system

Abstract

Provide a system. A terminal receives image data of a customer photographed by a user and transmits the image data together with customer information to a server; An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance. A server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and on the customer's past treatment history and preferences, and transmits the results to the user's terminal; A system including suggestions displayed on a user's device.

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] In today's beauty and aesthetics industries, it is important to accurately diagnose each customer's skin and hair condition and recommend the most appropriate treatments and products. However, current systems require significant time and experience from hairdressers and aestheticians, and the diagnosis is subjective, making it difficult to make accurate recommendations. It is also difficult to quickly understand a customer's past treatment history and preferences and make recommendations based on them. Therefore, there is a need for a system that reduces the burden on hairdressers and aestheticians and allows them to make faster and more accurate recommendations. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a terminal that allows a user to capture image data of a customer and transmits the image data and customer information to a server. The server is equipped with an artificial intelligence module that analyzes the received image data and diagnoses the customer's skin condition, hair condition, and facial feature balance. Furthermore, the server generates optimal products and treatment menus based on the AI ​​module's diagnosis results and past treatment history and customer preferences, and transmits the resulting menu to the user's terminal. This system allows hairdressers and estheticians to make accurate recommendations in a short amount of time and enables more effective recommendations based on the customer's past treatment history and preferences.

[0006] A "user" is a person, such as a hairdresser or aesthetician, who operates this system to take image data of a customer and input the information.

[0007] A "terminal" is a device used by a user, capable of taking a photo of a customer and inputting customer information, and capable of transmitting this data to a server.

[0008] "Image data" is photographic data of a customer's face and hair taken by a user using a terminal.

[0009] "Customer information" is text data such as the customer's name, past treatment history, preferences, etc.

[0010] The "server" is a device that receives image data and customer information sent from a user terminal, sends it to an artificial intelligence module, and receives the analysis results.

[0011] The "artificial intelligence module" is a program that has an algorithm that analyzes image data sent from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance.

[0012] The "diagnosis results" are information about the customer's skin condition, hair condition, and facial feature balance obtained by the artificial intelligence module analyzing image data.

[0013] "Past treatment history" refers to data relating to the treatments a customer has previously received and the products they have used.

[0014] "Proposal content" refers to information including the most suitable products and treatment menus for a customer, generated by the server based on the diagnostic results of the artificial intelligence module and past treatment history.

[0015] "Encoding" is the process by which the terminal converts captured image data and entered customer information into an appropriate format when sending the data to the server.

[0016] The "database" is a system installed on a server for storing and managing data such as customer information, treatment history, and diagnostic results from the artificial intelligence module.

[0017] The "pre-processing means" is a process by which the server converts image data sent from the user terminal into a format suitable for the artificial intelligence module. [Brief explanation of the drawings]

[0018] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0040] System configuration

[0041] This system mainly consists of a user terminal, a server, and an artificial intelligence module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The server receives the data sent from the user terminal, analyzes it in conjunction with the artificial intelligence module, and generates optimal suggestions. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0042] Operation on the user device

[0043] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (name, past treatment history, etc.) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to the server.

[0044] Data reception and analysis on the server

[0045] The server receives the image data and customer information sent from the user terminal. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. If the received data is determined to be problem-free, the server encodes the image data and converts it into an appropriate format. The converted data is then sent to the artificial intelligence module.

[0046] Image analysis using artificial intelligence modules

[0047] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0048] Data integration and proposal generation on the server

[0049] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. It then comprehensively considers the past data and the diagnosis results to select the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0050] Displaying suggestions on the user's device

[0051] The user terminal receives the proposal sent from the server. The proposal is displayed on the application screen in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0052] Specific examples

[0053] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button on the screen to send the data to the server. The server receives the data and sends it to an artificial intelligence module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0054] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0058] Step 2:

[0059] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0060] Step 3:

[0061] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data and the entered customer information and sends them to the server using the HTTPS protocol.

[0062] Step 4:

[0063] The server receives the image data and customer information sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0064] Step 5:

[0065] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0066] Step 6:

[0067] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0068] Step 7:

[0069] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0070] Step 8:

[0071] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0072] Step 9:

[0073] The server receives the diagnosis returned by the AI ​​module and stores it in a database, which then compares it with the customer's past treatment history and preferences.

[0074] Step 10:

[0075] The server generates proposals based on the diagnosis results and past data to select the most suitable products and treatment menus for each customer.

[0076] Step 11:

[0077] The server transmits the generated proposal to the user terminal.

[0078] Step 12:

[0079] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0080] In this way, the user, terminal, server, and artificial intelligence module work in cooperation with each other, making it possible to provide customers with prompt and accurate proposals.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Conventional customer image analysis systems in the beauty and aesthetics industries often had low accuracy in diagnosing a customer's skin condition, hair condition, and facial feature balance, and the recommendations often did not fully address each customer's past treatment history or preferences. This made it difficult to quickly and accurately recommend the optimal products and treatment menus for each customer. Furthermore, there were security issues with data transmission and reception, raising the risk of unauthorized data being mixed in or information being leaked.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes means for checking the integrity of data sent from the user terminal, encoding the image data, and converting it into a format suitable for the machine learning module means, means for comparing the diagnosis results from the machine learning module means with data on the customer's past treatment history and preferences to select the most suitable products and treatment menus, and application software means for displaying the proposal contents in an easy-to-use format on the user terminal. This confirms the integrity of the data, prevents the mixing of fraudulent data, and enables the most suitable proposals to be made to the customer quickly and accurately.

[0086] The term "mobile terminal means" refers to a small electronic device that allows a user to take a picture of a customer's image data, input customer information, and transmit the data to a server.

[0087] "Data Server" refers to a computer system that receives image data and customer information sent from user terminals and manages and processes them for analysis.

[0088] "Machine learning module means" refers to algorithms and programs that analyze image data and diagnose a customer's skin condition, hair condition, and facial feature balance.

[0089] "User interface means" refers to a system that displays the proposal content sent from the data server and supports operations in a format that is easily understandable to the user.

[0090] "Encryption" refers to the process of encoding data for secure transmission, thereby ensuring confidentiality of the data.

[0091] "Integrity check measures" refers to processes used to verify that received data is correct and does not contain any malicious data.

[0092] "Pre-processing means" refers to a process for converting received image data into a format suitable for the machine learning module means.

[0093] "Proposal generation means" refers to the process of comparing diagnostic results with past treatment history and customer preference data to generate optimal products and treatment menus.

[0094] "Application software means" refers to a program that runs on a user terminal and displays the content of the proposal.

[0095] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0096] System configuration

[0097] This system mainly consists of a user terminal, a data server, and a machine learning module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The data server receives the data sent from the user terminal, analyzes it in conjunction with the machine learning module, and generates optimal suggestions. The machine learning module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0098] Operation on the user device

[0099] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (such as name and past treatment history) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to a data server. For example, an iPhone (registered trademark), an ANDROID (registered trademark) smartphone, or a dedicated tablet device can be used as the user device.

[0100] Data reception and analysis on the server

[0101] The server receives the image data and customer information sent from the user terminal. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. For example, a cloud server from Amazon Web Services (AWS (registered trademark)) or Google (registered trademark) Cloud Platform (GCP) is used. If the received data is determined to be problem-free, the image data is encoded into JPEG or PNG format and converted into an appropriate format. The converted data is then sent to the machine learning module.

[0102] Image analysis using artificial intelligence modules

[0103] The machine learning module analyzes the received image data. This is implemented using, for example, Google's TENSORFLOW (registered trademark) or Facebook's PyTorch. Specifically, it uses a facial recognition algorithm to identify the location of each facial feature, and then analyzes the skin tone, wrinkles, blemishes, and pore condition. It also diagnoses hair quality, shine, and damage. These diagnostic results are compiled as text data and sent back to the server in JSON format.

[0104] Data integration and proposal generation on the server

[0105] The server receives the diagnosis results from the machine learning module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. For example, MySQL (registered trademark) or PostgreSQL is used as the database. Taking into consideration the past data and the diagnosis results comprehensively, the server selects the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0106] Displaying suggestions on user devices

[0107] The user terminal receives the proposal sent from the data server. The proposal is displayed on the application screen in a format that is easy for the user to understand. For example, it is displayed using text and images on the user interface of a dedicated application. The user (hairdresser or aesthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0108] Specific examples and examples of prompts to input to the generative AI model

[0109] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button to send the data to the server. The server receives the data and sends it to the machine learning module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0110] By inputting a prompt sentence like the one below into the generative AI model, a detailed description of the system will be automatically generated.

[0111] Please explain the system that analyzes customer facial photos and suggests optimal products and treatments. This system sends facial photos taken on the user's device and customer information to a data server, where a machine learning module performs image analysis and suggests optimal products and treatments. Please provide a detailed explanation, including specific operating procedures and the software and hardware used.

[0112] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Program processing flow

[0115] Step 1: Collect data on the user's device

[0116] The user (hairdresser or aesthetician) launches a dedicated application on a device such as a smartphone and takes a photo of the customer's face. Next, they enter the customer's basic information (name, past treatment history, etc.). The input data is saved as image data (JPEG format) and text data (JSON format). Specifically, the system works by taking a high-resolution photo with the camera and entering the text information into the application form.

[0117] Step 2: Send data to the server

[0118] The user presses the "Start Diagnosis" button on the dedicated application. This causes the user device to send the photograph taken and the entered customer information to the data server. The transmitted data is encrypted using the HTTPS protocol. Image data and text data are input, and encrypted data is sent as output. Specifically, pressing the send button sends the data to the server.

[0119] Step 3: Receiving and encoding data on the server

[0120] The server receives data sent from the user terminal. First, the server checks the integrity of the data to ensure that it does not contain any invalid data. The input data is encrypted image data and customer information, and the output is encoded JPEG image data. Specifically, the server stores the data in a database and converts the image data into an appropriate format.

[0121] Step 4: Sending data to the machine learning module

[0122] The server sends the encoded image data and customer information to the machine learning module. This data transmission uses a REST API. The input data is the encoded image data and text data, and an API request is generated as the output. Specifically, the request is sent to the API endpoint.

[0123] Step 5: Image analysis using machine learning modules

[0124] The machine learning module analyzes the received image data. It uses a facial recognition algorithm to identify the location of each facial feature and evaluates the condition of skin tone, wrinkles, blemishes, and pores. The input data is image data, and the output is a text-based analysis result. Specifically, it runs the image analysis algorithm and summarizes the analysis results in JSON format.

[0125] Step 6: Integration of analysis results and generation of proposals on the server

[0126] The server receives the diagnosis results from the machine learning module. It compares them with the customer's past treatment history and preference data to generate the optimal product and treatment menu. The input data is the analysis results and customer history data, and the output is the generated recommendations. Specifically, it executes a database query, aggregates the results, and determines the optimal recommendations.

[0127] Step 7: Displaying suggestions on the user's device

[0128] The user device receives the proposal sent from the server. A dedicated application parses the proposal and displays it on the screen in a format that is easy for the user to understand. The proposal is the input data, and a display screen is generated as the output. Specifically, the application displays text and images in the user interface.

[0129] Through the above steps, the system can quickly and accurately provide optimal proposals to customers.

[0130] (Application example 1)

[0131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] In conventional beauty and aesthetic treatments, it was difficult to grasp the condition of a customer's skin and hair in real time and quickly recommend the most appropriate treatments and products. Furthermore, there was a lack of efficient ways to provide personalized recommendations based on the customer's preferences and past treatment history. This resulted in a heavy workload for hairdressers and aestheticians, making it difficult to improve customer satisfaction.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0134] In this invention, the server includes a device that receives image data of a customer taken by a user and transmits it to the server together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, a server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and based on past treatment history and customer preferences and transmits them to the user device, a system including the proposals displayed on the user device, a device that acquires captured images of the customer from a built-in camera and checks the customer's skin and hair condition in real time, means for transmitting customer image data and customer information and acquiring analysis results, and means for displaying the analysis results on a display. This makes it possible to check the customer's skin and hair condition in real time and efficiently provide optimal treatments and products.

[0135] A "user" is a person who uses the system to photograph and input customer information and request treatment suggestions.

[0136] "Device" refers to a hardware device that allows a user to capture image data of a customer and input and transmit customer information.

[0137] The "server" is a computer system that receives data sent from a user device, analyzes the data using an artificial intelligence module, and generates optimal products and treatment menus.

[0138] The "artificial intelligence module" refers to algorithms and software that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[0139] "Image data" refers to an image file of the customer's face taken by the user with the device.

[0140] "Customer information" refers to data that includes basic information such as the customer's name and past treatment history.

[0141] The "diagnosis results" are an evaluation of the customer's skin condition, hair condition, and facial balance obtained through image analysis by an artificial intelligence module.

[0142] A "treatment menu" refers to specific beauty and aesthetic treatments proposed based on the diagnosis, past treatment history, and customer preferences.

[0143] "Proposal content" refers to information generated based on the diagnosis results, including optimal products and treatment menus.

[0144] The "built-in camera" refers to a camera that is built into the device and is used to capture image data.

[0145] A "display" refers to a screen for displaying information on a device, and is responsible for displaying proposals and other information to customers.

[0146] "Real-time" refers to the state in which the acquisition of customer image data and the display of analysis results are carried out immediately without delay.

[0147] The present invention is a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0148] System configuration

[0149] This system mainly consists of a user device, a server, and an artificial intelligence module. The user device is hardware used by hairdressers and estheticians to take images of their customers and input their information. The server receives the data sent from the user device, analyzes it in conjunction with the artificial intelligence module, and generates optimal recommendations. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial balance.

[0150] Hardware and software used

[0151] Hardware: Smart glasses (built-in camera, display), user device (smartphone, etc.)

[0152] Software: Python, OpenCV (image processing library), requests (library for sending HTTP requests)

[0153] Operation on user device

[0154] The user (beautician or esthetician) first puts on the smart glasses and takes a photo of the customer's face with the built-in camera. Next, they enter the customer's basic information (name, past treatment history, etc.) into the device's dedicated application. The image data and customer information are then sent to the server.

[0155] Data reception and analysis on the server

[0156] The server receives image data and customer information sent from the user device. When data is sent, the user device encodes the data and sends it to the server. The server checks the consistency of the received image data and customer information to ensure that no invalid data is included. Once the consistency is confirmed, the data is converted into an appropriate format and sent to the artificial intelligence module.

[0157] Image analysis using artificial intelligence modules

[0158] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0159] Data integration and proposal generation on the server

[0160] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preferences to generate the optimal product and treatment menu. The proposed content is sent to the user device and provided to the customer.

[0161] Displaying Offers on User Devices

[0162] The user device receives the proposed content sent from the server. The proposed content is displayed on the device's display in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposed content to the customer while looking at it, and begins the treatment only after obtaining approval.

[0163] Specific examples

[0164] For example, when a new customer visits a salon, a hairdresser wearing smart glasses takes a photo of the customer's face and enters their name and past treatment history. The data is then sent to a server, where it is analyzed by an artificial intelligence module, and a diagnosis is returned. Based on the analysis results, the most appropriate treatment menu and products are suggested and displayed on the smart glasses' display. The hairdresser then explains the suggestions to the customer and, after obtaining their approval, performs the treatment.

[0165] Example prompt sentence:

[0166] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0167] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] A user puts on the smart glasses and takes a photo of the customer's face. The smart glasses' built-in camera is used to capture the image data. The input is the customer's face photo, and the output is the image data captured by the built-in camera.

[0171] Step 2:

[0172] The user inputs customer information into the dedicated application on the smart glasses. The input is basic customer information (such as name and past treatment history), and the output is the collected customer information data.

[0173] Step 3:

[0174] The user device encodes the captured customer image data and customer information when transmitting them to the server. The input is the image data and customer information, and the output is the encoded data.

[0175] Step 4:

[0176] The server receives the encoded data sent from the user device, with the input being the encoded image data and customer information, and the output being the result of checking the integrity of the data.

[0177] Step 5:

[0178] The server converts the received image data into a suitable format and sends it to the artificial intelligence module, where the input is the encoded data and the output is the converted image data.

[0179] Step 6:

[0180] The AI ​​module analyzes the received image data and executes a facial recognition algorithm to identify the location of each facial feature. The input is the transformed image data and the output is the location information of the facial features.

[0181] Step 7:

[0182] The AI ​​module analyzes the condition of the skin and hair and measures the balance of facial features. The input is the position information of the facial features, and the output is the diagnosis results of the skin condition, hair condition, and balance of the facial features.

[0183] Step 8:

[0184] The server receives the diagnosis results sent from the AI ​​module, compares them with the customer's past treatment history and preference data, and generates the optimal product and treatment menu. The input is the diagnosis results and the customer's past data, and the output is the optimal proposal.

[0185] Step 9:

[0186] The server sends the generated optimal proposal to the user device, where the input is the optimal proposal and the output is the proposal received by the user device.

[0187] Step 10:

[0188] The user device receives the suggestions sent from the server and displays them on the display of the smart glasses. The input is the suggestions, and the output is the suggestions displayed on the display.

[0189] Specific examples

[0190] Example prompt sentence:

[0191] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] The present invention relates to a system that analyzes various data, including user emotions, and recommends optimal beauty treatments and products. The system includes a user terminal, a server, an artificial intelligence module, and an emotion engine. Specific embodiments for implementing the present invention are described in detail below.

[0194] System configuration

[0195] The system consists of the following main components:

[0196] 1. User Device

[0197] 2. Server

[0198] 3. Artificial Intelligence Module

[0199] 4. Emotion Engine

[0200] User terminal

[0201] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. It also has a function to acquire user emotional data (changes in facial expressions and voice) in real time using an emotion engine and send it to the server.

[0202] server

[0203] The server is a central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes the data. The server then sends the received data to the artificial intelligence module and emotion engine, and receives the analysis results. It also compares the analysis results with the customer's past treatment history to generate optimal proposals.

[0204] Artificial Intelligence Module

[0205] The AI ​​module is equipped with algorithms that analyze image data and diagnose the customer's skin condition, hair condition, and facial feature balance. The module uses facial recognition technology to identify the location of the customer's facial features and also provides a detailed analysis of the skin and hair condition.

