system

Abstract

Provide a system. [Solution] A means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size; means for receiving the input information; means for predicting future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; A system including:

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] Children grow rapidly, making it difficult to select the right size of clothes and shoes at the right time. Parents must frequently measure their children's sizes and purchase new clothes and shoes, which takes a lot of time and money. It is also difficult to predict the right size based on growth, resulting in an increase in the amount of disposable clothes and shoes. This increases the amount of clothing discarded, creating an environmental burden. Therefore, there is a need to develop a system that can predict children's growth and efficiently select the right size of clothes and shoes. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: A means for inputting information about a child's current height, weight, age, parents' heights, past time-series data, and foot size is provided. A means for receiving the input information and predicting future height, weight, and foot size using a growth curve is provided. A system is also provided that includes a means for calculating appropriate clothing and shoe sizes based on the predicted information. The system includes a means for verifying the format of the input information and checking whether required fields are included, inputting the information into a growth curve algorithm, and making predictions using an AI model. The predicted future height, weight, and foot size are converted into JSON format and sent to a terminal. The system also includes a means for providing a user-viewable interface and displaying the prediction results and recommended clothing and shoe sizes. This allows parents to efficiently select appropriate clothing and shoe sizes, saving time, money, and reducing environmental impact.

[0006] definition statement

[0007] "Child's current height" refers to the child's vertical length at the time of using the system.

[0008] "Child's weight" refers to the child's total weight at the time of using the system.

[0009] "Age" refers to the number of years that have passed since the child's date of birth at the time of using the system.

[0010] "Parental height" refers to the vertical length of each of the child's biological mother and father.

[0011] "Past time series data" refers to data that has been organized in chronological order, including records of children's height and weight measured in the past.

[0012] "Foot size" refers to the length of a child's foot at the time of using the system, expressed as shoe size.

[0013] "Input means" refers to an interface for a user to input the child's current height, weight, age, parents' height, past time series data, and shoe size into the system.

[0014] "Means for receiving" refers to the method or process by which the system receives input data.

[0015] A "growth curve" refers to graphs and data used to predict a child's growth based on commonly used statistical models.

[0016] "Predictive means" refers to a method or process for estimating a child's future height, weight, and shoe size using growth curves or algorithms.

[0017] "Means of calculation" refers to the method or process of calculating appropriate clothing and shoe sizes based on predicted information.

[0018] "Means for displaying" refers to an interface that allows the user to visually confirm the prediction results and recommended clothing and shoe sizes.

[0019] "Validation" refers to the process of checking the suitability and completeness of entered information and ensuring that it conforms to the required format and scope.

[0020] "AI model" refers to a machine learning algorithm that has been trained to use artificial intelligence to analyze data and make predictions.

[0021] "JSON format" refers to a way of structuring data in a text format and representing information in a lightweight, human-readable format.

[0022] "Interface" refers to the means or screen through which a user interacts with a system. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. The system aims to reduce unnecessary replacement purchases and clothing disposal by predicting future growth based on data provided by the user and suggesting appropriate sizes.

[0045] 1. Data Entry

[0046] Users use a smartphone or computer to enter the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. Users enter this information using a dedicated input form.

[0047] 2. Sending and Receiving Data

[0048] The terminal sends the data entered by the user to the server using the HTTPS protocol, structured in JSON format, after which the server receives it and prepares it for analysis.

[0049] 3. Data Validation

[0050] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[0051] 4. Conducting growth forecasts

[0052] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0053] 5. Calculation and format of forecast results

[0054] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[0055] 6. Sending and displaying result data

[0056] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[0057] Specific examples

[0058] Data Entry Example

[0059] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm).

[0060] Data reception and growth forecasting

[0061] The server receives the input data, validates it, and then uses the AI ​​model to make predictions, such as predicting that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[0062] Displaying the results

[0063] The device analyzes the received data and displays the predicted results to the user, such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm."

[0064] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, realizing efficient shopping and reducing environmental impact.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] Users enter their child's current height, weight, age, shoe size, past time series data, and the heights of their parents into a dedicated input form, which is displayed on a smartphone or computer application.

[0068] Step 2:

[0069] The terminal converts the data entered by the user into JSON format, which is then sent to the server using the HTTPS protocol.

[0070] Step 3:

[0071] The server receives the HTTPS request and parses the received JSON data to extract each data item, including height, weight, age, shoe size, past data, and parent's heights.

[0072] Step 4:

[0073] The server validates the received data, checking that it is in the correct format and that all required fields are included (for example, height is greater than 0 cm, age is not a decimal point, etc.).

