system

Abstract

The system according to this embodiment aims to objectively evaluate the user's fashion sense and suggest areas for improvement and new outfit combinations. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, and a suggestion unit. The reception unit receives a full-body photo uploaded by the user. The evaluation unit analyzes the full-body photo received by the reception unit and evaluates the user's fashion sense. The suggestion unit proposes improvements and new outfits to the user based on the results evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's fashion sense has not been objectively evaluated sufficiently, and there is room for improvement in proposing improvement points and new coordinates.

[0005] The system according to the embodiment aims to objectively evaluate the user's fashion sense and propose improvement points and new coordinates.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, and a suggestion unit. The reception unit receives a full-body photograph uploaded by the user. The evaluation unit analyzes the full-body photograph received by the reception unit and evaluates the user's fashion sense. Based on the evaluation by the evaluation unit, the suggestion unit proposes areas for improvement and new outfits to the user. [Effects of the Invention]

[0007] The system according to this embodiment can objectively evaluate the user's fashion sense and suggest areas for improvement and new outfit combinations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fashion evaluation system according to an embodiment of the present invention is a system that analyzes a full-body photograph uploaded by a user, evaluates and scores the user's fashion sense, and suggests areas for improvement and new outfits. This fashion evaluation system analyzes a full-body photograph uploaded by a user and evaluates the user's fashion sense. Based on the evaluation results, the generating AI assigns points and suggests areas for improvement and new outfits. This system allows users to improve their fashion sense. For example, a user uploads a full-body photograph. In this case, the user can choose a photograph that suits their body type and preferences. For example, they can upload photographs of various styles, such as casual or formal attire. This information is input into the generating AI. Next, the generating AI analyzes the uploaded photograph. The generating AI has learned the latest fashion trends and provides styling that suits the user's body type and preferences. For example, the generating AI can suggest clothing silhouettes and colors that suit the user's body type. This allows the user to find the style that is best suited to them. Based on the results of the photograph analysis, the generating AI evaluates the user's fashion sense. The evaluation is done in a point format, and the user's fashion sense is quantified. For example, the system evaluates whether a user's outfit is on-trend and whether the color combinations are appropriate. Based on the evaluation results, the generating AI suggests improvements and new outfits to the user. For instance, it might suggest changing the color combinations or combining different items with the user's outfit. This allows users to receive specific advice to improve their fashion sense. This system enables users to improve their fashion sense. For example, it is extremely useful for users who lack confidence in their fashion sense or who want to try new styles but don't know how. It is also convenient for busy modern people as it reduces the time spent choosing clothes every day. Furthermore, the generating AI can provide personalized fashion advice tailored to the user's needs.This enables unique suggestions tailored to each individual user. For example, it can suggest styling that matches the user's preferences or outfits suited to specific events. In this way, by utilizing generative AI, it is possible to improve users' fashion sense and create a society where people can live each day with confidence. Thus, the fashion evaluation system can improve users' fashion sense.

[0029] The fashion evaluation system according to this embodiment comprises a reception unit, an evaluation unit, and a suggestion unit. The reception unit receives full-body photos uploaded by the user. Full-body photos uploaded by the user include, but are not limited to, photos of various styles, such as casual or formal attire. The reception unit allows, for example, the user to select photos that suit their body type and preferences. The evaluation unit analyzes the full-body photos received by the reception unit and evaluates the user's fashion sense. The evaluation is performed based on criteria such as color combinations, clothing silhouettes, and degree of conformity to trends, but is not limited to such examples. The evaluation unit can, for example, use generative AI to analyze the user's full-body photos and evaluate color combinations and clothing silhouettes. The evaluation unit can also use generative AI to quantify the user's fashion sense. The suggestion unit proposes areas for improvement and new outfits to the user based on the evaluation results from the evaluation unit. The suggestions may, for example, be suggestions to change color combinations or to combine different items, but are not limited to such examples. The suggestion unit can, for example, use generative AI to propose specific areas for improvement and new outfits based on the user's evaluation results. The suggestion section can also use generative AI to suggest styling tailored to the user's preferences or outfits suited to specific events. This allows the fashion evaluation system according to the embodiment to evaluate the user's fashion sense and suggest areas for improvement or new outfits.

[0030] The reception desk accepts full-body photos uploaded by users. These photos may include, but are not limited to, photos in various styles, such as casual or formal attire. The reception desk allows users to select photos that suit their body type and preferences. Specifically, the reception desk provides an interface for users to upload photos, making it easy for them to select and upload images. Users can upload photos from smartphones or computers, and the reception desk automatically accepts and stores the uploaded photos in the system. Furthermore, the reception desk can check the quality of user-uploaded photos and, if necessary, adjust the resolution and brightness. For example, if a photo is too dark or has low resolution, the reception desk can automatically correct the photo so that the evaluation desk can analyze it accurately. The reception desk can also allow users to upload multiple photos, accepting photos from different angles and poses. This allows the evaluation desk to more accurately assess the user's fashion. Additionally, the reception desk can perform privacy-protecting processing on user-uploaded photos. For example, it can provide features such as automatically blurring faces or removing backgrounds containing personal information. This allows users to upload photos with peace of mind.