[0206] Emotion Engine

[0207] The emotion engine analyzes the user's facial expressions and voice changes to identify the user's emotional state, thereby understanding the user's emotions in real time and sending that information to the server.

[0208] Example

[0209] Let's take a specific example of a new customer visiting a beauty salon. First, hairdresser B takes a photo of the customer's face using a user device and enters basic information (such as name and past treatment history) into a dedicated application. Next, B activates the emotion engine and configures it to capture conversations with the customer and changes in facial expressions.

[0210] Next, Person B presses the "Start Diagnosis" button on the device, which sends the photo data, customer information, and emotional data to the server. The server receives this data and sends the image data to the artificial intelligence module and the emotional data to the emotion engine. The artificial intelligence module then diagnoses the customer's skin condition, hair condition, and facial feature balance, and the emotion engine analyzes the user's emotional state.

[0211] The analysis results are sent back to the server, which compares these results with the customer's past treatment history and generates the optimal proposal. The generated proposal is sent to the user's device and displayed on the device. Based on the displayed proposal, Mr. B explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[0212] In this way, the system analyzes a variety of data, including the user's emotional state, enabling it to provide more accurate and effective beauty treatments and product recommendations, thereby reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0213] The processing flow will be explained below.

[0214] Step 1:

[0215] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0216] Step 2:

[0217] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0218] Step 3:

[0219] The user enables the emotion engine, which allows the device to capture changes in the customer's facial expressions and voice in real time and collect emotion data.

[0220] Step 4:

[0221] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data, the entered customer information, and the acquired emotion data, and sends them to the server using the HTTPS protocol.

[0222] Step 5:

[0223] The server receives the image data, customer information, and emotion data sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0224] Step 6:

[0225] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0226] Step 7:

[0227] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0228] Step 8:

[0229] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0230] Step 9:

[0231] The emotion engine receives emotion data sent from the user's device, analyzes the user's facial expressions and voice characteristics, identifies emotions (happiness, sadness, surprise, etc.), and sends the results back to the server.

[0232] Step 10:

[0233] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0234] Step 11:

[0235] The server receives the diagnosis results and emotion data returned by the AI ​​module and emotion engine, stores them in a database, and then compares them with the customer's past treatment history and preferences.

[0236] Step 12:

[0237] The server generates proposals based on the diagnosis results, emotional data, and past data to select the most suitable products and treatment menus for each customer.

[0238] Step 13:

[0239] The server transmits the generated proposal to the user terminal.

[0240] Step 14:

[0241] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0242] In this way, the user, terminal, server, artificial intelligence module, and emotion engine work together to provide customers with prompt and accurate proposals, and the use of emotion data in particular makes it possible to provide services that are in tune with the customer's emotions.

[0243] Example 2

[0244] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0245] Conventional beauty treatment and product recommendation systems only take into account physical data such as the customer's skin and hair condition, and do not reflect psychological data such as the customer's emotional state, making it difficult to maximize customer satisfaction.In addition, recommendations based on treatment history and customer preferences are insufficient, making it difficult to make optimal recommendations for each individual customer.

[0246] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving image data and customer information of a customer photographed by a user and acquiring emotional data from changes in facial expressions and voice in real time, means for transmitting the acquired image data, customer information, and emotional data to the server, an artificial intelligence module for analyzing the image data received from the server and diagnosing the customer's skin condition, hair condition, and facial balance, an emotion engine for analyzing the emotional data and identifying the customer's emotional state, means for generating optimal products and treatment menus based on the analysis results of the artificial intelligence module and the emotion engine by comparing them with past treatment history and transmitting the generated recommendations to the user terminal, and a recommendation displayed on the user terminal. This makes it possible to propose optimal beauty treatments and products to individual customers, taking into account both physical and psychological data.

[0247] A "user terminal" is a device used by a user, which has the function of acquiring image data and customer information of a customer and transmitting it to a server.

[0248] A "server" is a central computer system that receives and analyzes data sent from user terminals, and is a device that generates optimal proposals based on the analysis results and transmits them to user terminals.

[0249] "Image data" refers to visual data such as a photo of the customer's face taken on a user device, and this data is used to analyze the customer's skin and hair condition.

[0250] "Customer Information" refers to information that includes data such as a customer's name, past treatment history, and preferences.

[0251] "Emotion data" is data that indicates the psychological state of a customer, obtained in real time from changes in facial expressions and voice.

[0252] The "artificial intelligence module" refers to algorithms and software that analyze image data and diagnose the customer's skin condition, hair condition, and facial balance.

[0253] An "emotion engine" refers to a system or software that analyzes emotional data and identifies a customer's emotional state.

[0254] "Analysis results" refers to the results of data analysis by the artificial intelligence module and emotion engine, based on which optimal suggestions are generated.

[0255] The "proposal content" includes optimal products and treatment menus generated based on the analysis results and past treatment history, and is displayed on the user's terminal.

[0256] "Encoding means" refers to methods or techniques used to encrypt or compress data before transmission, thereby increasing the security or efficiency of communications.

[0257] "Pre-processing means" refers to a method or system that performs processing to convert data sent from a user terminal into a format that can be analyzed by the artificial intelligence module and emotion engine.

[0258] The present invention is implemented using the following specific hardware and software components: The components of the overall system include a user terminal, a server, an artificial intelligence module, and an emotion engine.

[0259] User terminal

[0260] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. The user terminal is also equipped with an emotion engine, which has the function of capturing changes in the customer's facial expressions and voice in real time. Specifically, the camera function of a smartphone or tablet is used to take photos, and a keyboard or touch screen is used to input data. An example of an application used is the "BeautyPro" app.

[0261] server

[0262] The server is the central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes this data. Specifically, a database management system (e.g., MySQL) is installed on the server, and the received data is distributed to the artificial intelligence module and emotion engine.

[0263] Artificial Intelligence Module

[0264] The AI ​​module analyzes the received image data and is equipped with algorithms to diagnose the customer's skin condition, hair condition, and facial balance. This module uses the "FaceAnalyzer" algorithm developed in Python. For example, it analyzes the captured image for blemishes, wrinkles, and hair damage, and sends the results back to the server.

[0265] Emotion Engine

[0266] The emotion engine analyzes the customer's emotional state in real time from changes in facial expressions and voice captured on the user's device. The software used is "EmotionDetect." This engine identifies whether the customer is relaxed or tense and sends that information to the server.

[0267] Example

[0268] A specific example using this system will be described below.

[0269] When a new customer visits a beauty salon, the hairdresser (user) uses a tablet device. The user takes a photo of the customer's face and enters the customer's basic information (e.g., name, past treatment history) into the dedicated app "BeautyPro." In addition, the user activates the emotion engine (EmotionDetect) within the app to capture changes in the customer's facial expressions and voice.

[0270] When the user presses the "Start Diagnosis" button, the captured image data, customer information, and emotional data are sent to the server. The server receives this data and sends the image data to the artificial intelligence module (FaceAnalyzer) and the emotional data to the emotion engine (EmotionDetect).

[0271] The AI ​​module analyzes the image data and diagnoses the customer's skin condition, hair condition, and facial balance. The analysis results are sent back to the server. Similarly, the emotion engine analyzes the emotion data, identifies the customer's emotional state, and sends the results back to the server.

[0272] Based on the analysis results, the server compares them with the customer's past treatment history (stored in a MySQL database) and generates the optimal product and treatment menu. The generated proposals are sent to the user's device and displayed on the device. The user explains the displayed proposals to the customer and begins treatment after obtaining the customer's approval.

[0273] Prompt Sentence Examples

[0274] "Take a photo of your customer's face, analyze their emotions in real time, and recommend the most appropriate beauty treatment."

[0275] In this way, the system can take into account both physical and psychological data to recommend the most suitable beauty treatments and products for each customer, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The user (e.g., a hairdresser) launches the dedicated app "BeautyPro" on the user's device (e.g., a tablet).

[0279] Input: The user enters the customer's basic information (name, past treatment history) into the app and uses the camera function to take a photo of the customer's face.

[0280] What happens: The user uses the tablet's keyboard and touchscreen to enter customer information and takes a photo with the tablet's camera.

[0281] Output: The customer's basic information and facial photo are recorded on the user's terminal.

[0282] Step 2:

[0283] The user enables the emotion engine (EmotionDetect) within the application to capture changes in the customer's facial expressions and voice in real time.

[0284] Input: The user presses the "Start Emotion Engine" button.

[0285] Specific operation: Throughout the conversation with the customer, the tablet's microphone and camera detect changes in facial expressions and voice in real time.

[0286] Output: Real-time facial expression data and voice change data are recorded on the user's device.

[0287] Step 3:

[0288] The terminal transmits the acquired facial photograph, customer information, and emotion data to the server.

[0289] Input: Facial photo, customer information, emotional data.

[0290] Specific operation: The device sends data to the server via Wi-Fi.

[0291] Output: The server receives the face photo, customer information, and emotion data.

[0292] Step 4:

[0293] The server distributes the received data to the artificial intelligence module and emotion engine.

[0294] Input: Facial photo, customer information, emotional data.

[0295] Specific operation: The server sends the face photo to the artificial intelligence module (FaceAnalyzer) and the emotion data to the emotion engine (EmotionDetect).

[0296] Output: Data is sent for analysis by the artificial intelligence module and emotion engine.

[0297] Step 5:

[0298] An artificial intelligence module analyzes image data and diagnoses the customer's skin condition, hair condition, and facial balance.

[0299] Input: Facial photo data sent from the server.

[0300] Specific operation: The artificial intelligence module (FaceAnalyzer) uses image analysis algorithms to diagnose uneven skin tone, blemishes, wrinkles, hair damage, and facial balance.

[0301] Output: Diagnostic results are generated and sent back to the server.

[0302] Step 6:

[0303] An emotion engine analyzes the emotion data to identify the customer's emotional state.

[0304] Input: Emotion data sent from the server.

[0305] Specific behavior: The emotion engine (EmotionDetect) analyzes facial expressions and vocal changes to identify the customer's emotional state (relaxed, tense, etc.).

[0306] Output: Parsed emotional state data is generated and sent back to the server.

[0307] Step 7:

[0308] The server receives the analysis results from the artificial intelligence module and emotion engine, compares them with past treatment history, and generates optimal products and treatment menus.

[0309] Input: Analysis results returned from the artificial intelligence module and emotion engine, and the customer's past treatment history.

[0310] Specific operation: The server retrieves treatment history from the MySQL database, compares it with the analysis results, and executes an algorithm to make optimal recommendations.

[0311] Output: The generated recommendations for optimal products and treatment menus.

[0312] Step 8:

[0313] The server transmits the generated proposal content to the user terminal.

[0314] Input: Suggestions for optimal products and treatment menus.

[0315] Specific operation: The server sends the proposal content to the user's device via Wi-Fi.

[0316] Output: The proposal is displayed on the user's terminal.

[0317] Step 9:

[0318] Based on the proposal displayed on the user terminal, the user explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[0319] Input: The suggestion displayed on the user's device.

[0320] Specific operation: The user explains the proposal to the customer and obtains the customer's approval.

[0321] Output: Treatment begins after customer consent is obtained.

[0322] (Application example 2)

[0323] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] While conventional beauty treatment systems can analyze physical data such as a customer's skin and hair condition, they have difficulty making recommendations that take into account the customer's emotional state. Furthermore, while changes in a customer's emotions are considered an important factor in improving the suitability of treatment and product recommendations, there has been a lack of means to acquire and analyze this information in real time. This has resulted in reduced recommendation accuracy and made it difficult to improve customer satisfaction.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0326] In this invention, the server includes a terminal that receives image data of a customer taken by a user and transmits it together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, an emotion engine that analyzes changes in the customer's facial expression and voice and identifies their emotional state, and a means for generating optimal products and treatment menus based on the diagnosis results from the artificial intelligence module and emotion engine and taking into account past treatment history and customer preferences, and transmitting the results to the user terminal. This enables more appropriate beauty treatments and product recommendations that take into account not only the customer's physical condition but also their emotional state.

[0327] A "user terminal" is a device that acquires image data and customer information of a customer and transmits them to a server.

[0328] A "server" is a central device that receives, manages, and analyzes data sent from user terminals.

[0329] The "artificial intelligence module" is a system equipped with algorithms that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[0330] The "emotion engine" is a system that analyzes changes in a customer's facial expression and voice to identify their emotional state.

[0331] "Image data analysis" is the process of analyzing a customer's image and diagnosing their skin condition, hair condition, and facial feature balance.

[0332] "Emotional state" refers to a psychological state that can be identified from changes in facial expressions and voice that a customer shows in real time.

[0333] "Treatment history" refers to records of beauty treatments that a customer has received in the past.

[0334] "Products and treatment menus" refer to specific beauty items and services suggested based on the customer's condition and preferences.

[0335] The present invention is a system that analyzes a customer's image data and emotional data to recommend optimal beauty treatments and products. The system is composed of a user terminal, a server, an artificial intelligence module, and an emotion engine. An embodiment of the system is described in detail below.

[0336] User terminal

[0337] A user terminal is a device that acquires customer image data and customer information and sends them to a server. For example, smart glasses used by hairdressers are examples of such devices. By wearing the smart glasses, it is possible to capture customer facial photos and facial expression data in real time. The captured data is encoded and sent to a server.

[0338] server

[0339] The server is a central device that receives image data and customer information sent from user devices and sends the data to the AI ​​module and emotion engine for analysis. The server preprocesses the image data using image processing software such as OpenCV and converts the data format as necessary.

[0340] Artificial Intelligence Module

[0341] The AI ​​module is equipped with algorithms that analyze the received image data and diagnose the customer's skin condition, hair condition, and facial feature balance. This module also uses generative AI models to realize facial recognition technology, which can pinpoint the location of the customer's facial features and perform a detailed analysis.

[0342] Emotion Engine

[0343] The emotion engine is a system that analyzes changes in a customer's facial expression and voice to identify the customer's emotional state. In an embodiment, the emotion engine acquires the customer's emotional state in real time and transmits the information to a server.

[0344] Analysis results

[0345] The analysis results are sent back to the server, which uses these results to generate optimal products and treatment menus based on past treatment history and customer preferences. The generated proposals are sent to the user's terminal and displayed on the user's device.

[0346] Specific examples

[0347] When a new customer visits a beauty salon, the hairdresser puts on the smart glasses and takes a photo of the customer's face. The photo and customer information are then encoded and sent from the smart glasses to a server. The server then sends the image data to an artificial intelligence module and emotion data to an emotion engine. The data is analyzed, and the results are sent back to the server. Based on the analysis results, the server generates optimal suggestions based on the customer's past treatment history and preferences, and displays them on the smart glasses' display.

[0348] Prompt Sentence Examples

[0349] Customer Name: Yamada Hanako

[0350] Past treatment history: Cut, coloring

[0351] Current Emotion: Relaxed

[0352] Analysis results:

[0353] 1. Skin condition: Dry

[0354] 2. Hair condition: Healthy

[0355] suggestion:

[0356] Moisturizing Treatment

[0357] Moisturizing hair care products

[0358] As described above, this system makes it possible to propose beauty treatments and products that take into account both the physical and emotional state of the customer, thereby providing more appropriate services that will increase customer satisfaction.

[0359] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0360] Step 1:

[0361] The user terminal acquires the photo data and customer information.

[0362] Specifically, a hairdresser wearing smart glasses takes a photo of a customer's face and stores the data on the device. The input is the customer's face photo and basic information, and the output is encoded data.

[0363] Step 2:

[0364] The terminal transmits the photographed customer's face photo and customer information to the server.

[0365] Specifically, the terminal sends the encoded data to the server as an HTTP request. The input is the encoded face photo and customer information, and the output is the data sent to the server.

[0366] Step 3:

[0367] The server preprocesses the received data and converts it into a format that can be parsed.

[0368] Specifically, the server uses image processing software such as OpenCV to convert image data into a format that is easy for the AI ​​module to analyze. The input is encoded facial photo data, and the output is the converted image data.

[0369] Step 4:

[0370] The server sends the image data to an artificial intelligence module, which analyzes the condition of the skin, hair, and balance of facial features.

[0371] Specifically, the server inputs the converted image data into the generative AI model and obtains the analysis results. The input is the converted image data, and the output is the skin condition, hair condition, and facial feature balance.

[0372] Step 5:

[0373] The server sends data on facial expressions and voice changes to the emotion engine, which analyzes the emotional state.

[0374] Specifically, the server sends data on conversations with customers and changes in facial expressions to the emotion engine, which then identifies their emotional state. The input is data on changes in facial expressions and voice, and the output is their emotional state.

[0375] Step 6:

[0376] The server integrates the analysis results from the artificial intelligence module and emotion engine to generate optimal recommendations based on treatment history and customer preferences.

[0377] Specifically, the server generates optimal products and treatment menus based on the integrated analysis results, taking into account treatment history and customer preferences. The inputs are the analysis results and treatment history, and the output is the proposal content.

[0378] Step 7:

[0379] The server transmits the generated proposal content to the user terminal, and the user terminal displays the proposal content.

[0380] Specifically, the server sends the generated proposal to the user's device as an HTTP response, and the device displays the proposal on its screen. The input is the optimal proposal, and the output is the proposal displayed on the user's device.

[0381] 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.

[0382] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0383] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 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.

[0387] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0388] 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.

[0389] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0390] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0391] 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.

[0392] 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.

[0393] 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 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.

[0394] 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.

[0395] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0396] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0397] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0398] System configuration

[0399] This system mainly consists of a user terminal, a server, and an artificial intelligence module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The server receives the data sent from the user terminal, analyzes it in conjunction with the artificial intelligence module, and generates optimal suggestions. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0400] Operation on the user device

[0401] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (name, past treatment history, etc.) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to the server.

[0402] Data reception and analysis on the server

[0403] The server receives the image data and customer information sent from the user terminal. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. If the received data is determined to be problem-free, the server encodes the image data and converts it into an appropriate format. The converted data is then sent to the artificial intelligence module.

