System

Abstract

A system is provided.SOLUTION: A system comprising: data input means for inputting a brand and size of a garment currently being worn; data input means for inputting medical examination data; communication means for receiving the inputted data; size recommendation means for calculating an appropriate size of another brand based on the received data; and result display means for outputting the calculated size.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] With the spread of online shopping, it is becoming increasingly difficult for consumers to accurately select clothing sizes that fit their body types. This problem is particularly pronounced among people in their teens and twenties, whose bodies change dramatically with each generation, causing them to struggle with choosing the right size. Furthermore, companies are faced with the problem of consumers abandoning their purchases because they cannot find the right size, as well as the costs of returns, which can lead to environmental issues such as mass waste. Therefore, there is a need for technology that can solve these issues and bring benefits to both consumers and companies. [Means for solving the problem]

[0006] The present invention provides a system that includes a data input means for inputting the brand and size of clothing currently worn by a user and their health checkup data, a communication means for receiving this data, and a size recommendation means for calculating the appropriate size for other brands based on the received data. Specifically, the size recommendation means has the function of retrieving size charts from a database of multiple different brands and performing calculations using an AI algorithm. Furthermore, by taking into account health checkup data such as chest circumference, waist circumference, and weight, it is possible to recommend a size that fits the user's latest body shape. This allows consumers to instantly purchase clothes in the appropriate size online, and companies can prevent customer abandonment and reduce mass waste.

[0007] "Currently worn clothes" refers to the clothes that the user is currently wearing, including information about the brand and size of the clothes.

[0008] A "brand" refers to a name or logo mark used to identify a group of clothing or products offered by a particular company or designer.

[0009] "Size" is an indicator of how large a piece of clothing is relative to the user's body type, and is usually expressed as an alphabet (S, M, L, etc.) or a number (28, 30, 32, etc.).

[0010] "Medical checkup data" is information related to the user's physical measurements, including chest circumference, waist circumference, weight, etc.

[0011] "Data input means" refers to an interface through which a user inputs information, and typically corresponds to an input form for an application.

[0012] "Communication means" refers to the functions and protocols for sending and receiving data between a terminal and a server, including internet connections and APIs.

[0013] "Size Recommender" refers to an algorithm or software that has the ability to calculate and suggest appropriate sizes for other brands based on the data received.

[0014] The "result display means" refers to an interface or device for visually displaying the calculated recommended size to the user, and typically corresponds to the screen or display of the application. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] ---

[0037] The present invention is a system that allows a user to input the brand and size of clothing currently worn and health checkup data, and then recommends appropriate sizes for other brands based on the input data. Specifically, the system includes a data input means, a communication means, a size recommendation means, and a result display means. Each means will be described in detail below.

[0038] User data entry

[0039] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a T-shirt (size M) from "Brand A," they enter "Brand A" and "Size M." They can also add their chest circumference, waist circumference, and weight as health checkup data. This provides the system with the user's latest body shape information.

[0040] Data transmission by the terminal

[0041] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[0042] Data reception and analysis by the server

[0043] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to compare.

[0044] Calculations based on size recommendations

[0045] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0046] Sending and displaying calculation results

[0047] The server sends the calculated results to the device, which receives the results and displays them visually to the user. Specifically, the device displays information such as "Brand B, size L is recommended" on the screen.

[0048] Specific examples

[0049] Specifically, let's say a user is wearing a T-shirt (size M) from brand A and enters their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This data is sent to the server, which calculates the size of brand B that corresponds to size M from brand A. Using brand B's size chart and an AI algorithm, the appropriate size (for example, size L) is derived. The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0050] The system according to the present invention thus enables consumers to instantly purchase clothes of the right size online, which contributes to preventing customers from abandoning their purchases and reducing mass waste for businesses.

[0051] The processing flow will be explained below.

[0052] ---

[0053] Step 1:

[0054] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter health checkup data such as chest circumference, waist circumference, and weight.

[0055] Step 2:

[0056] The device receives the data entered by the user. The device converts the entered data into JSON format. For example, { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70}.

[0057] Step 3:

[0058] The device checks the data for abnormal values, for example, to make sure there are no inconsistencies in the entered size or health check data. If there are no abnormalities, it creates an API request to send the data.

[0059] Step 4:

[0060] The device sends a data transmission request to the server, and uses the communication method to send the JSON data mentioned above to the server.

[0061] Step 5:

[0062] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information and health check data.

[0063] Step 6:

[0064] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[0065] Step 7:

[0066] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. The AI ​​algorithm uses size conversion logic between different brands and compares the size standards of each brand.

[0067] Step 8:

[0068] The server fine-tunes the size by taking into account health check data (chest circumference, waist circumference, weight, etc.). For example, if the user's weight has increased, it will recommend a larger size than the calculated size.

[0069] Step 9:

[0070] The server converts the calculation results into JSON format and sends a response to the device, which includes the recommended size for brand B.

[0071] Step 10:

[0072] The device analyzes the response received from the server, obtains the recommended size (e.g., brand B, size L), and displays it to the user.

[0073] Step 11:

[0074] The device will visually display a size recommendation to the user, for example, "Brand B, size Large is recommended for you," and may also provide a link to purchase.

[0075] ---

[0076] The above are the specific processing steps, which allow users to easily select clothes of the appropriate size online.

[0077] Example 1

[0078] 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."

[0079] Conventional clothing purchasing systems require users to take the time to compare sizes across different brands, making it difficult to select the appropriate size. Furthermore, they are unable to recommend sizes that take into account changes in body shape, increasing the risk of purchasing clothing of an inappropriate size. Furthermore, since they lack a size adjustment function based on health checkup data, it is difficult to accurately recommend a size that fits the user's actual body shape. Therefore, there is a need for a system that can improve consumer satisfaction while reducing purchase abandonment and return rates.

[0080] 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.

[0081] In this invention, the server includes a data input means for inputting the brand and size of clothing currently being worn, a data input means for inputting health checkup data, a data conversion means for converting the input data into JSON format, a communication means for transmitting the converted data to the server, a data analysis means for analyzing the received data and obtaining a size chart for the corresponding brand, a size recommendation means for obtaining a size chart for a different brand and calculating an appropriate size for the other brand using an AI algorithm, a data transmission means for converting the calculated size into JSON format and transmitting it to the terminal, and a result display means for displaying the calculated size on the terminal. This allows users to easily and quickly compare and receive recommendations for appropriate sizes across different brands, significantly reducing the effort required for choosing a size and enabling them to purchase the optimal size that accommodates changes in body shape.

[0082] "Data input means" refers to a user interface or input device for inputting the brand and size of the clothing currently worn by the user, as well as health checkup data.

[0083] "Data conversion means" refers to software or algorithms that convert user-entered data into a specific data format, such as JSON format, for efficient processing.

[0084] "Communication Means" refers to the internet connection or other data transmission technology used to transmit the entered data to the server.

[0085] "Data analysis means" refers to software or algorithms that analyze the data received by the server and extract the necessary information.

[0086] "Size recommendation means" refers to a process or device that retrieves size charts of different brands from a database and calculates the appropriate size using an AI algorithm based on user input data.

[0087] "Data Transmission Means" refers to an Internet connection or other data transmission technology for transmitting the calculated results to the Terminal.

[0088] "Result display means" refers to a display device or UI software for visually displaying the calculated size information on the user's terminal.

[0089] The present invention is a system that quickly and accurately recommends suitable clothing sizes for users across different brands. The system includes a data input means, a data conversion means, a communication means, a data analysis means, a size recommendation means, a data transmission means, and a result display means. Specific embodiments are described below.

[0090] User data entry

[0091] Using a dedicated smartphone app, users input the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). For example, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This input is done using the user interface within the app. The app used is called "ClothFit" and is designed for efficient data input.

[0092] Data Conversion and Communication

[0093] The terminal converts the data entered by the user into JSON format and sends it to the server over the Internet. The data is converted as follows:

[0094] json

[0095] {

[0096] "brand": "Brand A",

[0097] "size": "M",

[0098] "chest": 90,

[0099] "waist": 80,

[0100] "weight": 70

[0101] }

[0102] The communication methods used are Wi-Fi and mobile data (4G / 5G), which ensures fast and secure data transmission.

[0103] Data reception and analysis

[0104] The server receives the JSON-formatted data sent from the device and parses it. Through this analysis, the brand name, size, and medical examination data are extracted. The server accesses the database to obtain the size chart for the target brand (Brand A) and also obtains size charts for other brands (Brand B). The database systematically manages size information.

[0105] Size recommendation calculation

[0106] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. The calculation is based on the user's health check data (chest circumference, waist circumference, and weight). For example, if the user has gained weight, a larger size (size L) is recommended. Specifically, the AI ​​model compares each brand's size chart with the user's data to determine the optimal size.

[0107] Sending and displaying calculation results

[0108] The calculation results are converted back to JSON format and sent to the device. The device analyzes this data and displays the results to the user through a user interface. Specifically, the device displays "Brand B, size L is recommended," along with the reason for this recommendation. The display is achieved using Flutter, a UI framework for smartphone apps.

[0109] Specific examples

[0110] As a specific example of how this works, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. This data is sent to the server, which uses an AI algorithm to calculate the appropriate size for Brand B. The recommended result is Brand B in size L, which is displayed on the screen of the "ClothFit" app.

[0111] Prompt Sentence Examples

[0112] Here are some example prompts to apply to generative AI models:

[0113] I wear a T-shirt (size M) from "Brand A," and I have a chest circumference of 90cm, a waist circumference of 80cm, and weigh 70kg. What size T-shirt from "Brand B" is appropriate?

[0114] The system of the present invention allows users to quickly and accurately select clothing of the appropriate size online, contributing to improved consumer satisfaction and preventing companies from abandoning their purchases.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user launches a dedicated smartphone app and inputs the brand and size of the clothing they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). Examples of input data include "Brand A T-shirt (size M)," chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg. When the user presses the "Submit" button, the data proceeds to the next step.

[0118] input:

[0119] Data entered by the user into the app (brand, size, chest circumference, waist circumference, weight)

[0120] output:

[0121] The input data is retained within the app.

[0122] Step 2:

[0123] The terminal will convert the input data into JSON format, which will look something like this:

[0124] json

[0125] {

[0126] "brand": "Brand A",

[0127] "size": "M",

[0128] "chest": 90,

[0129] "waist": 80,

[0130] "weight": 70

[0131] }

[0132] The converted data is sent to the server via Wi-Fi or mobile data (4G / 5G).

[0133] input:

[0134] Data stored within the app (brand, size, chest circumference, waist circumference, weight)

[0135] output:

[0136] The data converted to JSON format is sent to the server.

[0137] Step 3:

[0138] The server receives the JSON-formatted data sent from the device. It parses the received data and extracts the brand name, size, and medical examination data. Based on the extracted data, the server accesses the database to retrieve the size chart for the target brand (Brand A) and the size chart for the comparison brand (Brand B).

[0139] input:

[0140] JSON format data sent from the device

[0141] output:

[0142] Analyzed data and the brand size chart obtained based on it

[0143] Step 4:

[0144] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. At this time, the server also takes into account the user's health check data, and if, for example, the user has gained weight, it will recommend a larger size (size L).

[0145] input:

[0146] Extracted brand information, health check data, and size chart

[0147] output:

[0148] Calculated appropriate size from other brands (e.g. L size)

[0149] Step 5:

[0150] The calculation result is converted back to JSON format and sent to the terminal. The converted data has the following format:

[0151] json

[0152] {

[0153] "recommendedSize": "L",

[0154] "reason": "Based on your chest, waist, and weight data, it was determined that brand B, size L, would be appropriate."

[0155] }

[0156] This data transmission is also done via Wi-Fi or mobile data communication.

[0157] input:

[0158] Calculated size information for other brands

[0159] output:

[0160] Sending data converted to JSON format

[0161] Step 6:

[0162] The device analyzes the JSON-formatted data received from the server. Based on the analyzed data, the recommended size (brand B, size L) and the reason for the recommendation are visually displayed to the user. For example, the smartphone screen may display "Brand B, size L is recommended" along with an explanation of the reason.

[0163] input:

[0164] JSON format data received from the server

[0165] output:

[0166] What size recommendations do users see and why?

[0167] This series of processes allows users to easily select clothing of the appropriate size from different brands, resulting in a more satisfying online shopping experience.

[0168] (Application example 1)

[0169] 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."

[0170] In traditional online shopping, it is difficult for users to find the right size clothing. This often leads to poor user experiences and dissatisfaction due to returns or incorrect sizes. Furthermore, inconsistent size labels across different brands make it even more difficult for users to find the right size. This also causes inventory management issues and increased costs for businesses. Therefore, there is a need for a system that allows users to easily find the right size.

[0171] 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.

[0172] In this invention, the server includes a data input means for inputting the brand and size of the clothing currently being worn, a data input means for inputting health checkup data, a communication means for receiving the input data, a size recommendation means for calculating the appropriate size for other brands based on the received data, a result display means for outputting the calculated size, and an interface means for linking with a smartphone application. This allows users to easily find clothing in their correct size, improving the online shopping experience. It also helps companies reduce costs associated with returns and inventory management.

[0173] "Data input means" refers to a device or interface for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[0174] The "communication means" is a means for transmitting data received from the data input means to an external system such as a server. An internet connection is generally used.

[0175] "Size recommendation means" refers to a device or program for calculating the appropriate size of other brands based on received data, including the function of obtaining size charts of different brands and performing calculations using an AI algorithm.

[0176] The "result display means" is a device or interface for visually displaying the calculated recommended size to the user.

[0177] "Smartphone application" refers to application software that runs on a smartphone device and provides an interface that works in conjunction with each function of this system.

[0178] An "AI algorithm" is a calculation method or program that uses artificial intelligence technology to analyze data and derive optimal results.

[0179] A "database" is a storage device that stores size charts and other related data for different brands and is accessible by the server.

[0180] "Health checkup data" refers to the user's body measurement information such as chest circumference, waist circumference, and weight, which are important parameters for size recommendations.

[0181] An "interface means" is a device or software for transmitting and receiving data between a smartphone application and another system or device.

[0182] This invention is a system that recommends appropriate sizes for other brands of clothing based on the brand and size of the clothing currently worn by a user and health checkup data. The system includes a data input means, a communication means, a size recommendation means, a result display means, and an interface means that works in conjunction with a smartphone application.

[0183] Configuration and Functions

[0184] 1. Data entry method

[0185] The user inputs the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, weight, etc.) This data input is done through a smartphone application.

[0186] 2. Means of communication

[0187] The data entered by the user into the application is converted into JSON format and then sent to the server via a communication method, which is usually via the Internet.

[0188] 3. Size Recommendation Method

[0189] The server analyzes the received data and extracts brand and size information. It then accesses a database to retrieve the size chart for the current brand and the size chart for other brands (target brands). The server then uses an AI algorithm to calculate the optimal size.

[0190] 4. Results display means

[0191] The calculated results are sent from the server to a smartphone application, which then visually displays the recommended size to the user.

[0192] 5. Interface Methods

[0193] The smartphone application sends the data entered by the user to the server and generates a prompt sentence that calculates the recommended size.

[0194] Hardware and Software

[0195] Server: Hardware that runs the data analysis and size recommendation algorithms, using a web framework such as Flask.

[0196] Smartphone: A device for data entry and display of results, using a mobile application framework such as React Native.

[0197] Specific examples

[0198] For example, a user enters that they are wearing a "medium" size T-shirt from "brand A," and enters health checkup data showing a chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. The smartphone application sends this data to the server, which uses an AI algorithm to calculate the appropriate size (e.g., size L) for "brand B." The result is sent back to the smartphone application, which displays to the user, "Brand B size L is recommended."

[0199] Prompt Sentence Examples

[0200] "Based on the following data, please recommend the appropriate size of brand B that corresponds to brand A's size M. My chest circumference is 90cm, my waist circumference is 80cm, and I weigh 70kg."

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1: Data entry

[0203] Using a smartphone application, users input the brand and size of the clothes they are currently wearing, as well as health checkup data (chest circumference, waist circumference, weight, etc.). The input data is collected by a data input means within the application.

[0204] Input: Brand name, clothing size, chest circumference, waist circumference, weight

[0205] Output: Data entered into the application (brand name, clothing size, medical examination data)

[0206] Step 2: Send data

[0207] The device converts the data entered by the user into JSON format and sends it to the server via a communication method that utilizes an internet connection.

[0208] Input: Data entered into the application (brand name, clothing size, medical examination data)

[0209] Output: JSON formatted data sent to the server

[0210] Step 3: Data reception and analysis

[0211] The server receives the JSON-formatted data sent from the device and parses it, extracting the brand name, clothing size, and health checkup data.

[0212] Input: JSON format data received from the terminal

[0213] Output: Analyzed data (brand name, clothing size, medical checkup data)

[0214] Step 4: Get the size chart

[0215] Based on the analyzed data, the server retrieves the size chart for the relevant brand (current brand) from the database, and then retrieves the size charts for other brands (target brands) to be compared.

[0216] Input: Parsed data (brand name, clothing size)

[0217] Output: Size chart retrieved from the database (current brand and target brand)

[0218] Step 5: Size Calculation

[0219] The server uses the acquired size chart and health check data to calculate the optimal size using an AI algorithm. For example, it will recommend the appropriate size of brand B that corresponds to a size M of brand A based on chest, waist, and weight.

[0220] Input: Size chart (current brand and target brand), medical examination data

[0221] Output: Calculated recommended size

[0222] Step 6: Sending the calculation results

[0223] The server then sends the calculated recommended size to the device, and this communication also takes place over the Internet.

[0224] Input: Calculated preferred size

[0225] Output: Recommended size data sent to the device

[0226] Step 7: View the results

[0227] The device receives the recommended size data from the server and visually displays it to the user. The smartphone application presents information to the user such as "Brand B, size L is recommended."