[0031] The evaluation unit analyzes full-body photographs received by the reception unit and assesses the user's fashion sense. The evaluation is based on criteria such as color combinations, clothing silhouettes, and the degree of conformity with trends, but is not limited to these examples. The evaluation unit uses generative AI to analyze the user's full-body photographs and evaluate color combinations and clothing silhouettes. Specifically, the generative AI uses image recognition technology to extract the colors and shapes of clothing from the user's full-body photographs and evaluates the balance of color combinations and silhouettes. For example, the generative AI evaluates whether the color combinations are harmonious and whether the clothing silhouettes suit the body type, quantifying the user's fashion sense. The evaluation unit can also use generative AI to quantify the user's fashion sense. For example, the evaluation of color combinations is based on the balance of hue, saturation, and brightness, and the evaluation of silhouettes is based on how well the shape and lines of the clothing fit the body type. Furthermore, to evaluate the degree of conformity with trends, the evaluation unit refers to the latest fashion trend database and assesses how well the user's coordination matches current trends. This allows the evaluation unit to comprehensively assess the user's fashion sense and provide specific numerical data and evaluation results.

[0032] The suggestion department proposes improvements and new outfits to the user based on the evaluation results from the evaluation department. These suggestions may include, but are not limited to, suggestions to change color combinations or combine different items. The suggestion department uses generative AI to propose specific improvements and new outfits based on the user's evaluation results. Specifically, the generative AI analyzes the user's evaluation results and identifies areas for improvement in color combinations and silhouettes. For example, the generative AI suggests more harmonious color combinations or silhouettes that better suit the user's body type for their current outfit. Furthermore, the generative AI can suggest new items and styles considering the user's preferences and past outfit history. For example, if the user prefers a casual style, the generative AI will suggest a new outfit centered around casual items. In addition, the suggestion department can suggest outfits tailored to specific events or situations. For example, it can suggest outfits suitable for formal events such as weddings and parties, or casual outfits suitable for travel and outdoor activities. Through this, the suggestion department provides specific advice and new ideas to improve the user's fashion sense, enabling them to enjoy fashion with confidence.

[0033] The evaluation unit can analyze a user's full-body photograph and evaluate their fashion sense based on color combinations, clothing silhouettes, and the degree of matching with trends. For example, the evaluation unit can use a generative AI to analyze the user's full-body photograph and evaluate the color combinations. The evaluation unit can also use a generative AI to evaluate the clothing silhouettes. The evaluation unit can also use a generative AI to evaluate the degree of matching with trends. This allows for a detailed evaluation of the user's fashion sense. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the evaluation unit can input a user's full-body photograph into a generative AI and have the generative AI perform evaluations of color combinations, clothing silhouettes, and the degree of matching with trends.

[0034] The proposal unit can make suggestions to change color combinations or combine different items based on the evaluation results. For example, the proposal unit can use a generative AI to make suggestions to change color combinations based on the evaluation results. The proposal unit can also use a generative AI to make suggestions to combine different items. The proposal unit can also use a generative AI to suggest specific improvements or new outfits based on the user's evaluation results. This allows the proposal unit to suggest specific improvements or new outfits to the user. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal unit can input the evaluation results into a generative AI and have the generative AI execute suggestions to change color combinations or combine different items.

[0035] The suggestion unit can suggest styling tailored to the user's preferences or outfits suited to specific events. For example, the suggestion unit can use generative AI to suggest styling tailored to the user's preferences. The suggestion unit can also use generative AI to suggest outfits suited to specific events. The suggestion unit can also use generative AI to suggest specific areas for improvement or new outfits based on the user's evaluation results. This enables personalized suggestions tailored to the user's preferences and events. Some or all of the above processes in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input information about the user's preferences and events into the generative AI and have the generative AI perform styling and outfit suggestions.

[0036] The reception desk can analyze a user's past photo upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. The reception desk can also analyze the style of photos the user has uploaded in the past and recommend photos of a similar style. This allows the reception desk to suggest the optimal upload method based on the user's past history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past photo upload history into an AI and have the AI ​​select the optimal upload method.

[0037] The reception desk can filter full-body photos uploaded based on the user's current fashion style and areas of interest. For example, if the user prefers a casual style, the reception desk will prioritize uploading photos of casual attire. If the user has an upcoming formal event, the reception desk can also recommend photos of formal attire. If the user is interested in a particular brand, the reception desk can also upload photos that include items from that brand. This allows the reception desk to upload the most suitable photos based on the user's current style and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the user's current fashion style and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0038] The reception system can prioritize uploading highly relevant photos when a full-body photo is uploaded, taking into account the user's geographical location. For example, if the user is traveling, the reception system will prioritize uploading photos taken at their travel destination. If the user is in a specific region, the reception system can also prioritize uploading photos that match the fashion style of that region. If the user is at an event venue, the reception system can also prioritize uploading photos related to that event. This allows the system to upload the most suitable photos based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's geographical location into an AI and have the AI ​​select highly relevant photos.