[0404] Image analysis using artificial intelligence modules

[0405] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0406] Data integration and proposal generation on the server

[0407] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. It then comprehensively considers the past data and the diagnosis results to select the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0408] Displaying suggestions on the user's device

[0409] The user terminal receives the proposal sent from the server. The proposal is displayed on the application screen in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0410] Specific examples

[0411] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button on the screen to send the data to the server. The server receives the data and sends it to an artificial intelligence module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0412] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0413] The processing flow will be explained below.

[0414] Step 1:

[0415] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0416] Step 2:

[0417] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0418] Step 3:

[0419] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data and the entered customer information and sends them to the server using the HTTPS protocol.

[0420] Step 4:

[0421] The server receives the image data and customer information sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0422] Step 5:

[0423] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0424] Step 6:

[0425] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0426] Step 7:

[0427] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0428] Step 8:

[0429] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0430] Step 9:

[0431] The server receives the diagnosis returned by the AI ​​module and stores it in a database, which then compares it with the customer's past treatment history and preferences.

[0432] Step 10:

[0433] The server generates proposals based on the diagnosis results and past data to select the most suitable products and treatment menus for each customer.

[0434] Step 11:

[0435] The server transmits the generated proposal to the user terminal.

[0436] Step 12:

[0437] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0438] In this way, the user, terminal, server, and artificial intelligence module work in cooperation with each other, making it possible to provide customers with prompt and accurate proposals.

[0439] Example 1

[0440] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0441] Conventional customer image analysis systems in the beauty and aesthetics industries often had low accuracy in diagnosing a customer's skin condition, hair condition, and facial feature balance, and the recommendations often did not fully address each customer's past treatment history or preferences. This made it difficult to quickly and accurately recommend the optimal products and treatment menus for each customer. Furthermore, there were security issues with data transmission and reception, raising the risk of unauthorized data being mixed in or information being leaked.

[0442] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0443] In this invention, the server includes means for checking the integrity of data sent from the user terminal, encoding the image data, and converting it into a format suitable for the machine learning module means, means for comparing the diagnosis results from the machine learning module means with data on the customer's past treatment history and preferences to select the most suitable products and treatment menus, and application software means for displaying the proposal contents in an easy-to-use format on the user terminal. This confirms the integrity of the data, prevents the mixing of fraudulent data, and enables the most suitable proposals to be made to the customer quickly and accurately.

[0444] The term "mobile terminal means" refers to a small electronic device that allows a user to take a picture of a customer's image data, input customer information, and transmit the data to a server.

[0445] "Data Server" refers to a computer system that receives image data and customer information sent from user terminals and manages and processes them for analysis.

[0446] "Machine learning module means" refers to algorithms and programs that analyze image data and diagnose a customer's skin condition, hair condition, and facial feature balance.

[0447] "User interface means" refers to a system that displays the proposal content sent from the data server and supports operations in a format that is easily understandable to the user.

[0448] "Encryption" refers to the process of encoding data for secure transmission, thereby ensuring confidentiality of the data.

[0449] "Integrity check measures" refers to processes used to verify that received data is correct and does not contain any malicious data.

[0450] "Pre-processing means" refers to a process for converting received image data into a format suitable for the machine learning module means.

[0451] "Proposal generation means" refers to the process of comparing diagnostic results with past treatment history and customer preference data to generate optimal products and treatment menus.

[0452] "Application software means" refers to a program that runs on a user terminal and displays the content of the proposal.

[0453] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0454] System configuration

[0455] This system mainly consists of a user terminal, a data server, and a machine learning module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The data server receives the data sent from the user terminal, analyzes it in conjunction with the machine learning module, and generates optimal suggestions. The machine learning module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0456] Operation on the user device

[0457] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (such as name and past treatment history) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to a data server. For example, an iPhone, Android smartphone, or a dedicated tablet device can be used as the user device.

[0458] Data reception and analysis on the server

[0459] The server receives the image data and customer information sent from the user device. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. For example, a cloud server from Amazon Web Services (AWS) or Google Cloud Platform (GCP) is used. If the received data is determined to be problem-free, the image data is encoded into JPEG or PNG format and converted into an appropriate format. The converted data is then sent to the machine learning module.

[0460] Image analysis using artificial intelligence modules

[0461] The machine learning module analyzes the received image data. This is implemented using, for example, Google's TensorFlow or Facebook's PyTorch. Specifically, it uses a facial recognition algorithm to identify the location of each facial feature, and then analyzes the skin tone, wrinkles, blemishes, and pore condition. It also diagnoses hair quality, shine, and damage. These diagnostic results are compiled as text data and sent back to the server in JSON format.

[0462] Data integration and proposal generation on the server

[0463] The server receives the diagnosis results from the machine learning module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. For example, MySQL or PostgreSQL is used as the database. Taking into consideration the past data and the diagnosis results, the server selects the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0464] Displaying suggestions on user devices

[0465] The user terminal receives the proposal sent from the data server. The proposal is displayed on the application screen in a format that is easy for the user to understand. For example, it is displayed using text and images on the user interface of a dedicated application. The user (hairdresser or aesthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0466] Specific examples and examples of prompts to input to the generative AI model

[0467] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button to send the data to the server. The server receives the data and sends it to the machine learning module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0468] By inputting a prompt sentence like the one below into the generative AI model, a detailed description of the system will be automatically generated.

[0469] Please explain the system that analyzes customer facial photos and suggests optimal products and treatments. This system sends facial photos taken on the user's device and customer information to a data server, where a machine learning module performs image analysis and suggests optimal products and treatments. Please provide a detailed explanation, including specific operating procedures and the software and hardware used.

[0470] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Program processing flow

[0473] Step 1: Collect data on the user's device

[0474] The user (hairdresser or aesthetician) launches a dedicated application on a device such as a smartphone and takes a photo of the customer's face. Next, they enter the customer's basic information (name, past treatment history, etc.). The input data is saved as image data (JPEG format) and text data (JSON format). Specifically, the system works by taking a high-resolution photo with the camera and entering the text information into the application form.

[0475] Step 2: Send data to the server

[0476] The user presses the "Start Diagnosis" button on the dedicated application. This causes the user device to send the photograph taken and the entered customer information to the data server. The transmitted data is encrypted using the HTTPS protocol. Image data and text data are input, and encrypted data is sent as output. Specifically, pressing the send button sends the data to the server.

[0477] Step 3: Receiving and encoding data on the server

[0478] The server receives data sent from the user terminal. First, the server checks the integrity of the data to ensure that it does not contain any invalid data. The input data is encrypted image data and customer information, and the output is encoded JPEG image data. Specifically, the server stores the data in a database and converts the image data into an appropriate format.

[0479] Step 4: Sending data to the machine learning module

[0480] The server sends the encoded image data and customer information to the machine learning module. This data transmission uses a REST API. The input data is the encoded image data and text data, and an API request is generated as the output. Specifically, the request is sent to the API endpoint.

[0481] Step 5: Image analysis using machine learning modules

[0482] The machine learning module analyzes the received image data. It uses a facial recognition algorithm to identify the location of each facial feature and evaluates the condition of skin tone, wrinkles, blemishes, and pores. The input data is image data, and the output is a text-based analysis result. Specifically, it runs the image analysis algorithm and summarizes the analysis results in JSON format.

[0483] Step 6: Integration of analysis results and generation of proposals on the server

[0484] The server receives the diagnosis results from the machine learning module. It compares them with the customer's past treatment history and preference data to generate the optimal product and treatment menu. The input data is the analysis results and customer history data, and the output is the generated recommendations. Specifically, it executes a database query, aggregates the results, and determines the optimal recommendations.

[0485] Step 7: Displaying suggestions on the user's device

[0486] The user device receives the proposal sent from the server. A dedicated application parses the proposal and displays it on the screen in a format that is easy for the user to understand. The proposal is the input data, and a display screen is generated as the output. Specifically, the application displays text and images in the user interface.

[0487] Through the above steps, the system can quickly and accurately provide optimal proposals to customers.

[0488] (Application example 1)

[0489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0490] In conventional beauty and aesthetic treatments, it was difficult to grasp the condition of a customer's skin and hair in real time and quickly recommend the most appropriate treatments and products. Furthermore, there was a lack of efficient ways to provide personalized recommendations based on the customer's preferences and past treatment history. This resulted in a heavy workload for hairdressers and aestheticians, making it difficult to improve customer satisfaction.

[0491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0492] In this invention, the server includes a device that receives image data of a customer taken by a user and transmits it to the server together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, a server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and based on past treatment history and customer preferences and transmits them to the user device, a system including the proposals displayed on the user device, a device that acquires captured images of the customer from a built-in camera and checks the customer's skin and hair condition in real time, means for transmitting customer image data and customer information and acquiring analysis results, and means for displaying the analysis results on a display. This makes it possible to check the customer's skin and hair condition in real time and efficiently provide optimal treatments and products.

[0493] A "user" is a person who uses the system to photograph and input customer information and request treatment suggestions.

[0494] "Device" refers to a hardware device that allows a user to capture image data of a customer and input and transmit customer information.

[0495] The "server" is a computer system that receives data sent from a user device, analyzes the data using an artificial intelligence module, and generates optimal products and treatment menus.

[0496] The "artificial intelligence module" refers to algorithms and software that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[0497] "Image data" refers to an image file of the customer's face taken by the user with the device.

[0498] "Customer information" refers to data that includes basic information such as the customer's name and past treatment history.

[0499] The "diagnosis results" are an evaluation of the customer's skin condition, hair condition, and facial balance obtained through image analysis by an artificial intelligence module.

[0500] A "treatment menu" refers to specific beauty and aesthetic treatments proposed based on the diagnosis, past treatment history, and customer preferences.

[0501] "Proposal content" refers to information generated based on the diagnosis results, including optimal products and treatment menus.

[0502] The "built-in camera" refers to a camera that is built into the device and is used to capture image data.

[0503] A "display" refers to a screen for displaying information on a device, and is responsible for displaying proposals and other information to customers.

[0504] "Real-time" refers to the state in which the acquisition of customer image data and the display of analysis results are carried out immediately without delay.

[0505] The present invention is a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0506] System configuration

[0507] This system mainly consists of a user device, a server, and an artificial intelligence module. The user device is hardware used by hairdressers and estheticians to take images of their customers and input their information. The server receives the data sent from the user device, analyzes it in conjunction with the artificial intelligence module, and generates optimal recommendations. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial balance.

[0508] Hardware and software used

[0509] Hardware: Smart glasses (built-in camera, display), user device (smartphone, etc.)

[0510] Software: Python, OpenCV (image processing library), requests (library for sending HTTP requests)

[0511] Operation on user device

[0512] The user (beautician or esthetician) first puts on the smart glasses and takes a photo of the customer's face with the built-in camera. Next, they enter the customer's basic information (name, past treatment history, etc.) into the device's dedicated application. The image data and customer information are then sent to the server.

[0513] Data reception and analysis on the server

[0514] The server receives image data and customer information sent from the user device. When data is sent, the user device encodes the data and sends it to the server. The server checks the consistency of the received image data and customer information to ensure that no invalid data is included. Once the consistency is confirmed, the data is converted into an appropriate format and sent to the artificial intelligence module.

[0515] Image analysis using artificial intelligence modules

[0516] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0517] Data integration and proposal generation on the server

[0518] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preferences to generate the optimal product and treatment menu. The proposed content is sent to the user device and provided to the customer.

[0519] Displaying Offers on User Devices

[0520] The user device receives the proposed content sent from the server. The proposed content is displayed on the device's display in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposed content to the customer while looking at it, and begins the treatment only after obtaining approval.

[0521] Specific examples

[0522] For example, when a new customer visits a salon, a hairdresser wearing smart glasses takes a photo of the customer's face and enters their name and past treatment history. The data is then sent to a server, where it is analyzed by an artificial intelligence module, and a diagnosis is returned. Based on the analysis results, the most appropriate treatment menu and products are suggested and displayed on the smart glasses' display. The hairdresser then explains the suggestions to the customer and, after obtaining their approval, performs the treatment.

[0523] Example prompt sentence:

[0524] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0525] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0527] Step 1:

[0528] A user puts on the smart glasses and takes a photo of the customer's face. The smart glasses' built-in camera is used to capture the image data. The input is the customer's face photo, and the output is the image data captured by the built-in camera.

[0529] Step 2:

[0530] The user inputs customer information into the dedicated application on the smart glasses. The input is basic customer information (such as name and past treatment history), and the output is the collected customer information data.

[0531] Step 3:

[0532] The user device encodes the captured customer image data and customer information when transmitting them to the server. The input is the image data and customer information, and the output is the encoded data.

[0533] Step 4:

[0534] The server receives the encoded data sent from the user device, with the input being the encoded image data and customer information, and the output being the result of checking the integrity of the data.

[0535] Step 5:

[0536] The server converts the received image data into a suitable format and sends it to the artificial intelligence module, where the input is the encoded data and the output is the converted image data.

[0537] Step 6:

[0538] The AI ​​module analyzes the received image data and executes a facial recognition algorithm to identify the location of each facial feature. The input is the transformed image data and the output is the location information of the facial features.

[0539] Step 7:

[0540] The AI ​​module analyzes the condition of the skin and hair and measures the balance of facial features. The input is the position information of the facial features, and the output is the diagnosis results of the skin condition, hair condition, and balance of the facial features.

[0541] Step 8:

[0542] The server receives the diagnosis results sent from the AI ​​module, compares them with the customer's past treatment history and preference data, and generates the optimal product and treatment menu. The input is the diagnosis results and the customer's past data, and the output is the optimal proposal.

[0543] Step 9:

[0544] The server sends the generated optimal proposal to the user device, where the input is the optimal proposal and the output is the proposal received by the user device.

[0545] Step 10:

[0546] The user device receives the suggestions sent from the server and displays them on the display of the smart glasses. The input is the suggestions, and the output is the suggestions displayed on the display.

[0547] Specific examples

[0548] Example prompt sentence:

[0549] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0550] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0551] The present invention relates to a system that analyzes various data, including user emotions, and recommends optimal beauty treatments and products. The system includes a user terminal, a server, an artificial intelligence module, and an emotion engine. Specific embodiments for implementing the present invention are described in detail below.

[0552] System configuration

[0553] The system consists of the following main components:

[0554] 1. User Device

[0555] 2. Server

[0556] 3. Artificial Intelligence Module

[0557] 4. Emotion Engine

[0558] User terminal

[0559] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. It also has a function to acquire user emotional data (changes in facial expressions and voice) in real time using an emotion engine and send it to the server.

[0560] server

[0561] The server is a central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes the data. The server then sends the received data to the artificial intelligence module and emotion engine, and receives the analysis results. It also compares the analysis results with the customer's past treatment history to generate optimal proposals.

[0562] Artificial Intelligence Module

[0563] The AI ​​module is equipped with algorithms that analyze image data and diagnose the customer's skin condition, hair condition, and facial feature balance. The module uses facial recognition technology to identify the location of the customer's facial features and also provides a detailed analysis of the skin and hair condition.

[0564] Emotion Engine

[0565] The emotion engine analyzes the user's facial expressions and voice changes to identify the user's emotional state, thereby understanding the user's emotions in real time and sending that information to the server.

[0566] Example

[0567] Let's take a specific example of a new customer visiting a beauty salon. First, hairdresser B takes a photo of the customer's face using a user device and enters basic information (such as name and past treatment history) into a dedicated application. Next, B activates the emotion engine and configures it to capture conversations with the customer and changes in facial expressions.

[0568] Next, Person B presses the "Start Diagnosis" button on the device, which sends the photo data, customer information, and emotional data to the server. The server receives this data and sends the image data to the artificial intelligence module and the emotional data to the emotion engine. The artificial intelligence module then diagnoses the customer's skin condition, hair condition, and facial feature balance, and the emotion engine analyzes the user's emotional state.

[0569] The analysis results are sent back to the server, which compares these results with the customer's past treatment history and generates the optimal proposal. The generated proposal is sent to the user's device and displayed on the device. Based on the displayed proposal, Mr. B explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[0570] In this way, the system analyzes a variety of data, including the user's emotional state, enabling it to provide more accurate and effective beauty treatments and product recommendations, thereby reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0574] Step 2:

[0575] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0576] Step 3:

[0577] The user enables the emotion engine, which allows the device to capture changes in the customer's facial expressions and voice in real time and collect emotion data.

[0578] Step 4:

[0579] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data, the entered customer information, and the acquired emotion data, and sends them to the server using the HTTPS protocol.

[0580] Step 5:

[0581] The server receives the image data, customer information, and emotion data sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0582] Step 6:

[0583] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0584] Step 7:

[0585] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0586] Step 8:

[0587] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0588] Step 9:

[0589] The emotion engine receives emotion data sent from the user's device, analyzes the user's facial expressions and voice characteristics, identifies emotions (happiness, sadness, surprise, etc.), and sends the results back to the server.

[0590] Step 10:

[0591] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0592] Step 11:

[0593] The server receives the diagnosis results and emotion data returned by the AI ​​module and emotion engine, stores them in a database, and then compares them with the customer's past treatment history and preferences.

[0594] Step 12:

[0595] The server generates proposals based on the diagnosis results, emotional data, and past data to select the most suitable products and treatment menus for each customer.

[0596] Step 13:

[0597] The server transmits the generated proposal to the user terminal.

[0598] Step 14:

[0599] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0600] In this way, the user, terminal, server, artificial intelligence module, and emotion engine work together to provide customers with prompt and accurate proposals, and the use of emotion data in particular makes it possible to provide services that are in tune with the customer's emotions.

[0601] Example 2

[0602] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Conventional beauty treatment and product recommendation systems only take into account physical data such as the customer's skin and hair condition, and do not reflect psychological data such as the customer's emotional state, making it difficult to maximize customer satisfaction.In addition, recommendations based on treatment history and customer preferences are insufficient, making it difficult to make optimal recommendations for each individual customer.