[0228] Input: Recommended size data received from the server

[0229] Output: A visual recommendation of the size to the user

[0230] Through these steps, users can easily find the size of other brands that suits them.

[0231] 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.

[0232] ---

[0233] The present invention is a system that combines a system that inputs the brand and size of the clothing a user currently wears and their health checkup data, and then recommends appropriate sizes for other brands based on that input, with an emotion engine that recognizes the user's emotions. Specifically, this system includes a data input means, a communication means, a size recommendation means, a result display means, and an emotion engine. Each means will be described in detail below.

[0234] User data entry

[0235] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a "medium-sized T-shirt from brand A," they enter "brand A" and "medium size." They also enter health checkup data such as chest circumference, waist circumference, and weight. This data provides the system with the user's latest body shape information.

[0236] Data transmission by the terminal

[0237] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[0238] Data reception and analysis by the server

[0239] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information, as well as health check data. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to be compared.

[0240] Calculations based on size recommendations

[0241] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0242] Analysis by emotion engine

[0243] The system also includes an emotion engine that analyzes the user's emotions from facial expressions, voice, or text input. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothing item they are considering purchasing. This emotion data is also reflected in the size recommendation tool, resulting in more accurate size recommendations. For example, if the user is feeling stressed, the system will prioritize suggested sizes that fit well and help them relax.

[0244] Sending and displaying calculation results

[0245] The server converts the calculated result into JSON format and sends a response to the device. The response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B size L is recommended."

[0246] Specific examples

[0247] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0248] The system of this invention allows consumers to instantly purchase clothes in the right size online, which helps companies prevent abandonment and reduce mass waste. Furthermore, by incorporating an emotion engine, it becomes possible to recommend sizes that take into account the user's emotional state, further improving customer satisfaction.

[0249] The processing flow will be explained below.

[0250] ---

[0251] Step 1:

[0252] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter their health checkup data: chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg.

[0253] Step 2:

[0254] The terminal receives data entered by the user and converts it into JSON format.

[0255] Step 3:

[0256] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice in order to analyze the user's emotions. For example, the camera can detect emotions from the user's smile or voice.

[0257] Step 4:

[0258] The device receives the emotion data analyzed by the emotion engine and adds it to the JSON data. For example, it adds "Excitement level: High" as emotion data.

[0259] Step 5:

[0260] The device checks for abnormal values, confirming that there are no abnormalities in the input data and emotion data, and if there are no abnormalities, creates an API request to send the data.

[0261] Step 6:

[0262] The device sends a data transmission request to the server using a communication method, for example, in the format { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70, "emotion": "excited"}.

[0263] Step 7:

[0264] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information, health check data, and emotion data.

[0265] Step 8:

[0266] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[0267] Step 9:

[0268] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. For example, the AI ​​calculates the size of brand B that corresponds to size M of brand A.

[0269] Step 10:

[0270] The server will fine-tune the size based on your health check data (chest circumference, waist circumference, weight, etc.). For example, if you have gained weight, it will recommend a larger size.

[0271] Step 11:

[0272] The server takes emotional data into account to further refine the size recommendation: for example, if the user is highly excited, it may recommend a size that fits slightly better than the normal size.

[0273] Step 12:

[0274] The server converts the final calculated recommended size into JSON format and sends a response to the device, which may include, for example, "Brand B, size L."

[0275] Step 13:

[0276] The device analyzes the response received from the server and displays the recommended size to the user, for example, a message saying "Brand B size L is suitable for you."

[0277] Step 14:

[0278] The device can provide users with recommended sizes and related information through a visual interface, as well as purchase links.

[0279] ---

[0280] The above are the specific processing steps and the operations at each step. This process allows users to quickly and accurately receive appropriate size recommendations for different brands, taking into account their emotional data. It also helps companies prevent customer abandonment and improve the efficiency of inventory management.

[0281] Example 2

[0282] 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."

[0283] In conventional online shopping, it was difficult for users to choose the right size for their body type, and it was not possible to select the optimal size that reflected the user's emotional state. This resulted in returns and exchanges after purchase, which led to poor user experience and a large number of returns, which became a challenge for companies.

[0284] 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 a data input means for inputting the brand and size of the clothes currently being worn, a data input means for inputting medical examination data, a communication means for receiving the input data, a size recommendation means for calculating an appropriate size for another brand based on the received data, an emotion engine for analyzing the user's emotion data, a size recommendation means for adjusting the recommended size using the emotion data, and a result display means for outputting the calculated size. This makes it possible to recommend not only an appropriate size that suits the user's body type but also a size recommendation that takes the user's emotional state into consideration.

[0285] The "data input means" is a means for inputting the brand and size of the clothes the user is currently wearing, and health checkup data.

[0286] The "communication means" is a means for transmitting input data from a terminal to a server and receiving a response from the server at the terminal.

[0287] "Size recommendation tool" means a tool for calculating the appropriate size of other brands based on the received data, characterized by the use of an AI algorithm.

[0288] The "emotion engine" is a means for analyzing the user's emotional data and adjusting the recommended size based on the results.

[0289] The "result display means" is a means for visually displaying the calculated recommended size to the user.

[0290] "Health checkup data" refers to physical data such as the user's chest circumference, waist circumference, and weight.

[0291] The present invention is a system that combines a system that inputs the brand and size of the clothing currently worn by the user and health check data, and recommends appropriate sizes for other brands based on the input, with an emotion engine that recognizes the user's emotions. Detailed embodiments for implementing the present invention will be described below.

[0292] User data entry

[0293] The user inputs the brand and size of the clothing they are currently wearing through an application installed on a smartphone or tablet. For example, if the user is wearing a "medium-sized T-shirt from brand A," they input "brand A" and "medium size." They also input health checkup data such as chest circumference, waist circumference, and weight in the same way. This data provides the system with the user's latest body shape information. The hardware used includes a smartphone or tablet, and the software used includes a dedicated application.

[0294] Data transmission by the terminal

[0295] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection, such as Wi-Fi or mobile data.

[0296] Data reception and analysis by the server

[0297] The server receives the data sent from the device. This data includes brand, size information, and health check data. It analyzes the received data and extracts the necessary information. The server then accesses a database to retrieve the size chart for the relevant brand (e.g., Brand A). It also retrieves size charts for other brands to be compared (e.g., Brand B). The software used includes a database (e.g., MySQL) and an AI engine for analysis (e.g., TensorFlow).

[0298] Calculations based on size recommendations

[0299] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. If the user has provided health checkup data, this data will also be taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0300] Analysis by emotion engine

[0301] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data. It reads emotions from the user's facial expressions, voice, and text, and adjusts the recommended size based on that information. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothes they are considering purchasing. For example, if the user is feeling stressed, it will prioritize suggesting sizes that fit well and are relaxing.

[0302] Sending and displaying calculation results

[0303] Once the calculation is complete, the server converts the result back into JSON format and sends it as a response to the device. This response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B, size L is recommended."

[0304] Specific examples

[0305] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0306] This system will enable users to easily find clothing of the right size online, and is expected to help companies reduce return rates and improve customer satisfaction.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Step 1: User Data Entry

[0309] The user opens the application installed on their smartphone or tablet and inputs the brand and size of the clothes they are currently wearing. They also input health checkup data such as chest circumference, waist circumference, and weight. The input data includes the brand name (e.g., "Brand A"), size (e.g., "Size M"), and physical data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg). This provides the system with the user's latest body shape information.

[0310] Specific behavior:

[0311] The user launches the app and enters the required information.

[0312] Input: clothing brand, size, medical examination data

[0313] Output: A set of user input data (brand name, size, body data)

[0314] Step 2: Send data by device

[0315] The device receives data entered by the user, converts it into JSON format, and then sends the converted data to the server via a communication method that utilizes an internet connection.

[0316] Specific behavior:

[0317] Converting user-supplied data into JSON format

[0318] Send data to a server using your internet connection

[0319] Input: A set of user-entered data

[0320] Output: Data converted to JSON format

[0321] Step 3: Data reception and analysis by the server

[0322] The server receives the JSON-formatted data sent from the device, parses it, and extracts the brand name, size information, and medical examination data. It also retrieves the size charts for the relevant brands (Brand A and Brand B for comparison) from the database.

[0323] Specific behavior:

[0324] Receiving JSON format data

[0325] Analyze the data and extract the necessary information

[0326] Retrieving size charts from the database

[0327] Input: JSON format data

[0328] Output: Extracted data (brand name, size information, medical examination data), size chart

[0329] Step 4: Server performs sizing recommendations

[0330] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. It also takes into account health checkup data, for example, recommending a larger size if the user has gained weight.

[0331] Specific behavior:

[0332] Calculate data using AI algorithms

[0333] Taking health check data into account

[0334] Input: Extracted data, size chart

[0335] Output: Brand B appropriate size

[0336] Step 5: Running the Emotion Engine on the Server

[0337] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data, reads the user's emotions from facial expressions, voice, and text input, and adjusts the recommended size based on that information.

[0338] Specific behavior:

[0339] Analyzing Emotional Data

[0340] Adjust size recommendations based on sentiment data

[0341] Input: User emotion data

[0342] Output: Adjusted preferred size

[0343] Step 6: Server sends the calculation results

[0344] The server converts the calculation result into JSON format and sends it to the device as a response, which includes the recommended size.

[0345] Specific behavior:

[0346] Convert the calculation results to JSON format

[0347] Send JSON format data to the terminal

[0348] Input: Adjusted preferred size

[0349] Output: Calculation results in JSON format

[0350] Step 7: Displaying the results on the terminal

[0351] The device analyzes the calculation results received from the server and visually displays them to the user, for example, a message saying "Brand B, size L is recommended."

[0352] Specific behavior:

[0353] Receive a response from the server

[0354] Parse the response data and display it to the user

[0355] Input: Calculation result in JSON format

[0356] Output: Message to display to the user

[0357] (Application example 2)

[0358] 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."

[0359] Conventional clothing size recommendation systems recommend sizes based on the user's physical data, but because they do not take into account the user's emotions or psychological state, they may not always recommend the optimal size. In particular, when a user is purchasing clothing from a new brand or design on an online shopping site, their emotions, expectations, and anxieties about the clothing can significantly influence their size selection. For this reason, there is a need for more accurate size recommendations that also take into account the user's emotions.

[0360] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for inputting the brand and size of the currently worn clothing, a data input means for inputting health checkup data, an emotion recognition means for inputting and analyzing facial expressions and voice to recognize the user's emotions, a communication means for receiving the input data, a size recommendation means for calculating appropriate sizes for other brands based on the received data, an emotion-reflecting size recommendation means for inputting data from the emotion recognition means to the size recommendation means and recommending an appropriate size taking the emotion data into consideration, and a result display means for outputting the calculated size. This enables highly accurate size recommendations that take into consideration not only the user's physical data but also their emotion data.

[0361] "Data input means" refers to a device or software for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[0362] "Communication means" refers to a network communication device or protocol for transmitting data entered by a user to a server.

[0363] The "size recommendation means" is a device or software that has the function of calculating the appropriate size of other brands based on the received data and recommending it to the user.

[0364] A "result display means" is a display device or interface for visually displaying the recommended size to the user.

[0365] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and voice and acquiring the user's emotional data.

[0366] The "emotion-reflecting size recommendation means" is a device or software that has the function of recommending a more appropriate size using emotion data acquired by the emotion recognition means.

[0367] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze received data and calculate the optimal size.

[0368] The "database" is an information management system for storing and managing size information and health checkup data for each brand.

[0369] The present invention is a system that combines a system that inputs the brand and size of the clothing a user is currently wearing and health checkup data, and then recommends appropriate sizes for other brands based on that information, with an emotion engine that recognizes the user's emotions.

[0370] System Overview

[0371] The system consists of the following main components:

[0372] 1. Data entry method

[0373] It is a way for users to input the brand and size of the clothes they are currently wearing, as well as health checkup data such as the user's chest circumference, waist circumference, and weight.

[0374] 2. Means of communication

[0375] The terminal receives data entered by the user, converts this data into JSON format, and sends it to a server over the Internet.

[0376] 3. Size Recommendation Method

[0377] The server uses AI algorithms to calculate the appropriate size for other brands based on the data received.

[0378] 4. Results display means

[0379] This is a means to visually display the calculated size to the user, for example, by showing the recommended size on the display of a smartphone.

[0380] 5. Emotion recognition means

[0381] This is a means of analyzing the user's facial expressions and voice to obtain emotional data about the user, which can then be used to read the user's feelings (excitement, anxiety, etc.) about the clothes they are considering purchasing.

[0382] 6. Recommended emotional reflection size

[0383] It has the ability to recommend more appropriate sizes using the acquired emotional data, enabling highly accurate size recommendations that take user emotions into account.

[0384] Program processing overview

[0385] The overall system makes size recommendations based on user input and also takes into account user sentiment data in the process. Details are provided below.

[0386] 1. User data entry

[0387] Through the application, users input the brand and size of the clothes they are currently wearing, as well as their health check data, and this information is stored on the device.

[0388] 2. Data transmission by the terminal

[0389] The device converts the data entered by the user into JSON format and sends it to the server using a communication method that uses the Internet.

[0390] 3. Data reception and analysis by the server

[0391] The server receives and analyzes the data sent from the device, retrieves brand size charts from the database, and uses AI algorithms to calculate the appropriate size for other brands.

[0392] 4. Emotion analysis using an emotion engine

[0393] Emotion recognition is implemented on the device or server to analyze the user's facial expressions and voice to obtain emotional data, which is then reflected in the size recommendation.

[0394] 5. Displaying the calculation results

[0395] The server converts the calculated results into JSON format and sends a response to the device, which receives the results and displays them visually to the user.

[0396] Specific examples

[0397] Suppose a user is wearing a T-shirt (size M) from brand A, and has entered their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. The emotion engine also recognizes that the user is excited about the brand they are considering purchasing. When this data is sent to the server, the server calculates the size of brand B that corresponds to size M from brand A, and recommends the optimal size (for example, size L) taking into account the emotion data. The device displays the message "Brand B size L is recommended."

[0398] Prompt Sentence Examples

[0399] By inputting prompt sentences like the following into the generative AI model, the accuracy of the system's size recommendations can be improved.

[0400] "The user is wearing a UNIQLO medium shirt, and the data indicates that the chest circumference is 90cm, waist circumference is 80cm, and weight is 70kg. The emotion engine then analyzes the user's facial expression and determines that the user is excited. Based on this data, the system will recommend appropriate sizes for other brands, and the result will be a program that recommends a large size from brand B."

[0401] The above is a specific embodiment of the present invention.

[0402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0403] Step 1:

[0404] The user inputs the brand and size of the clothes they are currently wearing and their health check data.

[0405] Input: Brand (e.g., Brand A) and size (e.g., size M) of clothing currently worn by the user, and health check data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg).

[0406] Output: The input data is saved in the application.

[0407] Specific operation: The user enters each item into the input form on the application's data input screen and presses the "Submit" button.

[0408] Step 2:

[0409] The terminal receives input data, converts it into JSON format, and sends it to the server.

[0410] Input: Data entered by the user.

[0411] Output: The data converted to JSON format is sent to the server.

[0412] Specific operation: The application receives user input data, converts it to JSON format within the program, and then sends the data to the server over the Internet.

[0413] Step 3:

[0414] The server parses the received data and extracts brand and size information and medical examination data.

[0415] Input: JSON format data sent from the terminal.

[0416] Output: Brand and size information, medical examination data are extracted.

[0417] Specific operation: The server parses the received JSON data and accesses the database to search for the required information.

[0418] Step 4:

[0419] The server retrieves the size chart of the brand in question and the size charts of other brands to be compared from the database.

[0420] Input: Brand and size information.

[0421] Output: Size chart of the brand, size chart of the brand to compare.

[0422] Specific operation: The server searches an internal database to obtain the size chart of brand B that corresponds to size M of brand A.

[0423] Step 5:

[0424] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A.

[0425] Input: Brand A size chart, Brand B size chart, medical examination data.

[0426] Output: Brand B appropriate size.

[0427] Specific operation: The server uses an AI algorithm to calculate the appropriate size of brand B corresponding to size M of brand A, taking into account the user's health check data.

[0428] Step 6:

[0429] Emotion recognition means implemented in the terminal or server is used to analyze the user's facial expressions and voice and obtain emotion data.

[0430] Input: User's facial expression data, voice data.

[0431] Output: User emotion data.

[0432] Specific operation: Emotion recognition software installed on the device or server uses the camera and microphone to analyze the user's facial expressions and voice in real time and obtain emotional data.

[0433] Step 7:

[0434] The server uses the sentiment data to recalculate and recommend a more appropriate size.

[0435] Input: Brand B's initial size recommendation, sentiment data.

[0436] Output: Final recommended size for Brand B taking sentiment into account.

[0437] What it does: The server re-runs the AI ​​algorithm, taking into account the emotional data, and recommends a more accurate size.

[0438] Step 8:

[0439] The server converts the calculated result into JSON format and sends a response to the terminal.

[0440] Input: Final recommended size.

[0441] Output: The result data converted to JSON format.

[0442] Specific operation: The server converts the calculation result into JSON format and sends it to the terminal.

[0443] Step 9:

[0444] The terminal visually displays the received results to the user.

[0445] Input: JSON formatted result data sent from the server.

[0446] Output: The recommended size that will be displayed to the user.

[0447] Specific behavior: The application analyzes the received result data and visually displays to the user, "Brand B, size L is recommended."

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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."

[0464] ---

[0465] The present invention is a system that allows a user to input the brand and size of clothing currently worn and health checkup data, and then recommends appropriate sizes for other brands based on the input data. Specifically, the system includes a data input means, a communication means, a size recommendation means, and a result display means. Each means will be described in detail below.

