System

Abstract

An object of a system according to an embodiment is to enable a user to easily purchase a wig that suits the user while at home.SOLUTION: A system includes a reception part, an analysis part, a proposal part, an order part, and a consultation part. The receiving unit uploads the face picture of the user. The analysis unit analyzes the face photograph uploaded by the reception unit, and analyzes a feature of the face. The suggestion section suggests a wig based on the facial features analyzed by the analysis section. The ordering unit orders the wig proposed by the proposal unit online. The consulting unit consults about the order cut of the wig ordered by the ordering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to purchase satisfactory wigs for appearance care in response to hair loss caused by anti-cancer drug treatment.

[0005] The system according to the embodiment aims to enable users to easily purchase wigs that suit them from the comfort of their own homes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, an ordering unit, and a consultation unit. The reception unit uploads a facial photo of the user. The analysis unit analyzes the facial photo uploaded by the reception unit and analyzes facial features. The proposal unit proposes a wig based on the facial features analyzed by the analysis unit. The ordering unit orders the wig proposed by the proposal unit online. The consultation unit provides consultation on custom cutting of the wig ordered by the ordering unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily purchase a wig that suits them from the comfort of their own home. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A wig purchasing system according to an embodiment of the present invention allows users to simulate wigs that suit them from the comfort of their own home and easily purchase and consult about custom haircuts. The wig purchasing system allows users to simulate wigs that suit them from the comfort of their own home. Users can then purchase wigs through mail order, or visit a store near their home to consult about custom haircuts. This allows users to easily purchase wigs that satisfy them. Furthermore, the wig purchasing system aims to popularize wigs for both men and women, as the market for men's wigs is small. For example, the wig purchasing system allows users to simulate wigs that suit them from the comfort of their own home. Users upload a photo of their face, and AI suggests the most suitable wig based on their face shape and skin color. For example, when a user uploads a photo of their face, AI automatically analyzes their facial features and selects the most suitable wig from multiple wigs. The wig purchasing system then allows users to order the wig they selected in the simulation online. This allows users to easily purchase wigs from the comfort of their own home. Furthermore, the wig purchasing system allows users to bring the wig they have purchased and consult with a professional stylist at the store. This allows users to have the best cut and style suggested for them, and to obtain a wig that satisfies them. This allows the wig purchasing system to easily purchase a wig that satisfies them as appearance care for hair loss caused by anti-cancer drug treatment. This allows the wig purchasing system to simulate a wig that suits them from the comfort of their own home, and to easily purchase and consult about custom cutting. For example, users can easily find a wig that suits them from the comfort of their own home. Also, users can easily purchase a wig from the comfort of their own home. This allows users to have the best cut and style suggested for them, and to obtain a wig that satisfies them.This makes it possible for people to easily purchase a satisfactory wig as appearance care for hair loss caused by anti-cancer drug treatment.

[0029] A wig purchasing system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, an order unit, and a consultation unit. The reception unit uploads a facial photo of a user. The facial photo of a user includes, for example, resolution, file format, and shooting conditions, but is not limited to these examples. The reception unit allows a user to upload a facial photo using, for example, a smartphone or a personal computer. The reception unit can also directly upload a facial photo taken by the user with a camera. The reception unit can also allow a user to select and upload an existing photo. For example, the reception unit uploads a facial photo taken by the user with a smartphone camera. The reception unit can also upload a facial photo taken by the user with a personal computer camera. The reception unit can also allow a user to select and upload an existing photo. The analysis unit uses AI to analyze the facial photo uploaded by the reception unit and analyze facial features. The analysis is performed based on, for example, the algorithm used and the accuracy of the analysis, but is not limited to these examples. For example, the analysis unit analyzes facial features using deep learning. The analysis unit can also analyze facial features using a support vector machine. The analysis unit may also use a specific algorithm for extracting facial features. For example, the analysis unit may analyze facial features using deep learning. For example, the analysis unit may analyze facial features using a support vector machine. For example, the analysis unit may use a specific algorithm for extracting facial features. The suggestion unit may use AI to suggest an optimal wig based on the facial features analyzed by the analysis unit. The suggestion may be made based on, for example, a suggestion algorithm or a suggestion criterion, but is not limited to such examples. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig. For example, the suggestion unit may use a specific algorithm for suggesting an optimal wig based on the user's facial features. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig.The suggestion unit may also use a specific algorithm for suggesting an optimal wig based on the user's facial features. The ordering unit orders the wig suggested by the suggestion unit online. The order is placed based on, for example, an online ordering process and a payment method, but is not limited to such examples. For example, the ordering unit provides an interface through which the user can order a wig online. The ordering unit may also provide a payment method through which the user can order a wig online. The ordering unit may also provide a specific procedure through which the user can order a wig online. For example, the ordering unit provides an interface through which the user can order a wig online. The ordering unit may also provide a payment method through which the user can order a wig online. The ordering unit may also provide a specific procedure through which the user can order a wig online. The consultation unit provides a consultation on an order cutting for the wig ordered by the ordering unit. The consultation is placed based on, for example, a means of consultation and a content of consultation, but is not limited to such examples. For example, the consultation unit provides an interface through which the user can consult on an order cutting online. The consultation unit may also provide a means through which the user can consult on an order cutting over the phone. The consultation unit can also provide a means for the user to consult about an order cut in person. For example, the consultation unit provides an interface for the user to consult about an order cut online. The consultation unit can also provide a means for the user to consult about an order cut over the phone. The consultation unit can also provide a means for the user to consult about an order cut in person. As a result, the wig purchasing system according to the embodiment allows the user to simulate a wig that suits them from the comfort of their own home and easily purchase and consult about an order cut.

[0030] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods (camera, gallery, etc.) that the user has used in the past. The reception unit can also analyze the time periods when the user previously uploaded and suggest the optimal timing. The reception unit can also suggest the method with the highest success rate based on the user's past upload history. This makes it possible to suggest the optimal upload method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal upload method.