[0039] The reception desk can analyze a user's social media activity when they upload a full-body photo and upload relevant photos. For example, the reception desk can prioritize uploading photos that the user has shared on social media. If the user is using a specific hashtag, the reception desk can also upload photos related to that hashtag. The reception desk can also upload photos that resemble the style of influencers the user follows. This allows the reception desk to upload the most suitable photos based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​select relevant photos.

[0040] The evaluation unit can adjust the level of detail in its evaluation based on the user's body type and preferences. For example, the evaluation unit can evaluate silhouettes that suit the user's body type. The evaluation unit can also evaluate color combinations based on the user's preferences. The evaluation unit can also evaluate how well trends match the user's style. This allows for optimal evaluation based on the user's body type and preferences. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail in its evaluation.

[0041] The evaluation unit can apply different evaluation algorithms depending on the latest fashion trends during the evaluation process. For example, the evaluation unit can evaluate color combinations based on the latest fashion trends. The evaluation unit can also evaluate silhouettes based on the latest fashion trends. The evaluation unit can also evaluate accessory selection based on the latest fashion trends. This allows for optimal evaluation based on the latest fashion trends. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information on the latest fashion trends into a generative AI and have the generative AI execute the application of the evaluation algorithm.

[0042] The evaluation unit can improve the accuracy of its evaluations by referring to the user's past fashion history during the evaluation process. For example, the evaluation unit performs evaluations by referring to the user's past outfits. The evaluation unit can also improve the accuracy of its evaluations based on the user's past evaluation results. The evaluation unit can also analyze changes in trends from the user's past fashion history and reflect them in the evaluation. This allows for the provision of optimal evaluations based on the user's past fashion history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of the evaluations.

[0043] The evaluation unit can perform evaluations while considering the user's purchase history of related fashion items. For example, the evaluation unit can consider the brands of the items the user has purchased when performing evaluations. The evaluation unit can also consider the styles of the items the user has purchased when performing evaluations. The evaluation unit can also consider the color combinations of the items the user has purchased when performing evaluations. This allows for the provision of optimal evaluations based on the user's purchase history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's purchase history into a generative AI and have the generative AI perform the evaluation.

[0044] The suggestion unit can adjust the level of detail of its suggestions based on the user's body type and preferences. For example, the suggestion unit can suggest silhouettes that suit the user's body type. The suggestion unit can also suggest color combinations based on the user's preferences. The suggestion unit can also suggest a degree of trend matching that suits the user's style. This allows the suggestion unit to provide optimal suggestions based on the user's body type and preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0045] The suggestion unit can apply different suggestion algorithms depending on the specific event or situation when making a suggestion. For example, the suggestion unit can suggest an outfit suitable for a formal event. It can also suggest an outfit suitable for a casual situation. It can also suggest an outfit suitable for a specific season. This allows for optimal suggestions to be made according to the specific event or situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about a specific event or situation into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0046] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past fashion history. For example, the suggestion unit makes suggestions by referring to the user's past outfits. The suggestion unit can also improve the accuracy of its suggestions based on the user's past evaluation results. The suggestion unit can also analyze changes in trends from the user's past fashion history and reflect them in its suggestions. This allows it to make optimal suggestions based on the user's past fashion history. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of its suggestions.

[0047] The suggestion unit can make suggestions by considering the user's purchase history of related fashion items. For example, the suggestion unit can make suggestions by considering the brands of items the user has purchased. The suggestion unit can also make suggestions by considering the styles of items the user has purchased. The suggestion unit can also make suggestions by considering the color combinations of items the user has purchased. This allows the system to make optimal suggestions based on the user's purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's purchase history into a generative AI and have the generative AI execute the suggestions.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The evaluation unit can improve the accuracy of its evaluation of a user's fashion sense by referring to the user's past fashion history. For example, the evaluation unit can perform an evaluation by referring to the user's past outfits. The evaluation unit can also improve the accuracy of its evaluation based on the user's past evaluation results. The evaluation unit can also analyze changes in trends from the user's past fashion history and reflect them in the evaluation. This allows for the provision of an optimal evaluation based on the user's past fashion history. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of the evaluation.

[0050] The reception desk can analyze a user's past photo upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. The reception desk can also analyze the style of photos the user has uploaded in the past and recommend photos of a similar style. This allows the reception desk to suggest the optimal upload method based on the user's past history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past photo upload history into an AI and have the AI ​​select the optimal upload method.

[0051] The suggestion unit can adjust the level of detail of its suggestions based on the user's body type and preferences. For example, the suggestion unit can suggest silhouettes that suit the user's body type. The suggestion unit can also suggest color combinations based on the user's preferences. The suggestion unit can also suggest a degree of trend matching that suits the user's style. This allows the suggestion unit to provide optimal suggestions based on the user's body type and preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail of its suggestions.