[0466] User data entry

[0467] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a T-shirt (size M) from "Brand A," they enter "Brand A" and "Size M." They can also add their chest circumference, waist circumference, and weight as health checkup data. This provides the system with the user's latest body shape information.

[0468] Data transmission by the terminal

[0469] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[0470] Data reception and analysis by the server

[0471] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to compare.

[0472] Calculations based on size recommendations

[0473] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0474] Sending and displaying calculation results

[0475] The server sends the calculated results to the device, which receives the results and displays them visually to the user. Specifically, the device displays information such as "Brand B, size L is recommended" on the screen.

[0476] Specific examples

[0477] Specifically, let's say a user is wearing a T-shirt (size M) from brand A and enters their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This data is sent to the server, which calculates the size of brand B that corresponds to size M from brand A. Using brand B's size chart and an AI algorithm, the appropriate size (for example, size L) is derived. The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0478] The system according to the present invention thus enables consumers to instantly purchase clothes of the right size online, which contributes to preventing customers from abandoning their purchases and reducing mass waste for businesses.

[0479] The processing flow will be explained below.

[0480] ---

[0481] Step 1:

[0482] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter health checkup data such as chest circumference, waist circumference, and weight.

[0483] Step 2:

[0484] The device receives the data entered by the user. The device converts the entered data into JSON format. For example, { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70}.

[0485] Step 3:

[0486] The device checks the data for abnormal values, for example, to make sure there are no inconsistencies in the entered size or health check data. If there are no abnormalities, it creates an API request to send the data.

[0487] Step 4:

[0488] The device sends a data transmission request to the server, and uses the communication method to send the JSON data mentioned above to the server.

[0489] Step 5:

[0490] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information and health check data.

[0491] Step 6:

[0492] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[0493] Step 7:

[0494] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. The AI ​​algorithm uses size conversion logic between different brands and compares the size standards of each brand.

[0495] Step 8:

[0496] The server fine-tunes the size by taking into account health check data (chest circumference, waist circumference, weight, etc.). For example, if the user's weight has increased, it will recommend a larger size than the calculated size.

[0497] Step 9:

[0498] The server converts the calculation results into JSON format and sends a response to the device, which includes the recommended size for brand B.

[0499] Step 10:

[0500] The device analyzes the response received from the server, obtains the recommended size (e.g., brand B, size L), and displays it to the user.

[0501] Step 11:

[0502] The device will visually display a size recommendation to the user, for example, "Brand B, size Large is recommended for you," and may also provide a link to purchase.

[0503] ---

[0504] The above are the specific processing steps, which allow users to easily select clothes of the appropriate size online.

[0505] Example 1

[0506] 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."

[0507] Conventional clothing purchasing systems require users to take the time to compare sizes across different brands, making it difficult to select the appropriate size. Furthermore, they are unable to recommend sizes that take into account changes in body shape, increasing the risk of purchasing clothing of an inappropriate size. Furthermore, since they lack a size adjustment function based on health checkup data, it is difficult to accurately recommend a size that fits the user's actual body shape. Therefore, there is a need for a system that can improve consumer satisfaction while reducing purchase abandonment and return rates.

[0508] 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.

[0509] In this invention, the server includes a data input means for inputting the brand and size of clothing currently being worn, a data input means for inputting health checkup data, a data conversion means for converting the input data into JSON format, a communication means for transmitting the converted data to the server, a data analysis means for analyzing the received data and obtaining a size chart for the corresponding brand, a size recommendation means for obtaining a size chart for a different brand and calculating an appropriate size for the other brand using an AI algorithm, a data transmission means for converting the calculated size into JSON format and transmitting it to the terminal, and a result display means for displaying the calculated size on the terminal. This allows users to easily and quickly compare and receive recommendations for appropriate sizes across different brands, significantly reducing the effort required for choosing a size and enabling them to purchase the optimal size that accommodates changes in body shape.

[0510] "Data input means" refers to a user interface or input device for inputting the brand and size of the clothing currently worn by the user, as well as health checkup data.

[0511] "Data conversion means" refers to software or algorithms that convert user-entered data into a specific data format, such as JSON format, for efficient processing.

[0512] "Communication Means" refers to the internet connection or other data transmission technology used to transmit the entered data to the server.

[0513] "Data analysis means" refers to software or algorithms that analyze the data received by the server and extract the necessary information.

[0514] "Size recommendation means" refers to a process or device that retrieves size charts of different brands from a database and calculates the appropriate size using an AI algorithm based on user input data.

[0515] "Data Transmission Means" refers to an Internet connection or other data transmission technology for transmitting the calculated results to the Terminal.

[0516] "Result display means" refers to a display device or UI software for visually displaying the calculated size information on the user's terminal.

[0517] The present invention is a system that quickly and accurately recommends suitable clothing sizes for users across different brands. The system includes a data input means, a data conversion means, a communication means, a data analysis means, a size recommendation means, a data transmission means, and a result display means. Specific embodiments are described below.

[0518] User data entry

[0519] Using a dedicated smartphone app, users input the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). For example, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This input is done using the user interface within the app. The app used is called "ClothFit" and is designed for efficient data input.

[0520] Data Conversion and Communication

[0521] The terminal converts the data entered by the user into JSON format and sends it to the server over the Internet. The data is converted as follows:

[0522] json

[0523] {

[0524] "brand": "Brand A",

[0525] "size": "M",

[0526] "chest": 90,

[0527] "waist": 80,

[0528] "weight": 70

[0529] }

[0530] The communication methods used are Wi-Fi and mobile data (4G / 5G), which ensures fast and secure data transmission.

[0531] Data reception and analysis

[0532] The server receives the JSON-formatted data sent from the device and parses it. Through this analysis, the brand name, size, and medical examination data are extracted. The server accesses the database to obtain the size chart for the target brand (Brand A) and also obtains size charts for other brands (Brand B). The database systematically manages size information.

[0533] Size recommendation calculation

[0534] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. The calculation is based on the user's health check data (chest circumference, waist circumference, and weight). For example, if the user has gained weight, a larger size (size L) is recommended. Specifically, the AI ​​model compares each brand's size chart with the user's data to determine the optimal size.

[0535] Sending and displaying calculation results

[0536] The calculation results are converted back to JSON format and sent to the device. The device analyzes this data and displays the results to the user through a user interface. Specifically, the device displays "Brand B, size L is recommended," along with the reason for this recommendation. The display is achieved using Flutter, a UI framework for smartphone apps.

[0537] Specific examples

[0538] As a specific example of how this works, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. This data is sent to the server, which uses an AI algorithm to calculate the appropriate size for Brand B. The recommended result is Brand B in size L, which is displayed on the screen of the "ClothFit" app.

[0539] Prompt Sentence Examples

[0540] Here are some example prompts to apply to generative AI models:

[0541] I wear a T-shirt (size M) from "Brand A," and I have a chest circumference of 90cm, a waist circumference of 80cm, and weigh 70kg. What size T-shirt from "Brand B" is appropriate?

[0542] The system of the present invention allows users to quickly and accurately select clothing of the appropriate size online, contributing to improved consumer satisfaction and preventing companies from abandoning their purchases.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] The user launches a dedicated smartphone app and inputs the brand and size of the clothing they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). Examples of input data include "Brand A T-shirt (size M)," chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg. When the user presses the "Submit" button, the data proceeds to the next step.

[0546] input:

[0547] Data entered by the user into the app (brand, size, chest circumference, waist circumference, weight)

[0548] output:

[0549] The input data is retained within the app.

[0550] Step 2:

[0551] The terminal will convert the input data into JSON format, which will look something like this:

[0552] json

[0553] {

[0554] "brand": "Brand A",

[0555] "size": "M",

[0556] "chest": 90,

[0557] "waist": 80,

[0558] "weight": 70

[0559] }

[0560] The converted data is sent to the server via Wi-Fi or mobile data (4G / 5G).

[0561] input:

[0562] Data stored within the app (brand, size, chest circumference, waist circumference, weight)

[0563] output:

[0564] The data converted to JSON format is sent to the server.

[0565] Step 3:

[0566] The server receives the JSON-formatted data sent from the device. It parses the received data and extracts the brand name, size, and medical examination data. Based on the extracted data, the server accesses the database to retrieve the size chart for the target brand (Brand A) and the size chart for the comparison brand (Brand B).

[0567] input:

[0568] JSON format data sent from the device

[0569] output:

[0570] Analyzed data and the brand size chart obtained based on it

[0571] Step 4:

[0572] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. At this time, the server also takes into account the user's health check data, and if, for example, the user has gained weight, it will recommend a larger size (size L).

[0573] input:

[0574] Extracted brand information, health check data, and size chart

[0575] output:

[0576] Calculated appropriate size from other brands (e.g. L size)

[0577] Step 5:

[0578] The calculation result is converted back to JSON format and sent to the terminal. The converted data has the following format:

[0579] json

[0580] {

[0581] "recommendedSize": "L",

[0582] "reason": "Based on your chest, waist, and weight data, it was determined that brand B, size L, would be appropriate."

[0583] }

[0584] This data transmission is also done via Wi-Fi or mobile data communication.

[0585] input:

[0586] Calculated size information for other brands

[0587] output:

[0588] Sending data converted to JSON format

[0589] Step 6:

[0590] The device analyzes the JSON-formatted data received from the server. Based on the analyzed data, the recommended size (brand B, size L) and the reason for the recommendation are visually displayed to the user. For example, the smartphone screen may display "Brand B, size L is recommended" along with an explanation of the reason.

[0591] input:

[0592] JSON format data received from the server

[0593] output:

[0594] What size recommendations do users see and why?

[0595] This series of processes allows users to easily select clothing of the appropriate size from different brands, resulting in a more satisfying online shopping experience.

[0596] (Application example 1)

[0597] 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."

[0598] In traditional online shopping, it is difficult for users to find the right size clothing. This often leads to poor user experiences and dissatisfaction due to returns or incorrect sizes. Furthermore, inconsistent size labels across different brands make it even more difficult for users to find the right size. This also causes inventory management issues and increased costs for businesses. Therefore, there is a need for a system that allows users to easily find the right size.

[0599] 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.

[0600] In this invention, the server includes a data input means for inputting the brand and size of the clothing currently being worn, a data input means for inputting health checkup data, a communication means for receiving the input data, a size recommendation means for calculating the appropriate size for other brands based on the received data, a result display means for outputting the calculated size, and an interface means for linking with a smartphone application. This allows users to easily find clothing in their correct size, improving the online shopping experience. It also helps companies reduce costs associated with returns and inventory management.

[0601] "Data input means" refers to a device or interface for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[0602] The "communication means" is a means for transmitting data received from the data input means to an external system such as a server. An internet connection is generally used.

[0603] "Size recommendation means" refers to a device or program for calculating the appropriate size of other brands based on received data, including the function of obtaining size charts of different brands and performing calculations using an AI algorithm.

[0604] The "result display means" is a device or interface for visually displaying the calculated recommended size to the user.

[0605] "Smartphone application" refers to application software that runs on a smartphone device and provides an interface that works in conjunction with each function of this system.

[0606] An "AI algorithm" is a calculation method or program that uses artificial intelligence technology to analyze data and derive optimal results.

[0607] A "database" is a storage device that stores size charts and other related data for different brands and is accessible by the server.

[0608] "Health checkup data" refers to the user's body measurement information such as chest circumference, waist circumference, and weight, which are important parameters for size recommendations.

[0609] An "interface means" is a device or software for transmitting and receiving data between a smartphone application and another system or device.

[0610] This invention is a system that recommends appropriate sizes for other brands of clothing based on the brand and size of the clothing currently worn by a user and health checkup data. The system includes a data input means, a communication means, a size recommendation means, a result display means, and an interface means that works in conjunction with a smartphone application.

[0611] Configuration and Functions

[0612] 1. Data entry method

[0613] The user inputs the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, weight, etc.) This data input is done through a smartphone application.

[0614] 2. Means of communication

[0615] The data entered by the user into the application is converted into JSON format and then sent to the server via a communication method, which is usually via the Internet.

[0616] 3. Size Recommendation Method

[0617] The server analyzes the received data and extracts brand and size information. It then accesses a database to retrieve the size chart for the current brand and the size chart for other brands (target brands). The server then uses an AI algorithm to calculate the optimal size.

[0618] 4. Results display means

[0619] The calculated results are sent from the server to a smartphone application, which then visually displays the recommended size to the user.

[0620] 5. Interface Methods

[0621] The smartphone application sends the data entered by the user to the server and generates a prompt sentence that calculates the recommended size.

[0622] Hardware and Software

[0623] Server: Hardware that runs the data analysis and size recommendation algorithms, using a web framework such as Flask.

[0624] Smartphone: A device for data entry and display of results, using a mobile application framework such as React Native.

[0625] Specific examples

[0626] For example, a user enters that they are wearing a "medium" size T-shirt from "brand A," and enters health checkup data showing a chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. The smartphone application sends this data to the server, which uses an AI algorithm to calculate the appropriate size (e.g., size L) for "brand B." The result is sent back to the smartphone application, which displays to the user, "Brand B size L is recommended."

[0627] Prompt Sentence Examples

[0628] "Based on the following data, please recommend the appropriate size of brand B that corresponds to brand A's size M. My chest circumference is 90cm, my waist circumference is 80cm, and I weigh 70kg."

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1: Data entry

[0631] Using a smartphone application, users input the brand and size of the clothes they are currently wearing, as well as health checkup data (chest circumference, waist circumference, weight, etc.). The input data is collected by a data input means within the application.

[0632] Input: Brand name, clothing size, chest circumference, waist circumference, weight

[0633] Output: Data entered into the application (brand name, clothing size, medical examination data)

[0634] Step 2: Send data

[0635] The device converts the data entered by the user into JSON format and sends it to the server via a communication method that utilizes an internet connection.

[0636] Input: Data entered into the application (brand name, clothing size, medical examination data)

[0637] Output: JSON formatted data sent to the server

[0638] Step 3: Data reception and analysis

[0639] The server receives the JSON-formatted data sent from the device and parses it, extracting the brand name, clothing size, and health checkup data.

[0640] Input: JSON format data received from the terminal

[0641] Output: Analyzed data (brand name, clothing size, medical checkup data)

[0642] Step 4: Get the size chart

[0643] Based on the analyzed data, the server retrieves the size chart for the relevant brand (current brand) from the database, and then retrieves the size charts for other brands (target brands) to be compared.

[0644] Input: Parsed data (brand name, clothing size)

[0645] Output: Size chart retrieved from the database (current brand and target brand)

[0646] Step 5: Size Calculation

[0647] The server uses the acquired size chart and health check data to calculate the optimal size using an AI algorithm. For example, it will recommend the appropriate size of brand B that corresponds to a size M of brand A based on chest, waist, and weight.

[0648] Input: Size chart (current brand and target brand), medical examination data

[0649] Output: Calculated recommended size

[0650] Step 6: Sending the calculation results

[0651] The server then sends the calculated recommended size to the device, and this communication also takes place over the Internet.

[0652] Input: Calculated preferred size

[0653] Output: Recommended size data sent to the device

[0654] Step 7: View the results

[0655] The device receives the recommended size data from the server and visually displays it to the user. The smartphone application presents information to the user such as "Brand B, size L is recommended."

[0656] Input: Recommended size data received from the server

[0657] Output: A visual recommendation of the size to the user

[0658] Through these steps, users can easily find the size of other brands that suits them.

[0659] 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.

[0660] ---

[0661] The present invention is a system that combines a system that inputs the brand and size of the clothing a user currently wears and their health checkup data, and then recommends appropriate sizes for other brands based on that input, with an emotion engine that recognizes the user's emotions. Specifically, this system includes a data input means, a communication means, a size recommendation means, a result display means, and an emotion engine. Each means will be described in detail below.

[0662] User data entry

[0663] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a "medium-sized T-shirt from brand A," they enter "brand A" and "medium size." They also enter health checkup data such as chest circumference, waist circumference, and weight. This data provides the system with the user's latest body shape information.

[0664] Data transmission by the terminal

[0665] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[0666] Data reception and analysis by the server

[0667] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information, as well as health check data. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to be compared.

[0668] Calculations based on size recommendations

[0669] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0670] Analysis by emotion engine

[0671] The system also includes an emotion engine that analyzes the user's emotions from facial expressions, voice, or text input. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothing item they are considering purchasing. This emotion data is also reflected in the size recommendation tool, resulting in more accurate size recommendations. For example, if the user is feeling stressed, the system will prioritize suggested sizes that fit well and help them relax.

[0672] Sending and displaying calculation results

[0673] The server converts the calculated result into JSON format and sends a response to the device. The response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B size L is recommended."

[0674] Specific examples

[0675] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0676] The system of this invention allows consumers to instantly purchase clothes in the right size online, which helps companies prevent abandonment and reduce mass waste. Furthermore, by incorporating an emotion engine, it becomes possible to recommend sizes that take into account the user's emotional state, further improving customer satisfaction.

[0677] The processing flow will be explained below.

[0678] ---

[0679] Step 1:

[0680] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter their health checkup data: chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg.

[0681] Step 2:

[0682] The terminal receives data entered by the user and converts it into JSON format.

[0683] Step 3:

[0684] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice in order to analyze the user's emotions. For example, the camera can detect emotions from the user's smile or voice.

[0685] Step 4:

[0686] The device receives the emotion data analyzed by the emotion engine and adds it to the JSON data. For example, it adds "Excitement level: High" as emotion data.

[0687] Step 5:

[0688] The device checks for abnormal values, confirming that there are no abnormalities in the input data and emotion data, and if there are no abnormalities, creates an API request to send the data.

[0689] Step 6:

[0690] The device sends a data transmission request to the server using a communication method, for example, in the format { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70, "emotion": "excited"}.

[0691] Step 7:

[0692] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information, health check data, and emotion data.