[0604] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving image data and customer information of a customer photographed by a user and acquiring emotional data from changes in facial expressions and voice in real time, means for transmitting the acquired image data, customer information, and emotional data to the server, an artificial intelligence module for analyzing the image data received from the server and diagnosing the customer's skin condition, hair condition, and facial balance, an emotion engine for analyzing the emotional data and identifying the customer's emotional state, means for generating optimal products and treatment menus based on the analysis results of the artificial intelligence module and the emotion engine by comparing them with past treatment history and transmitting the generated recommendations to the user terminal, and a recommendation displayed on the user terminal. This makes it possible to propose optimal beauty treatments and products to individual customers, taking into account both physical and psychological data.

[0605] A "user terminal" is a device used by a user, which has the function of acquiring image data and customer information of a customer and transmitting it to a server.

[0606] A "server" is a central computer system that receives and analyzes data sent from user terminals, and is a device that generates optimal proposals based on the analysis results and transmits them to user terminals.

[0607] "Image data" refers to visual data such as a photo of the customer's face taken on a user device, and this data is used to analyze the customer's skin and hair condition.

[0608] "Customer Information" refers to information that includes data such as a customer's name, past treatment history, and preferences.

[0609] "Emotion data" is data that indicates the psychological state of a customer, obtained in real time from changes in facial expressions and voice.

[0610] The "artificial intelligence module" refers to algorithms and software that analyze image data and diagnose the customer's skin condition, hair condition, and facial balance.

[0611] An "emotion engine" refers to a system or software that analyzes emotional data and identifies a customer's emotional state.

[0612] "Analysis results" refers to the results of data analysis by the artificial intelligence module and emotion engine, based on which optimal suggestions are generated.

[0613] The "proposal content" includes optimal products and treatment menus generated based on the analysis results and past treatment history, and is displayed on the user's terminal.

[0614] "Encoding means" refers to methods or techniques used to encrypt or compress data before transmission, thereby increasing the security or efficiency of communications.

[0615] "Pre-processing means" refers to a method or system that performs processing to convert data sent from a user terminal into a format that can be analyzed by the artificial intelligence module and emotion engine.

[0616] The present invention is implemented using the following specific hardware and software components: The components of the overall system include a user terminal, a server, an artificial intelligence module, and an emotion engine.

[0617] User terminal

[0618] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. The user terminal is also equipped with an emotion engine, which has the function of capturing changes in the customer's facial expressions and voice in real time. Specifically, the camera function of a smartphone or tablet is used to take photos, and a keyboard or touch screen is used to input data. An example of an application used is the "BeautyPro" app.

[0619] server

[0620] The server is the central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes this data. Specifically, a database management system (e.g., MySQL) is installed on the server, and the received data is distributed to the artificial intelligence module and emotion engine.

[0621] Artificial Intelligence Module

[0622] The AI ​​module analyzes the received image data and is equipped with algorithms to diagnose the customer's skin condition, hair condition, and facial balance. This module uses the "FaceAnalyzer" algorithm developed in Python. For example, it analyzes the captured image for blemishes, wrinkles, and hair damage, and sends the results back to the server.

[0623] Emotion Engine

[0624] The emotion engine analyzes the customer's emotional state in real time from changes in facial expressions and voice captured on the user's device. The software used is "EmotionDetect." This engine identifies whether the customer is relaxed or tense and sends that information to the server.

[0625] Example

[0626] A specific example using this system will be described below.

[0627] When a new customer visits a beauty salon, the hairdresser (user) uses a tablet device. The user takes a photo of the customer's face and enters the customer's basic information (e.g., name, past treatment history) into the dedicated app "BeautyPro." In addition, the user activates the emotion engine (EmotionDetect) within the app to capture changes in the customer's facial expressions and voice.

[0628] When the user presses the "Start Diagnosis" button, the captured image data, customer information, and emotional data are sent to the server. The server receives this data and sends the image data to the artificial intelligence module (FaceAnalyzer) and the emotional data to the emotion engine (EmotionDetect).

[0629] The AI ​​module analyzes the image data and diagnoses the customer's skin condition, hair condition, and facial balance. The analysis results are sent back to the server. Similarly, the emotion engine analyzes the emotion data, identifies the customer's emotional state, and sends the results back to the server.

[0630] Based on the analysis results, the server compares them with the customer's past treatment history (stored in a MySQL database) and generates the optimal product and treatment menu. The generated proposals are sent to the user's device and displayed on the device. The user explains the displayed proposals to the customer and begins treatment after obtaining the customer's approval.

[0631] Prompt Sentence Examples

[0632] "Take a photo of your customer's face, analyze their emotions in real time, and recommend the most appropriate beauty treatment."

[0633] In this way, the system can take into account both physical and psychological data to recommend the most suitable beauty treatments and products for each customer, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0635] Step 1:

[0636] The user (e.g., a hairdresser) launches the dedicated app "BeautyPro" on the user's device (e.g., a tablet).

[0637] Input: The user enters the customer's basic information (name, past treatment history) into the app and uses the camera function to take a photo of the customer's face.

[0638] What happens: The user uses the tablet's keyboard and touchscreen to enter customer information and takes a photo with the tablet's camera.

[0639] Output: The customer's basic information and facial photo are recorded on the user's terminal.

[0640] Step 2:

[0641] The user enables the emotion engine (EmotionDetect) within the application to capture changes in the customer's facial expressions and voice in real time.

[0642] Input: The user presses the "Start Emotion Engine" button.

[0643] Specific operation: Throughout the conversation with the customer, the tablet's microphone and camera detect changes in facial expressions and voice in real time.

[0644] Output: Real-time facial expression data and voice change data are recorded on the user's device.

[0645] Step 3:

[0646] The terminal transmits the acquired facial photograph, customer information, and emotion data to the server.

[0647] Input: Facial photo, customer information, emotional data.

[0648] Specific operation: The device sends data to the server via Wi-Fi.

[0649] Output: The server receives the face photo, customer information, and emotion data.

[0650] Step 4:

[0651] The server distributes the received data to the artificial intelligence module and emotion engine.

[0652] Input: Facial photo, customer information, emotional data.

[0653] Specific operation: The server sends the face photo to the artificial intelligence module (FaceAnalyzer) and the emotion data to the emotion engine (EmotionDetect).

[0654] Output: Data is sent for analysis by the artificial intelligence module and emotion engine.

[0655] Step 5:

[0656] An artificial intelligence module analyzes image data and diagnoses the customer's skin condition, hair condition, and facial balance.

[0657] Input: Facial photo data sent from the server.

[0658] Specific operation: The artificial intelligence module (FaceAnalyzer) uses image analysis algorithms to diagnose uneven skin tone, blemishes, wrinkles, hair damage, and facial balance.

[0659] Output: Diagnostic results are generated and sent back to the server.

[0660] Step 6:

[0661] An emotion engine analyzes the emotion data to identify the customer's emotional state.

[0662] Input: Emotion data sent from the server.

[0663] Specific behavior: The emotion engine (EmotionDetect) analyzes facial expressions and vocal changes to identify the customer's emotional state (relaxed, tense, etc.).

[0664] Output: Parsed emotional state data is generated and sent back to the server.

[0665] Step 7:

[0666] The server receives the analysis results from the artificial intelligence module and emotion engine, compares them with past treatment history, and generates optimal products and treatment menus.

[0667] Input: Analysis results returned from the artificial intelligence module and emotion engine, and the customer's past treatment history.

[0668] Specific operation: The server retrieves treatment history from the MySQL database, compares it with the analysis results, and executes an algorithm to make optimal recommendations.

[0669] Output: The generated recommendations for optimal products and treatment menus.

[0670] Step 8:

[0671] The server transmits the generated proposal content to the user terminal.

[0672] Input: Suggestions for optimal products and treatment menus.

[0673] Specific operation: The server sends the proposal content to the user's device via Wi-Fi.

[0674] Output: The proposal is displayed on the user's terminal.

[0675] Step 9:

[0676] Based on the proposal displayed on the user terminal, the user explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[0677] Input: The suggestion displayed on the user's device.

[0678] Specific operation: The user explains the proposal to the customer and obtains the customer's approval.

[0679] Output: Treatment begins after customer consent is obtained.

[0680] (Application example 2)

[0681] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0682] While conventional beauty treatment systems can analyze physical data such as a customer's skin and hair condition, they have difficulty making recommendations that take into account the customer's emotional state. Furthermore, while changes in a customer's emotions are considered an important factor in improving the suitability of treatment and product recommendations, there has been a lack of means to acquire and analyze this information in real time. This has resulted in reduced recommendation accuracy and made it difficult to improve customer satisfaction.

[0683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0684] In this invention, the server includes a terminal that receives image data of a customer taken by a user and transmits it together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, an emotion engine that analyzes changes in the customer's facial expression and voice and identifies their emotional state, and a means for generating optimal products and treatment menus based on the diagnosis results from the artificial intelligence module and emotion engine and taking into account past treatment history and customer preferences, and transmitting the results to the user terminal. This enables more appropriate beauty treatments and product recommendations that take into account not only the customer's physical condition but also their emotional state.

[0685] A "user terminal" is a device that acquires image data and customer information of a customer and transmits them to a server.

[0686] A "server" is a central device that receives, manages, and analyzes data sent from user terminals.

[0687] The "artificial intelligence module" is a system equipped with algorithms that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[0688] The "emotion engine" is a system that analyzes changes in a customer's facial expression and voice to identify their emotional state.

[0689] "Image data analysis" is the process of analyzing a customer's image and diagnosing their skin condition, hair condition, and facial feature balance.

[0690] "Emotional state" refers to a psychological state that can be identified from changes in facial expressions and voice that a customer shows in real time.

[0691] "Treatment history" refers to records of beauty treatments that a customer has received in the past.

[0692] "Products and treatment menus" refer to specific beauty items and services suggested based on the customer's condition and preferences.

[0693] The present invention is a system that analyzes a customer's image data and emotional data to recommend optimal beauty treatments and products. The system is composed of a user terminal, a server, an artificial intelligence module, and an emotion engine. An embodiment of the system is described in detail below.

[0694] User terminal

[0695] A user terminal is a device that acquires customer image data and customer information and sends them to a server. For example, smart glasses used by hairdressers are examples of such devices. By wearing the smart glasses, it is possible to capture customer facial photos and facial expression data in real time. The captured data is encoded and sent to a server.

[0696] server

[0697] The server is a central device that receives image data and customer information sent from user devices and sends the data to the AI ​​module and emotion engine for analysis. The server preprocesses the image data using image processing software such as OpenCV and converts the data format as necessary.

[0698] Artificial Intelligence Module

[0699] The AI ​​module is equipped with algorithms that analyze the received image data and diagnose the customer's skin condition, hair condition, and facial feature balance. This module also uses generative AI models to realize facial recognition technology, which can pinpoint the location of the customer's facial features and perform a detailed analysis.

[0700] Emotion Engine

[0701] The emotion engine is a system that analyzes changes in a customer's facial expression and voice to identify the customer's emotional state. In an embodiment, the emotion engine acquires the customer's emotional state in real time and transmits the information to a server.

[0702] Analysis results

[0703] The analysis results are sent back to the server, which uses these results to generate optimal products and treatment menus based on past treatment history and customer preferences. The generated proposals are sent to the user's terminal and displayed on the user's device.

[0704] Specific examples

[0705] When a new customer visits a beauty salon, the hairdresser puts on the smart glasses and takes a photo of the customer's face. The photo and customer information are then encoded and sent from the smart glasses to a server. The server then sends the image data to an artificial intelligence module and emotion data to an emotion engine. The data is analyzed, and the results are sent back to the server. Based on the analysis results, the server generates optimal suggestions based on the customer's past treatment history and preferences, and displays them on the smart glasses' display.

[0706] Prompt Sentence Examples

[0707] Customer Name: Yamada Hanako

[0708] Past treatment history: Cut, coloring

[0709] Current Emotion: Relaxed

[0710] Analysis results:

[0711] 1. Skin condition: Dry

[0712] 2. Hair condition: Healthy

[0713] suggestion:

[0714] Moisturizing Treatment

[0715] Moisturizing hair care products

[0716] As described above, this system makes it possible to propose beauty treatments and products that take into account both the physical and emotional state of the customer, thereby providing more appropriate services that will increase customer satisfaction.

[0717] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0718] Step 1:

[0719] The user terminal acquires the photo data and customer information.

[0720] Specifically, a hairdresser wearing smart glasses takes a photo of a customer's face and stores the data on the device. The input is the customer's face photo and basic information, and the output is encoded data.

[0721] Step 2:

[0722] The terminal transmits the photographed customer's face photo and customer information to the server.

[0723] Specifically, the terminal sends the encoded data to the server as an HTTP request. The input is the encoded face photo and customer information, and the output is the data sent to the server.

[0724] Step 3:

[0725] The server preprocesses the received data and converts it into a format that can be parsed.

[0726] Specifically, the server uses image processing software such as OpenCV to convert image data into a format that is easy for the AI ​​module to analyze. The input is encoded facial photo data, and the output is the converted image data.

[0727] Step 4:

[0728] The server sends the image data to an artificial intelligence module, which analyzes the condition of the skin, hair, and balance of facial features.

[0729] Specifically, the server inputs the converted image data into the generative AI model and obtains the analysis results. The input is the converted image data, and the output is the skin condition, hair condition, and facial feature balance.

[0730] Step 5:

[0731] The server sends data on facial expressions and voice changes to the emotion engine, which analyzes the emotional state.

[0732] Specifically, the server sends data on conversations with customers and changes in facial expressions to the emotion engine, which then identifies their emotional state. The input is data on changes in facial expressions and voice, and the output is their emotional state.

[0733] Step 6:

[0734] The server integrates the analysis results from the artificial intelligence module and emotion engine to generate optimal recommendations based on treatment history and customer preferences.

[0735] Specifically, the server generates optimal products and treatment menus based on the integrated analysis results, taking into account treatment history and customer preferences. The inputs are the analysis results and treatment history, and the output is the proposal content.

[0736] Step 7:

[0737] The server transmits the generated proposal content to the user terminal, and the user terminal displays the proposal content.

[0738] Specifically, the server sends the generated proposal to the user's device as an HTTP response, and the device displays the proposal on its screen. The input is the optimal proposal, and the output is the proposal displayed on the user's device.

[0739] 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.

[0740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0741] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0742] [Third embodiment]

[0743] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0744] 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.

[0745] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0746] 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.

[0747] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0748] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0749] 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.

[0750] 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.

[0751] 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 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.

[0752] 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.

[0753] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0754] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0755] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0756] System configuration

[0757] This system mainly consists of a user terminal, a server, and an artificial intelligence module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The server receives the data sent from the user terminal, analyzes it in conjunction with the artificial intelligence module, and generates optimal suggestions. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0758] Operation on the user device

[0759] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (name, past treatment history, etc.) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to the server.

[0760] Data reception and analysis on the server

[0761] The server receives the image data and customer information sent from the user terminal. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. If the received data is determined to be problem-free, the server encodes the image data and converts it into an appropriate format. The converted data is then sent to the artificial intelligence module.

[0762] Image analysis using artificial intelligence modules

[0763] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0764] Data integration and proposal generation on the server

[0765] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. It then comprehensively considers the past data and the diagnosis results to select the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0766] Displaying suggestions on the user's device

[0767] The user terminal receives the proposal sent from the server. The proposal is displayed on the application screen in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0768] Specific examples

[0769] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button on the screen to send the data to the server. The server receives the data and sends it to an artificial intelligence module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0770] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0774] Step 2:

[0775] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0776] Step 3:

[0777] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data and the entered customer information and sends them to the server using the HTTPS protocol.

[0778] Step 4:

[0779] The server receives the image data and customer information sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0780] Step 5:

[0781] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0782] Step 6:

[0783] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0784] Step 7:

[0785] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0786] Step 8:

[0787] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0788] Step 9:

[0789] The server receives the diagnosis returned by the AI ​​module and stores it in a database, which then compares it with the customer's past treatment history and preferences.

[0790] Step 10:

[0791] The server generates proposals based on the diagnosis results and past data to select the most suitable products and treatment menus for each customer.

[0792] Step 11:

[0793] The server transmits the generated proposal to the user terminal.

[0794] Step 12:

[0795] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0796] In this way, the user, terminal, server, and artificial intelligence module work in cooperation with each other, making it possible to provide customers with prompt and accurate proposals.

[0797] Example 1

[0798] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0799] Conventional customer image analysis systems in the beauty and aesthetics industries often had low accuracy in diagnosing a customer's skin condition, hair condition, and facial feature balance, and the recommendations often did not fully address each customer's past treatment history or preferences. This made it difficult to quickly and accurately recommend the optimal products and treatment menus for each customer. Furthermore, there were security issues with data transmission and reception, raising the risk of unauthorized data being mixed in or information being leaked.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0801] In this invention, the server includes means for checking the integrity of data sent from the user terminal, encoding the image data, and converting it into a format suitable for the machine learning module means, means for comparing the diagnosis results from the machine learning module means with data on the customer's past treatment history and preferences to select the most suitable products and treatment menus, and application software means for displaying the proposal contents in an easy-to-use format on the user terminal. This confirms the integrity of the data, prevents the mixing of fraudulent data, and enables the most suitable proposals to be made to the customer quickly and accurately.

[0802] The term "mobile terminal means" refers to a small electronic device that allows a user to take a picture of a customer's image data, input customer information, and transmit the data to a server.

[0803] "Data Server" refers to a computer system that receives image data and customer information sent from user terminals and manages and processes them for analysis.

[0804] "Machine learning module means" refers to algorithms and programs that analyze image data and diagnose a customer's skin condition, hair condition, and facial feature balance.

[0805] "User interface means" refers to a system that displays the proposal content sent from the data server and supports operations in a format that is easily understandable to the user.

[0806] "Encryption" refers to the process of encoding data for secure transmission, thereby ensuring confidentiality of the data.

[0807] "Integrity check measures" refers to processes used to verify that received data is correct and does not contain any malicious data.