[0052] The reception desk can filter full-body photos uploaded based on the user's current fashion style and areas of interest. For example, if the reception desk prefers a casual style, it can prioritize uploading photos of casual attire. If the reception desk has an upcoming formal event, it can also recommend photos of formal attire. If the reception desk is interested in a particular brand, it can also upload photos that include items from that brand. This allows the reception desk to upload the most suitable photos based on the user's current style and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the user's current fashion style and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0053] The evaluation unit can adjust the level of detail in its evaluation based on the user's body type and preferences. For example, the evaluation unit can evaluate silhouettes that suit the user's body type. The evaluation unit can also evaluate color combinations based on the user's preferences. The evaluation unit can also evaluate how well trends match the user's style. This allows for optimal evaluation based on the user's body type and preferences. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail in its evaluation.

[0054] The suggestion unit can apply different suggestion algorithms depending on the specific event or situation when making a suggestion. For example, the suggestion unit can suggest outfits suitable for a formal event. It can also suggest outfits suitable for a casual situation. It can also suggest outfits suitable for a specific season. This allows for optimal suggestions depending on the specific event or situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about a specific event or situation into a generative AI and have the generative AI apply the suggestion algorithm.

[0055] The evaluation unit can apply different evaluation algorithms depending on the latest fashion trends during the evaluation process. For example, the evaluation unit can evaluate color combinations based on the latest fashion trends. The evaluation unit can also evaluate silhouettes based on the latest fashion trends. The evaluation unit can also evaluate accessory selection based on the latest fashion trends. This allows for optimal evaluation based on the latest fashion trends. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information on the latest fashion trends into a generative AI and have the generative AI apply the evaluation algorithm.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives full-body photos uploaded by users. These photos may include various styles, such as casual or formal attire. The reception desk allows users to select photos that suit their body type and preferences. Step 2: The evaluation unit analyzes the full-body photos received by the reception unit and assesses the user's fashion sense. The evaluation is based on criteria such as color combinations, clothing silhouettes, and the degree of matching with trends. The evaluation unit can also use generative AI to analyze the user's full-body photos, evaluate color combinations and clothing silhouettes, and quantify the user's fashion sense. Step 3: The suggestion department proposes improvements and new outfits to the user based on the evaluation results from the evaluation department. These suggestions may include changing color combinations or combining different items. Using generative AI, the suggestion department can propose specific improvements and new outfits based on the user's evaluation results, and can also suggest styling tailored to the user's preferences or outfits suitable for specific events.

[0058] (Example of form 2) The fashion evaluation system according to an embodiment of the present invention is a system that analyzes a full-body photograph uploaded by a user, evaluates and scores the user's fashion sense, and suggests areas for improvement and new outfits. This fashion evaluation system analyzes a full-body photograph uploaded by a user and evaluates the user's fashion sense. Based on the evaluation results, the generating AI assigns points and suggests areas for improvement and new outfits. This system allows users to improve their fashion sense. For example, a user uploads a full-body photograph. In this case, the user can choose a photograph that suits their body type and preferences. For example, they can upload photographs of various styles, such as casual or formal attire. This information is input into the generating AI. Next, the generating AI analyzes the uploaded photograph. The generating AI has learned the latest fashion trends and provides styling that suits the user's body type and preferences. For example, the generating AI can suggest clothing silhouettes and colors that suit the user's body type. This allows the user to find the style that is best suited to them. Based on the results of the photograph analysis, the generating AI evaluates the user's fashion sense. The evaluation is done in a point format, and the user's fashion sense is quantified. For example, the system evaluates whether a user's outfit is on-trend and whether the color combinations are appropriate. Based on the evaluation results, the generating AI suggests improvements and new outfits to the user. For instance, it might suggest changing the color combinations or combining different items with the user's outfit. This allows users to receive specific advice to improve their fashion sense. This system enables users to improve their fashion sense. For example, it is extremely useful for users who lack confidence in their fashion sense or who want to try new styles but don't know how. It is also convenient for busy modern people as it reduces the time spent choosing clothes every day. Furthermore, the generating AI can provide personalized fashion advice tailored to the user's needs.This enables unique suggestions tailored to each individual user. For example, it can suggest styling that matches the user's preferences or outfits suited to specific events. In this way, by utilizing generative AI, it is possible to improve users' fashion sense and create a society where people can live each day with confidence. Thus, the fashion evaluation system can improve users' fashion sense.