[0693] Step 8:

[0694] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[0695] Step 9:

[0696] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. For example, the AI ​​calculates the size of brand B that corresponds to size M of brand A.

[0697] Step 10:

[0698] The server will fine-tune the size based on your health check data (chest circumference, waist circumference, weight, etc.). For example, if you have gained weight, it will recommend a larger size.

[0699] Step 11:

[0700] The server takes emotional data into account to further refine the size recommendation: for example, if the user is highly excited, it may recommend a size that fits slightly better than the normal size.

[0701] Step 12:

[0702] The server converts the final calculated recommended size into JSON format and sends a response to the device, which may include, for example, "Brand B, size L."

[0703] Step 13:

[0704] The device analyzes the response received from the server and displays the recommended size to the user, for example, a message saying "Brand B size L is suitable for you."

[0705] Step 14:

[0706] The device can provide users with recommended sizes and related information through a visual interface, as well as purchase links.

[0707] ---

[0708] The above are the specific processing steps and the operations at each step. This process allows users to quickly and accurately receive appropriate size recommendations for different brands, taking into account their emotional data. It also helps companies prevent customer abandonment and improve the efficiency of inventory management.

[0709] Example 2

[0710] 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."

[0711] In conventional online shopping, it was difficult for users to choose the right size for their body type, and it was not possible to select the optimal size that reflected the user's emotional state. This resulted in returns and exchanges after purchase, which led to poor user experience and a large number of returns, which became a challenge for companies.

[0712] 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 a data input means for inputting the brand and size of the clothes currently being worn, a data input means for inputting medical examination data, a communication means for receiving the input data, a size recommendation means for calculating an appropriate size for another brand based on the received data, an emotion engine for analyzing the user's emotion data, a size recommendation means for adjusting the recommended size using the emotion data, and a result display means for outputting the calculated size. This makes it possible to recommend not only an appropriate size that suits the user's body type but also a size recommendation that takes the user's emotional state into consideration.

[0713] The "data input means" is a means for inputting the brand and size of the clothes the user is currently wearing, and health checkup data.

[0714] The "communication means" is a means for transmitting input data from a terminal to a server and receiving a response from the server at the terminal.

[0715] "Size recommendation tool" means a tool for calculating the appropriate size of other brands based on the received data, characterized by the use of an AI algorithm.

[0716] The "emotion engine" is a means for analyzing the user's emotional data and adjusting the recommended size based on the results.

[0717] The "result display means" is a means for visually displaying the calculated recommended size to the user.

[0718] "Health checkup data" refers to physical data such as the user's chest circumference, waist circumference, and weight.

[0719] The present invention is a system that combines a system that inputs the brand and size of the clothing currently worn by the user and health check data, and recommends appropriate sizes for other brands based on the input, with an emotion engine that recognizes the user's emotions. Detailed embodiments for implementing the present invention will be described below.

[0720] User data entry

[0721] The user inputs the brand and size of the clothing they are currently wearing through an application installed on a smartphone or tablet. For example, if the user is wearing a "medium-sized T-shirt from brand A," they input "brand A" and "medium size." They also input health checkup data such as chest circumference, waist circumference, and weight in the same way. This data provides the system with the user's latest body shape information. The hardware used includes a smartphone or tablet, and the software used includes a dedicated application.

[0722] Data transmission by the terminal

[0723] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection, such as Wi-Fi or mobile data.

[0724] Data reception and analysis by the server

[0725] The server receives the data sent from the device. This data includes brand, size information, and health check data. It analyzes the received data and extracts the necessary information. The server then accesses a database to retrieve the size chart for the relevant brand (e.g., Brand A). It also retrieves size charts for other brands to be compared (e.g., Brand B). The software used includes a database (e.g., MySQL) and an AI engine for analysis (e.g., TensorFlow).

[0726] Calculations based on size recommendations

[0727] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. If the user has provided health checkup data, this data will also be taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0728] Analysis by emotion engine

[0729] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data. It reads emotions from the user's facial expressions, voice, and text, and adjusts the recommended size based on that information. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothes they are considering purchasing. For example, if the user is feeling stressed, it will prioritize suggesting sizes that fit well and are relaxing.

[0730] Sending and displaying calculation results

[0731] Once the calculation is complete, the server converts the result back into JSON format and sends it as a response to the device. This response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B, size L is recommended."

[0732] Specific examples

[0733] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0734] This system will enable users to easily find clothing of the right size online, and is expected to help companies reduce return rates and improve customer satisfaction.

[0735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0736] Step 1: User Data Entry

[0737] The user opens the application installed on their smartphone or tablet and inputs the brand and size of the clothes they are currently wearing. They also input health checkup data such as chest circumference, waist circumference, and weight. The input data includes the brand name (e.g., "Brand A"), size (e.g., "Size M"), and physical data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg). This provides the system with the user's latest body shape information.

[0738] Specific behavior:

[0739] The user launches the app and enters the required information.

[0740] Input: clothing brand, size, medical examination data

[0741] Output: A set of user input data (brand name, size, body data)

[0742] Step 2: Send data by device

[0743] The device receives data entered by the user, converts it into JSON format, and then sends the converted data to the server via a communication method that utilizes an internet connection.

[0744] Specific behavior:

[0745] Converting user-supplied data into JSON format

[0746] Send data to a server using your internet connection

[0747] Input: A set of user-entered data

[0748] Output: Data converted to JSON format

[0749] Step 3: Data reception and analysis by the server

[0750] The server receives the JSON-formatted data sent from the device, parses it, and extracts the brand name, size information, and medical examination data. It also retrieves the size charts for the relevant brands (Brand A and Brand B for comparison) from the database.

[0751] Specific behavior:

[0752] Receiving JSON format data

[0753] Analyze the data and extract the necessary information

[0754] Retrieving size charts from the database

[0755] Input: JSON format data

[0756] Output: Extracted data (brand name, size information, medical examination data), size chart

[0757] Step 4: Server performs sizing recommendations

[0758] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. It also takes into account health checkup data, for example, recommending a larger size if the user has gained weight.

[0759] Specific behavior:

[0760] Calculate data using AI algorithms

[0761] Taking health check data into account

[0762] Input: Extracted data, size chart

[0763] Output: Brand B appropriate size

[0764] Step 5: Running the Emotion Engine on the Server

[0765] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data, reads the user's emotions from facial expressions, voice, and text input, and adjusts the recommended size based on that information.

[0766] Specific behavior:

[0767] Analyzing Emotional Data

[0768] Adjust size recommendations based on sentiment data

[0769] Input: User emotion data

[0770] Output: Adjusted preferred size

[0771] Step 6: Server sends the calculation results

[0772] The server converts the calculation result into JSON format and sends it to the device as a response, which includes the recommended size.

[0773] Specific behavior:

[0774] Convert the calculation results to JSON format

[0775] Send JSON format data to the terminal

[0776] Input: Adjusted preferred size

[0777] Output: Calculation results in JSON format

[0778] Step 7: Displaying the results on the terminal

[0779] The device analyzes the calculation results received from the server and visually displays them to the user, for example, a message saying "Brand B, size L is recommended."

[0780] Specific behavior:

[0781] Receive a response from the server

[0782] Parse the response data and display it to the user

[0783] Input: Calculation result in JSON format

[0784] Output: Message to display to the user

[0785] (Application example 2)

[0786] 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."

[0787] Conventional clothing size recommendation systems recommend sizes based on the user's physical data, but because they do not take into account the user's emotions or psychological state, they may not always recommend the optimal size. In particular, when a user is purchasing clothing from a new brand or design on an online shopping site, their emotions, expectations, and anxieties about the clothing can significantly influence their size selection. For this reason, there is a need for more accurate size recommendations that also take into account the user's emotions.

[0788] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for inputting the brand and size of the currently worn clothing, a data input means for inputting health checkup data, an emotion recognition means for inputting and analyzing facial expressions and voice to recognize the user's emotions, a communication means for receiving the input data, a size recommendation means for calculating appropriate sizes for other brands based on the received data, an emotion-reflecting size recommendation means for inputting data from the emotion recognition means to the size recommendation means and recommending an appropriate size taking the emotion data into consideration, and a result display means for outputting the calculated size. This enables highly accurate size recommendations that take into consideration not only the user's physical data but also their emotion data.

[0789] "Data input means" refers to a device or software for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[0790] "Communication means" refers to a network communication device or protocol for transmitting data entered by a user to a server.

[0791] The "size recommendation means" is a device or software that has the function of calculating the appropriate size of other brands based on the received data and recommending it to the user.

[0792] A "result display means" is a display device or interface for visually displaying the recommended size to the user.

[0793] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and voice and acquiring the user's emotional data.

[0794] The "emotion-reflecting size recommendation means" is a device or software that has the function of recommending a more appropriate size using emotion data acquired by the emotion recognition means.

[0795] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze received data and calculate the optimal size.

[0796] The "database" is an information management system for storing and managing size information and health checkup data for each brand.

[0797] The present invention is a system that combines a system that inputs the brand and size of the clothing a user is currently wearing and health checkup data, and then recommends appropriate sizes for other brands based on that information, with an emotion engine that recognizes the user's emotions.

[0798] System Overview

[0799] The system consists of the following main components:

[0800] 1. Data entry method

[0801] It is a way for users to input the brand and size of the clothes they are currently wearing, as well as health checkup data such as the user's chest circumference, waist circumference, and weight.

[0802] 2. Means of communication

[0803] The terminal receives data entered by the user, converts this data into JSON format, and sends it to a server over the Internet.

[0804] 3. Size Recommendation Method

[0805] The server uses AI algorithms to calculate the appropriate size for other brands based on the data received.

[0806] 4. Results display means

[0807] This is a means to visually display the calculated size to the user, for example, by showing the recommended size on the display of a smartphone.

[0808] 5. Emotion recognition means

[0809] This is a means of analyzing the user's facial expressions and voice to obtain emotional data about the user, which can then be used to read the user's feelings (excitement, anxiety, etc.) about the clothes they are considering purchasing.

[0810] 6. Recommended emotional reflection size

[0811] It has the ability to recommend more appropriate sizes using the acquired emotional data, enabling highly accurate size recommendations that take user emotions into account.

[0812] Program processing overview

[0813] The overall system makes size recommendations based on user input and also takes into account user sentiment data in the process. Details are provided below.

[0814] 1. User data entry

[0815] Through the application, users input the brand and size of the clothes they are currently wearing, as well as their health check data, and this information is stored on the device.

[0816] 2. Data transmission by the terminal

[0817] The device converts the data entered by the user into JSON format and sends it to the server using a communication method that uses the Internet.

[0818] 3. Data reception and analysis by the server

[0819] The server receives and analyzes the data sent from the device, retrieves brand size charts from the database, and uses AI algorithms to calculate the appropriate size for other brands.

[0820] 4. Emotion analysis using an emotion engine

[0821] Emotion recognition is implemented on the device or server to analyze the user's facial expressions and voice to obtain emotional data, which is then reflected in the size recommendation.

[0822] 5. Displaying the calculation results

[0823] The server converts the calculated results into JSON format and sends a response to the device, which receives the results and displays them visually to the user.

[0824] Specific examples

[0825] Suppose a user is wearing a T-shirt (size M) from brand A, and has entered their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. The emotion engine also recognizes that the user is excited about the brand they are considering purchasing. When this data is sent to the server, the server calculates the size of brand B that corresponds to size M from brand A, and recommends the optimal size (for example, size L) taking into account the emotion data. The device displays the message "Brand B size L is recommended."

[0826] Prompt Sentence Examples

[0827] By inputting prompt sentences like the following into the generative AI model, the accuracy of the system's size recommendations can be improved.

[0828] "The user is wearing a UNIQLO medium shirt, and the data indicates that the chest circumference is 90cm, waist circumference is 80cm, and weight is 70kg. The emotion engine then analyzes the user's facial expression and determines that the user is excited. Based on this data, the system will recommend appropriate sizes for other brands, and the result will be a program that recommends a large size from brand B."

[0829] The above is a specific embodiment of the present invention.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The user inputs the brand and size of the clothes they are currently wearing and their health check data.

[0833] Input: Brand (e.g., Brand A) and size (e.g., size M) of clothing currently worn by the user, and health check data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg).

[0834] Output: The input data is saved in the application.

[0835] Specific operation: The user enters each item into the input form on the application's data input screen and presses the "Submit" button.

[0836] Step 2:

[0837] The terminal receives input data, converts it into JSON format, and sends it to the server.

[0838] Input: Data entered by the user.

[0839] Output: The data converted to JSON format is sent to the server.

[0840] Specific operation: The application receives user input data, converts it to JSON format within the program, and then sends the data to the server over the Internet.

[0841] Step 3:

[0842] The server parses the received data and extracts brand and size information and medical examination data.

[0843] Input: JSON format data sent from the terminal.

[0844] Output: Brand and size information, medical examination data are extracted.

[0845] Specific operation: The server parses the received JSON data and accesses the database to search for the required information.

[0846] Step 4:

[0847] The server retrieves the size chart of the brand in question and the size charts of other brands to be compared from the database.

[0848] Input: Brand and size information.

[0849] Output: Size chart of the brand, size chart of the brand to compare.

[0850] Specific operation: The server searches an internal database to obtain the size chart of brand B that corresponds to size M of brand A.

[0851] Step 5:

[0852] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A.

[0853] Input: Brand A size chart, Brand B size chart, medical examination data.

[0854] Output: Brand B appropriate size.

[0855] Specific operation: The server uses an AI algorithm to calculate the appropriate size of brand B corresponding to size M of brand A, taking into account the user's health check data.

[0856] Step 6:

[0857] Emotion recognition means implemented in the terminal or server is used to analyze the user's facial expressions and voice and obtain emotion data.

[0858] Input: User's facial expression data, voice data.

[0859] Output: User emotion data.

[0860] Specific operation: Emotion recognition software installed on the device or server uses the camera and microphone to analyze the user's facial expressions and voice in real time and obtain emotional data.

[0861] Step 7:

[0862] The server uses the sentiment data to recalculate and recommend a more appropriate size.

[0863] Input: Brand B's initial size recommendation, sentiment data.

[0864] Output: Final recommended size for Brand B taking sentiment into account.

[0865] What it does: The server re-runs the AI ​​algorithm, taking into account the emotional data, and recommends a more accurate size.

[0866] Step 8:

[0867] The server converts the calculated result into JSON format and sends a response to the terminal.

[0868] Input: Final recommended size.

[0869] Output: The result data converted to JSON format.

[0870] Specific operation: The server converts the calculation result into JSON format and sends it to the terminal.

[0871] Step 9:

[0872] The terminal visually displays the received results to the user.

[0873] Input: JSON formatted result data sent from the server.

[0874] Output: The recommended size that will be displayed to the user.

[0875] Specific behavior: The application analyzes the received result data and visually displays to the user, "Brand B, size L is recommended."

[0876] 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.

[0877] 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.

[0878] 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.

[0879] [Third embodiment]

[0880] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0881] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0882] 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).

[0883] 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.

[0884] 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.

[0885] 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).

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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."

[0892] ---

[0893] The present invention is a system that allows a user to input the brand and size of clothing currently worn and health checkup data, and then recommends appropriate sizes for other brands based on the input data. Specifically, the system includes a data input means, a communication means, a size recommendation means, and a result display means. Each means will be described in detail below.

[0894] User data entry

[0895] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a T-shirt (size M) from "Brand A," they enter "Brand A" and "Size M." They can also add their chest circumference, waist circumference, and weight as health checkup data. This provides the system with the user's latest body shape information.

[0896] Data transmission by the terminal

[0897] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[0898] Data reception and analysis by the server

[0899] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to compare.

[0900] Calculations based on size recommendations

[0901] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[0902] Sending and displaying calculation results

[0903] The server sends the calculated results to the device, which receives the results and displays them visually to the user. Specifically, the device displays information such as "Brand B, size L is recommended" on the screen.

[0904] Specific examples

[0905] Specifically, let's say a user is wearing a T-shirt (size M) from brand A and enters their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This data is sent to the server, which calculates the size of brand B that corresponds to size M from brand A. Using brand B's size chart and an AI algorithm, the appropriate size (for example, size L) is derived. The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[0906] The system according to the present invention thus enables consumers to instantly purchase clothes of the right size online, which contributes to preventing customers from abandoning their purchases and reducing mass waste for businesses.

[0907] The processing flow will be explained below.

[0908] ---

[0909] Step 1:

[0910] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter health checkup data such as chest circumference, waist circumference, and weight.

[0911] Step 2:

[0912] The device receives the data entered by the user. The device converts the entered data into JSON format. For example, { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70}.

[0913] Step 3:

[0914] The device checks the data for abnormal values, for example, to make sure there are no inconsistencies in the entered size or health check data. If there are no abnormalities, it creates an API request to send the data.

[0915] Step 4:

[0916] The device sends a data transmission request to the server, and uses the communication method to send the JSON data mentioned above to the server.

[0917] Step 5:

[0918] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information and health check data.

[0919] Step 6:

[0920] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[0921] Step 7:

[0922] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. The AI ​​algorithm uses size conversion logic between different brands and compares the size standards of each brand.

[0923] Step 8:

[0924] The server fine-tunes the size by taking into account health check data (chest circumference, waist circumference, weight, etc.). For example, if the user's weight has increased, it will recommend a larger size than the calculated size.

[0925] Step 9:

[0926] The server converts the calculation results into JSON format and sends a response to the device, which includes the recommended size for brand B.

[0927] Step 10:

[0928] The device analyzes the response received from the server, obtains the recommended size (e.g., brand B, size L), and displays it to the user.

[0929] Step 11:

[0930] The device will visually display a size recommendation to the user, for example, "Brand B, size Large is recommended for you," and may also provide a link to purchase.

[0931] ---

[0932] The above are the specific processing steps, which allow users to easily select clothes of the appropriate size online.