[0074] Step 5:

[0075] The server passes the validated data to an AI model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents.

[0076] Step 6:

[0077] The server receives the AI ​​model's predictions and calculates the appropriate clothing and shoe size based on them. For example, if the predicted height in six months is 105 cm, weight 17 kg, and shoe size 16 cm, the server calculates the clothing size as 110 cm and shoe size as 17 cm.

[0078] Step 7:

[0079] The server converts the prediction results into JSON format and sends them to the device, which includes the predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[0080] Step 8:

[0081] The device receives and analyzes the JSON data sent from the server, and based on the analysis results, displays the predicted results and recommended sizes to the user.

[0082] Step 9:

[0083] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed.

[0084] Example 1

[0085] 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."

[0086] Currently, there is a lack of methods to predict appropriate clothing and shoe sizes as children grow, resulting in frequent replacements and incorrect size selection, resulting in a lot of waste. Incorrect growth predictions also increase unnecessary waste and increase the environmental burden. Therefore, there is a need for a system that can accurately predict children's growth and recommend appropriate clothing and shoe sizes based on that prediction.

[0087] 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.

[0088] In this invention, the server includes: means for a user to input information about the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for a terminal to transmit the input information to the server; means for the server to check the format of the received information and whether all required fields are included; means for the server to use a generative AI model to predict the child's future height, weight, and shoe size using a growth curve algorithm based on the received information; means for the server to calculate appropriate clothing and shoe sizes based on the predicted information; and means for the terminal to display the calculated clothing and shoe sizes to the user. This allows the user to select appropriate clothing and shoe sizes based on the child's growth, thereby reducing unnecessary replacement purchases and waste.

[0089] "User" refers to a person who utilizes the system to input their child's growth data and obtain growth predictions and size recommendations.

[0090] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to input data.

[0091] "Server" refers to a computer system that receives, analyzes, and processes data sent from a terminal.

[0092] "Information on height, weight, age, parents' height, past time series data, and shoe size" refers to basic data for a child's growth that the user inputs via the terminal.

[0093] "Data validation" refers to the process of checking whether the format and content of entered data are correct and whether all required fields are included.

[0094] A "growth curve algorithm" refers to a mathematical formula or calculation method for predicting a child's future growth based on past growth data.

[0095] A "generative AI model" refers to artificial intelligence that uses growth curve algorithms to predict future height, weight, and shoe size.

[0096] "JSON format" refers to a lightweight data exchange format for structuring and representing data.

[0097] "Method for calculating appropriate clothing and shoe sizes" refers to the process of calculating appropriate clothing and shoe sizes for a child's future growth based on growth projections.

[0098] "Means for displaying to the user" refers to the process of displaying the predicted height, weight, foot size, and recommended clothing and shoe sizes based on ... shoe sizes on the device.

[0099] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. Users of the system input their child's current height, weight, age, historical height and weight data, and the heights of their parents using a smartphone or computer.

[0100] Data Entry

[0101] The user uses a dedicated input form to input the child's current height, weight, age, shoe size, historical height and weight data, and the heights of the parents. For example, the user enters the following information:

[0102] Current height: 100 cm

[0103] Current weight: 15 kg

[0104] Age: 3 years old

[0105] Foot size: 15 cm

[0106] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0107] Parents' height: Mother 160 cm, Father 170 cm

[0108] Sending and Receiving Data

[0109] The terminal sends the input data to the server using the HTTPS protocol. The data format is JSON, and the sent data is saved on the server.

[0110] Data Validation

[0111] The server validates the data it receives, ensuring it is in the correct format and that all required fields are included. It checks for invalid values ​​for height and weight, decimals for age, etc. If validation is successful, it proceeds to the next step.

[0112] Conducting growth forecasts

[0113] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, the following predictions can be obtained:

[0114] Height after 6 months: 105 cm

[0115] Weight after 6 months: 17 kg

[0116] Foot size after 6 months: 16 cm

[0117] Calculating and formatting forecast results

[0118] The server calculates the appropriate clothing and shoe size based on the predicted data obtained from the AI ​​model. The prediction results are formatted in JSON format and include the following information:

[0119] Estimated height: 105 cm

[0120] Estimated weight: 17 kg

[0121] Estimated foot size: 16 cm

[0122] Recommended clothing size: 110 cm

[0123] Recommended shoe size: 17 cm

[0124] Sending and displaying result data

[0125] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[0126] Specific examples

[0127] A specific example of data entry would be the following prompt:

[0128] "My child is currently 100 cm tall, weighs 15 kg, is 3 years old, and has a shoe size of 15 cm. One month ago, his height was 98 cm and his weight was 14 kg. His parents' heights are 160 cm and 170 cm, respectively. Given this information, what are his predicted height, weight, shoe size, and recommended clothing and shoe sizes for the next 6 months?"