[0031] The reception unit can perform filtering based on the user's current lighting conditions and background when uploading a facial photo. For example, if the user uploads a facial photo in a dark place, the reception unit can apply a filter that corrects lighting. Furthermore, if the user uploads a facial photo in a place with a cluttered background, the reception unit can also apply a filter that blurs the background. Furthermore, if the user uploads a facial photo in natural light, the reception unit can also apply a filter that adjusts color tone. This allows the application of an optimal filter depending on the lighting and background. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's facial photo data to the generation AI and cause the generation AI to perform filtering based on the lighting conditions and background.

[0032] The reception unit can select an uploading means according to the user's input method when uploading a facial photo. For example, if the user selects voice input, the reception unit provides instructions for uploading a facial photo by following a voice guide. Furthermore, if the user selects text input, the reception unit can also provide instructions for uploading a facial photo by following a text guide. Furthermore, if the user selects image input, the reception unit can also provide instructions for uploading a facial photo by following an image guide. This makes it possible to provide an optimal uploading means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into a generation AI and cause the generation AI to select the optimal uploading means.

[0033] When uploading a facial photo, the reception unit can prioritize uploading highly relevant photos in consideration of the user's geographical location information. For example, if the user is at home, the reception unit can prioritize uploading facial photos taken at home. Furthermore, if the user is traveling, the reception unit can prioritize uploading facial photos taken at the travel destination. Furthermore, if the user is at work, the reception unit can prioritize uploading facial photos taken at work. This makes it possible to upload optimal photos based on the geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant photos.

[0034] The reception unit can analyze the user's social media activity when uploading a facial photo and upload related photos. For example, the reception unit prioritizes uploading facial photos posted by the user on social media. The reception unit can also analyze the user's social media activity and upload related facial photos. The reception unit can also upload related facial photos with reference to the activity of the user's friends on social media. This makes it possible to upload related photos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related photos.

[0035] The reception unit can customize the upload method by reflecting the user's past feedback when uploading a facial photo. For example, the reception unit can prioritize suggesting an upload method for which the user has provided past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal upload method. The reception unit can also improve the upload procedure based on the user's past feedback. This makes it possible to optimize the upload method based on the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the upload method.

[0036] When analyzing facial features, the analysis unit can adjust the level of detail of the analysis based on the importance of the face. For example, the analysis unit prioritizes analysis of important facial features (eyes, nose, mouth, etc.). The analysis unit can also adjust the level of detail of the analysis taking into account the overall balance of the face. The analysis unit can also analyze particularly prominent parts of the facial features in detail. This makes it possible to adjust the level of detail of the analysis according to the importance of the face. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input facial feature data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] When analyzing facial features, the analysis unit can apply different analysis algorithms depending on the facial category. For example, the analysis unit can apply different analysis algorithms depending on the facial features of men and women. The analysis unit can also apply different analysis algorithms depending on the facial features of young people and old people. The analysis unit can also apply different analysis algorithms depending on the facial features of different races. This makes it possible to apply the optimal analysis algorithm depending on the facial category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input facial category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past facial feature analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. This improves the accuracy of the analysis based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] When analyzing facial features, the analysis unit can determine the priority of analysis based on the time when the facial photograph was taken. For example, the analysis unit prioritizes analysis of the most recent facial photograph. The analysis unit can also prioritize analysis of facial photographs taken at a specific event. The analysis unit can also prioritize analysis of facial photographs taken within a period specified by the user. This allows the analysis priority to be determined based on the time when the photographs were taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the facial photograph was taken into the generation AI and have the generation AI determine the analysis priority.

[0040] When analyzing facial features, the analysis unit can adjust the order of analysis based on facial relevance. For example, the analysis unit prioritizes analysis of important facial features (eyes, nose, mouth, etc.). The analysis unit can also adjust the order of analysis taking into account the overall balance of the face. The analysis unit can also prioritize analysis of particularly prominent parts of the facial features. This makes it possible to adjust the order of analysis based on facial relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial feature data to a generation AI and have the generation AI adjust the order of analysis.

[0041] When analyzing facial features, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results using concise and easy-to-understand terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows the way the analysis results are presented to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] When proposing a wig, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the wig. For example, the suggestion unit suggests important wigs (such as for special events) in detail. The suggestion unit can also briefly suggest wigs for everyday use. The suggestion unit can also adjust the level of detail of the wig suggestion according to the user's request. This allows the level of detail of the suggestion to be adjusted according to the importance of the wig. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input wig importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0043] When proposing a wig, the suggestion unit can apply different suggestion algorithms depending on the wig category. For example, the suggestion unit can apply different suggestion algorithms depending on whether the wig is for a man or a woman. The suggestion unit can also apply different suggestion algorithms depending on whether the wig is for casual or formal wear. The suggestion unit can also apply different suggestion algorithms depending on different hair types and styles. This allows the most appropriate suggestion algorithm to be applied depending on the wig category. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input wig category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0044] When suggesting a wig, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, corrects the current suggestion based on the user's past wig suggestion results. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. This improves the accuracy of the suggestion based on the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0045] When suggesting wigs, the suggestion unit can determine the priority of the suggestions based on the time of submission of the wig. For example, if a user is looking for a wig for a specific event, the suggestion unit can determine the priority of the suggestions based on the time of the event. Furthermore, if a user is looking for a wig for everyday use, the suggestion unit can make suggestions regardless of the time of submission. Furthermore, if a user is in a hurry, the suggestion unit can prioritize the wig that can be obtained the soonest. This allows the priority of the suggestions to be determined based on the time of submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wig submission time data into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0046] When suggesting wigs, the suggestion unit can adjust the order of suggestions based on the relevance of the wigs. For example, if a user desires a specific style, the suggestion unit can prioritize suggesting wigs related to that style. Furthermore, if a user desires a specific hair type, the suggestion unit can prioritize suggesting wigs related to that hair type. Furthermore, if a user desires a specific brand, the suggestion unit can prioritize suggesting wigs related to that brand. This allows the order of suggestions to be adjusted based on the relevance of the wigs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wig relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When proposing a wig, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a wig suggestion using detailed technical terminology. Alternatively, if the user does not have technical expertise, the suggestion unit can make a wig suggestion using concise and easy-to-understand terminology. The suggestion unit can also adjust the way the suggestion is expressed according to the user's level of expertise. This allows the way the suggestion is expressed to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When placing an order, the order unit can analyze the user's past order history and select the optimal order method. For example, the order unit can prioritize and suggest ordering methods (online, telephone, etc.) that the user has used in the past. The order unit can also suggest the order method with the highest success rate based on the user's past order history. The order unit can also analyze the user's past order history and provide the optimal ordering procedure. This makes it possible to suggest the optimal order method based on the past order history. Some or all of the above-mentioned processing in the order unit may be performed using, for example, AI, or may be performed without using AI. For example, the order unit can input the user's past order history data into the generation AI and have the generation AI select the optimal order method.