[0933] Example 1

[0934] 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."

[0935] Conventional clothing purchasing systems require users to take the time to compare sizes across different brands, making it difficult to select the appropriate size. Furthermore, they are unable to recommend sizes that take into account changes in body shape, increasing the risk of purchasing clothing of an inappropriate size. Furthermore, since they lack a size adjustment function based on health checkup data, it is difficult to accurately recommend a size that fits the user's actual body shape. Therefore, there is a need for a system that can improve consumer satisfaction while reducing purchase abandonment and return rates.

[0936] 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.

[0937] In this invention, the server includes a data input means for inputting the brand and size of clothing currently being worn, a data input means for inputting health checkup data, a data conversion means for converting the input data into JSON format, a communication means for transmitting the converted data to the server, a data analysis means for analyzing the received data and obtaining a size chart for the corresponding brand, a size recommendation means for obtaining a size chart for a different brand and calculating an appropriate size for the other brand using an AI algorithm, a data transmission means for converting the calculated size into JSON format and transmitting it to the terminal, and a result display means for displaying the calculated size on the terminal. This allows users to easily and quickly compare and receive recommendations for appropriate sizes across different brands, significantly reducing the effort required for choosing a size and enabling them to purchase the optimal size that accommodates changes in body shape.

[0938] "Data input means" refers to a user interface or input device for inputting the brand and size of the clothing currently worn by the user, as well as health checkup data.

[0939] "Data conversion means" refers to software or algorithms that convert user-entered data into a specific data format, such as JSON format, for efficient processing.

[0940] "Communication Means" refers to the internet connection or other data transmission technology used to transmit the entered data to the server.

[0941] "Data analysis means" refers to software or algorithms that analyze the data received by the server and extract the necessary information.

[0942] "Size recommendation means" refers to a process or device that retrieves size charts of different brands from a database and calculates the appropriate size using an AI algorithm based on user input data.

[0943] "Data Transmission Means" refers to an Internet connection or other data transmission technology for transmitting the calculated results to the Terminal.

[0944] "Result display means" refers to a display device or UI software for visually displaying the calculated size information on the user's terminal.

[0945] The present invention is a system that quickly and accurately recommends suitable clothing sizes for users across different brands. The system includes a data input means, a data conversion means, a communication means, a data analysis means, a size recommendation means, a data transmission means, and a result display means. Specific embodiments are described below.

[0946] User data entry

[0947] Using a dedicated smartphone app, users input the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). For example, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This input is done using the user interface within the app. The app used is called "ClothFit" and is designed for efficient data input.

[0948] Data Conversion and Communication

[0949] The terminal converts the data entered by the user into JSON format and sends it to the server over the Internet. The data is converted as follows:

[0950] json

[0951] {

[0952] "brand": "Brand A",

[0953] "size": "M",

[0954] "chest": 90,

[0955] "waist": 80,

[0956] "weight": 70

[0957] }

[0958] The communication methods used are Wi-Fi and mobile data (4G / 5G), which ensures fast and secure data transmission.

[0959] Data reception and analysis

[0960] The server receives the JSON-formatted data sent from the device and parses it. Through this analysis, the brand name, size, and medical examination data are extracted. The server accesses the database to obtain the size chart for the target brand (Brand A) and also obtains size charts for other brands (Brand B). The database systematically manages size information.

[0961] Size recommendation calculation

[0962] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. The calculation is based on the user's health check data (chest circumference, waist circumference, and weight). For example, if the user has gained weight, a larger size (size L) is recommended. Specifically, the AI ​​model compares each brand's size chart with the user's data to determine the optimal size.

[0963] Sending and displaying calculation results

[0964] The calculation results are converted back to JSON format and sent to the device. The device analyzes this data and displays the results to the user through a user interface. Specifically, the device displays "Brand B, size L is recommended," along with the reason for this recommendation. The display is achieved using Flutter, a UI framework for smartphone apps.

[0965] Specific examples

[0966] As a specific example of how this works, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. This data is sent to the server, which uses an AI algorithm to calculate the appropriate size for Brand B. The recommended result is Brand B in size L, which is displayed on the screen of the "ClothFit" app.

[0967] Prompt Sentence Examples

[0968] Here are some example prompts to apply to generative AI models:

[0969] I wear a T-shirt (size M) from "Brand A," and I have a chest circumference of 90cm, a waist circumference of 80cm, and weigh 70kg. What size T-shirt from "Brand B" is appropriate?

[0970] The system of the present invention allows users to quickly and accurately select clothing of the appropriate size online, contributing to improved consumer satisfaction and preventing companies from abandoning their purchases.

[0971] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0972] Step 1:

[0973] The user launches a dedicated smartphone app and inputs the brand and size of the clothing they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). Examples of input data include "Brand A T-shirt (size M)," chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg. When the user presses the "Submit" button, the data proceeds to the next step.

[0974] input:

[0975] Data entered by the user into the app (brand, size, chest circumference, waist circumference, weight)

[0976] output:

[0977] The input data is retained within the app.

[0978] Step 2:

[0979] The terminal will convert the input data into JSON format, which will look something like this:

[0980] json

[0981] {

[0982] "brand": "Brand A",

[0983] "size": "M",

[0984] "chest": 90,

[0985] "waist": 80,

[0986] "weight": 70

[0987] }

[0988] The converted data is sent to the server via Wi-Fi or mobile data (4G / 5G).

[0989] input:

[0990] Data stored within the app (brand, size, chest circumference, waist circumference, weight)

[0991] output:

[0992] The data converted to JSON format is sent to the server.

[0993] Step 3:

[0994] The server receives the JSON-formatted data sent from the device. It parses the received data and extracts the brand name, size, and medical examination data. Based on the extracted data, the server accesses the database to retrieve the size chart for the target brand (Brand A) and the size chart for the comparison brand (Brand B).

[0995] input:

[0996] JSON format data sent from the device

[0997] output:

[0998] Analyzed data and the brand size chart obtained based on it

[0999] Step 4:

[1000] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. At this time, the server also takes into account the user's health check data, and if, for example, the user has gained weight, it will recommend a larger size (size L).

[1001] input:

[1002] Extracted brand information, health check data, and size chart

[1003] output:

[1004] Calculated appropriate size from other brands (e.g. L size)

[1005] Step 5:

[1006] The calculation result is converted back to JSON format and sent to the terminal. The converted data has the following format:

[1007] json

[1008] {

[1009] "recommendedSize": "L",

[1010] "reason": "Based on your chest, waist, and weight data, it was determined that brand B, size L, would be appropriate."

[1011] }

[1012] This data transmission is also done via Wi-Fi or mobile data communication.

[1013] input:

[1014] Calculated size information for other brands

[1015] output:

[1016] Sending data converted to JSON format

[1017] Step 6:

[1018] The device analyzes the JSON-formatted data received from the server. Based on the analyzed data, the recommended size (brand B, size L) and the reason for the recommendation are visually displayed to the user. For example, the smartphone screen may display "Brand B, size L is recommended" along with an explanation of the reason.

[1019] input:

[1020] JSON format data received from the server

[1021] output:

[1022] What size recommendations do users see and why?

[1023] This series of processes allows users to easily select clothing of the appropriate size from different brands, resulting in a more satisfying online shopping experience.

[1024] (Application example 1)

[1025] 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."

[1026] In traditional online shopping, it is difficult for users to find the right size clothing. This often leads to poor user experiences and dissatisfaction due to returns or incorrect sizes. Furthermore, inconsistent size labels across different brands make it even more difficult for users to find the right size. This also causes inventory management issues and increased costs for businesses. Therefore, there is a need for a system that allows users to easily find the right size.

[1027] 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.

[1028] In this invention, the server includes a data input means for inputting the brand and size of the clothing currently being worn, a data input means for inputting health checkup data, a communication means for receiving the input data, a size recommendation means for calculating the appropriate size for other brands based on the received data, a result display means for outputting the calculated size, and an interface means for linking with a smartphone application. This allows users to easily find clothing in their correct size, improving the online shopping experience. It also helps companies reduce costs associated with returns and inventory management.

[1029] "Data input means" refers to a device or interface for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[1030] The "communication means" is a means for transmitting data received from the data input means to an external system such as a server. An internet connection is generally used.

[1031] "Size recommendation means" refers to a device or program for calculating the appropriate size of other brands based on received data, including the function of obtaining size charts of different brands and performing calculations using an AI algorithm.

[1032] The "result display means" is a device or interface for visually displaying the calculated recommended size to the user.

[1033] "Smartphone application" refers to application software that runs on a smartphone device and provides an interface that works in conjunction with each function of this system.

[1034] An "AI algorithm" is a calculation method or program that uses artificial intelligence technology to analyze data and derive optimal results.

[1035] A "database" is a storage device that stores size charts and other related data for different brands and is accessible by the server.

[1036] "Health checkup data" refers to the user's body measurement information such as chest circumference, waist circumference, and weight, which are important parameters for size recommendations.

[1037] An "interface means" is a device or software for transmitting and receiving data between a smartphone application and another system or device.

[1038] This invention is a system that recommends appropriate sizes for other brands of clothing based on the brand and size of the clothing currently worn by a user and health checkup data. The system includes a data input means, a communication means, a size recommendation means, a result display means, and an interface means that works in conjunction with a smartphone application.

[1039] Configuration and Functions

[1040] 1. Data entry method

[1041] The user inputs the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, weight, etc.) This data input is done through a smartphone application.

[1042] 2. Means of communication

[1043] The data entered by the user into the application is converted into JSON format and then sent to the server via a communication method, which is usually via the Internet.

[1044] 3. Size Recommendation Method

[1045] The server analyzes the received data and extracts brand and size information. It then accesses a database to retrieve the size chart for the current brand and the size chart for other brands (target brands). The server then uses an AI algorithm to calculate the optimal size.

[1046] 4. Results display means

[1047] The calculated results are sent from the server to a smartphone application, which then visually displays the recommended size to the user.

[1048] 5. Interface Methods

[1049] The smartphone application sends the data entered by the user to the server and generates a prompt sentence that calculates the recommended size.

[1050] Hardware and Software

[1051] Server: Hardware that runs the data analysis and size recommendation algorithms, using a web framework such as Flask.

[1052] Smartphone: A device for data entry and display of results, using a mobile application framework such as React Native.

[1053] Specific examples

[1054] For example, a user enters that they are wearing a "medium" size T-shirt from "brand A," and enters health checkup data showing a chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. The smartphone application sends this data to the server, which uses an AI algorithm to calculate the appropriate size (e.g., size L) for "brand B." The result is sent back to the smartphone application, which displays to the user, "Brand B size L is recommended."

[1055] Prompt Sentence Examples

[1056] "Based on the following data, please recommend the appropriate size of brand B that corresponds to brand A's size M. My chest circumference is 90cm, my waist circumference is 80cm, and I weigh 70kg."

[1057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1058] Step 1: Data entry

[1059] Using a smartphone application, users input the brand and size of the clothes they are currently wearing, as well as health checkup data (chest circumference, waist circumference, weight, etc.). The input data is collected by a data input means within the application.

[1060] Input: Brand name, clothing size, chest circumference, waist circumference, weight

[1061] Output: Data entered into the application (brand name, clothing size, medical examination data)

[1062] Step 2: Send data

[1063] The device converts the data entered by the user into JSON format and sends it to the server via a communication method that utilizes an internet connection.

[1064] Input: Data entered into the application (brand name, clothing size, medical examination data)

[1065] Output: JSON formatted data sent to the server

[1066] Step 3: Data reception and analysis

[1067] The server receives the JSON-formatted data sent from the device and parses it, extracting the brand name, clothing size, and health checkup data.

[1068] Input: JSON format data received from the terminal

[1069] Output: Analyzed data (brand name, clothing size, medical checkup data)

[1070] Step 4: Get the size chart

[1071] Based on the analyzed data, the server retrieves the size chart for the relevant brand (current brand) from the database, and then retrieves the size charts for other brands (target brands) to be compared.

[1072] Input: Parsed data (brand name, clothing size)

[1073] Output: Size chart retrieved from the database (current brand and target brand)

[1074] Step 5: Size Calculation

[1075] The server uses the acquired size chart and health check data to calculate the optimal size using an AI algorithm. For example, it will recommend the appropriate size of brand B that corresponds to a size M of brand A based on chest, waist, and weight.

[1076] Input: Size chart (current brand and target brand), medical examination data

[1077] Output: Calculated recommended size

[1078] Step 6: Sending the calculation results

[1079] The server then sends the calculated recommended size to the device, and this communication also takes place over the Internet.

[1080] Input: Calculated preferred size

[1081] Output: Recommended size data sent to the device

[1082] Step 7: View the results

[1083] The device receives the recommended size data from the server and visually displays it to the user. The smartphone application presents information to the user such as "Brand B, size L is recommended."

[1084] Input: Recommended size data received from the server

[1085] Output: A visual recommendation of the size to the user

[1086] Through these steps, users can easily find the size of other brands that suits them.

[1087] 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.

[1088] ---

[1089] The present invention is a system that combines a system that inputs the brand and size of the clothing a user currently wears and their health checkup data, and then recommends appropriate sizes for other brands based on that input, with an emotion engine that recognizes the user's emotions. Specifically, this system includes a data input means, a communication means, a size recommendation means, a result display means, and an emotion engine. Each means will be described in detail below.

[1090] User data entry

[1091] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a "medium-sized T-shirt from brand A," they enter "brand A" and "medium size." They also enter health checkup data such as chest circumference, waist circumference, and weight. This data provides the system with the user's latest body shape information.

[1092] Data transmission by the terminal

[1093] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[1094] Data reception and analysis by the server

[1095] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information, as well as health check data. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to be compared.

[1096] Calculations based on size recommendations

[1097] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[1098] Analysis by emotion engine

[1099] The system also includes an emotion engine that analyzes the user's emotions from facial expressions, voice, or text input. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothing item they are considering purchasing. This emotion data is also reflected in the size recommendation tool, resulting in more accurate size recommendations. For example, if the user is feeling stressed, the system will prioritize suggested sizes that fit well and help them relax.

[1100] Sending and displaying calculation results

[1101] The server converts the calculated result into JSON format and sends a response to the device. The response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B size L is recommended."

[1102] Specific examples

[1103] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[1104] The system of this invention allows consumers to instantly purchase clothes in the right size online, which helps companies prevent abandonment and reduce mass waste. Furthermore, by incorporating an emotion engine, it becomes possible to recommend sizes that take into account the user's emotional state, further improving customer satisfaction.

[1105] The processing flow will be explained below.

[1106] ---

[1107] Step 1:

[1108] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter their health checkup data: chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg.

[1109] Step 2:

[1110] The terminal receives data entered by the user and converts it into JSON format.

[1111] Step 3:

[1112] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice in order to analyze the user's emotions. For example, the camera can detect emotions from the user's smile or voice.

[1113] Step 4:

[1114] The device receives the emotion data analyzed by the emotion engine and adds it to the JSON data. For example, it adds "Excitement level: High" as emotion data.

[1115] Step 5:

[1116] The device checks for abnormal values, confirming that there are no abnormalities in the input data and emotion data, and if there are no abnormalities, creates an API request to send the data.

[1117] Step 6:

[1118] The device sends a data transmission request to the server using a communication method, for example, in the format { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70, "emotion": "excited"}.

[1119] Step 7:

[1120] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information, health check data, and emotion data.

[1121] Step 8:

[1122] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[1123] Step 9:

[1124] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. For example, the AI ​​calculates the size of brand B that corresponds to size M of brand A.

[1125] Step 10:

[1126] The server will fine-tune the size based on your health check data (chest circumference, waist circumference, weight, etc.). For example, if you have gained weight, it will recommend a larger size.

[1127] Step 11:

[1128] The server takes emotional data into account to further refine the size recommendation: for example, if the user is highly excited, it may recommend a size that fits slightly better than the normal size.

[1129] Step 12:

[1130] The server converts the final calculated recommended size into JSON format and sends a response to the device, which may include, for example, "Brand B, size L."

[1131] Step 13:

[1132] The device analyzes the response received from the server and displays the recommended size to the user, for example, a message saying "Brand B size L is suitable for you."

[1133] Step 14:

[1134] The device can provide users with recommended sizes and related information through a visual interface, as well as purchase links.

[1135] ---

[1136] The above are the specific processing steps and the operations at each step. This process allows users to quickly and accurately receive appropriate size recommendations for different brands, taking into account their emotional data. It also helps companies prevent customer abandonment and improve the efficiency of inventory management.

[1137] Example 2

[1138] 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."

[1139] In conventional online shopping, it was difficult for users to choose the right size for their body type, and it was not possible to select the optimal size that reflected the user's emotional state. This resulted in returns and exchanges after purchase, which led to poor user experience and a large number of returns, which became a challenge for companies.

[1140] 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 a data input means for inputting the brand and size of the clothes currently being worn, a data input means for inputting medical examination data, a communication means for receiving the input data, a size recommendation means for calculating an appropriate size for another brand based on the received data, an emotion engine for analyzing the user's emotion data, a size recommendation means for adjusting the recommended size using the emotion data, and a result display means for outputting the calculated size. This makes it possible to recommend not only an appropriate size that suits the user's body type but also a size recommendation that takes the user's emotional state into consideration.

[1141] The "data input means" is a means for inputting the brand and size of the clothes the user is currently wearing, and health checkup data.

[1142] The "communication means" is a means for transmitting input data from a terminal to a server and receiving a response from the server at the terminal.

[1143] "Size recommendation tool" means a tool for calculating the appropriate size of other brands based on the received data, characterized by the use of an AI algorithm.

[1144] The "emotion engine" is a means for analyzing the user's emotional data and adjusting the recommended size based on the results.

[1145] The "result display means" is a means for visually displaying the calculated recommended size to the user.