[0129] This system allows users to select the optimal size of clothes and shoes as their child grows, reducing unnecessary replacements and waste.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1: Data entry

[0132] The user inputs the child's current height, weight, age, past time series data, shoe size, and parents' heights into a dedicated input form. For example, the following information is input:

[0133] Current height: 100 cm

[0134] Current weight: 15 kg

[0135] Age: 3 years old

[0136] Foot size: 15 cm

[0137] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0138] Parents' height: Mother 160 cm, Father 170 cm

[0139] Input data: Multiple data items entered by the user

[0140] Output data: Child growth data entered in the input form

[0141] Step 2: Sending data

[0142] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. For example, the input data is converted into the following JSON format.

[0143] json

[0144] {

[0145] "height": 100,

[0146] "weight": 15,

[0147] "age": 3,

[0148] "footSize": 15,

[0149] "pastData": [

[0150] {"monthsAgo": 1, "height": 98, "weight": 14},

[0151] {"monthsAgo": 3, "height": 95, "weight": 13},

[0152] {"monthsAgo": 6, "height": 92, "weight": 12}

[0153] ],

[0154] "parentHeights": {"mother": 160, "father": 170}

[0155] }

[0156] Input data: Growth data entered by the user

[0157] Output data: Data converted to JSON format

[0158] Step 3: Receiving the data

[0159] The server receives the JSON format data sent from the device using the HTTPS protocol. The data is stored on the server and used for subsequent processing.

[0160] Input data: JSON format data sent via HTTPS protocol

[0161] Output data: Growth data stored on the server

[0162] Step 4: Validate the data

[0163] The server validates the received data. Specifically, it checks whether the data format is correct and whether all required fields are included. For example, it checks whether height and weight are negative values ​​and whether age is a decimal. If validation is successful, it proceeds to the next step.

[0164] Input data: Growth data stored on the server

[0165] Output data: Validation result (pass or fail)

[0166] Step 5: Perform growth projections

[0167] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict future height, weight, and shoe size. For example, the following prediction results can be obtained:

[0168] Height after 6 months: 105 cm

[0169] Weight after 6 months: 17 kg

[0170] Foot size after 6 months: 16 cm

[0171] Input data: Growth data that has passed validation

[0172] Output data: Prediction results (estimated height, weight, and shoe size for the next 6 months)

[0173] Step 6: Calculate and format the forecast results

[0174] The server calculates the appropriate clothing and shoe sizes based on the predicted data obtained from the generative AI model and formats it in JSON format. For example, it generates JSON data containing the following information:

[0175] json

[0176] {

[0177] "predictedHeight": 105,

[0178] "predictedWeight": 17,

[0179] "predictedFootSize": 16,

[0180] "recommendedClothingSize": 110,

[0181] "recommendedShoeSize": 17

[0182] }

[0183] Input data: Prediction data from a generative AI model

[0184] Output data: Result data formatted in JSON format

[0185] Step 7: Send and display result data

[0186] The server sends the resulting data, formatted in JSON, to the device, which receives, analyzes, and displays it to the user. The displayed information includes predicted future height, weight, foot size, and recommended clothing and shoe sizes, allowing the user to choose the optimal size for their next purchase.

[0187] Input data: Result data sent from the server (JSON format)

[0188] Output data: What is displayed to the user (predicted results and recommended sizes)

[0189] Through these steps, the system accurately predicts a child's growth and suggests optimal clothing and shoe sizes, thereby reducing unnecessary replacement purchases and waste.

[0190] (Application example 1)

[0191] 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."

[0192] Traditionally, it has been difficult for many parents to select the appropriate clothing and shoe sizes as their children grow. In particular, when shopping in anticipation of future growth, mistakes in size selection frequently occur, resulting in unnecessary replacements and disposal. Furthermore, the lack of systems that suggest appropriate sized products based on growth predictions is one factor that degrades the online shopping experience. For this reason, there has been a demand for a system that provides accurate size selection, efficient shopping, and reduces environmental impact.

[0193] 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.