[0808] "Pre-processing means" refers to a process for converting received image data into a format suitable for the machine learning module means.

[0809] "Proposal generation means" refers to the process of comparing diagnostic results with past treatment history and customer preference data to generate optimal products and treatment menus.

[0810] "Application software means" refers to a program that runs on a user terminal and displays the content of the proposal.

[0811] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0812] System configuration

[0813] This system mainly consists of a user terminal, a data server, and a machine learning module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The data server receives the data sent from the user terminal, analyzes it in conjunction with the machine learning module, and generates optimal suggestions. The machine learning module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[0814] Operation on the user device

[0815] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (such as name and past treatment history) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to a data server. For example, an iPhone, Android smartphone, or a dedicated tablet device can be used as the user device.

[0816] Data reception and analysis on the server

[0817] The server receives the image data and customer information sent from the user device. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. For example, a cloud server from Amazon Web Services (AWS) or Google Cloud Platform (GCP) is used. If the received data is determined to be problem-free, the image data is encoded into JPEG or PNG format and converted into an appropriate format. The converted data is then sent to the machine learning module.

[0818] Image analysis using artificial intelligence modules

[0819] The machine learning module analyzes the received image data. This is implemented using, for example, Google's TensorFlow or Facebook's PyTorch. Specifically, it uses a facial recognition algorithm to identify the location of each facial feature, and then analyzes the skin tone, wrinkles, blemishes, and pore condition. It also diagnoses hair quality, shine, and damage. These diagnostic results are compiled as text data and sent back to the server in JSON format.

[0820] Data integration and proposal generation on the server

[0821] The server receives the diagnosis results from the machine learning module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. For example, MySQL or PostgreSQL is used as the database. Taking into consideration the past data and the diagnosis results, the server selects the most suitable proposal for the customer. The proposal is then sent to the user's device.

[0822] Displaying suggestions on user devices

[0823] The user terminal receives the proposal sent from the data server. The proposal is displayed on the application screen in a format that is easy for the user to understand. For example, it is displayed using text and images on the user interface of a dedicated application. The user (hairdresser or aesthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[0824] Specific examples and examples of prompts to input to the generative AI model

[0825] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button to send the data to the server. The server receives the data and sends it to the machine learning module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[0826] By inputting a prompt sentence like the one below into the generative AI model, a detailed description of the system will be automatically generated.

[0827] Please explain the system that analyzes customer facial photos and suggests optimal products and treatments. This system sends facial photos taken on the user's device and customer information to a data server, where a machine learning module performs image analysis and suggests optimal products and treatments. Please provide a detailed explanation, including specific operating procedures and the software and hardware used.

[0828] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0829] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0830] Program processing flow

[0831] Step 1: Collect data on the user's device

[0832] The user (hairdresser or aesthetician) launches a dedicated application on a device such as a smartphone and takes a photo of the customer's face. Next, they enter the customer's basic information (name, past treatment history, etc.). The input data is saved as image data (JPEG format) and text data (JSON format). Specifically, the system works by taking a high-resolution photo with the camera and entering the text information into the application form.

[0833] Step 2: Send data to the server

[0834] The user presses the "Start Diagnosis" button on the dedicated application. This causes the user device to send the photograph taken and the entered customer information to the data server. The transmitted data is encrypted using the HTTPS protocol. Image data and text data are input, and encrypted data is sent as output. Specifically, pressing the send button sends the data to the server.

[0835] Step 3: Receiving and encoding data on the server

[0836] The server receives data sent from the user terminal. First, the server checks the integrity of the data to ensure that it does not contain any invalid data. The input data is encrypted image data and customer information, and the output is encoded JPEG image data. Specifically, the server stores the data in a database and converts the image data into an appropriate format.

[0837] Step 4: Sending data to the machine learning module

[0838] The server sends the encoded image data and customer information to the machine learning module. This data transmission uses a REST API. The input data is the encoded image data and text data, and an API request is generated as the output. Specifically, the request is sent to the API endpoint.

[0839] Step 5: Image analysis using machine learning modules

[0840] The machine learning module analyzes the received image data. It uses a facial recognition algorithm to identify the location of each facial feature and evaluates the condition of skin tone, wrinkles, blemishes, and pores. The input data is image data, and the output is a text-based analysis result. Specifically, it runs the image analysis algorithm and summarizes the analysis results in JSON format.

[0841] Step 6: Integration of analysis results and generation of proposals on the server

[0842] The server receives the diagnosis results from the machine learning module. It compares them with the customer's past treatment history and preference data to generate the optimal product and treatment menu. The input data is the analysis results and customer history data, and the output is the generated recommendations. Specifically, it executes a database query, aggregates the results, and determines the optimal recommendations.

[0843] Step 7: Displaying suggestions on the user's device

[0844] The user device receives the proposal sent from the server. A dedicated application parses the proposal and displays it on the screen in a format that is easy for the user to understand. The proposal is the input data, and a display screen is generated as the output. Specifically, the application displays text and images in the user interface.

[0845] Through the above steps, the system can quickly and accurately provide optimal proposals to customers.

[0846] (Application example 1)

[0847] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0848] In conventional beauty and aesthetic treatments, it was difficult to grasp the condition of a customer's skin and hair in real time and quickly recommend the most appropriate treatments and products. Furthermore, there was a lack of efficient ways to provide personalized recommendations based on the customer's preferences and past treatment history. This resulted in a heavy workload for hairdressers and aestheticians, making it difficult to improve customer satisfaction.

[0849] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0850] In this invention, the server includes a device that receives image data of a customer taken by a user and transmits it to the server together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, a server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and based on past treatment history and customer preferences and transmits them to the user device, a system including the proposals displayed on the user device, a device that acquires captured images of the customer from a built-in camera and checks the customer's skin and hair condition in real time, means for transmitting customer image data and customer information and acquiring analysis results, and means for displaying the analysis results on a display. This makes it possible to check the customer's skin and hair condition in real time and efficiently provide optimal treatments and products.

[0851] A "user" is a person who uses the system to photograph and input customer information and request treatment suggestions.

[0852] "Device" refers to a hardware device that allows a user to capture image data of a customer and input and transmit customer information.

[0853] The "server" is a computer system that receives data sent from a user device, analyzes the data using an artificial intelligence module, and generates optimal products and treatment menus.

[0854] The "artificial intelligence module" refers to algorithms and software that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[0855] "Image data" refers to an image file of the customer's face taken by the user with the device.

[0856] "Customer information" refers to data that includes basic information such as the customer's name and past treatment history.

[0857] The "diagnosis results" are an evaluation of the customer's skin condition, hair condition, and facial balance obtained through image analysis by an artificial intelligence module.

[0858] A "treatment menu" refers to specific beauty and aesthetic treatments proposed based on the diagnosis, past treatment history, and customer preferences.

[0859] "Proposal content" refers to information generated based on the diagnosis results, including optimal products and treatment menus.

[0860] The "built-in camera" refers to a camera that is built into the device and is used to capture image data.

[0861] A "display" refers to a screen for displaying information on a device, and is responsible for displaying proposals and other information to customers.

[0862] "Real-time" refers to the state in which the acquisition of customer image data and the display of analysis results are carried out immediately without delay.

[0863] The present invention is a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[0864] System configuration

[0865] This system mainly consists of a user device, a server, and an artificial intelligence module. The user device is hardware used by hairdressers and estheticians to take images of their customers and input their information. The server receives the data sent from the user device, analyzes it in conjunction with the artificial intelligence module, and generates optimal recommendations. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial balance.

[0866] Hardware and software used

[0867] Hardware: Smart glasses (built-in camera, display), user device (smartphone, etc.)

[0868] Software: Python, OpenCV (image processing library), requests (library for sending HTTP requests)

[0869] Operation on user device

[0870] The user (beautician or esthetician) first puts on the smart glasses and takes a photo of the customer's face with the built-in camera. Next, they enter the customer's basic information (name, past treatment history, etc.) into the device's dedicated application. The image data and customer information are then sent to the server.

[0871] Data reception and analysis on the server

[0872] The server receives image data and customer information sent from the user device. When data is sent, the user device encodes the data and sends it to the server. The server checks the consistency of the received image data and customer information to ensure that no invalid data is included. Once the consistency is confirmed, the data is converted into an appropriate format and sent to the artificial intelligence module.

[0873] Image analysis using artificial intelligence modules

[0874] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[0875] Data integration and proposal generation on the server

[0876] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preferences to generate the optimal product and treatment menu. The proposed content is sent to the user device and provided to the customer.

[0877] Displaying Offers on User Devices

[0878] The user device receives the proposed content sent from the server. The proposed content is displayed on the device's display in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposed content to the customer while looking at it, and begins the treatment only after obtaining approval.

[0879] Specific examples

[0880] For example, when a new customer visits a salon, a hairdresser wearing smart glasses takes a photo of the customer's face and enters their name and past treatment history. The data is then sent to a server, where it is analyzed by an artificial intelligence module, and a diagnosis is returned. Based on the analysis results, the most appropriate treatment menu and products are suggested and displayed on the smart glasses' display. The hairdresser then explains the suggestions to the customer and, after obtaining their approval, performs the treatment.

[0881] Example prompt sentence:

[0882] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0883] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0884] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0885] Step 1:

[0886] A user puts on the smart glasses and takes a photo of the customer's face. The smart glasses' built-in camera is used to capture the image data. The input is the customer's face photo, and the output is the image data captured by the built-in camera.

[0887] Step 2:

[0888] The user inputs customer information into the dedicated application on the smart glasses. The input is basic customer information (such as name and past treatment history), and the output is the collected customer information data.

[0889] Step 3:

[0890] The user device encodes the captured customer image data and customer information when transmitting them to the server. The input is the image data and customer information, and the output is the encoded data.

[0891] Step 4:

[0892] The server receives the encoded data sent from the user device, with the input being the encoded image data and customer information, and the output being the result of checking the integrity of the data.

[0893] Step 5:

[0894] The server converts the received image data into a suitable format and sends it to the artificial intelligence module, where the input is the encoded data and the output is the converted image data.

[0895] Step 6:

[0896] The AI ​​module analyzes the received image data and executes a facial recognition algorithm to identify the location of each facial feature. The input is the transformed image data and the output is the location information of the facial features.

[0897] Step 7:

[0898] The AI ​​module analyzes the condition of the skin and hair and measures the balance of facial features. The input is the position information of the facial features, and the output is the diagnosis results of the skin condition, hair condition, and balance of the facial features.

[0899] Step 8:

[0900] The server receives the diagnosis results sent from the AI ​​module, compares them with the customer's past treatment history and preference data, and generates the optimal product and treatment menu. The input is the diagnosis results and the customer's past data, and the output is the optimal proposal.

[0901] Step 9:

[0902] The server sends the generated optimal proposal to the user device, where the input is the optimal proposal and the output is the proposal received by the user device.

[0903] Step 10:

[0904] The user device receives the suggestions sent from the server and displays them on the display of the smart glasses. The input is the suggestions, and the output is the suggestions displayed on the display.

[0905] Specific examples

[0906] Example prompt sentence:

[0907] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[0908] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0909] The present invention relates to a system that analyzes various data, including user emotions, and recommends optimal beauty treatments and products. The system includes a user terminal, a server, an artificial intelligence module, and an emotion engine. Specific embodiments for implementing the present invention are described in detail below.

[0910] System configuration

[0911] The system consists of the following main components:

[0912] 1. User Device

[0913] 2. Server

[0914] 3. Artificial Intelligence Module

[0915] 4. Emotion Engine

[0916] User terminal

[0917] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. It also has a function to acquire user emotional data (changes in facial expressions and voice) in real time using an emotion engine and send it to the server.

[0918] server

[0919] The server is a central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes the data. The server then sends the received data to the artificial intelligence module and emotion engine, and receives the analysis results. It also compares the analysis results with the customer's past treatment history to generate optimal proposals.

[0920] Artificial Intelligence Module

[0921] The AI ​​module is equipped with algorithms that analyze image data and diagnose the customer's skin condition, hair condition, and facial feature balance. The module uses facial recognition technology to identify the location of the customer's facial features and also provides a detailed analysis of the skin and hair condition.

[0922] Emotion Engine

[0923] The emotion engine analyzes the user's facial expressions and voice changes to identify the user's emotional state, thereby understanding the user's emotions in real time and sending that information to the server.

[0924] Example

[0925] Let's take a specific example of a new customer visiting a beauty salon. First, hairdresser B takes a photo of the customer's face using a user device and enters basic information (such as name and past treatment history) into a dedicated application. Next, B activates the emotion engine and configures it to capture conversations with the customer and changes in facial expressions.

[0926] Next, Person B presses the "Start Diagnosis" button on the device, which sends the photo data, customer information, and emotional data to the server. The server receives this data and sends the image data to the artificial intelligence module and the emotional data to the emotion engine. The artificial intelligence module then diagnoses the customer's skin condition, hair condition, and facial feature balance, and the emotion engine analyzes the user's emotional state.

[0927] The analysis results are sent back to the server, which compares these results with the customer's past treatment history and generates the optimal proposal. The generated proposal is sent to the user's device and displayed on the device. Based on the displayed proposal, Mr. B explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[0928] In this way, the system analyzes a variety of data, including the user's emotional state, enabling it to provide more accurate and effective beauty treatments and product recommendations, thereby reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0929] The processing flow will be explained below.

[0930] Step 1:

[0931] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[0932] Step 2:

[0933] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[0934] Step 3:

[0935] The user enables the emotion engine, which allows the device to capture changes in the customer's facial expressions and voice in real time and collect emotion data.

[0936] Step 4:

[0937] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data, the entered customer information, and the acquired emotion data, and sends them to the server using the HTTPS protocol.

[0938] Step 5:

[0939] The server receives the image data, customer information, and emotion data sent from the user terminal, verifies the format of the received data, and confirms its validity.

[0940] Step 6:

[0941] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[0942] Step 7:

[0943] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[0944] Step 8:

[0945] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[0946] Step 9:

[0947] The emotion engine receives emotion data sent from the user's device, analyzes the user's facial expressions and voice characteristics, identifies emotions (happiness, sadness, surprise, etc.), and sends the results back to the server.

[0948] Step 10:

[0949] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[0950] Step 11:

[0951] The server receives the diagnosis results and emotion data returned by the AI ​​module and emotion engine, stores them in a database, and then compares them with the customer's past treatment history and preferences.

[0952] Step 12:

[0953] The server generates proposals based on the diagnosis results, emotional data, and past data to select the most suitable products and treatment menus for each customer.

[0954] Step 13:

[0955] The server transmits the generated proposal to the user terminal.

[0956] Step 14:

[0957] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[0958] In this way, the user, terminal, server, artificial intelligence module, and emotion engine work together to provide customers with prompt and accurate proposals, and the use of emotion data in particular makes it possible to provide services that are in tune with the customer's emotions.

[0959] Example 2

[0960] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0961] Conventional beauty treatment and product recommendation systems only take into account physical data such as the customer's skin and hair condition, and do not reflect psychological data such as the customer's emotional state, making it difficult to maximize customer satisfaction.In addition, recommendations based on treatment history and customer preferences are insufficient, making it difficult to make optimal recommendations for each individual customer.

[0962] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving image data and customer information of a customer photographed by a user and acquiring emotional data from changes in facial expressions and voice in real time, means for transmitting the acquired image data, customer information, and emotional data to the server, an artificial intelligence module for analyzing the image data received from the server and diagnosing the customer's skin condition, hair condition, and facial balance, an emotion engine for analyzing the emotional data and identifying the customer's emotional state, means for generating optimal products and treatment menus based on the analysis results of the artificial intelligence module and the emotion engine by comparing them with past treatment history and transmitting the generated recommendations to the user terminal, and a recommendation displayed on the user terminal. This makes it possible to propose optimal beauty treatments and products to individual customers, taking into account both physical and psychological data.

[0963] A "user terminal" is a device used by a user, which has the function of acquiring image data and customer information of a customer and transmitting it to a server.

[0964] A "server" is a central computer system that receives and analyzes data sent from user terminals, and is a device that generates optimal proposals based on the analysis results and transmits them to user terminals.

[0965] "Image data" refers to visual data such as a photo of the customer's face taken on a user device, and this data is used to analyze the customer's skin and hair condition.

[0966] "Customer Information" refers to information that includes data such as a customer's name, past treatment history, and preferences.

[0967] "Emotion data" is data that indicates the psychological state of a customer, obtained in real time from changes in facial expressions and voice.

[0968] The "artificial intelligence module" refers to algorithms and software that analyze image data and diagnose the customer's skin condition, hair condition, and facial balance.

[0969] An "emotion engine" refers to a system or software that analyzes emotional data and identifies a customer's emotional state.

[0970] "Analysis results" refers to the results of data analysis by the artificial intelligence module and emotion engine, based on which optimal suggestions are generated.

[0971] The "proposal content" includes optimal products and treatment menus generated based on the analysis results and past treatment history, and is displayed on the user's terminal.

[0972] "Encoding means" refers to methods or techniques used to encrypt or compress data before transmission, thereby increasing the security or efficiency of communications.

[0973] "Pre-processing means" refers to a method or system that performs processing to convert data sent from a user terminal into a format that can be analyzed by the artificial intelligence module and emotion engine.

[0974] The present invention is implemented using the following specific hardware and software components: The components of the overall system include a user terminal, a server, an artificial intelligence module, and an emotion engine.

[0975] User terminal

[0976] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. The user terminal is also equipped with an emotion engine, which has the function of capturing changes in the customer's facial expressions and voice in real time. Specifically, the camera function of a smartphone or tablet is used to take photos, and a keyboard or touch screen is used to input data. An example of an application used is the "BeautyPro" app.

[0977] server

[0978] The server is the central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes this data. Specifically, a database management system (e.g., MySQL) is installed on the server, and the received data is distributed to the artificial intelligence module and emotion engine.

[0979] Artificial Intelligence Module

[0980] The AI ​​module analyzes the received image data and is equipped with algorithms to diagnose the customer's skin condition, hair condition, and facial balance. This module uses the "FaceAnalyzer" algorithm developed in Python. For example, it analyzes the captured image for blemishes, wrinkles, and hair damage, and sends the results back to the server.