[1146] "Health checkup data" refers to physical data such as the user's chest circumference, waist circumference, and weight.

[1147] The present invention is a system that combines a system that inputs the brand and size of the clothing currently worn by the user and health check data, and recommends appropriate sizes for other brands based on the input, with an emotion engine that recognizes the user's emotions. Detailed embodiments for implementing the present invention will be described below.

[1148] User data entry

[1149] The user inputs the brand and size of the clothing they are currently wearing through an application installed on a smartphone or tablet. For example, if the user is wearing a "medium-sized T-shirt from brand A," they input "brand A" and "medium size." They also input health checkup data such as chest circumference, waist circumference, and weight in the same way. This data provides the system with the user's latest body shape information. The hardware used includes a smartphone or tablet, and the software used includes a dedicated application.

[1150] Data transmission by the terminal

[1151] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection, such as Wi-Fi or mobile data.

[1152] Data reception and analysis by the server

[1153] The server receives the data sent from the device. This data includes brand, size information, and health check data. It analyzes the received data and extracts the necessary information. The server then accesses a database to retrieve the size chart for the relevant brand (e.g., Brand A). It also retrieves size charts for other brands to be compared (e.g., Brand B). The software used includes a database (e.g., MySQL) and an AI engine for analysis (e.g., TensorFlow).

[1154] Calculations based on size recommendations

[1155] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. If the user has provided health checkup data, this data will also be taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[1156] Analysis by emotion engine

[1157] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data. It reads emotions from the user's facial expressions, voice, and text, and adjusts the recommended size based on that information. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothes they are considering purchasing. For example, if the user is feeling stressed, it will prioritize suggesting sizes that fit well and are relaxing.

[1158] Sending and displaying calculation results

[1159] Once the calculation is complete, the server converts the result back into JSON format and sends it as a response to the device. This response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B, size L is recommended."

[1160] Specific examples

[1161] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[1162] This system will enable users to easily find clothing of the right size online, and is expected to help companies reduce return rates and improve customer satisfaction.

[1163] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1164] Step 1: User Data Entry

[1165] The user opens the application installed on their smartphone or tablet and inputs the brand and size of the clothes they are currently wearing. They also input health checkup data such as chest circumference, waist circumference, and weight. The input data includes the brand name (e.g., "Brand A"), size (e.g., "Size M"), and physical data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg). This provides the system with the user's latest body shape information.

[1166] Specific behavior:

[1167] The user launches the app and enters the required information.

[1168] Input: clothing brand, size, medical examination data

[1169] Output: A set of user input data (brand name, size, body data)

[1170] Step 2: Send data by device

[1171] The device receives data entered by the user, converts it into JSON format, and then sends the converted data to the server via a communication method that utilizes an internet connection.

[1172] Specific behavior:

[1173] Converting user-supplied data into JSON format

[1174] Send data to a server using your internet connection

[1175] Input: A set of user-entered data

[1176] Output: Data converted to JSON format

[1177] Step 3: Data reception and analysis by the server

[1178] The server receives the JSON-formatted data sent from the device, parses it, and extracts the brand name, size information, and medical examination data. It also retrieves the size charts for the relevant brands (Brand A and Brand B for comparison) from the database.

[1179] Specific behavior:

[1180] Receiving JSON format data

[1181] Analyze the data and extract the necessary information

[1182] Retrieving size charts from the database

[1183] Input: JSON format data

[1184] Output: Extracted data (brand name, size information, medical examination data), size chart

[1185] Step 4: Server performs sizing recommendations

[1186] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. It also takes into account health checkup data, for example, recommending a larger size if the user has gained weight.

[1187] Specific behavior:

[1188] Calculate data using AI algorithms

[1189] Taking health check data into account

[1190] Input: Extracted data, size chart

[1191] Output: Brand B appropriate size

[1192] Step 5: Running the Emotion Engine on the Server

[1193] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data, reads the user's emotions from facial expressions, voice, and text input, and adjusts the recommended size based on that information.

[1194] Specific behavior:

[1195] Analyzing Emotional Data

[1196] Adjust size recommendations based on sentiment data

[1197] Input: User emotion data

[1198] Output: Adjusted preferred size

[1199] Step 6: Server sends the calculation results

[1200] The server converts the calculation result into JSON format and sends it to the device as a response, which includes the recommended size.

[1201] Specific behavior:

[1202] Convert the calculation results to JSON format

[1203] Send JSON format data to the terminal

[1204] Input: Adjusted preferred size

[1205] Output: Calculation results in JSON format

[1206] Step 7: Displaying the results on the terminal

[1207] The device analyzes the calculation results received from the server and visually displays them to the user, for example, a message saying "Brand B, size L is recommended."

[1208] Specific behavior:

[1209] Receive a response from the server

[1210] Parse the response data and display it to the user

[1211] Input: Calculation result in JSON format

[1212] Output: Message to display to the user

[1213] (Application example 2)

[1214] 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."

[1215] Conventional clothing size recommendation systems recommend sizes based on the user's physical data, but because they do not take into account the user's emotions or psychological state, they may not always recommend the optimal size. In particular, when a user is purchasing clothing from a new brand or design on an online shopping site, their emotions, expectations, and anxieties about the clothing can significantly influence their size selection. For this reason, there is a need for more accurate size recommendations that also take into account the user's emotions.

[1216] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for inputting the brand and size of the currently worn clothing, a data input means for inputting health checkup data, an emotion recognition means for inputting and analyzing facial expressions and voice to recognize the user's emotions, a communication means for receiving the input data, a size recommendation means for calculating appropriate sizes for other brands based on the received data, an emotion-reflecting size recommendation means for inputting data from the emotion recognition means to the size recommendation means and recommending an appropriate size taking the emotion data into consideration, and a result display means for outputting the calculated size. This enables highly accurate size recommendations that take into consideration not only the user's physical data but also their emotion data.

[1217] "Data input means" refers to a device or software for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[1218] "Communication means" refers to a network communication device or protocol for transmitting data entered by a user to a server.

[1219] The "size recommendation means" is a device or software that has the function of calculating the appropriate size of other brands based on the received data and recommending it to the user.

[1220] A "result display means" is a display device or interface for visually displaying the recommended size to the user.

[1221] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and voice and acquiring the user's emotional data.

[1222] The "emotion-reflecting size recommendation means" is a device or software that has the function of recommending a more appropriate size using emotion data acquired by the emotion recognition means.

[1223] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze received data and calculate the optimal size.

[1224] The "database" is an information management system for storing and managing size information and health checkup data for each brand.

[1225] The present invention is a system that combines a system that inputs the brand and size of the clothing a user is currently wearing and health checkup data, and then recommends appropriate sizes for other brands based on that information, with an emotion engine that recognizes the user's emotions.

[1226] System Overview

[1227] The system consists of the following main components:

[1228] 1. Data entry method

[1229] It is a way for users to input the brand and size of the clothes they are currently wearing, as well as health checkup data such as the user's chest circumference, waist circumference, and weight.

[1230] 2. Means of communication

[1231] The terminal receives data entered by the user, converts this data into JSON format, and sends it to a server over the Internet.

[1232] 3. Size Recommendation Method

[1233] The server uses AI algorithms to calculate the appropriate size for other brands based on the data received.

[1234] 4. Results display means

[1235] This is a means to visually display the calculated size to the user, for example, by showing the recommended size on the display of a smartphone.

[1236] 5. Emotion recognition means

[1237] This is a means of analyzing the user's facial expressions and voice to obtain emotional data about the user, which can then be used to read the user's feelings (excitement, anxiety, etc.) about the clothes they are considering purchasing.

[1238] 6. Recommended emotional reflection size

[1239] It has the ability to recommend more appropriate sizes using the acquired emotional data, enabling highly accurate size recommendations that take user emotions into account.

[1240] Program processing overview

[1241] The overall system makes size recommendations based on user input and also takes into account user sentiment data in the process. Details are provided below.

[1242] 1. User data entry

[1243] Through the application, users input the brand and size of the clothes they are currently wearing, as well as their health check data, and this information is stored on the device.

[1244] 2. Data transmission by the terminal

[1245] The device converts the data entered by the user into JSON format and sends it to the server using a communication method that uses the Internet.

[1246] 3. Data reception and analysis by the server

[1247] The server receives and analyzes the data sent from the device, retrieves brand size charts from the database, and uses AI algorithms to calculate the appropriate size for other brands.

[1248] 4. Emotion analysis using an emotion engine

[1249] Emotion recognition is implemented on the device or server to analyze the user's facial expressions and voice to obtain emotional data, which is then reflected in the size recommendation.

[1250] 5. Displaying the calculation results

[1251] The server converts the calculated results into JSON format and sends a response to the device, which receives the results and displays them visually to the user.

[1252] Specific examples

[1253] Suppose a user is wearing a T-shirt (size M) from brand A, and has entered their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. The emotion engine also recognizes that the user is excited about the brand they are considering purchasing. When this data is sent to the server, the server calculates the size of brand B that corresponds to size M from brand A, and recommends the optimal size (for example, size L) taking into account the emotion data. The device displays the message "Brand B size L is recommended."

[1254] Prompt Sentence Examples

[1255] By inputting prompt sentences like the following into the generative AI model, the accuracy of the system's size recommendations can be improved.

[1256] "The user is wearing a UNIQLO medium shirt, and the data indicates that the chest circumference is 90cm, waist circumference is 80cm, and weight is 70kg. The emotion engine then analyzes the user's facial expression and determines that the user is excited. Based on this data, the system will recommend appropriate sizes for other brands, and the result will be a program that recommends a large size from brand B."

[1257] The above is a specific embodiment of the present invention.

[1258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1259] Step 1:

[1260] The user inputs the brand and size of the clothes they are currently wearing and their health check data.

[1261] Input: Brand (e.g., Brand A) and size (e.g., size M) of clothing currently worn by the user, and health check data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg).

[1262] Output: The input data is saved in the application.

[1263] Specific operation: The user enters each item into the input form on the application's data input screen and presses the "Submit" button.

[1264] Step 2:

[1265] The terminal receives input data, converts it into JSON format, and sends it to the server.

[1266] Input: Data entered by the user.

[1267] Output: The data converted to JSON format is sent to the server.

[1268] Specific operation: The application receives user input data, converts it to JSON format within the program, and then sends the data to the server over the Internet.

[1269] Step 3:

[1270] The server parses the received data and extracts brand and size information and medical examination data.

[1271] Input: JSON format data sent from the terminal.

[1272] Output: Brand and size information, medical examination data are extracted.

[1273] Specific operation: The server parses the received JSON data and accesses the database to search for the required information.

[1274] Step 4:

[1275] The server retrieves the size chart of the brand in question and the size charts of other brands to be compared from the database.

[1276] Input: Brand and size information.

[1277] Output: Size chart of the brand, size chart of the brand to compare.

[1278] Specific operation: The server searches an internal database to obtain the size chart of brand B that corresponds to size M of brand A.

[1279] Step 5:

[1280] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A.

[1281] Input: Brand A size chart, Brand B size chart, medical examination data.

[1282] Output: Brand B appropriate size.

[1283] Specific operation: The server uses an AI algorithm to calculate the appropriate size of brand B corresponding to size M of brand A, taking into account the user's health check data.

[1284] Step 6:

[1285] Emotion recognition means implemented in the terminal or server is used to analyze the user's facial expressions and voice and obtain emotion data.

[1286] Input: User's facial expression data, voice data.

[1287] Output: User emotion data.

[1288] Specific operation: Emotion recognition software installed on the device or server uses the camera and microphone to analyze the user's facial expressions and voice in real time and obtain emotional data.

[1289] Step 7:

[1290] The server uses the sentiment data to recalculate and recommend a more appropriate size.

[1291] Input: Brand B's initial size recommendation, sentiment data.

[1292] Output: Final recommended size for Brand B taking sentiment into account.

[1293] What it does: The server re-runs the AI ​​algorithm, taking into account the emotional data, and recommends a more accurate size.

[1294] Step 8:

[1295] The server converts the calculated result into JSON format and sends a response to the terminal.

[1296] Input: Final recommended size.

[1297] Output: The result data converted to JSON format.

[1298] Specific operation: The server converts the calculation result into JSON format and sends it to the terminal.

[1299] Step 9:

[1300] The terminal visually displays the received results to the user.

[1301] Input: JSON formatted result data sent from the server.

[1302] Output: The recommended size that will be displayed to the user.

[1303] Specific behavior: The application analyzes the received result data and visually displays to the user, "Brand B, size L is recommended."

[1304] 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.

[1305] 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.

[1306] 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.

[1307] [Fourth embodiment]

[1308] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1309] 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.

[1310] 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).

[1311] 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.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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."

[1321] ---

[1322] The present invention is a system that allows a user to input the brand and size of clothing currently worn and health checkup data, and then recommends appropriate sizes for other brands based on the input data. Specifically, the system includes a data input means, a communication means, a size recommendation means, and a result display means. Each means will be described in detail below.

[1323] User data entry

[1324] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a T-shirt (size M) from "Brand A," they enter "Brand A" and "Size M." They can also add their chest circumference, waist circumference, and weight as health checkup data. This provides the system with the user's latest body shape information.

[1325] Data transmission by the terminal

[1326] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[1327] Data reception and analysis by the server

[1328] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to compare.

[1329] Calculations based on size recommendations

[1330] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[1331] Sending and displaying calculation results

[1332] The server sends the calculated results to the device, which receives the results and displays them visually to the user. Specifically, the device displays information such as "Brand B, size L is recommended" on the screen.

[1333] Specific examples

[1334] Specifically, let's say a user is wearing a T-shirt (size M) from brand A and enters their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This data is sent to the server, which calculates the size of brand B that corresponds to size M from brand A. Using brand B's size chart and an AI algorithm, the appropriate size (for example, size L) is derived. The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[1335] The system according to the present invention thus enables consumers to instantly purchase clothes of the right size online, which contributes to preventing customers from abandoning their purchases and reducing mass waste for businesses.

[1336] The processing flow will be explained below.

[1337] ---

[1338] Step 1:

[1339] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter health checkup data such as chest circumference, waist circumference, and weight.

[1340] Step 2:

[1341] The device receives the data entered by the user. The device converts the entered data into JSON format. For example, { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70}.

[1342] Step 3:

[1343] The device checks the data for abnormal values, for example, to make sure there are no inconsistencies in the entered size or health check data. If there are no abnormalities, it creates an API request to send the data.

[1344] Step 4:

[1345] The device sends a data transmission request to the server, and uses the communication method to send the JSON data mentioned above to the server.

[1346] Step 5:

[1347] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information and health check data.

[1348] Step 6:

[1349] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[1350] Step 7:

[1351] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. The AI ​​algorithm uses size conversion logic between different brands and compares the size standards of each brand.

[1352] Step 8:

[1353] The server fine-tunes the size by taking into account health check data (chest circumference, waist circumference, weight, etc.). For example, if the user's weight has increased, it will recommend a larger size than the calculated size.

[1354] Step 9:

[1355] The server converts the calculation results into JSON format and sends a response to the device, which includes the recommended size for brand B.

[1356] Step 10:

[1357] The device analyzes the response received from the server, obtains the recommended size (e.g., brand B, size L), and displays it to the user.

[1358] Step 11:

[1359] The device will visually display a size recommendation to the user, for example, "Brand B, size Large is recommended for you," and may also provide a link to purchase.

[1360] ---

[1361] The above are the specific processing steps, which allow users to easily select clothes of the appropriate size online.

[1362] Example 1

[1363] 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."

[1364] Conventional clothing purchasing systems require users to take the time to compare sizes across different brands, making it difficult to select the appropriate size. Furthermore, they are unable to recommend sizes that take into account changes in body shape, increasing the risk of purchasing clothing of an inappropriate size. Furthermore, since they lack a size adjustment function based on health checkup data, it is difficult to accurately recommend a size that fits the user's actual body shape. Therefore, there is a need for a system that can improve consumer satisfaction while reducing purchase abandonment and return rates.

[1365] 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.

[1366] In this invention, the server includes a data input means for inputting the brand and size of clothing currently being worn, a data input means for inputting health checkup data, a data conversion means for converting the input data into JSON format, a communication means for transmitting the converted data to the server, a data analysis means for analyzing the received data and obtaining a size chart for the corresponding brand, a size recommendation means for obtaining a size chart for a different brand and calculating an appropriate size for the other brand using an AI algorithm, a data transmission means for converting the calculated size into JSON format and transmitting it to the terminal, and a result display means for displaying the calculated size on the terminal. This allows users to easily and quickly compare and receive recommendations for appropriate sizes across different brands, significantly reducing the effort required for choosing a size and enabling them to purchase the optimal size that accommodates changes in body shape.

[1367] "Data input means" refers to a user interface or input device for inputting the brand and size of the clothing currently worn by the user, as well as health checkup data.

[1368] "Data conversion means" refers to software or algorithms that convert user-entered data into a specific data format, such as JSON format, for efficient processing.

[1369] "Communication Means" refers to the internet connection or other data transmission technology used to transmit the entered data to the server.

[1370] "Data analysis means" refers to software or algorithms that analyze the data received by the server and extract the necessary information.

[1371] "Size recommendation means" refers to a process or device that retrieves size charts of different brands from a database and calculates the appropriate size using an AI algorithm based on user input data.

[1372] "Data Transmission Means" refers to an Internet connection or other data transmission technology for transmitting the calculated results to the Terminal.

[1373] "Result display means" refers to a display device or UI software for visually displaying the calculated size information on the user's terminal.

[1374] The present invention is a system that quickly and accurately recommends suitable clothing sizes for users across different brands. The system includes a data input means, a data conversion means, a communication means, a data analysis means, a size recommendation means, a data transmission means, and a result display means. Specific embodiments are described below.