[0049] The ordering unit can customize the ordering method based on the user's current living situation when placing an order. For example, if the user is busy, the ordering unit can provide a method that allows the user to complete the order in simple steps. The ordering unit can also provide detailed ordering instructions if the user is relaxed. Furthermore, if the user is using a specific device, the ordering unit can provide ordering instructions optimized for that device. This makes it possible to provide the optimal ordering method based on the user's current living situation. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can input the user's living situation data into a generation AI and have the generation AI customize the ordering method.

[0050] The ordering unit can improve the ordering method by reflecting user feedback when placing an order. The ordering unit can improve the ordering procedure, for example, based on feedback provided by the user in the past. The ordering unit can also analyze the user's feedback and suggest the optimal ordering method. The ordering unit can also customize the ordering procedure by referring to the user's feedback. This allows the ordering method to be improved based on the feedback. Some or all of the above-mentioned processing in the ordering unit can be performed, for example, using AI or without using AI. For example, the ordering unit can input user feedback data into a generation AI and have the generation AI improve the ordering method.

[0051] When placing an order, the order unit can select the optimal ordering method by taking into account the user's geographical location information. For example, if the user is at home, the order unit can preferentially suggest online ordering. Furthermore, if the user is near a store, the order unit can also preferentially suggest ordering at the store. Furthermore, if the user is traveling, the order unit can also suggest ordering at the nearest store. This makes it possible to provide the optimal ordering method based on the geographical location information. Some or all of the above-mentioned processing in the order unit may be performed using, or without, AI, for example. For example, the order unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal ordering method.

[0052] When placing an order, the ordering unit can analyze the user's social media activity and suggest an ordering method. For example, the ordering unit can prioritize suggesting products that the user has shared on social media. The ordering unit can also analyze the user's social media activity and suggest related products. The ordering unit can also suggest related products by referring to the activity of the user's friends on social media. This makes it possible to provide the optimal ordering method based on social media activity. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal ordering method.

[0053] When placing an order, the ordering unit can customize the ordering method by reflecting the user's past feedback. The ordering unit can improve the ordering procedure, for example, based on feedback provided by the user in the past. The ordering unit can also analyze the user's feedback and suggest the optimal ordering method. The ordering unit can also customize the ordering procedure by referring to the user's feedback. This allows the ordering method to be optimized based on the past feedback. Some or all of the above-mentioned processing in the ordering unit can be performed, for example, using AI or without using AI. For example, the ordering unit can input the user's feedback data into a generation AI and have the generation AI customize the ordering method.

[0054] At the time of consultation, the consultation unit can select the optimal consultation method by analyzing the user's past consultation history. For example, the consultation unit can prioritize and suggest consultation methods (online, telephone, etc.) that the user has used in the past. The consultation unit can also suggest the consultation method with the highest success rate based on the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide the optimal consultation procedure. This makes it possible to provide the optimal consultation method based on the past consultation history. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past consultation history data into the generation AI and have the generation AI select the optimal consultation method.

[0055] The consultation unit can customize the consultation method based on the user's current living situation during the consultation. For example, if the user is busy, the consultation unit can provide a method that allows the user to complete the consultation in simple steps. The consultation unit can also provide detailed consultation procedures if the user is relaxed. Furthermore, if the user is using a specific device, the consultation unit can provide consultation procedures optimized for that device. This makes it possible to provide the optimal consultation method based on the current living situation. Some or all of the above-mentioned processing in the consultation unit may be performed using AI, for example, or may be performed without using AI. For example, the consultation unit can input the user's living situation data into a generation AI and have the generation AI customize the consultation method.

[0056] The consultation unit can improve the consultation method by reflecting the user's feedback during the consultation. The consultation unit can improve the consultation procedure, for example, based on feedback provided by the user in the past. The consultation unit can also analyze the user's feedback and suggest the optimal consultation method. The consultation unit can also customize the consultation procedure by referring to the user's feedback. This allows the consultation method to be improved based on the feedback. Some or all of the above-mentioned processing in the consultation unit can be performed using AI, for example, or can be performed without using AI. For example, the consultation unit can input the user's feedback data into a generation AI and have the generation AI improve the consultation method.

[0057] At the time of consultation, the consultation unit can select the optimal consultation method taking into account the user's geographical location information. For example, if the user is at home, the consultation unit can preferentially suggest online consultation. Furthermore, if the user is near a store, the consultation unit can also preferentially suggest in-store consultation. Furthermore, if the user is traveling, the consultation unit can also suggest consultation at the nearest store. This makes it possible to provide the optimal consultation method based on the geographical location information. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal consultation method.

[0058] At the time of consultation, the consultation unit can analyze the user's social media activity and suggest a means of consultation. For example, the consultation unit can prioritize suggestions for consultation regarding products that the user has shared on social media. The consultation unit can also analyze the user's social media activity and suggest related means of consultation. The consultation unit can also suggest related means of consultation by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal means of consultation based on the social media activity. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal means of consultation.

[0059] The consultation unit can customize the consultation method by reflecting the user's past feedback during the consultation. The consultation unit can improve the consultation procedure based on, for example, feedback provided by the user in the past. The consultation unit can also analyze the user's feedback and suggest an optimal consultation method. The consultation unit can also customize the consultation procedure by referring to the user's feedback. This allows the consultation method to be optimized based on the past feedback. Some or all of the above-mentioned processing in the consultation unit can be performed using, for example, AI, or can be performed without using AI. For example, the consultation unit can input the user's feedback data into a generation AI and have the generation AI customize the consultation method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The suggestion unit can analyze the user's past purchase history and suggest a new wig based on the style and color of wigs previously purchased by the user. For example, data on wigs previously purchased by the user can be used to suggest wigs of similar style and color. The suggestion unit can also take into account the ratings of wigs previously purchased by the user and preferentially suggest wigs similar to highly rated wigs. Furthermore, the suggestion unit can analyze the frequency of use of wigs previously purchased by the user and suggest wigs similar to the frequently used wigs. This makes it possible to suggest the optimal wig based on the user's past purchase history.