[0059] The fashion evaluation system according to this embodiment comprises a reception unit, an evaluation unit, and a suggestion unit. The reception unit receives full-body photos uploaded by the user. Full-body photos uploaded by the user include, but are not limited to, photos of various styles, such as casual or formal attire. The reception unit allows, for example, the user to select photos that suit their body type and preferences. The evaluation unit analyzes the full-body photos received by the reception unit and evaluates the user's fashion sense. The evaluation is performed based on criteria such as color combinations, clothing silhouettes, and degree of conformity to trends, but is not limited to such examples. The evaluation unit can, for example, use generative AI to analyze the user's full-body photos and evaluate color combinations and clothing silhouettes. The evaluation unit can also use generative AI to quantify the user's fashion sense. The suggestion unit proposes areas for improvement and new outfits to the user based on the evaluation results from the evaluation unit. The suggestions may, for example, be suggestions to change color combinations or to combine different items, but are not limited to such examples. The suggestion unit can, for example, use generative AI to propose specific areas for improvement and new outfits based on the user's evaluation results. The suggestion section can also use generative AI to suggest styling tailored to the user's preferences or outfits suited to specific events. This allows the fashion evaluation system according to the embodiment to evaluate the user's fashion sense and suggest areas for improvement or new outfits.

[0060] The reception desk accepts full-body photos uploaded by users. These photos may include, but are not limited to, photos in various styles, such as casual or formal attire. The reception desk allows users to select photos that suit their body type and preferences. Specifically, the reception desk provides an interface for users to upload photos, making it easy for them to select and upload images. Users can upload photos from smartphones or computers, and the reception desk automatically accepts and stores the uploaded photos in the system. Furthermore, the reception desk can check the quality of user-uploaded photos and, if necessary, adjust the resolution and brightness. For example, if a photo is too dark or has low resolution, the reception desk can automatically correct the photo so that the evaluation desk can analyze it accurately. The reception desk can also allow users to upload multiple photos, accepting photos from different angles and poses. This allows the evaluation desk to more accurately assess the user's fashion. Additionally, the reception desk can perform privacy-protecting processing on user-uploaded photos. For example, it can provide features such as automatically blurring faces or removing backgrounds containing personal information. This allows users to upload photos with peace of mind.

[0061] The evaluation unit analyzes full-body photographs received by the reception unit and assesses the user's fashion sense. The evaluation is based on criteria such as color combinations, clothing silhouettes, and the degree of conformity with trends, but is not limited to these examples. The evaluation unit uses generative AI to analyze the user's full-body photographs and evaluate color combinations and clothing silhouettes. Specifically, the generative AI uses image recognition technology to extract the colors and shapes of clothing from the user's full-body photographs and evaluates the balance of color combinations and silhouettes. For example, the generative AI evaluates whether the color combinations are harmonious and whether the clothing silhouettes suit the body type, quantifying the user's fashion sense. The evaluation unit can also use generative AI to quantify the user's fashion sense. For example, the evaluation of color combinations is based on the balance of hue, saturation, and brightness, and the evaluation of silhouettes is based on how well the shape and lines of the clothing fit the body type. Furthermore, to evaluate the degree of conformity with trends, the evaluation unit refers to the latest fashion trend database and assesses how well the user's coordination matches current trends. This allows the evaluation unit to comprehensively assess the user's fashion sense and provide specific numerical data and evaluation results.

[0062] The suggestion department proposes improvements and new outfits to the user based on the evaluation results from the evaluation department. These suggestions may include, but are not limited to, suggestions to change color combinations or combine different items. The suggestion department uses generative AI to propose specific improvements and new outfits based on the user's evaluation results. Specifically, the generative AI analyzes the user's evaluation results and identifies areas for improvement in color combinations and silhouettes. For example, the generative AI suggests more harmonious color combinations or silhouettes that better suit the user's body type for their current outfit. Furthermore, the generative AI can suggest new items and styles considering the user's preferences and past outfit history. For example, if the user prefers a casual style, the generative AI will suggest a new outfit centered around casual items. In addition, the suggestion department can suggest outfits tailored to specific events or situations. For example, it can suggest outfits suitable for formal events such as weddings and parties, or casual outfits suitable for travel and outdoor activities. Through this, the suggestion department provides specific advice and new ideas to improve the user's fashion sense, enabling them to enjoy fashion with confidence.

[0063] The evaluation unit can analyze a user's full-body photograph and evaluate their fashion sense based on color combinations, clothing silhouettes, and the degree of matching with trends. For example, the evaluation unit can use a generative AI to analyze the user's full-body photograph and evaluate the color combinations. The evaluation unit can also use a generative AI to evaluate the clothing silhouettes. The evaluation unit can also use a generative AI to evaluate the degree of matching with trends. This allows for a detailed evaluation of the user's fashion sense. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the evaluation unit can input a user's full-body photograph into a generative AI and have the generative AI perform evaluations of color combinations, clothing silhouettes, and the degree of matching with trends.

[0064] The proposal unit can make suggestions to change color combinations or combine different items based on the evaluation results. For example, the proposal unit can use a generative AI to make suggestions to change color combinations based on the evaluation results. The proposal unit can also use a generative AI to make suggestions to combine different items. The proposal unit can also use a generative AI to suggest specific improvements or new outfits based on the user's evaluation results. This allows the proposal unit to suggest specific improvements or new outfits to the user. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal unit can input the evaluation results into a generative AI and have the generative AI execute suggestions to change color combinations or combine different items.