[0981] Emotion Engine

[0982] The emotion engine analyzes the customer's emotional state in real time from changes in facial expressions and voice captured on the user's device. The software used is "EmotionDetect." This engine identifies whether the customer is relaxed or tense and sends that information to the server.

[0983] Example

[0984] A specific example using this system will be described below.

[0985] When a new customer visits a beauty salon, the hairdresser (user) uses a tablet device. The user takes a photo of the customer's face and enters the customer's basic information (e.g., name, past treatment history) into the dedicated app "BeautyPro." In addition, the user activates the emotion engine (EmotionDetect) within the app to capture changes in the customer's facial expressions and voice.

[0986] When the user presses the "Start Diagnosis" button, the captured image data, customer information, and emotional data are sent to the server. The server receives this data and sends the image data to the artificial intelligence module (FaceAnalyzer) and the emotional data to the emotion engine (EmotionDetect).

[0987] The AI ​​module analyzes the image data and diagnoses the customer's skin condition, hair condition, and facial balance. The analysis results are sent back to the server. Similarly, the emotion engine analyzes the emotion data, identifies the customer's emotional state, and sends the results back to the server.

[0988] Based on the analysis results, the server compares them with the customer's past treatment history (stored in a MySQL database) and generates the optimal product and treatment menu. The generated proposals are sent to the user's device and displayed on the device. The user explains the displayed proposals to the customer and begins treatment after obtaining the customer's approval.

[0989] Prompt Sentence Examples

[0990] "Take a photo of your customer's face, analyze their emotions in real time, and recommend the most appropriate beauty treatment."

[0991] In this way, the system can take into account both physical and psychological data to recommend the most suitable beauty treatments and products for each customer, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[0992] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0993] Step 1:

[0994] The user (e.g., a hairdresser) launches the dedicated app "BeautyPro" on the user's device (e.g., a tablet).

[0995] Input: The user enters the customer's basic information (name, past treatment history) into the app and uses the camera function to take a photo of the customer's face.

[0996] What happens: The user uses the tablet's keyboard and touchscreen to enter customer information and takes a photo with the tablet's camera.

[0997] Output: The customer's basic information and facial photo are recorded on the user's terminal.

[0998] Step 2:

[0999] The user enables the emotion engine (EmotionDetect) within the application to capture changes in the customer's facial expressions and voice in real time.

[1000] Input: The user presses the "Start Emotion Engine" button.

[1001] Specific operation: Throughout the conversation with the customer, the tablet's microphone and camera detect changes in facial expressions and voice in real time.

[1002] Output: Real-time facial expression data and voice change data are recorded on the user's device.

[1003] Step 3:

[1004] The terminal transmits the acquired facial photograph, customer information, and emotion data to the server.

[1005] Input: Facial photo, customer information, emotional data.

[1006] Specific operation: The device sends data to the server via Wi-Fi.

[1007] Output: The server receives the face photo, customer information, and emotion data.

[1008] Step 4:

[1009] The server distributes the received data to the artificial intelligence module and emotion engine.

[1010] Input: Facial photo, customer information, emotional data.

[1011] Specific operation: The server sends the face photo to the artificial intelligence module (FaceAnalyzer) and the emotion data to the emotion engine (EmotionDetect).

[1012] Output: Data is sent for analysis by the artificial intelligence module and emotion engine.

[1013] Step 5:

[1014] An artificial intelligence module analyzes image data and diagnoses the customer's skin condition, hair condition, and facial balance.

[1015] Input: Facial photo data sent from the server.

[1016] Specific operation: The artificial intelligence module (FaceAnalyzer) uses image analysis algorithms to diagnose uneven skin tone, blemishes, wrinkles, hair damage, and facial balance.

[1017] Output: Diagnostic results are generated and sent back to the server.

[1018] Step 6:

[1019] An emotion engine analyzes the emotion data to identify the customer's emotional state.

[1020] Input: Emotion data sent from the server.

[1021] Specific behavior: The emotion engine (EmotionDetect) analyzes facial expressions and vocal changes to identify the customer's emotional state (relaxed, tense, etc.).

[1022] Output: Parsed emotional state data is generated and sent back to the server.

[1023] Step 7:

[1024] The server receives the analysis results from the artificial intelligence module and emotion engine, compares them with past treatment history, and generates optimal products and treatment menus.

[1025] Input: Analysis results returned from the artificial intelligence module and emotion engine, and the customer's past treatment history.

[1026] Specific operation: The server retrieves treatment history from the MySQL database, compares it with the analysis results, and executes an algorithm to make optimal recommendations.

[1027] Output: The generated recommendations for optimal products and treatment menus.

[1028] Step 8:

[1029] The server transmits the generated proposal content to the user terminal.

[1030] Input: Suggestions for optimal products and treatment menus.

[1031] Specific operation: The server sends the proposal content to the user's device via Wi-Fi.

[1032] Output: The proposal is displayed on the user's terminal.

[1033] Step 9:

[1034] Based on the proposal displayed on the user terminal, the user explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[1035] Input: The suggestion displayed on the user's device.

[1036] Specific operation: The user explains the proposal to the customer and obtains the customer's approval.

[1037] Output: Treatment begins after customer consent is obtained.

[1038] (Application example 2)

[1039] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1040] While conventional beauty treatment systems can analyze physical data such as a customer's skin and hair condition, they have difficulty making recommendations that take into account the customer's emotional state. Furthermore, while changes in a customer's emotions are considered an important factor in improving the suitability of treatment and product recommendations, there has been a lack of means to acquire and analyze this information in real time. This has resulted in reduced recommendation accuracy and made it difficult to improve customer satisfaction.

[1041] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1042] In this invention, the server includes a terminal that receives image data of a customer taken by a user and transmits it together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, an emotion engine that analyzes changes in the customer's facial expression and voice and identifies their emotional state, and a means for generating optimal products and treatment menus based on the diagnosis results from the artificial intelligence module and emotion engine and taking into account past treatment history and customer preferences, and transmitting the results to the user terminal. This enables more appropriate beauty treatments and product recommendations that take into account not only the customer's physical condition but also their emotional state.

[1043] A "user terminal" is a device that acquires image data and customer information of a customer and transmits them to a server.

[1044] A "server" is a central device that receives, manages, and analyzes data sent from user terminals.

[1045] The "artificial intelligence module" is a system equipped with algorithms that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[1046] The "emotion engine" is a system that analyzes changes in a customer's facial expression and voice to identify their emotional state.

[1047] "Image data analysis" is the process of analyzing a customer's image and diagnosing their skin condition, hair condition, and facial feature balance.

[1048] "Emotional state" refers to a psychological state that can be identified from changes in facial expressions and voice that a customer shows in real time.

[1049] "Treatment history" refers to records of beauty treatments that a customer has received in the past.

[1050] "Products and treatment menus" refer to specific beauty items and services suggested based on the customer's condition and preferences.

[1051] The present invention is a system that analyzes a customer's image data and emotional data to recommend optimal beauty treatments and products. The system is composed of a user terminal, a server, an artificial intelligence module, and an emotion engine. An embodiment of the system is described in detail below.

[1052] User terminal

[1053] A user terminal is a device that acquires customer image data and customer information and sends them to a server. For example, smart glasses used by hairdressers are examples of such devices. By wearing the smart glasses, it is possible to capture customer facial photos and facial expression data in real time. The captured data is encoded and sent to a server.

[1054] server

[1055] The server is a central device that receives image data and customer information sent from user devices and sends the data to the AI ​​module and emotion engine for analysis. The server preprocesses the image data using image processing software such as OpenCV and converts the data format as necessary.

[1056] Artificial Intelligence Module

[1057] The AI ​​module is equipped with algorithms that analyze the received image data and diagnose the customer's skin condition, hair condition, and facial feature balance. This module also uses generative AI models to realize facial recognition technology, which can pinpoint the location of the customer's facial features and perform a detailed analysis.

[1058] Emotion Engine

[1059] The emotion engine is a system that analyzes changes in a customer's facial expression and voice to identify the customer's emotional state. In an embodiment, the emotion engine acquires the customer's emotional state in real time and transmits the information to a server.

[1060] Analysis results

[1061] The analysis results are sent back to the server, which uses these results to generate optimal products and treatment menus based on past treatment history and customer preferences. The generated proposals are sent to the user's terminal and displayed on the user's device.

[1062] Specific examples

[1063] When a new customer visits a beauty salon, the hairdresser puts on the smart glasses and takes a photo of the customer's face. The photo and customer information are then encoded and sent from the smart glasses to a server. The server then sends the image data to an artificial intelligence module and emotion data to an emotion engine. The data is analyzed, and the results are sent back to the server. Based on the analysis results, the server generates optimal suggestions based on the customer's past treatment history and preferences, and displays them on the smart glasses' display.

[1064] Prompt Sentence Examples

[1065] Customer Name: Yamada Hanako

[1066] Past treatment history: Cut, coloring

[1067] Current Emotion: Relaxed

[1068] Analysis results:

[1069] 1. Skin condition: Dry

[1070] 2. Hair condition: Healthy

[1071] suggestion:

[1072] Moisturizing Treatment

[1073] Moisturizing hair care products

[1074] As described above, this system makes it possible to propose beauty treatments and products that take into account both the physical and emotional state of the customer, thereby providing more appropriate services that will increase customer satisfaction.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] The user terminal acquires the photo data and customer information.

[1078] Specifically, a hairdresser wearing smart glasses takes a photo of a customer's face and stores the data on the device. The input is the customer's face photo and basic information, and the output is encoded data.

[1079] Step 2:

[1080] The terminal transmits the photographed customer's face photo and customer information to the server.

[1081] Specifically, the terminal sends the encoded data to the server as an HTTP request. The input is the encoded face photo and customer information, and the output is the data sent to the server.

[1082] Step 3:

[1083] The server preprocesses the received data and converts it into a format that can be parsed.

[1084] Specifically, the server uses image processing software such as OpenCV to convert image data into a format that is easy for the AI ​​module to analyze. The input is encoded facial photo data, and the output is the converted image data.

[1085] Step 4:

[1086] The server sends the image data to an artificial intelligence module, which analyzes the condition of the skin, hair, and balance of facial features.

[1087] Specifically, the server inputs the converted image data into the generative AI model and obtains the analysis results. The input is the converted image data, and the output is the skin condition, hair condition, and facial feature balance.

[1088] Step 5:

[1089] The server sends data on facial expressions and voice changes to the emotion engine, which analyzes the emotional state.

[1090] Specifically, the server sends data on conversations with customers and changes in facial expressions to the emotion engine, which then identifies their emotional state. The input is data on changes in facial expressions and voice, and the output is their emotional state.

[1091] Step 6:

[1092] The server integrates the analysis results from the artificial intelligence module and emotion engine to generate optimal recommendations based on treatment history and customer preferences.

[1093] Specifically, the server generates optimal products and treatment menus based on the integrated analysis results, taking into account treatment history and customer preferences. The inputs are the analysis results and treatment history, and the output is the proposal content.

[1094] Step 7:

[1095] The server transmits the generated proposal content to the user terminal, and the user terminal displays the proposal content.

[1096] Specifically, the server sends the generated proposal to the user's device as an HTTP response, and the device displays the proposal on its screen. The input is the optimal proposal, and the output is the proposal displayed on the user's device.

[1097] 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.

[1098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1099] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1100] [Fourth embodiment]

[1101] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1102] 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.

[1103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1104] 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.

[1105] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1106] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1107] 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.

[1108] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1109] 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.

[1110] 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 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.

[1111] 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.

[1112] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1113] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1114] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[1115] System configuration

[1116] This system mainly consists of a user terminal, a server, and an artificial intelligence module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The server receives the data sent from the user terminal, analyzes it in conjunction with the artificial intelligence module, and generates optimal suggestions. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[1117] Operation on the user device

[1118] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (name, past treatment history, etc.) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to the server.

[1119] Data reception and analysis on the server

[1120] The server receives the image data and customer information sent from the user terminal. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. If the received data is determined to be problem-free, the server encodes the image data and converts it into an appropriate format. The converted data is then sent to the artificial intelligence module.

[1121] Image analysis using artificial intelligence modules

[1122] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[1123] Data integration and proposal generation on the server

[1124] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. It then comprehensively considers the past data and the diagnosis results to select the most suitable proposal for the customer. The proposal is then sent to the user's device.

[1125] Displaying suggestions on the user's device

[1126] The user terminal receives the proposal sent from the server. The proposal is displayed on the application screen in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[1127] Specific examples

[1128] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button on the screen to send the data to the server. The server receives the data and sends it to an artificial intelligence module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[1129] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[1130] The processing flow will be explained below.

[1131] Step 1:

[1132] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[1133] Step 2:

[1134] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[1135] Step 3:

[1136] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data and the entered customer information and sends them to the server using the HTTPS protocol.

[1137] Step 4:

[1138] The server receives the image data and customer information sent from the user terminal, verifies the format of the received data, and confirms its validity.

[1139] Step 5:

[1140] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[1141] Step 6:

[1142] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[1143] Step 7:

[1144] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[1145] Step 8:

[1146] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[1147] Step 9:

[1148] The server receives the diagnosis returned by the AI ​​module and stores it in a database, which then compares it with the customer's past treatment history and preferences.

[1149] Step 10:

[1150] The server generates proposals based on the diagnosis results and past data to select the most suitable products and treatment menus for each customer.

[1151] Step 11:

[1152] The server transmits the generated proposal to the user terminal.

[1153] Step 12:

[1154] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[1155] In this way, the user, terminal, server, and artificial intelligence module work in cooperation with each other, making it possible to provide customers with prompt and accurate proposals.

[1156] Example 1

[1157] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1158] Conventional customer image analysis systems in the beauty and aesthetics industries often had low accuracy in diagnosing a customer's skin condition, hair condition, and facial feature balance, and the recommendations often did not fully address each customer's past treatment history or preferences. This made it difficult to quickly and accurately recommend the optimal products and treatment menus for each customer. Furthermore, there were security issues with data transmission and reception, raising the risk of unauthorized data being mixed in or information being leaked.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1160] In this invention, the server includes means for checking the integrity of data sent from the user terminal, encoding the image data, and converting it into a format suitable for the machine learning module means, means for comparing the diagnosis results from the machine learning module means with data on the customer's past treatment history and preferences to select the most suitable products and treatment menus, and application software means for displaying the proposal contents in an easy-to-use format on the user terminal. This confirms the integrity of the data, prevents the mixing of fraudulent data, and enables the most suitable proposals to be made to the customer quickly and accurately.

[1161] The term "mobile terminal means" refers to a small electronic device that allows a user to take a picture of a customer's image data, input customer information, and transmit the data to a server.

[1162] "Data Server" refers to a computer system that receives image data and customer information sent from user terminals and manages and processes them for analysis.

[1163] "Machine learning module means" refers to algorithms and programs that analyze image data and diagnose a customer's skin condition, hair condition, and facial feature balance.

[1164] "User interface means" refers to a system that displays the proposal content sent from the data server and supports operations in a format that is easily understandable to the user.

[1165] "Encryption" refers to the process of encoding data for secure transmission, thereby ensuring confidentiality of the data.

[1166] "Integrity check measures" refers to processes used to verify that received data is correct and does not contain any malicious data.

[1167] "Pre-processing means" refers to a process for converting received image data into a format suitable for the machine learning module means.

[1168] "Proposal generation means" refers to the process of comparing diagnostic results with past treatment history and customer preference data to generate optimal products and treatment menus.

[1169] "Application software means" refers to a program that runs on a user terminal and displays the content of the proposal.

[1170] The present invention relates to a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[1171] System configuration

[1172] This system mainly consists of a user terminal, a data server, and a machine learning module. The user terminal is a device used by hairdressers and estheticians to take photos of their customers' faces and enter their information. The data server receives the data sent from the user terminal, analyzes it in conjunction with the machine learning module, and generates optimal suggestions. The machine learning module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial feature balance.

[1173] Operation on the user device

[1174] The user (hairdresser or esthetician) first takes a photo of the customer's face using a user device such as a smartphone. Next, they enter the customer's basic information (such as name and past treatment history) into a dedicated application on the device. After that, the user presses the "Start Diagnosis" button, which sends the photo and customer information to a data server. For example, an iPhone, Android smartphone, or a dedicated tablet device can be used as the user device.

[1175] Data reception and analysis on the server

[1176] The server receives the image data and customer information sent from the user device. At this time, it checks the consistency of the image data and customer information to ensure that no invalid data is included. For example, a cloud server from Amazon Web Services (AWS) or Google Cloud Platform (GCP) is used. If the received data is determined to be problem-free, the image data is encoded into JPEG or PNG format and converted into an appropriate format. The converted data is then sent to the machine learning module.

[1177] Image analysis using artificial intelligence modules

[1178] The machine learning module analyzes the received image data. This is implemented using, for example, Google's TensorFlow or Facebook's PyTorch. Specifically, it uses a facial recognition algorithm to identify the location of each facial feature, and then analyzes the skin tone, wrinkles, blemishes, and pore condition. It also diagnoses hair quality, shine, and damage. These diagnostic results are compiled as text data and sent back to the server in JSON format.

[1179] Data integration and proposal generation on the server

[1180] The server receives the diagnosis results from the machine learning module. It then compares them with the customer's past treatment history and preference data to generate the optimal products and treatment menu. For example, MySQL or PostgreSQL is used as the database. Taking into consideration the past data and the diagnosis results, the server selects the most suitable proposal for the customer. The proposal is then sent to the user's device.

[1181] Displaying suggestions on user devices

[1182] The user terminal receives the proposal sent from the data server. The proposal is displayed on the application screen in a format that is easy for the user to understand. For example, it is displayed using text and images on the user interface of a dedicated application. The user (hairdresser or aesthetician) explains the proposal to the customer while looking at the displayed proposal, and begins the treatment after obtaining approval.