[0062] The analysis unit can analyze not only the user's facial features but also the user's hair type and hair volume to suggest the most suitable wig. For example, the analysis unit can analyze the user's hair type (straight, wavy, curly, etc.) and suggest a wig that suits it. The analysis unit can also analyze the user's hair volume (thin, normal, thick, etc.) and suggest a wig accordingly. Furthermore, the analysis unit can analyze the user's hair color and suggest the most suitable wig color to achieve a natural look. This makes it possible to suggest the most suitable wig based on the user's hair features.

[0063] The reception unit can provide information on the nearest wig store taking into consideration the user's geographical location information. For example, if the user is at home, information on the wig store closest to the user's home can be provided. If the user is traveling, information on the wig store closest to the user's travel destination can also be provided. Furthermore, if the user is at work, information on the wig store closest to the user's workplace can also be provided. This makes it possible to provide optimal wig store information based on the user's geographical location information.

[0064] The suggestion unit can analyze the user's social media activity and suggest wig styles that the user is interested in. For example, it can suggest wig styles that the user has liked or commented on on social media. It can also suggest wig styles used by influencers the user follows. It can also suggest wig styles that the user has shared. This makes it possible to suggest the most suitable wig style based on the user's social media activity.

[0065] The analysis unit analyzes not only the user's facial features but also the user's body type and height, and can suggest a wig that takes into account the user's overall balance. For example, the analysis unit can suggest the length of the wig based on the user's height. It can also suggest the volume of the wig based on the user's body type. It can also suggest a wig that matches the user's overall style. This makes it possible to suggest the optimal wig that takes into account the user's overall balance.

[0066] The ordering unit can analyze the user's past order history and prioritize suggestions for payment methods and delivery methods previously selected by the user. For example, if the user has previously paid by credit card, the same payment method can be suggested. It can also prioritize suggestions for delivery methods previously selected by the user (such as home delivery or convenience store pickup). Furthermore, it can also suggest the optimal ordering method by taking into account coupons and discount information used by the user in the past. This makes it possible to suggest the optimal ordering method based on the user's past order history.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit uploads a photo of the user's face. Users can upload a photo of their face using a smartphone or computer. They can also upload a photo of their face taken with a camera directly, or they can select an existing photo and upload it. Step 2: The analysis unit uses AI to analyze the facial photo uploaded by the reception unit and analyze facial features. The analysis is performed using algorithms such as deep learning and support vector machines. Step 3: The suggestion unit suggests the most suitable wig based on the facial features analyzed by the analysis unit. The suggestion is made using a recommendation system or machine learning algorithm. Step 4: The ordering unit orders the wig suggested by the suggestion unit online. The ordering unit provides an interface and a payment method for the user to order a wig online. Step 5: The consultation department provides consultation on custom cutting of the wig ordered by the ordering department. The consultation department provides a means for users to consult on custom cutting online, by phone, or in person.

[0069] (Example 2) A wig purchasing system according to an embodiment of the present invention allows users to simulate wigs that suit them from the comfort of their own home and easily purchase and consult about custom haircuts. The wig purchasing system allows users to simulate wigs that suit them from the comfort of their own home. Users can then purchase wigs through mail order, or visit a store near their home to consult about custom haircuts. This allows users to easily purchase wigs that satisfy them. Furthermore, the wig purchasing system aims to popularize wigs for both men and women, as the market for men's wigs is small. For example, the wig purchasing system allows users to simulate wigs that suit them from the comfort of their own home. Users upload a photo of their face, and AI suggests the most suitable wig based on their face shape and skin color. For example, when a user uploads a photo of their face, AI automatically analyzes their facial features and selects the most suitable wig from multiple wigs. The wig purchasing system then allows users to order the wig they selected in the simulation online. This allows users to easily purchase wigs from the comfort of their own home. Furthermore, the wig purchasing system allows users to bring the wig they have purchased and consult with a professional stylist at the store. This allows users to have the best cut and style suggested for them, and to obtain a wig that satisfies them. This allows the wig purchasing system to easily purchase a wig that satisfies them as appearance care for hair loss caused by anti-cancer drug treatment. This allows the wig purchasing system to simulate a wig that suits them from the comfort of their own home, and to easily purchase and consult about custom cutting. For example, users can easily find a wig that suits them from the comfort of their own home. Also, users can easily purchase a wig from the comfort of their own home. This allows users to have the best cut and style suggested for them, and to obtain a wig that satisfies them.This makes it possible for people to easily purchase a satisfactory wig as appearance care for hair loss caused by anti-cancer drug treatment.