[0065] The suggestion unit can suggest styling tailored to the user's preferences or outfits suited to specific events. For example, the suggestion unit can use generative AI to suggest styling tailored to the user's preferences. The suggestion unit can also use generative AI to suggest outfits suited to specific events. The suggestion unit can also use generative AI to suggest specific areas for improvement or new outfits based on the user's evaluation results. This enables personalized suggestions tailored to the user's preferences and events. Some or all of the above processes in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input information about the user's preferences and events into the generative AI and have the generative AI perform styling and outfit suggestions.

[0066] The reception desk can estimate the user's emotions and adjust the timing of the full-body photo upload based on the estimated emotions. For example, if the user is relaxed, the reception desk can allow the user to freely choose the upload timing. If the user is stressed, the reception desk can also allow the user to upload with simple operations. If the user is in a hurry, the reception desk can also allow the user to upload quickly using voice input. This allows the full-body photo to be uploaded at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the upload timing.

[0067] The reception desk can analyze a user's past photo upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. The reception desk can also analyze the style of photos the user has uploaded in the past and recommend photos of a similar style. This allows the reception desk to suggest the optimal upload method based on the user's past history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past photo upload history into an AI and have the AI ​​select the optimal upload method.

[0068] The reception desk can filter full-body photos uploaded based on the user's current fashion style and areas of interest. For example, if the user prefers a casual style, the reception desk will prioritize uploading photos of casual attire. If the user has an upcoming formal event, the reception desk can also recommend photos of formal attire. If the user is interested in a particular brand, the reception desk can also upload photos that include items from that brand. This allows the reception desk to upload the most suitable photos based on the user's current style and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the user's current fashion style and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0069] The reception desk can estimate the user's emotions and determine the priority of photos to upload based on the estimated emotions. For example, if the user is confident, the reception desk may prioritize uploading the most recent photos. If the user is feeling anxious, the reception desk may also prioritize uploading photos that have received high ratings in the past. If the user is excited, the reception desk may also prioritize uploading photos taken at a specific event. This allows for the uploading of the most suitable photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of photos.

[0070] The reception system can prioritize uploading highly relevant photos when a full-body photo is uploaded, taking into account the user's geographical location. For example, if the user is traveling, the reception system will prioritize uploading photos taken at their travel destination. If the user is in a specific region, the reception system can also prioritize uploading photos that match the fashion style of that region. If the user is at an event venue, the reception system can also prioritize uploading photos related to that event. This allows the system to upload the most suitable photos based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's geographical location into an AI and have the AI ​​select highly relevant photos.

[0071] The reception desk can analyze a user's social media activity when they upload a full-body photo and upload relevant photos. For example, the reception desk can prioritize uploading photos that the user has shared on social media. If the user is using a specific hashtag, the reception desk can also upload photos related to that hashtag. The reception desk can also upload photos that resemble the style of influencers the user follows. This allows the reception desk to upload the most suitable photos based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​select relevant photos.

[0072] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for fashion sense based on the estimated user emotions. For example, if the user is confident, the evaluation unit may apply strict evaluation criteria. If the user is feeling anxious, the evaluation unit may also apply gentle evaluation criteria. If the user is relaxed, the evaluation unit may also apply balanced evaluation criteria. This allows the evaluation unit to apply the optimal evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0073] The evaluation unit can adjust the level of detail in its evaluation based on the user's body type and preferences. For example, the evaluation unit can evaluate silhouettes that suit the user's body type. The evaluation unit can also evaluate color combinations based on the user's preferences. The evaluation unit can also evaluate how well trends match the user's style. This allows for optimal evaluation based on the user's body type and preferences. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail in its evaluation.

[0074] The evaluation unit can apply different evaluation algorithms depending on the latest fashion trends during the evaluation process. For example, the evaluation unit can evaluate color combinations based on the latest fashion trends. The evaluation unit can also evaluate silhouettes based on the latest fashion trends. The evaluation unit can also evaluate accessory selection based on the latest fashion trends. This allows for optimal evaluation based on the latest fashion trends. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information on the latest fashion trends into a generative AI and have the generative AI execute the application of the evaluation algorithm.

[0075] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is confident, the evaluation unit can display detailed evaluation results. If the user is feeling anxious, the evaluation unit can also emphasize positive feedback. If the user is relaxed, the evaluation unit can also display balanced evaluation results. This allows for the display of optimal evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the evaluation results.

[0076] The evaluation unit can improve the accuracy of its evaluations by referring to the user's past fashion history during the evaluation process. For example, the evaluation unit performs evaluations by referring to the user's past outfits. The evaluation unit can also improve the accuracy of its evaluations based on the user's past evaluation results. The evaluation unit can also analyze changes in trends from the user's past fashion history and reflect them in the evaluation. This allows for the provision of optimal evaluations based on the user's past fashion history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of the evaluations.