[1183] Specific examples and examples of prompts to input to the generative AI model

[1184] For example, when a new customer visits a beauty salon, hairdresser A takes a photo of the customer's face with his smartphone and enters some basic information. He then presses the "Start Diagnosis" button to send the data to the server. The server receives the data and sends it to the machine learning module to begin analysis. After the analysis results are returned, hairdresser A receives the optimal proposal based on the customer's past treatment history, explains the proposal to the customer, and performs the optimal treatment after obtaining their approval.

[1185] By inputting a prompt sentence like the one below into the generative AI model, a detailed description of the system will be automatically generated.

[1186] Please explain the system that analyzes customer facial photos and suggests optimal products and treatments. This system sends facial photos taken on the user's device and customer information to a data server, where a machine learning module performs image analysis and suggests optimal products and treatments. Please provide a detailed explanation, including specific operating procedures and the software and hardware used.

[1187] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[1188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1189] Program processing flow

[1190] Step 1: Collect data on the user's device

[1191] The user (hairdresser or aesthetician) launches a dedicated application on a device such as a smartphone and takes a photo of the customer's face. Next, they enter the customer's basic information (name, past treatment history, etc.). The input data is saved as image data (JPEG format) and text data (JSON format). Specifically, the system works by taking a high-resolution photo with the camera and entering the text information into the application form.

[1192] Step 2: Send data to the server

[1193] The user presses the "Start Diagnosis" button on the dedicated application. This causes the user device to send the photograph taken and the entered customer information to the data server. The transmitted data is encrypted using the HTTPS protocol. Image data and text data are input, and encrypted data is sent as output. Specifically, pressing the send button sends the data to the server.

[1194] Step 3: Receiving and encoding data on the server

[1195] The server receives data sent from the user terminal. First, the server checks the integrity of the data to ensure that it does not contain any invalid data. The input data is encrypted image data and customer information, and the output is encoded JPEG image data. Specifically, the server stores the data in a database and converts the image data into an appropriate format.

[1196] Step 4: Sending data to the machine learning module

[1197] The server sends the encoded image data and customer information to the machine learning module. This data transmission uses a REST API. The input data is the encoded image data and text data, and an API request is generated as the output. Specifically, the request is sent to the API endpoint.

[1198] Step 5: Image analysis using machine learning modules

[1199] The machine learning module analyzes the received image data. It uses a facial recognition algorithm to identify the location of each facial feature and evaluates the condition of skin tone, wrinkles, blemishes, and pores. The input data is image data, and the output is a text-based analysis result. Specifically, it runs the image analysis algorithm and summarizes the analysis results in JSON format.

[1200] Step 6: Integration of analysis results and generation of proposals on the server

[1201] The server receives the diagnosis results from the machine learning module. It compares them with the customer's past treatment history and preference data to generate the optimal product and treatment menu. The input data is the analysis results and customer history data, and the output is the generated recommendations. Specifically, it executes a database query, aggregates the results, and determines the optimal recommendations.

[1202] Step 7: Displaying suggestions on the user's device

[1203] The user device receives the proposal sent from the server. A dedicated application parses the proposal and displays it on the screen in a format that is easy for the user to understand. The proposal is the input data, and a display screen is generated as the output. Specifically, the application displays text and images in the user interface.

[1204] Through the above steps, the system can quickly and accurately provide optimal proposals to customers.

[1205] (Application example 1)

[1206] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1207] In conventional beauty and aesthetic treatments, it was difficult to grasp the condition of a customer's skin and hair in real time and quickly recommend the most appropriate treatments and products. Furthermore, there was a lack of efficient ways to provide personalized recommendations based on the customer's preferences and past treatment history. This resulted in a heavy workload for hairdressers and aestheticians, making it difficult to improve customer satisfaction.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1209] In this invention, the server includes a device that receives image data of a customer taken by a user and transmits it to the server together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, a server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and based on past treatment history and customer preferences and transmits them to the user device, a system including the proposals displayed on the user device, a device that acquires captured images of the customer from a built-in camera and checks the customer's skin and hair condition in real time, means for transmitting customer image data and customer information and acquiring analysis results, and means for displaying the analysis results on a display. This makes it possible to check the customer's skin and hair condition in real time and efficiently provide optimal treatments and products.

[1210] A "user" is a person who uses the system to photograph and input customer information and request treatment suggestions.

[1211] "Device" refers to a hardware device that allows a user to capture image data of a customer and input and transmit customer information.

[1212] The "server" is a computer system that receives data sent from a user device, analyzes the data using an artificial intelligence module, and generates optimal products and treatment menus.

[1213] The "artificial intelligence module" refers to algorithms and software that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[1214] "Image data" refers to an image file of the customer's face taken by the user with the device.

[1215] "Customer information" refers to data that includes basic information such as the customer's name and past treatment history.

[1216] The "diagnosis results" are an evaluation of the customer's skin condition, hair condition, and facial balance obtained through image analysis by an artificial intelligence module.

[1217] A "treatment menu" refers to specific beauty and aesthetic treatments proposed based on the diagnosis, past treatment history, and customer preferences.

[1218] "Proposal content" refers to information generated based on the diagnosis results, including optimal products and treatment menus.

[1219] The "built-in camera" refers to a camera that is built into the device and is used to capture image data.

[1220] A "display" refers to a screen for displaying information on a device, and is responsible for displaying proposals and other information to customers.

[1221] "Real-time" refers to the state in which the acquisition of customer image data and the display of analysis results are carried out immediately without delay.

[1222] The present invention is a system for analyzing image data of a customer in the beauty and aesthetic industry and proposing optimal products and treatment menus. An embodiment of this system will be described in detail below.

[1223] System configuration

[1224] This system mainly consists of a user device, a server, and an artificial intelligence module. The user device is hardware used by hairdressers and estheticians to take images of their customers and input their information. The server receives the data sent from the user device, analyzes it in conjunction with the artificial intelligence module, and generates optimal recommendations. The artificial intelligence module uses image analysis algorithms to diagnose the customer's skin condition, hair condition, and facial balance.

[1225] Hardware and software used

[1226] Hardware: Smart glasses (built-in camera, display), user device (smartphone, etc.)

[1227] Software: Python, OpenCV (image processing library), requests (library for sending HTTP requests)

[1228] Operation on user device

[1229] The user (beautician or esthetician) first puts on the smart glasses and takes a photo of the customer's face with the built-in camera. Next, they enter the customer's basic information (name, past treatment history, etc.) into the device's dedicated application. The image data and customer information are then sent to the server.

[1230] Data reception and analysis on the server

[1231] The server receives image data and customer information sent from the user device. When data is sent, the user device encodes the data and sends it to the server. The server checks the consistency of the received image data and customer information to ensure that no invalid data is included. Once the consistency is confirmed, the data is converted into an appropriate format and sent to the artificial intelligence module.

[1232] Image analysis using artificial intelligence modules

[1233] The AI ​​module analyzes the received image data and runs a facial recognition algorithm to identify the location of each facial feature. It then analyzes skin tone, wrinkles, blemishes, and pore condition, and diagnoses hair quality, shine, and damage. It also measures the balance of facial features and diagnoses areas for aesthetic improvement. These diagnostic results are compiled as text data and sent back to the server.

[1234] Data integration and proposal generation on the server

[1235] The server receives the diagnosis results sent from the AI ​​module. It then compares them with the customer's past treatment history and preferences to generate the optimal product and treatment menu. The proposed content is sent to the user device and provided to the customer.

[1236] Displaying Offers on User Devices

[1237] The user device receives the proposed content sent from the server. The proposed content is displayed on the device's display in a format that is easy for the user to understand. The user (beautician or esthetician) explains the proposed content to the customer while looking at it, and begins the treatment only after obtaining approval.

[1238] Specific examples

[1239] For example, when a new customer visits a salon, a hairdresser wearing smart glasses takes a photo of the customer's face and enters their name and past treatment history. The data is then sent to a server, where it is analyzed by an artificial intelligence module, and a diagnosis is returned. Based on the analysis results, the most appropriate treatment menu and products are suggested and displayed on the smart glasses' display. The hairdresser then explains the suggestions to the customer and, after obtaining their approval, performs the treatment.

[1240] Example prompt sentence:

[1241] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[1242] In this way, the system can quickly and accurately provide optimal suggestions to customers, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[1243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1244] Step 1:

[1245] A user puts on the smart glasses and takes a photo of the customer's face. The smart glasses' built-in camera is used to capture the image data. The input is the customer's face photo, and the output is the image data captured by the built-in camera.

[1246] Step 2:

[1247] The user inputs customer information into the dedicated application on the smart glasses. The input is basic customer information (such as name and past treatment history), and the output is the collected customer information data.

[1248] Step 3:

[1249] The user device encodes the captured customer image data and customer information when transmitting them to the server. The input is the image data and customer information, and the output is the encoded data.

[1250] Step 4:

[1251] The server receives the encoded data sent from the user device, with the input being the encoded image data and customer information, and the output being the result of checking the integrity of the data.

[1252] Step 5:

[1253] The server converts the received image data into a suitable format and sends it to the artificial intelligence module, where the input is the encoded data and the output is the converted image data.

[1254] Step 6:

[1255] The AI ​​module analyzes the received image data and executes a facial recognition algorithm to identify the location of each facial feature. The input is the transformed image data and the output is the location information of the facial features.

[1256] Step 7:

[1257] The AI ​​module analyzes the condition of the skin and hair and measures the balance of facial features. The input is the position information of the facial features, and the output is the diagnosis results of the skin condition, hair condition, and balance of the facial features.

[1258] Step 8:

[1259] The server receives the diagnosis results sent from the AI ​​module, compares them with the customer's past treatment history and preference data, and generates the optimal product and treatment menu. The input is the diagnosis results and the customer's past data, and the output is the optimal proposal.

[1260] Step 9:

[1261] The server sends the generated optimal proposal to the user device, where the input is the optimal proposal and the output is the proposal received by the user device.

[1262] Step 10:

[1263] The user device receives the suggestions sent from the server and displays them on the display of the smart glasses. The input is the suggestions, and the output is the suggestions displayed on the display.

[1264] Specific examples

[1265] Example prompt sentence:

[1266] A new customer has come to your store. His name is Taro Yamada, and his past treatment history includes "coloring, cut, and perm." Please take a photo of Yamada's face using the smart glasses and suggest the most suitable treatments and products.

[1267] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1268] The present invention relates to a system that analyzes various data, including user emotions, and recommends optimal beauty treatments and products. The system includes a user terminal, a server, an artificial intelligence module, and an emotion engine. Specific embodiments for implementing the present invention are described in detail below.

[1269] System configuration

[1270] The system consists of the following main components:

[1271] 1. User Device

[1272] 2. Server

[1273] 3. Artificial Intelligence Module

[1274] 4. Emotion Engine

[1275] User terminal

[1276] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. It also has a function to acquire user emotional data (changes in facial expressions and voice) in real time using an emotion engine and send it to the server.

[1277] server

[1278] The server is a central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes the data. The server then sends the received data to the artificial intelligence module and emotion engine, and receives the analysis results. It also compares the analysis results with the customer's past treatment history to generate optimal proposals.

[1279] Artificial Intelligence Module

[1280] The AI ​​module is equipped with algorithms that analyze image data and diagnose the customer's skin condition, hair condition, and facial feature balance. The module uses facial recognition technology to identify the location of the customer's facial features and also provides a detailed analysis of the skin and hair condition.

[1281] Emotion Engine

[1282] The emotion engine analyzes the user's facial expressions and voice changes to identify the user's emotional state, thereby understanding the user's emotions in real time and sending that information to the server.

[1283] Example

[1284] Let's take a specific example of a new customer visiting a beauty salon. First, hairdresser B takes a photo of the customer's face using a user device and enters basic information (such as name and past treatment history) into a dedicated application. Next, B activates the emotion engine and configures it to capture conversations with the customer and changes in facial expressions.

[1285] Next, Person B presses the "Start Diagnosis" button on the device, which sends the photo data, customer information, and emotional data to the server. The server receives this data and sends the image data to the artificial intelligence module and the emotional data to the emotion engine. The artificial intelligence module then diagnoses the customer's skin condition, hair condition, and facial feature balance, and the emotion engine analyzes the user's emotional state.

[1286] The analysis results are sent back to the server, which compares these results with the customer's past treatment history and generates the optimal proposal. The generated proposal is sent to the user's device and displayed on the device. Based on the displayed proposal, Mr. B explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[1287] In this way, the system analyzes a variety of data, including the user's emotional state, enabling it to provide more accurate and effective beauty treatments and product recommendations, thereby reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[1288] The processing flow will be explained below.

[1289] Step 1:

[1290] The user takes a photo of the customer's face using a device such as a smartphone, and the device temporarily stores the photo in its memory.

[1291] Step 2:

[1292] The user enters the customer's basic information (such as name and past treatment history) into the dedicated application on the device. Once the information has been entered, the device prepares to send the data.

[1293] Step 3:

[1294] The user enables the emotion engine, which allows the device to capture changes in the customer's facial expressions and voice in real time and collect emotion data.

[1295] Step 4:

[1296] When the user presses the "Start Diagnosis" button, the device encodes the captured photo data, the entered customer information, and the acquired emotion data, and sends them to the server using the HTTPS protocol.

[1297] Step 5:

[1298] The server receives the image data, customer information, and emotion data sent from the user terminal, verifies the format of the received data, and confirms its validity.

[1299] Step 6:

[1300] The server then converts the properly received image data into a format suitable for the artificial intelligence module. This pre-processing makes the image data suitable for analysis.

[1301] Step 7:

[1302] The server sends the pre-processed image data to the artificial intelligence module, which then begins analyzing the received data.

[1303] Step 8:

[1304] The AI ​​module runs a facial recognition algorithm to locate the customer's facial features, then analyzes the skin condition (tone, wrinkles, blemishes, pores) and hair condition (quality, shine, damage).

[1305] Step 9:

[1306] The emotion engine receives emotion data sent from the user's device, analyzes the user's facial expressions and voice characteristics, identifies emotions (happiness, sadness, surprise, etc.), and sends the results back to the server.

[1307] Step 10:

[1308] The artificial intelligence module compiles the analysis results and returns them to the server as text data.

[1309] Step 11:

[1310] The server receives the diagnosis results and emotion data returned by the AI ​​module and emotion engine, stores them in a database, and then compares them with the customer's past treatment history and preferences.

[1311] Step 12:

[1312] The server generates proposals based on the diagnosis results, emotional data, and past data to select the most suitable products and treatment menus for each customer.

[1313] Step 13:

[1314] The server transmits the generated proposal to the user terminal.

[1315] Step 14:

[1316] The user's device receives the proposal sent from the server and displays it on the application screen. The user (hairdresser or esthetician) explains the proposal to the customer while looking at it, and begins the treatment once they have given their approval.

[1317] In this way, the user, terminal, server, artificial intelligence module, and emotion engine work together to provide customers with prompt and accurate proposals, and the use of emotion data in particular makes it possible to provide services that are in tune with the customer's emotions.

[1318] Example 2

[1319] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1320] Conventional beauty treatment and product recommendation systems only take into account physical data such as the customer's skin and hair condition, and do not reflect psychological data such as the customer's emotional state, making it difficult to maximize customer satisfaction.In addition, recommendations based on treatment history and customer preferences are insufficient, making it difficult to make optimal recommendations for each individual customer.

[1321] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving image data and customer information of a customer photographed by a user and acquiring emotional data from changes in facial expressions and voice in real time, means for transmitting the acquired image data, customer information, and emotional data to the server, an artificial intelligence module for analyzing the image data received from the server and diagnosing the customer's skin condition, hair condition, and facial balance, an emotion engine for analyzing the emotional data and identifying the customer's emotional state, means for generating optimal products and treatment menus based on the analysis results of the artificial intelligence module and the emotion engine by comparing them with past treatment history and transmitting the generated recommendations to the user terminal, and a recommendation displayed on the user terminal. This makes it possible to propose optimal beauty treatments and products to individual customers, taking into account both physical and psychological data.

[1322] A "user terminal" is a device used by a user, which has the function of acquiring image data and customer information of a customer and transmitting it to a server.

[1323] A "server" is a central computer system that receives and analyzes data sent from user terminals, and is a device that generates optimal proposals based on the analysis results and transmits them to user terminals.

[1324] "Image data" refers to visual data such as a photo of the customer's face taken on a user device, and this data is used to analyze the customer's skin and hair condition.

[1325] "Customer Information" refers to information that includes data such as a customer's name, past treatment history, and preferences.

[1326] "Emotion data" is data that indicates the psychological state of a customer, obtained in real time from changes in facial expressions and voice.

[1327] The "artificial intelligence module" refers to algorithms and software that analyze image data and diagnose the customer's skin condition, hair condition, and facial balance.

[1328] An "emotion engine" refers to a system or software that analyzes emotional data and identifies a customer's emotional state.

[1329] "Analysis results" refers to the results of data analysis by the artificial intelligence module and emotion engine, based on which optimal suggestions are generated.

[1330] The "proposal content" includes optimal products and treatment menus generated based on the analysis results and past treatment history, and is displayed on the user's terminal.

[1331] "Encoding means" refers to methods or techniques used to encrypt or compress data before transmission, thereby increasing the security or efficiency of communications.

[1332] "Pre-processing means" refers to a method or system that performs processing to convert data sent from a user terminal into a format that can be analyzed by the artificial intelligence module and emotion engine.

[1333] The present invention is implemented using the following specific hardware and software components: The components of the overall system include a user terminal, a server, an artificial intelligence module, and an emotion engine.

[1334] User terminal

[1335] The user terminal is a device used by hairdressers and estheticians to take photos of customers' faces and input customer information. The user terminal is also equipped with an emotion engine, which has the function of capturing changes in the customer's facial expressions and voice in real time. Specifically, the camera function of a smartphone or tablet is used to take photos, and a keyboard or touch screen is used to input data. An example of an application used is the "BeautyPro" app.