[0070] A wig purchasing system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, an order unit, and a consultation unit. The reception unit uploads a facial photo of a user. The facial photo of a user includes, for example, resolution, file format, and shooting conditions, but is not limited to these examples. The reception unit allows a user to upload a facial photo using, for example, a smartphone or a personal computer. The reception unit can also directly upload a facial photo taken by the user with a camera. The reception unit can also allow a user to select and upload an existing photo. For example, the reception unit uploads a facial photo taken by the user with a smartphone camera. The reception unit can also upload a facial photo taken by the user with a personal computer camera. The reception unit can also allow a user to select and upload an existing photo. The analysis unit uses AI to analyze the facial photo uploaded by the reception unit and analyze facial features. The analysis is performed based on, for example, the algorithm used and the accuracy of the analysis, but is not limited to these examples. For example, the analysis unit analyzes facial features using deep learning. The analysis unit can also analyze facial features using a support vector machine. The analysis unit may also use a specific algorithm for extracting facial features. For example, the analysis unit may analyze facial features using deep learning. For example, the analysis unit may analyze facial features using a support vector machine. For example, the analysis unit may use a specific algorithm for extracting facial features. The suggestion unit may use AI to suggest an optimal wig based on the facial features analyzed by the analysis unit. The suggestion may be made based on, for example, a suggestion algorithm or a suggestion criterion, but is not limited to such examples. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig. For example, the suggestion unit may use a specific algorithm for suggesting an optimal wig based on the user's facial features. For example, the suggestion unit may use a recommendation system to suggest an optimal wig. For example, the suggestion unit may use a machine learning algorithm to suggest an optimal wig.The suggestion unit may also use a specific algorithm for suggesting an optimal wig based on the user's facial features. The ordering unit orders the wig suggested by the suggestion unit online. The order is placed based on, for example, an online ordering process and a payment method, but is not limited to such examples. For example, the ordering unit provides an interface through which the user can order a wig online. The ordering unit may also provide a payment method through which the user can order a wig online. The ordering unit may also provide a specific procedure through which the user can order a wig online. For example, the ordering unit provides an interface through which the user can order a wig online. The ordering unit may also provide a payment method through which the user can order a wig online. The ordering unit may also provide a specific procedure through which the user can order a wig online. The consultation unit provides a consultation on an order cutting for the wig ordered by the ordering unit. The consultation is placed based on, for example, a means of consultation and a content of consultation, but is not limited to such examples. For example, the consultation unit provides an interface through which the user can consult on an order cutting online. The consultation unit may also provide a means through which the user can consult on an order cutting over the phone. The consultation unit can also provide a means for the user to consult about an order cut in person. For example, the consultation unit provides an interface for the user to consult about an order cut online. The consultation unit can also provide a means for the user to consult about an order cut over the phone. The consultation unit can also provide a means for the user to consult about an order cut in person. As a result, the wig purchasing system according to the embodiment allows the user to simulate a wig that suits them from the comfort of their own home and easily purchase and consult about an order cut.

[0071] The reception unit can estimate the user's emotions and adjust the timing of uploading a facial photo based on the estimated user emotions. For example, if the user is nervous, the reception unit can display a guide to help the user relax and encourage the user to upload a facial photo. Furthermore, if the user is relaxed, the reception unit can also encourage the user to upload a facial photo immediately, providing a smooth operation. Furthermore, if the user is in a hurry, the reception unit can enable the user to upload a facial photo with a simple operation. This allows the user to upload a facial photo at the optimal timing depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0072] The reception unit can analyze the user's past facial photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods (camera, gallery, etc.) that the user has used in the past. The reception unit can also analyze the time periods when the user previously uploaded and suggest the optimal timing. The reception unit can also suggest the method with the highest success rate based on the user's past upload history. This makes it possible to suggest the optimal upload method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal upload method.

[0073] The reception unit can perform filtering based on the user's current lighting conditions and background when uploading a facial photo. For example, if the user uploads a facial photo in a dark place, the reception unit can apply a filter that corrects lighting. Furthermore, if the user uploads a facial photo in a place with a cluttered background, the reception unit can also apply a filter that blurs the background. Furthermore, if the user uploads a facial photo in natural light, the reception unit can also apply a filter that adjusts color tone. This allows the application of an optimal filter depending on the lighting and background. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's facial photo data to the generation AI and cause the generation AI to perform filtering based on the lighting conditions and background.

[0074] The reception unit can select an uploading means according to the user's input method when uploading a facial photo. For example, if the user selects voice input, the reception unit provides instructions for uploading a facial photo by following a voice guide. Furthermore, if the user selects text input, the reception unit can also provide instructions for uploading a facial photo by following a text guide. Furthermore, if the user selects image input, the reception unit can also provide instructions for uploading a facial photo by following an image guide. This makes it possible to provide an optimal uploading means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into a generation AI and cause the generation AI to select the optimal uploading means.

[0075] The reception unit can estimate the user's emotions and determine the priority of facial photos to be uploaded based on the estimated user emotions. For example, if the user is nervous, the reception unit can display a guide to help the user relax and encourage the user to upload a facial photo. Furthermore, if the user is relaxed, the reception unit can also encourage the user to upload a facial photo immediately, providing a smooth operation. Furthermore, if the user is in a hurry, the reception unit can enable the user to upload a facial photo with a simple operation. This allows the priority of facial photo uploads to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0076] When uploading a facial photo, the reception unit can prioritize uploading highly relevant photos in consideration of the user's geographical location information. For example, if the user is at home, the reception unit can prioritize uploading facial photos taken at home. Furthermore, if the user is traveling, the reception unit can prioritize uploading facial photos taken at the travel destination. Furthermore, if the user is at work, the reception unit can prioritize uploading facial photos taken at work. This makes it possible to upload optimal photos based on the geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant photos.

[0077] The reception unit can analyze the user's social media activity when uploading a facial photo and upload related photos. For example, the reception unit prioritizes uploading facial photos posted by the user on social media. The reception unit can also analyze the user's social media activity and upload related facial photos. The reception unit can also upload related facial photos with reference to the activity of the user's friends on social media. This makes it possible to upload related photos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related photos.

[0078] The reception unit can customize the upload method by reflecting the user's past feedback when uploading a facial photo. For example, the reception unit can prioritize suggesting an upload method for which the user has provided past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal upload method. The reception unit can also improve the upload procedure based on the user's past feedback. This makes it possible to optimize the upload method based on the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the upload method.

[0079] The analysis unit can estimate the user's emotions and adjust the way the facial feature analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display detailed facial feature analysis results. Furthermore, if the user is nervous, the analysis unit can display concise facial feature analysis results. Furthermore, if the user is excited, the analysis unit can display visually appealing facial feature analysis results. This allows the way the analysis results are presented to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0080] When analyzing facial features, the analysis unit can adjust the level of detail of the analysis based on the importance of the face. For example, the analysis unit prioritizes analysis of important facial features (eyes, nose, mouth, etc.). The analysis unit can also adjust the level of detail of the analysis taking into account the overall balance of the face. The analysis unit can also analyze particularly prominent parts of the facial features in detail. This makes it possible to adjust the level of detail of the analysis according to the importance of the face. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input facial feature data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0081] When analyzing facial features, the analysis unit can apply different analysis algorithms depending on the facial category. For example, the analysis unit can apply different analysis algorithms depending on the facial features of men and women. The analysis unit can also apply different analysis algorithms depending on the facial features of young people and old people. The analysis unit can also apply different analysis algorithms depending on the facial features of different races. This makes it possible to apply the optimal analysis algorithm depending on the facial category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input facial category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0082] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past facial feature analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. This improves the accuracy of the analysis based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can analyze facial features in a short time. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can also provide a visually appealing analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0084] When analyzing facial features, the analysis unit can determine the priority of analysis based on the time when the facial photograph was taken. For example, the analysis unit prioritizes analysis of the most recent facial photograph. The analysis unit can also prioritize analysis of facial photographs taken at a specific event. The analysis unit can also prioritize analysis of facial photographs taken within a period specified by the user. This allows the analysis priority to be determined based on the time when the photographs were taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the facial photograph was taken into the generation AI and have the generation AI determine the analysis priority.