[0077] The evaluation unit can perform evaluations while considering the user's purchase history of related fashion items. For example, the evaluation unit can consider the brands of the items the user has purchased when performing evaluations. The evaluation unit can also consider the styles of the items the user has purchased when performing evaluations. The evaluation unit can also consider the color combinations of the items the user has purchased when performing evaluations. This allows for the provision of optimal evaluations based on the user's purchase history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's purchase history into a generative AI and have the generative AI perform the evaluation.

[0078] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is confident, the suggestion unit can provide detailed suggestions. If the user is feeling anxious, the suggestion unit can also provide positive suggestions. If the user is relaxed, the suggestion unit can provide balanced suggestions. This allows the suggestion unit to provide the most appropriate suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0079] The suggestion unit can adjust the level of detail of its suggestions based on the user's body type and preferences. For example, the suggestion unit can suggest silhouettes that suit the user's body type. The suggestion unit can also suggest color combinations based on the user's preferences. The suggestion unit can also suggest a degree of trend matching that suits the user's style. This allows the suggestion unit to provide optimal suggestions based on the user's body type and preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0080] The suggestion unit can apply different suggestion algorithms depending on the specific event or situation when making a suggestion. For example, the suggestion unit can suggest an outfit suitable for a formal event. It can also suggest an outfit suitable for a casual situation. It can also suggest an outfit suitable for a specific season. This allows for optimal suggestions to be made according to the specific event or situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about a specific event or situation into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0081] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is confident, the suggestion unit may prioritize suggesting the latest trends. If the user is feeling anxious, the suggestion unit may also prioritize suggesting outfits that have received high ratings in the past. If the user is relaxed, the suggestion unit may also provide balanced suggestions. This allows the system to prioritize the most suitable suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0082] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past fashion history. For example, the suggestion unit makes suggestions by referring to the user's past outfits. The suggestion unit can also improve the accuracy of its suggestions based on the user's past evaluation results. The suggestion unit can also analyze changes in trends from the user's past fashion history and reflect them in its suggestions. This allows it to make optimal suggestions based on the user's past fashion history. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of its suggestions.

[0083] The suggestion unit can make suggestions by considering the user's purchase history of related fashion items. For example, the suggestion unit can make suggestions by considering the brands of items the user has purchased. The suggestion unit can also make suggestions by considering the styles of items the user has purchased. The suggestion unit can also make suggestions by considering the color combinations of items the user has purchased. This allows the system to make optimal suggestions based on the user's purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's purchase history into a generative AI and have the generative AI execute the suggestions.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The evaluation unit can improve the accuracy of its evaluation of a user's fashion sense by referring to the user's past fashion history. For example, the evaluation unit can perform an evaluation by referring to the user's past outfits. The evaluation unit can also improve the accuracy of its evaluation based on the user's past evaluation results. The evaluation unit can also analyze changes in trends from the user's past fashion history and reflect them in the evaluation. This allows for the provision of an optimal evaluation based on the user's past fashion history. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the user's past fashion history into a generative AI and have the generative AI perform the task of improving the accuracy of the evaluation.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is confident, the suggestion unit can offer detailed suggestions. If the user is feeling anxious, the suggestion unit can offer positive suggestions. If the user is relaxed, the suggestion unit can offer balanced suggestions. This allows the suggestion unit to provide the most appropriate suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0087] The reception desk can analyze a user's past photo upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. The reception desk can also analyze the style of photos the user has uploaded in the past and recommend photos of a similar style. This allows the reception desk to suggest the optimal upload method based on the user's past history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past photo upload history into an AI and have the AI ​​select the optimal upload method.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for fashion sense based on the estimated emotions. For example, the evaluation unit can apply strict evaluation criteria if the user is confident. It can also apply gentle evaluation criteria if the user is feeling anxious. It can also apply balanced evaluation criteria if the user is relaxed. This allows the evaluation unit to apply the most appropriate evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0089] The suggestion unit can adjust the level of detail of its suggestions based on the user's body type and preferences. For example, the suggestion unit can suggest silhouettes that suit the user's body type. The suggestion unit can also suggest color combinations based on the user's preferences. The suggestion unit can also suggest a degree of trend matching that suits the user's style. This allows the suggestion unit to provide optimal suggestions based on the user's body type and preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail of its suggestions.

[0090] The reception desk can filter full-body photos uploaded based on the user's current fashion style and areas of interest. For example, if the reception desk prefers a casual style, it can prioritize uploading photos of casual attire. If the reception desk has an upcoming formal event, it can also recommend photos of formal attire. If the reception desk is interested in a particular brand, it can also upload photos that include items from that brand. This allows the reception desk to upload the most suitable photos based on the user's current style and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the user's current fashion style and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0091] The evaluation unit can adjust the level of detail in its evaluation based on the user's body type and preferences. For example, the evaluation unit can evaluate silhouettes that suit the user's body type. The evaluation unit can also evaluate color combinations based on the user's preferences. The evaluation unit can also evaluate how well trends match the user's style. This allows for optimal evaluation based on the user's body type and preferences. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information about the user's body type and preferences into a generative AI and have the generative AI adjust the level of detail in its evaluation.