[0194] In this invention, the server includes: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and foot size; means for receiving the input information; means for predicting the child's future height, weight, and foot size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for suggesting related product sizes using the calculated information; and means for linking the purchase information of the suggested products to an online shopping site. This allows users to select accurate clothing and shoe sizes that take future growth into consideration, thereby reducing unnecessary replacement and disposal. Furthermore, linking with an online shopping site improves the online shopping experience by suggesting products based on growth predictions.

[0195] "Information on the child's current height, weight, age, parents' height, past time series data, and foot size" refers to the basic data required to predict a child's growth, specifically including the child's current height, weight, age, parents' height, data on changes in height and weight over a certain period of time in the past, and foot size information.

[0196] "Input means" refers to the interface or device that allows the user to provide the system with the necessary information about the child's development.

[0197] "Means for receiving" refers to a mechanism or function that takes information entered by a user into the system and stores it in a processable format.

[0198] "Prediction means" refers to methods or algorithms for estimating future height, weight, and shoe size based on the received information using growth curves or AI models.

[0199] "Means for calculating" refers to the process or method for calculating appropriate clothing and shoe sizes based on predicted future information.

[0200] "Display means" refers to a screen or device for visually presenting the calculated clothing and shoe size information to the user.

[0201] "Means for suggesting" refers to a system and process for selecting sizes of related products based on the calculated size information and providing them to the user.

[0202] "Means of collaboration" refers to a system or interface that shares purchase information for proposed products with the online shopping site and facilitates the purchase process.

[0203] "Online shopping site" refers to a web-based platform that allows customers to search for, purchase, and complete delivery procedures for products online.

[0204] This invention is a system that predicts a child's growth and suggests appropriate clothing and shoe sizes. The system uses input growth data to predict the child's future height, weight, and shoe size, and then calculates and provides the most appropriate clothing and shoe sizes to the user. Furthermore, by linking this information with online shopping sites, the system facilitates the purchase process.

[0205] 1. Data Entry

[0206] The user uses a smartphone or computer to input the following information: current height, weight, age, past time series data (e.g., height and weight from 1 month ago, 3 months ago, and 6 months ago), shoe size, and parents' heights. This information is then provided to the system using a dedicated input form.

[0207] 2. Sending and Receiving Data

[0208] The terminal sends the data entered by the user to the server using the HTTPS protocol. The data is structured in JSON format. The server receives it and prepares it for data analysis.

[0209] 3. Data Validation

[0210] The server validates the data it receives, ensuring that the information entered is in the correct format and that all required fields are included. If this validation is successful, it proceeds to the next step.

[0211] 4. Conducting growth forecasts

[0212] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0213] 5. Calculation and format of forecast results

[0214] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[0215] 6. Sending and displaying result data

[0216] The server sends formatted JSON data to the device, which receives it and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[0217] 7. Collaboration with online shopping sites

[0218] The system uses the predicted size information to suggest appropriate size products from the product database of related online retailers, allowing users to easily purchase clothes and shoes that fit their growing child's size.

[0219] As a specific example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg), mother's height (160 cm), and father's height (170 cm). The server validates this data and makes a prediction using a generative AI model. For example, it predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm over the next 6 months. Based on this information, the device displays the recommended clothing size of 110 cm and shoe size of 17 cm to the user. It also connects to an online shopping site to find products that fit these sizes, making it easy for the user to purchase them.

[0220] An example prompt has the following format:

[0221] "Predict your child's growth. Use the following information: Current height: 100 cm Current weight: 15 kg Age: 3 years Shoe size: 15 cm Additionally, you have the following historical data: 1 month ago height: 98 cm, weight: 14 kg 3 months ago height: 95 cm, weight: 13 kg 6 months ago height: 92 cm, weight: 12 kg Parents' heights: Mother: 160 cm, Father: 170 cm"

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] The user enters information about the child's development.

[0225] Input: Current height, weight, age, shoe size, historical height and weight data, and parents' heights.

[0226] What it does: A user enters data into a special form on their smartphone or computer. The form is designed so that all required fields are filled in.

[0227] Step 2:

[0228] The terminal transmits the input data to the server.

[0229] Input: Growth-related data entered by the user.

[0230] Data processing or data operation: Data is structured in JSON format.

[0231] Output: Structured JSON data.

[0232] Specific operation: The terminal uses the HTTPS protocol to convert the data entered by the user into JSON format and send it to the server.

[0233] Step 3:

[0234] The server receives the submitted data and validates it.

[0235] Input: User data in JSON format.

[0236] Data processing or data calculation: Check the format of input data and check the existence of required fields.

[0237] Output: The data that was successfully validated, or an error message.