[0085] When analyzing facial features, the analysis unit can adjust the order of analysis based on facial relevance. For example, the analysis unit prioritizes analysis of important facial features (eyes, nose, mouth, etc.). The analysis unit can also adjust the order of analysis taking into account the overall balance of the face. The analysis unit can also prioritize analysis of particularly prominent parts of the facial features. This makes it possible to adjust the order of analysis based on facial relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial feature data to a generation AI and have the generation AI adjust the order of analysis.

[0086] When analyzing facial features, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results using concise and easy-to-understand terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows the way the analysis results are presented to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0087] The suggestion unit can estimate the user's emotions and adjust the manner in which the wig suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed wig suggestions. If the user is nervous, the suggestion unit can also provide concise wig suggestions. If the user is excited, the suggestion unit can also provide visually appealing wig suggestions. This allows the manner in which the wig suggestions are presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0088] When proposing a wig, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the wig. For example, the suggestion unit suggests important wigs (such as for special events) in detail. The suggestion unit can also briefly suggest wigs for everyday use. The suggestion unit can also adjust the level of detail of the wig suggestion according to the user's request. This allows the level of detail of the suggestion to be adjusted according to the importance of the wig. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input wig importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0089] When proposing a wig, the suggestion unit can apply different suggestion algorithms depending on the wig category. For example, the suggestion unit can apply different suggestion algorithms depending on whether the wig is for a man or a woman. The suggestion unit can also apply different suggestion algorithms depending on whether the wig is for casual or formal wear. The suggestion unit can also apply different suggestion algorithms depending on different hair types and styles. This allows the most appropriate suggestion algorithm to be applied depending on the wig category. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input wig category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0090] When suggesting a wig, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, corrects the current suggestion based on the user's past wig suggestion results. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. This improves the accuracy of the suggestion based on the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can suggest a short, concise wig. If the user is relaxed, the suggestion unit can suggest a longer wig with detailed explanations. If the user is excited, the suggestion unit can suggest a wig with visually stimulating effects. This allows the length of the suggestion to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0092] When suggesting wigs, the suggestion unit can determine the priority of the suggestions based on the time of submission of the wig. For example, if a user is looking for a wig for a specific event, the suggestion unit can determine the priority of the suggestions based on the time of the event. Furthermore, if a user is looking for a wig for everyday use, the suggestion unit can make suggestions regardless of the time of submission. Furthermore, if a user is in a hurry, the suggestion unit can prioritize the wig that can be obtained the soonest. This allows the priority of the suggestions to be determined based on the time of submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wig submission time data into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0093] When suggesting wigs, the suggestion unit can adjust the order of suggestions based on the relevance of the wigs. For example, if a user desires a specific style, the suggestion unit can prioritize suggesting wigs related to that style. Furthermore, if a user desires a specific hair type, the suggestion unit can prioritize suggesting wigs related to that hair type. Furthermore, if a user desires a specific brand, the suggestion unit can prioritize suggesting wigs related to that brand. This allows the order of suggestions to be adjusted based on the relevance of the wigs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wig relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0094] When proposing a wig, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a wig suggestion using detailed technical terminology. Alternatively, if the user does not have technical expertise, the suggestion unit can make a wig suggestion using concise and easy-to-understand terminology. The suggestion unit can also adjust the way the suggestion is expressed according to the user's level of expertise. This allows the way the suggestion is expressed to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0095] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated user emotions. For example, if the user is relaxed, the ordering unit can provide detailed ordering instructions. If the user is nervous, the ordering unit can also provide concise ordering instructions. If the user is in a hurry, the ordering unit can also provide instructions that allow the user to complete the order quickly. This allows the ordering method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the ordering unit can be performed using AI, for example, or without AI. For example, the ordering unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0096] When placing an order, the order unit can analyze the user's past order history and select the optimal order method. For example, the order unit can prioritize and suggest ordering methods (online, telephone, etc.) that the user has used in the past. The order unit can also suggest the order method with the highest success rate based on the user's past order history. The order unit can also analyze the user's past order history and provide the optimal ordering procedure. This makes it possible to suggest the optimal order method based on the past order history. Some or all of the above-mentioned processing in the order unit may be performed using, for example, AI, or may be performed without using AI. For example, the order unit can input the user's past order history data into the generation AI and have the generation AI select the optimal order method.

[0097] The ordering unit can customize the ordering method based on the user's current living situation when placing an order. For example, if the user is busy, the ordering unit can provide a method that allows the user to complete the order in simple steps. The ordering unit can also provide detailed ordering instructions if the user is relaxed. Furthermore, if the user is using a specific device, the ordering unit can provide ordering instructions optimized for that device. This makes it possible to provide the optimal ordering method based on the user's current living situation. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can input the user's living situation data into a generation AI and have the generation AI customize the ordering method.

[0098] The ordering unit can improve the ordering method by reflecting user feedback when placing an order. The ordering unit can improve the ordering procedure, for example, based on feedback provided by the user in the past. The ordering unit can also analyze the user's feedback and suggest the optimal ordering method. The ordering unit can also customize the ordering procedure by referring to the user's feedback. This allows the ordering method to be improved based on the feedback. Some or all of the above-mentioned processing in the ordering unit can be performed, for example, using AI or without using AI. For example, the ordering unit can input user feedback data into a generation AI and have the generation AI improve the ordering method.