[1375] User data entry

[1376] Using a dedicated smartphone app, users input the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). For example, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. This input is done using the user interface within the app. The app used is called "ClothFit" and is designed for efficient data input.

[1377] Data Conversion and Communication

[1378] The terminal converts the data entered by the user into JSON format and sends it to the server over the Internet. The data is converted as follows:

[1379] json

[1380] {

[1381] "brand": "Brand A",

[1382] "size": "M",

[1383] "chest": 90,

[1384] "waist": 80,

[1385] "weight": 70

[1386] }

[1387] The communication methods used are Wi-Fi and mobile data (4G / 5G), which ensures fast and secure data transmission.

[1388] Data reception and analysis

[1389] The server receives the JSON-formatted data sent from the device and parses it. Through this analysis, the brand name, size, and medical examination data are extracted. The server accesses the database to obtain the size chart for the target brand (Brand A) and also obtains size charts for other brands (Brand B). The database systematically manages size information.

[1390] Size recommendation calculation

[1391] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. The calculation is based on the user's health check data (chest circumference, waist circumference, and weight). For example, if the user has gained weight, a larger size (size L) is recommended. Specifically, the AI ​​model compares each brand's size chart with the user's data to determine the optimal size.

[1392] Sending and displaying calculation results

[1393] The calculation results are converted back to JSON format and sent to the device. The device analyzes this data and displays the results to the user through a user interface. Specifically, the device displays "Brand B, size L is recommended," along with the reason for this recommendation. The display is achieved using Flutter, a UI framework for smartphone apps.

[1394] Specific examples

[1395] As a specific example of how this works, consider the case where a user is wearing a T-shirt (size M) from "Brand A" and inputs their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. This data is sent to the server, which uses an AI algorithm to calculate the appropriate size for Brand B. The recommended result is Brand B in size L, which is displayed on the screen of the "ClothFit" app.

[1396] Prompt Sentence Examples

[1397] Here are some example prompts to apply to generative AI models:

[1398] I wear a T-shirt (size M) from "Brand A," and I have a chest circumference of 90cm, a waist circumference of 80cm, and weigh 70kg. What size T-shirt from "Brand B" is appropriate?

[1399] The system of the present invention allows users to quickly and accurately select clothing of the appropriate size online, contributing to improved consumer satisfaction and preventing companies from abandoning their purchases.

[1400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] The user launches a dedicated smartphone app and inputs the brand and size of the clothing they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, and weight). Examples of input data include "Brand A T-shirt (size M)," chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg. When the user presses the "Submit" button, the data proceeds to the next step.

[1403] input:

[1404] Data entered by the user into the app (brand, size, chest circumference, waist circumference, weight)

[1405] output:

[1406] The input data is retained within the app.

[1407] Step 2:

[1408] The terminal will convert the input data into JSON format, which will look something like this:

[1409] json

[1410] {

[1411] "brand": "Brand A",

[1412] "size": "M",

[1413] "chest": 90,

[1414] "waist": 80,

[1415] "weight": 70

[1416] }

[1417] The converted data is sent to the server via Wi-Fi or mobile data (4G / 5G).

[1418] input:

[1419] Data stored within the app (brand, size, chest circumference, waist circumference, weight)

[1420] output:

[1421] The data converted to JSON format is sent to the server.

[1422] Step 3:

[1423] The server receives the JSON-formatted data sent from the device. It parses the received data and extracts the brand name, size, and medical examination data. Based on the extracted data, the server accesses the database to retrieve the size chart for the target brand (Brand A) and the size chart for the comparison brand (Brand B).

[1424] input:

[1425] JSON format data sent from the device

[1426] output:

[1427] Analyzed data and the brand size chart obtained based on it

[1428] Step 4:

[1429] The server uses AI algorithms such as TensorFlow and PyTorch to calculate the appropriate size of brand B that corresponds to size M of brand A. At this time, the server also takes into account the user's health check data, and if, for example, the user has gained weight, it will recommend a larger size (size L).

[1430] input:

[1431] Extracted brand information, health check data, and size chart

[1432] output:

[1433] Calculated appropriate size from other brands (e.g. L size)

[1434] Step 5:

[1435] The calculation result is converted back to JSON format and sent to the terminal. The converted data has the following format:

[1436] json

[1437] {

[1438] "recommendedSize": "L",

[1439] "reason": "Based on your chest, waist, and weight data, it was determined that brand B, size L, would be appropriate."

[1440] }

[1441] This data transmission is also done via Wi-Fi or mobile data communication.

[1442] input:

[1443] Calculated size information for other brands

[1444] output:

[1445] Sending data converted to JSON format

[1446] Step 6:

[1447] The device analyzes the JSON-formatted data received from the server. Based on the analyzed data, the recommended size (brand B, size L) and the reason for the recommendation are visually displayed to the user. For example, the smartphone screen may display "Brand B, size L is recommended" along with an explanation of the reason.

[1448] input:

[1449] JSON format data received from the server

[1450] output:

[1451] What size recommendations do users see and why?

[1452] This series of processes allows users to easily select clothing of the appropriate size from different brands, resulting in a more satisfying online shopping experience.

[1453] (Application example 1)

[1454] 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."

[1455] In traditional online shopping, it is difficult for users to find the right size clothing. This often leads to poor user experiences and dissatisfaction due to returns or incorrect sizes. Furthermore, inconsistent size labels across different brands make it even more difficult for users to find the right size. This also causes inventory management issues and increased costs for businesses. Therefore, there is a need for a system that allows users to easily find the right size.

[1456] 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.

[1457] In this invention, the server includes a data input means for inputting the brand and size of the clothing currently being worn, a data input means for inputting health checkup data, a communication means for receiving the input data, a size recommendation means for calculating the appropriate size for other brands based on the received data, a result display means for outputting the calculated size, and an interface means for linking with a smartphone application. This allows users to easily find clothing in their correct size, improving the online shopping experience. It also helps companies reduce costs associated with returns and inventory management.

[1458] "Data input means" refers to a device or interface for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[1459] The "communication means" is a means for transmitting data received from the data input means to an external system such as a server. An internet connection is generally used.

[1460] "Size recommendation means" refers to a device or program for calculating the appropriate size of other brands based on received data, including the function of obtaining size charts of different brands and performing calculations using an AI algorithm.

[1461] The "result display means" is a device or interface for visually displaying the calculated recommended size to the user.

[1462] "Smartphone application" refers to application software that runs on a smartphone device and provides an interface that works in conjunction with each function of this system.

[1463] An "AI algorithm" is a calculation method or program that uses artificial intelligence technology to analyze data and derive optimal results.

[1464] A "database" is a storage device that stores size charts and other related data for different brands and is accessible by the server.

[1465] "Health checkup data" refers to the user's body measurement information such as chest circumference, waist circumference, and weight, which are important parameters for size recommendations.

[1466] An "interface means" is a device or software for transmitting and receiving data between a smartphone application and another system or device.

[1467] This invention is a system that recommends appropriate sizes for other brands of clothing based on the brand and size of the clothing currently worn by a user and health checkup data. The system includes a data input means, a communication means, a size recommendation means, a result display means, and an interface means that works in conjunction with a smartphone application.

[1468] Configuration and Functions

[1469] 1. Data entry method

[1470] The user inputs the brand and size of the clothes they are currently wearing, as well as their health checkup data (chest circumference, waist circumference, weight, etc.) This data input is done through a smartphone application.

[1471] 2. Means of communication

[1472] The data entered by the user into the application is converted into JSON format and then sent to the server via a communication method, which is usually via the Internet.

[1473] 3. Size Recommendation Method

[1474] The server analyzes the received data and extracts brand and size information. It then accesses a database to retrieve the size chart for the current brand and the size chart for other brands (target brands). The server then uses an AI algorithm to calculate the optimal size.

[1475] 4. Results display means

[1476] The calculated results are sent from the server to a smartphone application, which then visually displays the recommended size to the user.

[1477] 5. Interface Methods

[1478] The smartphone application sends the data entered by the user to the server and generates a prompt sentence that calculates the recommended size.

[1479] Hardware and Software

[1480] Server: Hardware that runs the data analysis and size recommendation algorithms, using a web framework such as Flask.

[1481] Smartphone: A device for data entry and display of results, using a mobile application framework such as React Native.

[1482] Specific examples

[1483] For example, a user enters that they are wearing a "medium" size T-shirt from "brand A," and enters health checkup data showing a chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg. The smartphone application sends this data to the server, which uses an AI algorithm to calculate the appropriate size (e.g., size L) for "brand B." The result is sent back to the smartphone application, which displays to the user, "Brand B size L is recommended."

[1484] Prompt Sentence Examples

[1485] "Based on the following data, please recommend the appropriate size of brand B that corresponds to brand A's size M. My chest circumference is 90cm, my waist circumference is 80cm, and I weigh 70kg."

[1486] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1487] Step 1: Data entry

[1488] Using a smartphone application, users input the brand and size of the clothes they are currently wearing, as well as health checkup data (chest circumference, waist circumference, weight, etc.). The input data is collected by a data input means within the application.

[1489] Input: Brand name, clothing size, chest circumference, waist circumference, weight

[1490] Output: Data entered into the application (brand name, clothing size, medical examination data)

[1491] Step 2: Send data

[1492] The device converts the data entered by the user into JSON format and sends it to the server via a communication method that utilizes an internet connection.

[1493] Input: Data entered into the application (brand name, clothing size, medical examination data)

[1494] Output: JSON formatted data sent to the server

[1495] Step 3: Data reception and analysis

[1496] The server receives the JSON-formatted data sent from the device and parses it, extracting the brand name, clothing size, and health checkup data.

[1497] Input: JSON format data received from the terminal

[1498] Output: Analyzed data (brand name, clothing size, medical checkup data)

[1499] Step 4: Get the size chart

[1500] Based on the analyzed data, the server retrieves the size chart for the relevant brand (current brand) from the database, and then retrieves the size charts for other brands (target brands) to be compared.

[1501] Input: Parsed data (brand name, clothing size)

[1502] Output: Size chart retrieved from the database (current brand and target brand)

[1503] Step 5: Size Calculation

[1504] The server uses the acquired size chart and health check data to calculate the optimal size using an AI algorithm. For example, it will recommend the appropriate size of brand B that corresponds to a size M of brand A based on chest, waist, and weight.

[1505] Input: Size chart (current brand and target brand), medical examination data

[1506] Output: Calculated recommended size

[1507] Step 6: Sending the calculation results

[1508] The server then sends the calculated recommended size to the device, and this communication also takes place over the Internet.

[1509] Input: Calculated preferred size

[1510] Output: Recommended size data sent to the device

[1511] Step 7: View the results

[1512] The device receives the recommended size data from the server and visually displays it to the user. The smartphone application presents information to the user such as "Brand B, size L is recommended."

[1513] Input: Recommended size data received from the server

[1514] Output: A visual recommendation of the size to the user

[1515] Through these steps, users can easily find the size of other brands that suits them.

[1516] 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.

[1517] ---

[1518] The present invention is a system that combines a system that inputs the brand and size of the clothing a user currently wears and their health checkup data, and then recommends appropriate sizes for other brands based on that input, with an emotion engine that recognizes the user's emotions. Specifically, this system includes a data input means, a communication means, a size recommendation means, a result display means, and an emotion engine. Each means will be described in detail below.

[1519] User data entry

[1520] The user enters the brand and size of the clothing they are currently wearing through the application. For example, if the user is wearing a "medium-sized T-shirt from brand A," they enter "brand A" and "medium size." They also enter health checkup data such as chest circumference, waist circumference, and weight. This data provides the system with the user's latest body shape information.

[1521] Data transmission by the terminal

[1522] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection.

[1523] Data reception and analysis by the server

[1524] The server receives the data sent from the device. It then analyzes this data and extracts brand and size information, as well as health check data. The server accesses a database to retrieve the size chart for the relevant brand (Brand A) and also retrieves the size chart for another brand (Brand B) to be compared.

[1525] Calculations based on size recommendations

[1526] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. If the user has provided health checkup data, this data is also taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[1527] Analysis by emotion engine

[1528] The system also includes an emotion engine that analyzes the user's emotions from facial expressions, voice, or text input. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothing item they are considering purchasing. This emotion data is also reflected in the size recommendation tool, resulting in more accurate size recommendations. For example, if the user is feeling stressed, the system will prioritize suggested sizes that fit well and help them relax.

[1529] Sending and displaying calculation results

[1530] The server converts the calculated result into JSON format and sends a response to the device. The response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B size L is recommended."

[1531] Specific examples

[1532] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[1533] The system of this invention allows consumers to instantly purchase clothes in the right size online, which helps companies prevent abandonment and reduce mass waste. Furthermore, by incorporating an emotion engine, it becomes possible to recommend sizes that take into account the user's emotional state, further improving customer satisfaction.

[1534] The processing flow will be explained below.

[1535] ---

[1536] Step 1:

[1537] The user starts the application and enters the brand and size of the clothing they are currently wearing. For example, if the user is wearing a "Brand A T-shirt, size M," they enter "Brand A" and "Size M." They also enter their health checkup data: chest circumference 90 cm, waist circumference 80 cm, and weight 70 kg.

[1538] Step 2:

[1539] The terminal receives data entered by the user and converts it into JSON format.

[1540] Step 3:

[1541] The emotion engine uses a camera and microphone to capture the user's facial expressions and voice in order to analyze the user's emotions. For example, the camera can detect emotions from the user's smile or voice.

[1542] Step 4:

[1543] The device receives the emotion data analyzed by the emotion engine and adds it to the JSON data. For example, it adds "Excitement level: High" as emotion data.

[1544] Step 5:

[1545] The device checks for abnormal values, confirming that there are no abnormalities in the input data and emotion data, and if there are no abnormalities, creates an API request to send the data.

[1546] Step 6:

[1547] The device sends a data transmission request to the server using a communication method, for example, in the format { "brand": "Brand A", "size": "M", "chest": 90, "waist": 80, "weight": 70, "emotion": "excited"}.

[1548] Step 7:

[1549] The server receives the data sent from the terminal, analyzes the received data, and extracts brand and size information, health check data, and emotion data.

[1550] Step 8:

[1551] The server accesses the database and retrieves the size chart for the input brand (Brand A), as well as the size chart for other brands to be compared (e.g., Brand B).

[1552] Step 9:

[1553] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A. For example, the AI ​​calculates the size of brand B that corresponds to size M of brand A.

[1554] Step 10:

[1555] The server will fine-tune the size based on your health check data (chest circumference, waist circumference, weight, etc.). For example, if you have gained weight, it will recommend a larger size.

[1556] Step 11:

[1557] The server takes emotional data into account to further refine the size recommendation: for example, if the user is highly excited, it may recommend a size that fits slightly better than the normal size.

[1558] Step 12:

[1559] The server converts the final calculated recommended size into JSON format and sends a response to the device, which may include, for example, "Brand B, size L."

[1560] Step 13:

[1561] The device analyzes the response received from the server and displays the recommended size to the user, for example, a message saying "Brand B size L is suitable for you."

[1562] Step 14:

[1563] The device can provide users with recommended sizes and related information through a visual interface, as well as purchase links.

[1564] ---

[1565] The above are the specific processing steps and the operations at each step. This process allows users to quickly and accurately receive appropriate size recommendations for different brands, taking into account their emotional data. It also helps companies prevent customer abandonment and improve the efficiency of inventory management.

[1566] Example 2

[1567] 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."

[1568] In conventional online shopping, it was difficult for users to choose the right size for their body type, and it was not possible to select the optimal size that reflected the user's emotional state. This resulted in returns and exchanges after purchase, which led to poor user experience and a large number of returns, which became a challenge for companies.

[1569] 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 a data input means for inputting the brand and size of the clothes currently being worn, a data input means for inputting medical examination data, a communication means for receiving the input data, a size recommendation means for calculating an appropriate size for another brand based on the received data, an emotion engine for analyzing the user's emotion data, a size recommendation means for adjusting the recommended size using the emotion data, and a result display means for outputting the calculated size. This makes it possible to recommend not only an appropriate size that suits the user's body type but also a size recommendation that takes the user's emotional state into consideration.

[1570] The "data input means" is a means for inputting the brand and size of the clothes the user is currently wearing, and health checkup data.

[1571] The "communication means" is a means for transmitting input data from a terminal to a server and receiving a response from the server at the terminal.

[1572] "Size recommendation tool" means a tool for calculating the appropriate size of other brands based on the received data, characterized by the use of an AI algorithm.

[1573] The "emotion engine" is a means for analyzing the user's emotional data and adjusting the recommended size based on the results.

[1574] The "result display means" is a means for visually displaying the calculated recommended size to the user.

[1575] "Health checkup data" refers to physical data such as the user's chest circumference, waist circumference, and weight.

[1576] The present invention is a system that combines a system that inputs the brand and size of the clothing currently worn by the user and health check data, and recommends appropriate sizes for other brands based on the input, with an emotion engine that recognizes the user's emotions. Detailed embodiments for implementing the present invention will be described below.

[1577] User data entry

[1578] The user inputs the brand and size of the clothing they are currently wearing through an application installed on a smartphone or tablet. For example, if the user is wearing a "medium-sized T-shirt from brand A," they input "brand A" and "medium size." They also input health checkup data such as chest circumference, waist circumference, and weight in the same way. This data provides the system with the user's latest body shape information. The hardware used includes a smartphone or tablet, and the software used includes a dedicated application.

[1579] Data transmission by the terminal

[1580] The device receives the data entered by the user, converts it into JSON format, and then sends it to the server via a communication method that uses an internet connection, such as Wi-Fi or mobile data.

[1581] Data reception and analysis by the server

[1582] The server receives the data sent from the device. This data includes brand, size information, and health check data. It analyzes the received data and extracts the necessary information. The server then accesses a database to retrieve the size chart for the relevant brand (e.g., Brand A). It also retrieves size charts for other brands to be compared (e.g., Brand B). The software used includes a database (e.g., MySQL) and an AI engine for analysis (e.g., TensorFlow).