[0238] Specific operation: The server parses the JSON data format and checks whether all the required information is included. If the check result is correct, it proceeds to the next step.

[0239] Step 4:

[0240] The server uses an AI model to predict growth.

[0241] Input: User data that passes validation.

[0242] Data processing or data calculation: Input into a generative AI model and apply a growth curve algorithm.

[0243] Output: Predicted future height, weight, and shoe size.

[0244] Specific operation: The server runs an AI model (e.g., TENSORFLOW®) to predict future height, weight, and shoe size based on the input data.

[0245] Step 5:

[0246] The server calculates appropriate clothing and shoe sizes based on the predicted data.

[0247] Input: Predicted growth data.

[0248] Data processing or data calculation: calculation of recommended size and formatting of data.

[0249] Output: Recommended clothing and shoe sizes, formatted JSON data.

[0250] What it does: The server analyzes the predicted data, calculates appropriate clothing and shoe sizes based on growth predictions, and formats the information into JSON format.

[0251] Step 6:

[0252] The server sends the formatted data to the terminal.

[0253] Input: Well-formed JSON data.

[0254] Data processing or data calculation: None (simple transfer of data).

[0255] Output: Sending data to the user's terminal.

[0256] Specific operation: The server again uses the HTTPS protocol to send the formatted JSON data to the terminal.

[0257] Step 7:

[0258] The terminal displays the received data to the user.

[0259] Input: Formatted JSON data received from the server.

[0260] Data processing or data operation: Parsing JSON data and converting the display format.

[0261] Output: Growth prediction results and recommended size displayed to the user.

[0262] Specific operation: The device parses the received JSON data and displays it on the user's screen as specific values ​​(estimated height, weight, foot size, and recommended clothing and shoe sizes).

[0263] Step 8:

[0264] The system will suggest products of appropriate sizes from related online shopping sites and link purchasing information.

[0265] Input: Recommended size information.

[0266] Data processing or data calculation: Matching with the product database of the online shopping site.

[0267] Output: Right-sized product suggestions and purchasing options.

[0268] Specific operation: Based on the predicted size information, the system searches the product database of related online shopping sites, suggests products of appropriate sizes to the user, and assists in the purchase process of the suggested products.

[0269] 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.

[0270] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[0271] 1. Data Entry and Emotion Recognition

[0272] Users use their smartphones or computers to input the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the parents' heights. Users enter this information using a dedicated input form. At the same time, the system recognizes the user's emotions in real time through the camera and microphone and analyzes them using an emotion engine.

[0273] 2. Sending and Receiving Data

[0274] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives it and prepares it for data analysis.

[0275] 3. Data Validation

[0276] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[0277] 4. Conducting growth forecasts

[0278] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size, taking into account past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0279] 5. Calculation and format of forecast results

[0280] The server calculates appropriate clothing and shoe sizes based on prediction data from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format and recommendations based on the user's emotional state. For example, if the user's emotion is not positive, it presents information in a more understandable and friendly format.

[0281] 6. Sending and displaying result data

[0282] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, recommended clothing and shoe sizes, and optimal display formatting based on emotion recognition.

[0283] Specific examples

[0284] Data Entry Example

[0285] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[0286] Data reception and growth forecasting

[0287] The server receives the input data and emotion data from the emotion engine, validates them, and then uses the AI ​​model to make predictions. For example, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months. Emotion data is also analyzed at the same time.

[0288] Displaying the results

[0289] The device receives the JSON data sent from the server and displays it to the user based on the prediction results and sentiment analysis data. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's sentiment is not positive, the information is displayed in a more friendly format.

[0290] This embodiment allows the user to select clothes and shoes of appropriate sizes according to the child's growth, and further provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[0291] The processing flow will be explained below.

[0292] Step 1:

[0293] The user launches the application and opens the "Child Growth Prediction" screen. They enter their child's current height, weight, age, shoe size, past time series data, and the parents' heights into the form on the screen. The camera and microphone are then activated, capturing the user's facial expressions and voice in real time and sending them to the emotion engine.

[0294] Step 2:

[0295] The device converts the input data and captured emotion data into JSON format and sends it to the server using the HTTPS protocol. The transmitted data includes specific numerical values ​​for each item and the emotion analysis results.

[0296] Step 3:

[0297] The server receives the HTTPS request and parses the JSON data to extract each data item, including height, weight, age, shoe size, historical time series data, parents' heights, and emotion data.