[0099] The ordering unit can estimate a user's emotions and prioritize orders based on the estimated user emotions. For example, the ordering unit prioritizes orders when the user is in a hurry. The ordering unit can also process orders in the normal order when the user is relaxed. The ordering unit can also provide a procedure for quickly completing an order when the user is nervous. This allows the prioritization of orders to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the ordering unit may be performed using AI, or may be performed without AI. For example, the ordering unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0100] When placing an order, the order unit can select the optimal ordering method by taking into account the user's geographical location information. For example, if the user is at home, the order unit can preferentially suggest online ordering. Furthermore, if the user is near a store, the order unit can also preferentially suggest ordering at the store. Furthermore, if the user is traveling, the order unit can also suggest ordering at the nearest store. This makes it possible to provide the optimal ordering method based on the geographical location information. Some or all of the above-mentioned processing in the order unit may be performed using, or without, AI, for example. For example, the order unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal ordering method.

[0101] When placing an order, the ordering unit can analyze the user's social media activity and suggest an ordering method. For example, the ordering unit can prioritize suggesting products that the user has shared on social media. The ordering unit can also analyze the user's social media activity and suggest related products. The ordering unit can also suggest related products by referring to the activity of the user's friends on social media. This makes it possible to provide the optimal ordering method based on social media activity. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal ordering method.

[0102] When placing an order, the ordering unit can customize the ordering method by reflecting the user's past feedback. The ordering unit can improve the ordering procedure, for example, based on feedback provided by the user in the past. The ordering unit can also analyze the user's feedback and suggest the optimal ordering method. The ordering unit can also customize the ordering procedure by referring to the user's feedback. This allows the ordering method to be optimized based on the past feedback. Some or all of the above-mentioned processing in the ordering unit can be performed, for example, using AI or without using AI. For example, the ordering unit can input the user's feedback data into a generation AI and have the generation AI customize the ordering method.

[0103] The consultation unit can estimate the user's emotions and adjust the consultation method based on the estimated user emotions. For example, if the user is relaxed, the consultation unit can provide detailed consultation procedures. Furthermore, if the user is nervous, the consultation unit can provide concise consultation procedures. Furthermore, if the user is in a hurry, the consultation unit can provide procedures that allow the consultation to be completed quickly. This allows the consultation method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0104] At the time of consultation, the consultation unit can select the optimal consultation method by analyzing the user's past consultation history. For example, the consultation unit can prioritize and suggest consultation methods (online, telephone, etc.) that the user has used in the past. The consultation unit can also suggest the consultation method with the highest success rate based on the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide the optimal consultation procedure. This makes it possible to provide the optimal consultation method based on the past consultation history. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past consultation history data into the generation AI and have the generation AI select the optimal consultation method.

[0105] The consultation unit can customize the consultation method based on the user's current living situation during the consultation. For example, if the user is busy, the consultation unit can provide a method that allows the user to complete the consultation in simple steps. The consultation unit can also provide detailed consultation procedures if the user is relaxed. Furthermore, if the user is using a specific device, the consultation unit can provide consultation procedures optimized for that device. This makes it possible to provide the optimal consultation method based on the current living situation. Some or all of the above-mentioned processing in the consultation unit may be performed using AI, for example, or may be performed without using AI. For example, the consultation unit can input the user's living situation data into a generation AI and have the generation AI customize the consultation method.

[0106] The consultation unit can improve the consultation method by reflecting the user's feedback during the consultation. The consultation unit can improve the consultation procedure, for example, based on feedback provided by the user in the past. The consultation unit can also analyze the user's feedback and suggest the optimal consultation method. The consultation unit can also customize the consultation procedure by referring to the user's feedback. This allows the consultation method to be improved based on the feedback. Some or all of the above-mentioned processing in the consultation unit can be performed using AI, for example, or can be performed without using AI. For example, the consultation unit can input the user's feedback data into a generation AI and have the generation AI improve the consultation method.

[0107] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated user emotions. For example, if the user is in a hurry, the consultation unit can prioritize consultations. Furthermore, if the user is relaxed, the consultation unit can process consultations in the normal order. Furthermore, if the user is nervous, the consultation unit can provide a procedure for quickly completing the consultation. This allows the priority of consultations to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0108] At the time of consultation, the consultation unit can select the optimal consultation method taking into account the user's geographical location information. For example, if the user is at home, the consultation unit can preferentially suggest online consultation. Furthermore, if the user is near a store, the consultation unit can also preferentially suggest in-store consultation. Furthermore, if the user is traveling, the consultation unit can also suggest consultation at the nearest store. This makes it possible to provide the optimal consultation method based on the geographical location information. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal consultation method.

[0109] At the time of consultation, the consultation unit can analyze the user's social media activity and suggest a means of consultation. For example, the consultation unit can prioritize suggestions for consultation regarding products that the user has shared on social media. The consultation unit can also analyze the user's social media activity and suggest related means of consultation. The consultation unit can also suggest related means of consultation by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal means of consultation based on the social media activity. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal means of consultation.

[0110] The consultation unit can customize the consultation method by reflecting the user's past feedback during the consultation. The consultation unit can improve the consultation procedure based on, for example, feedback provided by the user in the past. The consultation unit can also analyze the user's feedback and suggest an optimal consultation method. The consultation unit can also customize the consultation procedure by referring to the user's feedback. This allows the consultation method to be optimized based on the past feedback. Some or all of the above-mentioned processing in the consultation unit can be performed using, for example, AI, or can be performed without using AI. For example, the consultation unit can input the user's feedback data into a generation AI and have the generation AI customize the consultation method. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, ordering unit, and consultation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit uploads a photo of the user's face using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal wig based on the analysis results. The ordering unit is realized by the control unit 46A of the smart device 14 and orders a wig online. The consultation unit is realized by the control unit 46A of the smart device 14 and provides consultation on custom haircuts. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, ordering unit, and consultation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit uploads a photo of the user's face using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal wig based on the analysis results. The ordering unit is realized by the control unit 46A of the smart glasses 214 and orders a wig online. The consultation unit is realized by the control unit 46A of the smart glasses 214 and provides consultation on custom haircuts. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, ordering unit, and consultation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit uploads a photo of the user's face using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal wig based on the analysis results. The ordering unit is realized by the control unit 46A of the headset terminal 314 and orders a wig online. The consultation unit is realized by the control unit 46A of the headset terminal 314 and provides consultation on custom haircuts. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, ordering unit, and consultation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit uploads a photo of the user's face using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal wig based on the analysis results. The ordering unit is realized by the control unit 46A of the robot 414 and orders a wig online. The consultation unit is realized by the control unit 46A of the robot 414 and provides consultation on custom haircuts.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The suggestion unit can analyze the user's past purchase history and suggest a new wig based on the style and color of wigs previously purchased by the user. For example, data on wigs previously purchased by the user can be used to suggest wigs of similar style and color. The suggestion unit can also take into account the ratings of wigs previously purchased by the user and preferentially suggest wigs similar to highly rated wigs. Furthermore, the suggestion unit can analyze the frequency of use of wigs previously purchased by the user and suggest wigs similar to the frequently used wigs. This makes it possible to suggest the optimal wig based on the user's past purchase history.