[0092] The suggestion unit can apply different suggestion algorithms depending on the specific event or situation when making a suggestion. For example, the suggestion unit can suggest outfits suitable for a formal event. It can also suggest outfits suitable for a casual situation. It can also suggest outfits suitable for a specific season. This allows for optimal suggestions depending on the specific event or situation. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about a specific event or situation into a generative AI and have the generative AI apply the suggestion algorithm.

[0093] The reception desk can estimate the user's emotions and determine the priority of photos to upload based on the estimated emotions. For example, if the user is confident, the reception desk can prioritize uploading the most recent photos. If the user is feeling anxious, the reception desk can also prioritize uploading photos that have received high ratings in the past. If the user is excited, the reception desk can also prioritize uploading photos taken at a specific event. This allows for the uploading of the most suitable photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of photos.

[0094] The evaluation unit can apply different evaluation algorithms depending on the latest fashion trends during the evaluation process. For example, the evaluation unit can evaluate color combinations based on the latest fashion trends. The evaluation unit can also evaluate silhouettes based on the latest fashion trends. The evaluation unit can also evaluate accessory selection based on the latest fashion trends. This allows for optimal evaluation based on the latest fashion trends. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information on the latest fashion trends into a generative AI and have the generative AI apply the evaluation algorithm.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk receives full-body photos uploaded by users. These photos may include various styles, such as casual or formal attire. The reception desk allows users to select photos that suit their body type and preferences. Step 2: The evaluation unit analyzes the full-body photos received by the reception unit and assesses the user's fashion sense. The evaluation is based on criteria such as color combinations, clothing silhouettes, and the degree of matching with trends. The evaluation unit can also use generative AI to analyze the user's full-body photos, evaluate color combinations and clothing silhouettes, and quantify the user's fashion sense. Step 3: The suggestion department proposes improvements and new outfits to the user based on the evaluation results from the evaluation department. These suggestions may include changing color combinations or combining different items. Using generative AI, the suggestion department can propose specific improvements and new outfits based on the user's evaluation results, and can also suggest styling tailored to the user's preferences or outfits suitable for specific events.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the reception unit, evaluation unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives a full-body photograph uploaded by the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the full-body photograph using generating AI to evaluate the user's fashion sense. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes improvements and new outfits based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, evaluation unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives a full-body photograph uploaded by the user. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the full-body photograph using generating AI to evaluate the user's fashion sense. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements and new outfits based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, evaluation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives a full-body photograph uploaded by the user. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the full-body photograph using a generating AI to evaluate the user's fashion sense. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements and new outfits based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, evaluation unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives a full-body photograph uploaded by the user. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the full-body photograph using a generating AI to evaluate the user's fashion sense. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements and new outfits based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A reception area that accepts full-body photos uploaded by users, An evaluation unit analyzes the full-body photograph received by the reception unit and evaluates the user's fashion sense, The system includes a proposal unit that suggests improvements and new coordination ideas to the user based on the results of the evaluation by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The evaluation unit described above, The system analyzes full-body photos of users and evaluates their fashion sense based on color combinations, clothing silhouettes, and how well they match current trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the evaluation results, we will propose changes to the color combinations or suggestions for combining different items. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer styling suggestions tailored to the user's preferences, as well as outfit ideas for specific events. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of full-body photo uploads based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past photo upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When uploading full-body photos, the system filters them based on the user's current fashion style and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of photos to upload based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading a full-body photo, the system prioritizes uploading photos that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When uploading a full-body photo, the system analyzes the user's social media activity and uploads relevant photos. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, The system estimates the user's emotions and adjusts the criteria for evaluating fashion sense based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, During the evaluation process, the level of detail in the evaluation is adjusted based on the user's body type and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, During the evaluation process, different evaluation algorithms are applied according to the latest fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, During the evaluation process, we refer to the user's past fashion history to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, When evaluating a product, the user's purchase history of related fashion items will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's body type and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the specific event or situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, we refer to the user's past fashion history to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, we take into account the user's purchase history of related fashion items. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts full-body photos uploaded by users, An evaluation unit analyzes the full-body photograph received by the reception unit and evaluates the user's fashion sense, The system includes a proposal unit that suggests improvements and new coordination ideas to the user based on the results of the evaluation by the aforementioned evaluation unit. A system characterized by the following features.

2. The evaluation unit, The system analyzes full-body photos of users and evaluates their fashion sense based on color combinations, clothing silhouettes, and how well they match current trends. The system according to feature 1.

3. The aforementioned proposal section is, Based on the evaluation results, we will propose changes to the color combinations or suggestions for combining different items. The system according to feature 1.

4. The aforementioned proposal section is, We offer styling suggestions tailored to the user's preferences, as well as outfit ideas for specific events. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of full-body photo uploads based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past photo upload history and select the optimal upload method. The system according to feature 1.

7. The aforementioned reception unit is When uploading full-body photos, the system filters them based on the user's current fashion style and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of photos to upload based on the estimated emotions. The system according to feature 1.

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