[1583] Calculations based on size recommendations

[1584] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. If the user has provided health checkup data, this data will also be taken into account when adjusting the size. For example, if the user has gained weight, a larger size will be recommended.

[1585] Analysis by emotion engine

[1586] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data. It reads emotions from the user's facial expressions, voice, and text, and adjusts the recommended size based on that information. Emotional data includes the user's level of excitement, satisfaction, and anxiety about the clothes they are considering purchasing. For example, if the user is feeling stressed, it will prioritize suggesting sizes that fit well and are relaxing.

[1587] Sending and displaying calculation results

[1588] Once the calculation is complete, the server converts the result back into JSON format and sends it as a response to the device. This response includes the recommended size for brand B. The device receives this result and displays it visually to the user. For example, it displays a message on the screen saying, "Brand B, size L is recommended."

[1589] Specific examples

[1590] Specifically, if a user is wearing a T-shirt (size M) from brand A and has entered their chest circumference of 90 cm, waist circumference of 80 cm, and weight of 70 kg, this data is sent to the server. The server calculates the size of brand B that corresponds to size M from brand A, and then uses an emotion engine to analyze the user's emotional data. If the user is excited about the clothes they are considering purchasing, that information is taken into account to calculate the appropriate size (for example, size L). The result is sent to the device, and the message "Brand B size L is recommended" is displayed.

[1591] This system will enable users to easily find clothing of the right size online, and is expected to help companies reduce return rates and improve customer satisfaction.

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1: User Data Entry

[1594] The user opens the application installed on their smartphone or tablet and inputs the brand and size of the clothes they are currently wearing. They also input health checkup data such as chest circumference, waist circumference, and weight. The input data includes the brand name (e.g., "Brand A"), size (e.g., "Size M"), and physical data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg). This provides the system with the user's latest body shape information.

[1595] Specific behavior:

[1596] The user launches the app and enters the required information.

[1597] Input: clothing brand, size, medical examination data

[1598] Output: A set of user input data (brand name, size, body data)

[1599] Step 2: Send data by device

[1600] The device receives data entered by the user, converts it into JSON format, and then sends the converted data to the server via a communication method that utilizes an internet connection.

[1601] Specific behavior:

[1602] Converting user-supplied data into JSON format

[1603] Send data to a server using your internet connection

[1604] Input: A set of user-entered data

[1605] Output: Data converted to JSON format

[1606] Step 3: Data reception and analysis by the server

[1607] The server receives the JSON-formatted data sent from the device, parses it, and extracts the brand name, size information, and medical examination data. It also retrieves the size charts for the relevant brands (Brand A and Brand B for comparison) from the database.

[1608] Specific behavior:

[1609] Receiving JSON format data

[1610] Analyze the data and extract the necessary information

[1611] Retrieving size charts from the database

[1612] Input: JSON format data

[1613] Output: Extracted data (brand name, size information, medical examination data), size chart

[1614] Step 4: Server performs sizing recommendations

[1615] The server uses an AI algorithm based on the received data and the retrieved size chart to calculate the appropriate size of brand B corresponding to the size of brand A. It also takes into account health checkup data, for example, recommending a larger size if the user has gained weight.

[1616] Specific behavior:

[1617] Calculate data using AI algorithms

[1618] Taking health check data into account

[1619] Input: Extracted data, size chart

[1620] Output: Brand B appropriate size

[1621] Step 5: Running the Emotion Engine on the Server

[1622] The server uses an emotion engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotional data, reads the user's emotions from facial expressions, voice, and text input, and adjusts the recommended size based on that information.

[1623] Specific behavior:

[1624] Analyzing Emotional Data

[1625] Adjust size recommendations based on sentiment data

[1626] Input: User emotion data

[1627] Output: Adjusted preferred size

[1628] Step 6: Server sends the calculation results

[1629] The server converts the calculation result into JSON format and sends it to the device as a response, which includes the recommended size.

[1630] Specific behavior:

[1631] Convert the calculation results to JSON format

[1632] Send JSON format data to the terminal

[1633] Input: Adjusted preferred size

[1634] Output: Calculation results in JSON format

[1635] Step 7: Displaying the results on the terminal

[1636] The device analyzes the calculation results received from the server and visually displays them to the user, for example, a message saying "Brand B, size L is recommended."

[1637] Specific behavior:

[1638] Receive a response from the server

[1639] Parse the response data and display it to the user

[1640] Input: Calculation result in JSON format

[1641] Output: Message to display to the user

[1642] (Application example 2)

[1643] 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."

[1644] Conventional clothing size recommendation systems recommend sizes based on the user's physical data, but because they do not take into account the user's emotions or psychological state, they may not always recommend the optimal size. In particular, when a user is purchasing clothing from a new brand or design on an online shopping site, their emotions, expectations, and anxieties about the clothing can significantly influence their size selection. For this reason, there is a need for more accurate size recommendations that also take into account the user's emotions.

[1645] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for inputting the brand and size of the currently worn clothing, a data input means for inputting health checkup data, an emotion recognition means for inputting and analyzing facial expressions and voice to recognize the user's emotions, a communication means for receiving the input data, a size recommendation means for calculating appropriate sizes for other brands based on the received data, an emotion-reflecting size recommendation means for inputting data from the emotion recognition means to the size recommendation means and recommending an appropriate size taking the emotion data into consideration, and a result display means for outputting the calculated size. This enables highly accurate size recommendations that take into consideration not only the user's physical data but also their emotion data.

[1646] "Data input means" refers to a device or software for inputting the brand and size of the clothes the user is currently wearing and health checkup data.

[1647] "Communication means" refers to a network communication device or protocol for transmitting data entered by a user to a server.

[1648] The "size recommendation means" is a device or software that has the function of calculating the appropriate size of other brands based on the received data and recommending it to the user.

[1649] A "result display means" is a display device or interface for visually displaying the recommended size to the user.

[1650] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and voice and acquiring the user's emotional data.

[1651] The "emotion-reflecting size recommendation means" is a device or software that has the function of recommending a more appropriate size using emotion data acquired by the emotion recognition means.

[1652] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze received data and calculate the optimal size.

[1653] The "database" is an information management system for storing and managing size information and health checkup data for each brand.

[1654] The present invention is a system that combines a system that inputs the brand and size of the clothing a user is currently wearing and health checkup data, and then recommends appropriate sizes for other brands based on that information, with an emotion engine that recognizes the user's emotions.

[1655] System Overview

[1656] The system consists of the following main components:

[1657] 1. Data entry method

[1658] It is a way for users to input the brand and size of the clothes they are currently wearing, as well as health checkup data such as the user's chest circumference, waist circumference, and weight.

[1659] 2. Means of communication

[1660] The terminal receives data entered by the user, converts this data into JSON format, and sends it to a server over the Internet.

[1661] 3. Size Recommendation Method

[1662] The server uses AI algorithms to calculate the appropriate size for other brands based on the data received.

[1663] 4. Results display means

[1664] This is a means to visually display the calculated size to the user, for example, by showing the recommended size on the display of a smartphone.

[1665] 5. Emotion recognition means

[1666] This is a means of analyzing the user's facial expressions and voice to obtain emotional data about the user, which can then be used to read the user's feelings (excitement, anxiety, etc.) about the clothes they are considering purchasing.

[1667] 6. Recommended emotional reflection size

[1668] It has the ability to recommend more appropriate sizes using the acquired emotional data, enabling highly accurate size recommendations that take user emotions into account.

[1669] Program processing overview

[1670] The overall system makes size recommendations based on user input and also takes into account user sentiment data in the process. Details are provided below.

[1671] 1. User data entry

[1672] Through the application, users input the brand and size of the clothes they are currently wearing, as well as their health check data, and this information is stored on the device.

[1673] 2. Data transmission by the terminal

[1674] The device converts the data entered by the user into JSON format and sends it to the server using a communication method that uses the Internet.

[1675] 3. Data reception and analysis by the server

[1676] The server receives and analyzes the data sent from the device, retrieves brand size charts from the database, and uses AI algorithms to calculate the appropriate size for other brands.

[1677] 4. Emotion analysis using an emotion engine

[1678] Emotion recognition is implemented on the device or server to analyze the user's facial expressions and voice to obtain emotional data, which is then reflected in the size recommendation.

[1679] 5. Displaying the calculation results

[1680] The server converts the calculated results into JSON format and sends a response to the device, which receives the results and displays them visually to the user.

[1681] Specific examples

[1682] Suppose a user is wearing a T-shirt (size M) from brand A, and has entered their chest circumference of 90cm, waist circumference of 80cm, and weight of 70kg. The emotion engine also recognizes that the user is excited about the brand they are considering purchasing. When this data is sent to the server, the server calculates the size of brand B that corresponds to size M from brand A, and recommends the optimal size (for example, size L) taking into account the emotion data. The device displays the message "Brand B size L is recommended."

[1683] Prompt Sentence Examples

[1684] By inputting prompt sentences like the following into the generative AI model, the accuracy of the system's size recommendations can be improved.

[1685] "The user is wearing a UNIQLO medium shirt, and the data indicates that the chest circumference is 90cm, waist circumference is 80cm, and weight is 70kg. The emotion engine then analyzes the user's facial expression and determines that the user is excited. Based on this data, the system will recommend appropriate sizes for other brands, and the result will be a program that recommends a large size from brand B."

[1686] The above is a specific embodiment of the present invention.

[1687] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1688] Step 1:

[1689] The user inputs the brand and size of the clothes they are currently wearing and their health check data.

[1690] Input: Brand (e.g., Brand A) and size (e.g., size M) of clothing currently worn by the user, and health check data (e.g., chest circumference 90 cm, waist circumference 80 cm, weight 70 kg).

[1691] Output: The input data is saved in the application.

[1692] Specific operation: The user enters each item into the input form on the application's data input screen and presses the "Submit" button.

[1693] Step 2:

[1694] The terminal receives input data, converts it into JSON format, and sends it to the server.

[1695] Input: Data entered by the user.

[1696] Output: The data converted to JSON format is sent to the server.

[1697] Specific operation: The application receives user input data, converts it to JSON format within the program, and then sends the data to the server over the Internet.

[1698] Step 3:

[1699] The server parses the received data and extracts brand and size information and medical examination data.

[1700] Input: JSON format data sent from the terminal.

[1701] Output: Brand and size information, medical examination data are extracted.

[1702] Specific operation: The server parses the received JSON data and accesses the database to search for the required information.

[1703] Step 4:

[1704] The server retrieves the size chart of the brand in question and the size charts of other brands to be compared from the database.

[1705] Input: Brand and size information.

[1706] Output: Size chart of the brand, size chart of the brand to compare.

[1707] Specific operation: The server searches an internal database to obtain the size chart of brand B that corresponds to size M of brand A.

[1708] Step 5:

[1709] The server uses an AI algorithm to calculate the appropriate size of brand B that corresponds to size M of brand A.

[1710] Input: Brand A size chart, Brand B size chart, medical examination data.

[1711] Output: Brand B appropriate size.

[1712] Specific operation: The server uses an AI algorithm to calculate the appropriate size of brand B corresponding to size M of brand A, taking into account the user's health check data.

[1713] Step 6:

[1714] Emotion recognition means implemented in the terminal or server is used to analyze the user's facial expressions and voice and obtain emotion data.

[1715] Input: User's facial expression data, voice data.

[1716] Output: User emotion data.

[1717] Specific operation: Emotion recognition software installed on the device or server uses the camera and microphone to analyze the user's facial expressions and voice in real time and obtain emotional data.

[1718] Step 7:

[1719] The server uses the sentiment data to recalculate and recommend a more appropriate size.

[1720] Input: Brand B's initial size recommendation, sentiment data.

[1721] Output: Final recommended size for Brand B taking sentiment into account.

[1722] What it does: The server re-runs the AI ​​algorithm, taking into account the emotional data, and recommends a more accurate size.

[1723] Step 8:

[1724] The server converts the calculated result into JSON format and sends a response to the terminal.

[1725] Input: Final recommended size.

[1726] Output: The result data converted to JSON format.

[1727] Specific operation: The server converts the calculation result into JSON format and sends it to the terminal.

[1728] Step 9:

[1729] The terminal visually displays the received results to the user.

[1730] Input: JSON formatted result data sent from the server.

[1731] Output: The recommended size that will be displayed to the user.

[1732] Specific behavior: The application analyzes the received result data and visually displays to the user, "Brand B, size L is recommended."

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] 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.

[1738] 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.

[1739] 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).

[1740] 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.

[1741] 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."

[1742] 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.

[1743] 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).

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] The following is further disclosed regarding the above embodiment.

[1755] (Claim 1)

[1756] a data entry means for entering the brand and size of clothing currently being worn;

[1757] a data input means for inputting health checkup data;

[1758] a communication means for receiving the input data;

[1759] a size recommendation means for calculating suitable sizes for other brands based on the received data;

[1760] a result display means for outputting the calculated size;

[1761] A system including:

[1762] (Claim 2)

[1763] The system according to claim 1, characterized in that the size recommendation means includes means for retrieving size charts of a plurality of different brands from a database and performing calculations using an AI algorithm.

[1764] (Claim 3)

[1765] 2. The system of claim 1, wherein the medical examination data includes chest circumference, waist circumference, and weight.

[1766] "Example 1"

[1767] (Claim 1)

[1768] a data entry means for entering the brand and size of clothing currently being worn;

[1769] a data input means for inputting health checkup data;

[1770] a data conversion means for converting the input data into a JSON format;

[1771] a communication means for transmitting the converted data to a server;

[1772] a data analysis means for analyzing the received data and obtaining a size chart for the corresponding brand;

[1773] Size recommendation tool that takes size charts of different brands and uses AI algorithms to calculate the appropriate size for other brands;

[1774] a data transmission means for converting the calculated size into a JSON format and transmitting the converted size to a terminal;

[1775] a result display means for displaying the calculated size on a terminal;

[1776] A system including:

[1777] (Claim 2)

[1778] The system of claim 1, wherein the size recommendation means includes a process for calculating a size based on an AI algorithm using size charts of multiple different brands obtained from a database and the user's health checkup data.

[1779] (Claim 3)

[1780] 2. The system of claim 1, wherein the medical examination data includes chest circumference, waist circumference, and weight.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] a data entry means for entering the brand and size of clothing currently being worn;

[1784] a data input means for inputting health checkup data;

[1785] a communication means for receiving the input data;

[1786] a size recommendation means for calculating suitable sizes for other brands based on the received data;

[1787] a result display means for outputting the calculated size;

[1788] an interface means for interfacing with a smartphone application;

[1789] A system including:

[1790] (Claim 2)

[1791] The system according to claim 1, characterized in that the size recommendation means includes means for retrieving size charts of a plurality of different brands from a database and performing calculations using an AI algorithm.

[1792] (Claim 3)

[1793] 2. The system of claim 1, wherein the medical examination data includes chest circumference, waist circumference, and weight.

[1794] (Claim 4)

[1795] 2. The system according to claim 1, wherein the result display means visualizes the calculation results received from the server via the communication means and displays them to the user.

[1796] (Claim 5)

[1797] The system described in claim 1, characterized in that the smartphone application generates prompt text based on the brand and size of clothing currently worn by the user and health check data, sends the data to a server, and displays recommended sizes.

[1798] "Example 2: Combining Emotion Engines"

[1799] (Claim 1)

[1800] a data entry means for entering the brand and size of clothing currently being worn;

[1801] a data input means for inputting health checkup data;

[1802] a communication means for receiving the input data;

[1803] a size recommendation means for calculating suitable sizes for other brands based on the received data;

[1804] an emotion engine that analyzes user emotion data;

[1805] a size recommendation means for adjusting a recommended size using the emotion data;

[1806] a result display means for outputting the calculated size;

[1807] A system including:

[1808] (Claim 2)

[1809] The system according to claim 1, characterized in that the size recommendation means includes means for retrieving size charts of a plurality of different brands from a database and performing calculations using an AI algorithm.

[1810] (Claim 3)

[1811] 2. The system of claim 1, wherein the medical examination data includes chest circumference, waist circumference, and weight.

[1812] "Application example 2 when combining emotion engines"

[1813] (Claim 1)

[1814] a data entry means for entering the brand and size of clothing currently being worn;

[1815] a data input means for inputting health checkup data;

[1816] a communication means for receiving the input data;

[1817] a size recommendation means for calculating suitable sizes for other brands based on the received data;

[1818] a result display means for outputting the calculated size;

[1819] emotion recognition means for inputting and analyzing facial expressions and voice to recognize the emotions of a user;

[1820] The system includes emotion-reflecting size recommendation means for inputting data from the emotion recognition means into the size recommendation means, thereby recommending an appropriate size taking emotion data into consideration.

[1821] (Claim 2)

[1822] The system of claim 1, wherein the size recommendation means includes means for retrieving size charts of multiple different brands from a database and performing calculations using an AI algorithm, and further includes means for visually displaying the recommended size on a smartphone display.

[1823] (Claim 3)

[1824] 2. The system according to claim 1, wherein the health checkup data includes chest circumference, waist circumference, and weight, and the emotion recognition means analyzes the user's level of excitement and anxiety. [Explanation of symbols]

[1825] 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 data entry means for entering the brand and size of clothing currently being worn; a data input means for inputting health checkup data; a communication means for receiving the input data; a size recommendation means for calculating suitable sizes for other brands based on the received data; a result display means for outputting the calculated size; A system including:

2. The system according to claim 1, wherein the size recommendation means includes means for obtaining size charts of a plurality of different brands from a database and performing calculations using an AI algorithm.

3. 2. The system of claim 1, wherein the medical examination data includes chest circumference, waist circumference, and weight.

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