[0113] The analysis unit can analyze not only the user's facial features but also the user's hair type and hair volume to suggest the most suitable wig. For example, the analysis unit can analyze the user's hair type (straight, wavy, curly, etc.) and suggest a wig that suits it. The analysis unit can also analyze the user's hair volume (thin, normal, thick, etc.) and suggest a wig accordingly. Furthermore, the analysis unit can analyze the user's hair color and suggest the most suitable wig color to achieve a natural look. This makes it possible to suggest the most suitable wig based on the user's hair features.

[0114] The suggestion unit can estimate the user's emotions and suggest a wig style based on the estimated user's emotions. For example, if the user is relaxed, a casual style wig can be suggested. If the user is nervous, a formal style wig can be suggested. Furthermore, if the user is excited, a trendy style wig can be suggested. In this way, the optimal wig style can be suggested according to the user's emotions.

[0115] The order unit can estimate the user's emotions and suggest a payment method based on the estimated user's emotions. For example, if the user is relaxed, it can suggest a regular payment method (credit card, debit card, etc.). If the user is nervous, it can suggest an easy and quick payment method (electronic money, two-dimensional code payment (e.g., QR code payment), etc.). Furthermore, if the user is in a hurry, it can suggest a payment method that can be completed with one click. This allows the optimal payment method to be suggested according to the user's emotions.

[0116] The consultation unit can estimate the user's emotions and adjust the content of the consultation based on the estimated user's emotions. For example, if the user is relaxed, detailed consultation content can be provided. If the user is nervous, brief consultation content that focuses on the main points can be provided. Furthermore, if the user is in a hurry, content that allows the consultation to be completed quickly can be provided. In this way, optimal consultation content can be provided according to the user's emotions.

[0117] The reception unit can provide information on the nearest wig store taking into consideration the user's geographical location information. For example, if the user is at home, information on the wig store closest to the user's home can be provided. If the user is traveling, information on the wig store closest to the user's travel destination can also be provided. Furthermore, if the user is at work, information on the wig store closest to the user's workplace can also be provided. This makes it possible to provide optimal wig store information based on the user's geographical location information.

[0118] The suggestion unit can analyze the user's social media activity and suggest wig styles that the user is interested in. For example, it can suggest wig styles that the user has liked or commented on on social media. It can also suggest wig styles used by influencers the user follows. It can also suggest wig styles that the user has shared. This makes it possible to suggest the most suitable wig style based on the user's social media activity.

[0119] The analysis unit analyzes not only the user's facial features but also the user's body type and height, and can suggest a wig that takes into account the user's overall balance. For example, the analysis unit can suggest the length of the wig based on the user's height. It can also suggest the volume of the wig based on the user's body type. It can also suggest a wig that matches the user's overall style. This makes it possible to suggest the optimal wig that takes into account the user's overall balance.

[0120] The suggestion unit can estimate the user's emotions and suggest a wig color based on the estimated user's emotions. For example, if the user is relaxed, a wig with a natural color can be suggested. If the user is excited, a wig with a vivid color can be suggested. Furthermore, if the user is calm, a wig with a subdued color can be suggested. In this way, the optimal wig color can be suggested according to the user's emotions.

[0121] The ordering unit can analyze the user's past order history and prioritize suggestions for payment methods and delivery methods previously selected by the user. For example, if the user has previously paid by credit card, the same payment method can be suggested. It can also prioritize suggestions for delivery methods previously selected by the user (such as home delivery or convenience store pickup). Furthermore, it can also suggest the optimal ordering method by taking into account coupons and discount information used by the user in the past. This makes it possible to suggest the optimal ordering method based on the user's past order history.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit uploads a photo of the user's face. Users can upload a photo of their face using a smartphone or computer. They can also upload a photo of their face taken with a camera directly, or they can select an existing photo and upload it. Step 2: The analysis unit uses AI to analyze the facial photo uploaded by the reception unit and analyze facial features. The analysis is performed using algorithms such as deep learning and support vector machines. Step 3: The suggestion unit suggests the most suitable wig based on the facial features analyzed by the analysis unit. The suggestion is made using a recommendation system or machine learning algorithm. Step 4: The ordering unit orders the wig suggested by the suggestion unit online. The ordering unit provides an interface and a payment method for the user to order a wig online. Step 5: The consultation department provides consultation on custom cutting of the wig ordered by the ordering department. The consultation department provides a means for users to consult on custom cutting online, by phone, or in person.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for uploading a user's face photo; an analysis unit that analyzes the face photo uploaded by the reception unit and analyzes facial features; a suggestion unit that suggests wigs based on the facial features analyzed by the analysis unit; an ordering unit for ordering the wig suggested by the suggestion unit online; a consultation unit that provides consultation on custom cutting of the wig ordered by the ordering unit; A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of uploading face photos based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past face photo upload history and select the upload method 2. The system of claim 1.

4. The reception unit Filtering face photos based on the user's current lighting conditions and background when uploading them 2. The system of claim 1.

5. The reception unit When uploading a face photo, select the upload method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the face photos to be uploaded based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When uploading a face photo, prioritize uploading the most relevant photo based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When uploading a face photo, analyze the user's social media activity and upload relevant photos.

2. The system of claim 1.

Images