[1336] server

[1337] The server is the central device that receives image data, customer information, and emotion data sent from user devices, and manages and analyzes this data. Specifically, a database management system (e.g., MySQL) is installed on the server, and the received data is distributed to the artificial intelligence module and emotion engine.

[1338] Artificial Intelligence Module

[1339] The AI ​​module analyzes the received image data and is equipped with algorithms to diagnose the customer's skin condition, hair condition, and facial balance. This module uses the "FaceAnalyzer" algorithm developed in Python. For example, it analyzes the captured image for blemishes, wrinkles, and hair damage, and sends the results back to the server.

[1340] Emotion Engine

[1341] The emotion engine analyzes the customer's emotional state in real time from changes in facial expressions and voice captured on the user's device. The software used is "EmotionDetect." This engine identifies whether the customer is relaxed or tense and sends that information to the server.

[1342] Example

[1343] A specific example using this system will be described below.

[1344] When a new customer visits a beauty salon, the hairdresser (user) uses a tablet device. The user takes a photo of the customer's face and enters the customer's basic information (e.g., name, past treatment history) into the dedicated app "BeautyPro." In addition, the user activates the emotion engine (EmotionDetect) within the app to capture changes in the customer's facial expressions and voice.

[1345] When the user presses the "Start Diagnosis" button, the captured image data, customer information, and emotional data are sent to the server. The server receives this data and sends the image data to the artificial intelligence module (FaceAnalyzer) and the emotional data to the emotion engine (EmotionDetect).

[1346] The AI ​​module analyzes the image data and diagnoses the customer's skin condition, hair condition, and facial balance. The analysis results are sent back to the server. Similarly, the emotion engine analyzes the emotion data, identifies the customer's emotional state, and sends the results back to the server.

[1347] Based on the analysis results, the server compares them with the customer's past treatment history (stored in a MySQL database) and generates the optimal product and treatment menu. The generated proposals are sent to the user's device and displayed on the device. The user explains the displayed proposals to the customer and begins treatment after obtaining the customer's approval.

[1348] Prompt Sentence Examples

[1349] "Take a photo of your customer's face, analyze their emotions in real time, and recommend the most appropriate beauty treatment."

[1350] In this way, the system can take into account both physical and psychological data to recommend the most suitable beauty treatments and products for each customer, reducing the burden on hairdressers and estheticians and improving customer satisfaction.

[1351] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1352] Step 1:

[1353] The user (e.g., a hairdresser) launches the dedicated app "BeautyPro" on the user's device (e.g., a tablet).

[1354] Input: The user enters the customer's basic information (name, past treatment history) into the app and uses the camera function to take a photo of the customer's face.

[1355] What happens: The user uses the tablet's keyboard and touchscreen to enter customer information and takes a photo with the tablet's camera.

[1356] Output: The customer's basic information and facial photo are recorded on the user's terminal.

[1357] Step 2:

[1358] The user enables the emotion engine (EmotionDetect) within the application to capture changes in the customer's facial expressions and voice in real time.

[1359] Input: The user presses the "Start Emotion Engine" button.

[1360] Specific operation: Throughout the conversation with the customer, the tablet's microphone and camera detect changes in facial expressions and voice in real time.

[1361] Output: Real-time facial expression data and voice change data are recorded on the user's device.

[1362] Step 3:

[1363] The terminal transmits the acquired facial photograph, customer information, and emotion data to the server.

[1364] Input: Facial photo, customer information, emotional data.

[1365] Specific operation: The device sends data to the server via Wi-Fi.

[1366] Output: The server receives the face photo, customer information, and emotion data.

[1367] Step 4:

[1368] The server distributes the received data to the artificial intelligence module and emotion engine.

[1369] Input: Facial photo, customer information, emotional data.

[1370] Specific operation: The server sends the face photo to the artificial intelligence module (FaceAnalyzer) and the emotion data to the emotion engine (EmotionDetect).

[1371] Output: Data is sent for analysis by the artificial intelligence module and emotion engine.

[1372] Step 5:

[1373] An artificial intelligence module analyzes image data and diagnoses the customer's skin condition, hair condition, and facial balance.

[1374] Input: Facial photo data sent from the server.

[1375] Specific operation: The artificial intelligence module (FaceAnalyzer) uses image analysis algorithms to diagnose uneven skin tone, blemishes, wrinkles, hair damage, and facial balance.

[1376] Output: Diagnostic results are generated and sent back to the server.

[1377] Step 6:

[1378] An emotion engine analyzes the emotion data to identify the customer's emotional state.

[1379] Input: Emotion data sent from the server.

[1380] Specific behavior: The emotion engine (EmotionDetect) analyzes facial expressions and vocal changes to identify the customer's emotional state (relaxed, tense, etc.).

[1381] Output: Parsed emotional state data is generated and sent back to the server.

[1382] Step 7:

[1383] The server receives the analysis results from the artificial intelligence module and emotion engine, compares them with past treatment history, and generates optimal products and treatment menus.

[1384] Input: Analysis results returned from the artificial intelligence module and emotion engine, and the customer's past treatment history.

[1385] Specific operation: The server retrieves treatment history from the MySQL database, compares it with the analysis results, and executes an algorithm to make optimal recommendations.

[1386] Output: The generated recommendations for optimal products and treatment menus.

[1387] Step 8:

[1388] The server transmits the generated proposal content to the user terminal.

[1389] Input: Suggestions for optimal products and treatment menus.

[1390] Specific operation: The server sends the proposal content to the user's device via Wi-Fi.

[1391] Output: The proposal is displayed on the user's terminal.

[1392] Step 9:

[1393] Based on the proposal displayed on the user terminal, the user explains the treatment details and recommended products to the customer, and begins the treatment after obtaining approval.

[1394] Input: The suggestion displayed on the user's device.

[1395] Specific operation: The user explains the proposal to the customer and obtains the customer's approval.

[1396] Output: Treatment begins after customer consent is obtained.

[1397] (Application example 2)

[1398] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1399] While conventional beauty treatment systems can analyze physical data such as a customer's skin and hair condition, they have difficulty making recommendations that take into account the customer's emotional state. Furthermore, while changes in a customer's emotions are considered an important factor in improving the suitability of treatment and product recommendations, there has been a lack of means to acquire and analyze this information in real time. This has resulted in reduced recommendation accuracy and made it difficult to improve customer satisfaction.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1401] In this invention, the server includes a terminal that receives image data of a customer taken by a user and transmits it together with customer information, an artificial intelligence module that analyzes the image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance, an emotion engine that analyzes changes in the customer's facial expression and voice and identifies their emotional state, and a means for generating optimal products and treatment menus based on the diagnosis results from the artificial intelligence module and emotion engine and taking into account past treatment history and customer preferences, and transmitting the results to the user terminal. This enables more appropriate beauty treatments and product recommendations that take into account not only the customer's physical condition but also their emotional state.

[1402] A "user terminal" is a device that acquires image data and customer information of a customer and transmits them to a server.

[1403] A "server" is a central device that receives, manages, and analyzes data sent from user terminals.

[1404] The "artificial intelligence module" is a system equipped with algorithms that analyze received image data and diagnose the customer's skin condition, hair condition, and facial feature balance.

[1405] The "emotion engine" is a system that analyzes changes in a customer's facial expression and voice to identify their emotional state.

[1406] "Image data analysis" is the process of analyzing a customer's image and diagnosing their skin condition, hair condition, and facial feature balance.

[1407] "Emotional state" refers to a psychological state that can be identified from changes in facial expressions and voice that a customer shows in real time.

[1408] "Treatment history" refers to records of beauty treatments that a customer has received in the past.

[1409] "Products and treatment menus" refer to specific beauty items and services suggested based on the customer's condition and preferences.

[1410] The present invention is a system that analyzes a customer's image data and emotional data to recommend optimal beauty treatments and products. The system is composed of a user terminal, a server, an artificial intelligence module, and an emotion engine. An embodiment of the system is described in detail below.

[1411] User terminal

[1412] A user terminal is a device that acquires customer image data and customer information and sends them to a server. For example, smart glasses used by hairdressers are examples of such devices. By wearing the smart glasses, it is possible to capture customer facial photos and facial expression data in real time. The captured data is encoded and sent to a server.

[1413] server

[1414] The server is a central device that receives image data and customer information sent from user devices and sends the data to the AI ​​module and emotion engine for analysis. The server preprocesses the image data using image processing software such as OpenCV and converts the data format as necessary.

[1415] Artificial Intelligence Module

[1416] The AI ​​module is equipped with algorithms that analyze the received image data and diagnose the customer's skin condition, hair condition, and facial feature balance. This module also uses generative AI models to realize facial recognition technology, which can pinpoint the location of the customer's facial features and perform a detailed analysis.

[1417] Emotion Engine

[1418] The emotion engine is a system that analyzes changes in a customer's facial expression and voice to identify the customer's emotional state. In an embodiment, the emotion engine acquires the customer's emotional state in real time and transmits the information to a server.

[1419] Analysis results

[1420] The analysis results are sent back to the server, which uses these results to generate optimal products and treatment menus based on past treatment history and customer preferences. The generated proposals are sent to the user's terminal and displayed on the user's device.

[1421] Specific examples

[1422] When a new customer visits a beauty salon, the hairdresser puts on the smart glasses and takes a photo of the customer's face. The photo and customer information are then encoded and sent from the smart glasses to a server. The server then sends the image data to an artificial intelligence module and emotion data to an emotion engine. The data is analyzed, and the results are sent back to the server. Based on the analysis results, the server generates optimal suggestions based on the customer's past treatment history and preferences, and displays them on the smart glasses' display.

[1423] Prompt Sentence Examples

[1424] Customer Name: Yamada Hanako

[1425] Past treatment history: Cut, coloring

[1426] Current Emotion: Relaxed

[1427] Analysis results:

[1428] 1. Skin condition: Dry

[1429] 2. Hair condition: Healthy

[1430] suggestion:

[1431] Moisturizing Treatment

[1432] Moisturizing hair care products

[1433] As described above, this system makes it possible to propose beauty treatments and products that take into account both the physical and emotional state of the customer, thereby providing more appropriate services that will increase customer satisfaction.

[1434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1435] Step 1:

[1436] The user terminal acquires the photo data and customer information.

[1437] Specifically, a hairdresser wearing smart glasses takes a photo of a customer's face and stores the data on the device. The input is the customer's face photo and basic information, and the output is encoded data.

[1438] Step 2:

[1439] The terminal transmits the photographed customer's face photo and customer information to the server.

[1440] Specifically, the terminal sends the encoded data to the server as an HTTP request. The input is the encoded face photo and customer information, and the output is the data sent to the server.

[1441] Step 3:

[1442] The server preprocesses the received data and converts it into a format that can be parsed.

[1443] Specifically, the server uses image processing software such as OpenCV to convert image data into a format that is easy for the AI ​​module to analyze. The input is encoded facial photo data, and the output is the converted image data.

[1444] Step 4:

[1445] The server sends the image data to an artificial intelligence module, which analyzes the condition of the skin, hair, and balance of facial features.

[1446] Specifically, the server inputs the converted image data into the generative AI model and obtains the analysis results. The input is the converted image data, and the output is the skin condition, hair condition, and facial feature balance.

[1447] Step 5:

[1448] The server sends data on facial expressions and voice changes to the emotion engine, which analyzes the emotional state.

[1449] Specifically, the server sends data on conversations with customers and changes in facial expressions to the emotion engine, which then identifies their emotional state. The input is data on changes in facial expressions and voice, and the output is their emotional state.

[1450] Step 6:

[1451] The server integrates the analysis results from the artificial intelligence module and emotion engine to generate optimal recommendations based on treatment history and customer preferences.

[1452] Specifically, the server generates optimal products and treatment menus based on the integrated analysis results, taking into account treatment history and customer preferences. The inputs are the analysis results and treatment history, and the output is the proposal content.

[1453] Step 7:

[1454] The server transmits the generated proposal content to the user terminal, and the user terminal displays the proposal content.

[1455] Specifically, the server sends the generated proposal to the user's device as an HTTP response, and the device displays the proposal on its screen. The input is the optimal proposal, and the output is the proposal displayed on the user's device.

[1456] 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.

[1457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1458] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1459] 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.

[1460] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1461] 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.

[1462] 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).

[1463] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, 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.

[1464] 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."

[1465] 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.

[1466] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1467] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1472] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] The following is further disclosed regarding the above embodiment.

[1478] (Claim 1)

[1479] a terminal that receives image data of a customer photographed by a user and transmits the image data together with customer information to a server;

[1480] An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance.

[1481] A server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and on the customer's past treatment history and preferences, and transmits the results to the user's terminal;

[1482] A system including suggestions displayed on a user's device.

[1483] (Claim 2)

[1484] 2. The system according to claim 1, further comprising means for encoding data when the user terminal transmits the photographed customer image data and customer information to the server.

[1485] (Claim 3)

[1486] 2. The system according to claim 1, wherein the server includes a preprocessing means for converting customer image data sent from the user terminal into a format suitable for the artificial intelligence module and sending the converted data to the artificial intelligence module.

[1487] "Example 1"

[1488] (Claim 1)

[1489] a mobile terminal means for receiving image data of a customer photographed by a user and transmitting the image data together with customer information to a data server;

[1490] a machine learning module means for analyzing image data received from a data server and diagnosing the customer's skin condition, hair condition, and facial feature balance;

[1491] a data server means for generating optimal products and treatment menus based on the diagnosis results from the machine learning module means and based on the past treatment history and customer preferences, and transmitting the generated menus to a user terminal;

[1492] a user interface means for displaying the proposal content transmitted from the data server means;

[1493] means for encrypting data when transmitting image data and customer information captured by a user terminal to a data server;

[1494] A means for the data server to check the consistency of the image data and the customer information;

[1495] pre-processing means for encoding and converting the image data into a format suitable for the machine learning module means;

[1496] A means for comparing the diagnosis results from the machine learning module means with the customer's past treatment history and preference data to select the most suitable products and treatment menus;

[1497] application software means for displaying the proposals in a user-friendly format on a user terminal;

[1498] A system including:

[1499] (Claim 2)

[1500] 2. The system according to claim 1, further comprising means for converting and encrypting data when the user terminal transmits the photographed customer image data and customer information to the data server.

[1501] (Claim 3)

[1502] 2. The system according to claim 1, wherein the data server includes a preprocessing means for converting customer image data sent from the user terminal into a format suitable for the machine learning module means and sending the converted data to the machine learning module means.

[1503] "Application Example 1"

[1504] (Claim 1)

[1505] a device that receives image data of a customer photographed by a user and transmits the image data together with customer information to a server;

[1506] An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance.

[1507] a server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and on the customer's past treatment history and preferences, and transmits the menus to the user device;

[1508] a system including suggestions to be displayed on a user device;

[1509] A device that acquires the captured image of the customer using a built-in camera and checks the condition of the customer's skin and hair in real time.

[1510] A means for transmitting customer image data and customer information and acquiring analysis results;

[1511] The system includes a means for displaying the analysis results on a display.

[1512] (Claim 2)

[1513] 2. The system according to claim 1, further comprising means for encoding data when the user device transmits the captured customer image data and customer information to the server.

[1514] (Claim 3)

[1515] 2. The system according to claim 1, wherein the server includes a preprocessing means for converting the customer image data sent from the user device into a format suitable for the artificial intelligence module and sending the converted data to the artificial intelligence module.

[1516] "Example 2: Combining Emotion Engines"

[1517] (Claim 1)

[1518] A means for receiving image data and customer information of a customer photographed by a user and acquiring emotion data from changes in facial expressions and voice in real time;

[1519] means for transmitting the acquired image data, customer information, and emotion data to a server;

[1520] An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial balance.

[1521] an emotion engine that analyzes emotion data and identifies the customer's emotional state;

[1522] A means for generating optimal products and treatment menus based on the analysis results of the artificial intelligence module and emotion engine, by comparing them with past treatment history, and transmitting the results to the user's terminal;

[1523] A system including suggestions displayed on a user's device.

[1524] (Claim 2)

[1525] 2. The system according to claim 1, further comprising means for encoding data when the user terminal transmits the photographed customer image data, customer information, and emotion data to the server.

[1526] (Claim 3)

[1527] 2. The system according to claim 1, wherein the server includes a preprocessing means for converting customer image data sent from the user terminal into a format suitable for the artificial intelligence module and the emotion engine, and sending the converted data to the artificial intelligence module and the emotion engine.

[1528] "Application example 2 when combining emotion engines"

[1529] (Claim 1)

[1530] a terminal that receives image data of a customer photographed by a user and transmits the image data together with customer information to a server;

[1531] An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance.

[1532] An emotion engine that analyzes changes in customer facial expressions and voice to identify their emotional state;

[1533] A server that generates optimal products and treatment menus based on the diagnosis results from the artificial intelligence module and emotion engine, and based on past treatment history and customer preferences, and transmits the results to the user's terminal;

[1534] A system including suggestions displayed on a user's device.

[1535] (Claim 2)

[1536] 2. The system according to claim 1, further comprising means for encoding data when the user terminal transmits the photographed customer image data and customer information to the server.

[1537] (Claim 3)

[1538] 2. The system according to claim 1, wherein the server includes a preprocessing means for converting customer image data sent from the user terminal into a format suitable for the artificial intelligence module and sending the converted data to the artificial intelligence module and the emotion engine. [Explanation of symbols]

[1539] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal that receives image data of a customer photographed by a user and transmits the image data together with customer information to a server; An artificial intelligence module that analyzes image data received from the server and diagnoses the customer's skin condition, hair condition, and facial feature balance. A server that generates optimal products and treatment menus based on the diagnosis results of the artificial intelligence module and on the customer's past treatment history and preferences, and transmits the results to the user's terminal; A system including suggestions displayed on a user's device.

2. 2. The system according to claim 1, further comprising means for encoding data when the user terminal transmits the photographed customer image data and customer information to the server.

3. 2. The system according to claim 1, wherein the server includes a preprocessing means for converting the customer image data transmitted from the user terminal into a format suitable for the artificial intelligence module and transmitting the converted data to the artificial intelligence module.

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