliable analysis results can be provided.

[0103] The identification unit can estimate the user's emotions and adjust the color identification criteria based on the estimated emotions. For example, if the user is nervous, the color identification criteria can be made stricter to improve reliability. Alternatively, if the user is relaxed, the color identification criteria can be kept normal. Furthermore, if the user is in a hurry, the color identification criteria can be adjusted to provide quick results. Thus, by adjusting the color identification criteria based on the user's emotions, more reliable color identification is possible.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is nervous, a simple, highly visible suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. In this way, by adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be made.

[0105] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the information to be provided based on the user's emotions, more appropriate display is possible.

[0106] The reception unit can analyze the user's past answer history and automatically generate optimal questions. For example, it can automatically generate related questions based on the content of the user's past answers. It can also automatically generate questions that are likely to interest the user from the user's past answer history. It can also analyze the user's past answer history and automatically generate new questions that do not overlap. This allows for efficient information collection by generating optimal questions based on the user's past answer history.

[0107] When analyzing photos, the analysis unit can improve analysis accuracy by taking into account different lighting conditions and backgrounds. For example, if a photo is dark, image processing technology can be used to adjust the brightness to improve analysis accuracy. Also, if the photo has a complex background, the background can be automatically removed to improve analysis accuracy. Furthermore, if the photo is backlit, backlight correction can be performed to improve analysis accuracy. This improves the accuracy of photo analysis by taking into account different lighting conditions and backgrounds.

[0108] When identifying a color, the identification unit can improve the accuracy of identification by referring to the user's past fashion history. For example, the identification unit can identify a color by referring to the colors of items the user has previously purchased. The identification unit can also identify a color by referring to the colors of items the user has previously worn. The identification unit can also identify a color by referring to the colors of cosmetics the user has previously preferred. In this way, the accuracy of color identification can be improved by referring to the user's past fashion history.

[0109] When making a suggestion, the suggestion unit can suggest the most suitable item by referring to the user's past purchase history. For example, it can suggest related items by referring to the color and style of items the user has previously purchased. It can also suggest new items of the same brand by referring to brands the user has previously purchased. It can also analyze the frequency of use of items the user has previously purchased and suggest the most suitable item. In this way, it is possible to suggest the most suitable item by referring to the user's past purchase history.

[0110] The providing unit can provide a region-specific shop list based on the user's geographical location information at the time of providing. For example, if the user lives in a specific region, a list of shops in that region can be provided. Also, if the user is traveling, a list of shops in the region the user is visiting can be provided. Furthermore, if the user is planning to move, a list of shops in the new region can be provided. In this way, a region-specific shop list can be provided by taking the user's geographical location information into consideration.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives information from the user. The information from the user includes, for example, the user's profile information, photos, hobbies, and preferences. The reception unit interactively receives the user's characteristics, hobbies, and preferences. For example, the user interactively answers questions about their own characteristics, hobbies, and preferences, and inputs their skin color, hair color, eye color, favorite colors, and styles. Step 2: The analysis unit analyzes the photo based on the information received by the reception unit. The analysis unit analyzes the uploaded photo and diagnoses the user's skin color, hair color, eye color, etc. in real time. For example, if a user uploads a photo of their face, the generation AI analyzes the photo and identifies the user's personal color. Step 3: The identification unit identifies a personal color based on the results of the analysis by the analysis unit. The identification unit identifies colors that suit the user based on the information obtained through the interactive process and the results of the photo analysis. For example, it identifies clothing colors that match the user's skin color or accessory colors that match the user's hair color. Step 4: The suggestion unit suggests specific items based on the personal color identified by the identification unit. The suggested items include, for example, clothes, accessories, cosmetics, etc. The suggestion unit suggests clothes, accessories, cosmetics, etc. from a specific brand based on the user's personal color. Step 5: The providing unit provides a shopping list or a list of items to purchase that are suggested by the suggestion unit. The provided shopping list or list of items to purchase includes, for example, links to online shops and store information. The providing unit compiles and provides a list of recommended shopping items or shops. For example, it provides online shops where items that suit the user can be purchased, store information for specific brands, etc.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0127] In the 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the photograph based on the information received by the reception unit; an identification unit that identifies a personal color based on the results of the analysis by the analysis unit; a suggestion unit that suggests items based on the personal color identified by the identification unit; a providing unit that provides a shopping list or a shop list of items suggested by the suggesting unit. A system characterized by:

2. The reception unit Interactively accepts user characteristics and preferences 2. The system of claim 1.

3. The analysis unit Analyzes uploaded photos to diagnose users' skin, hair, and eye color 2. The system of claim 1.

4. The identification unit Identify colors that suit the user based on interactive information and photo analysis results 2. The system of claim 1.

5. The proposal unit Suggesting items based on the user's personal color 2. The system of claim 1.

6. The providing unit Providing a curated list of recommended purchases and shopping lists 2. The system of claim 1.

7. The reception unit Inferring user sentiment and adjusting the order or content of questions based on the estimated user sentiment 2. The system of claim 1.

8. The reception unit Analyze the user's past answer history and automatically generate the most appropriate questions 2. The system of claim 1.

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