[0298] Step 4:

[0299] The server validates the received data, specifically checking that the height and weight are within a reasonable range, that the age is not a decimal, that the shoe size is valid, and that all required information is present. If validation is successful, the server proceeds to the next step.

[0300] Step 5:

[0301] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents. For example, it predicts the next six months based on growth data from the past six months.

[0302] Step 6:

[0303] The server receives the prediction data from the AI ​​model and calculates the appropriate clothing and shoe size based on that data. At the same time, it also analyzes the received emotional data and adjusts the way the prediction results are presented. For example, if the user's emotional state is not positive, the information will be displayed in a more friendly manner.

[0304] Step 7:

[0305] The server then converts the prediction results and emotional data into the optimal display format in JSON format and sends it to the device. This data includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and emotional analysis results.

[0306] Step 8:

[0307] The device receives and analyzes the JSON data sent from the server. Based on the analysis results, it displays predictions and recommended sizes to the user. Displayed information includes "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," "Recommended shoe size: 17 cm," and so on. If the user's emotions are not positive, the display format is adjusted, such as by adding more explanatory text or using gentler colors.

[0308] Step 9:

[0309] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed. The friendly display provides a better user experience.

[0310] Example 2

[0311] 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."

[0312] Conventional child growth prediction systems predict growth curves and suggest appropriate sizes, but they do not provide information that takes user emotions into consideration, resulting in a poor user experience. Furthermore, input information is not properly checked, and prediction results based on incorrect data are sometimes displayed. Furthermore, there is a lack of systems that can make predictions using specific data formats.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0314] In this invention, the server includes: means for inputting information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for capturing and analyzing user emotion data; and means for adjusting the display format based on the emotion data. This allows for the provision of information that takes the user's emotions into consideration, providing a better user experience. Furthermore, by checking the format of the input information and confirming required fields, the accuracy of the prediction results can be improved.

[0315] "Child's current height" is the child's most recent height, which serves as the basis for the prediction.

[0316] "Weight" is the child's most recent weight, which serves as the basis for the prediction.

[0317] "Age" is the age of the child that the prediction is based on, expressed in months or years.

[0318] "Parental height" refers to the heights of both parents that affect the child's growth prediction. This usually includes the heights of the father and mother.

[0319] "Past time-series data" refers to height and weight data that a child has entered in the past, and is obtained at multiple points in time.

[0320] "Foot size" refers to the actual measurement of a child's current foot size, and is data used to determine the appropriate shoe size.

[0321] The "means for inputting" is a means for the user to input information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size.

[0322] The "receiving means" is a means by which the system receives information input by the user.

[0323] A "growth chart" is a graph showing normal growth patterns used to predict a child's growth.

[0324] A "means for predicting" is a means for predicting future height, weight, and shoe size using growth curves.

[0325] The "means for calculating" is a means for calculating the appropriate clothing size and shoe size based on the predicted information.

[0326] The "display means" is a means for displaying the calculated clothing and shoe sizes to the user.

[0327] The "means for capturing and analyzing emotional data" refers to a means for obtaining the user's emotions through a camera or microphone and analyzing the data.

[0328] The "means for adjusting the display format" is a means for optimizing the way information is displayed based on the user's emotional data.

[0329] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[0330] Data Entry and Emotion Recognition

[0331] Users use a smartphone or computer to enter their child's current height, weight, age, shoe size, past height and weight data over time, and the parents' heights into the system. As users enter information using the input form, the system uses a camera and microphone to recognize the user's emotions in real time and analyzes them using the emotion engine. For example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). They also enter the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter information.

[0332] Sending and Receiving Data

[0333] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives the data and prepares it for data analysis.

[0334] Data Validation

[0335] The server checks the format of the received data and verifies that the input information is correct, checking for required fields. For example, it checks that height and weight are numeric, and age is an integer. If all validations are successful, it proceeds to the next processing step.

[0336] Conducting growth forecasts

[0337] The server then launches a generative AI model based on the validated data. This AI model uses a growth curve algorithm and receives historical growth data and the parents' heights as input. Specifically, the AI ​​model predicts the child's height (e.g., 105 cm), weight (e.g., 17 kg), and shoe size (e.g., 16 cm) for the next six months.

[0338] Calculating and formatting forecast results

[0339] The server calculates appropriate clothing and shoe sizes based on the predictions obtained from the AI ​​model and the emotional data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. If the user's emotion is not positive, the server presents information in a more friendly format.

[0340] Sending and displaying result data

[0341] The server sends formatted JSON data to the device, which parses it and displays predictions to the user, including estimated future height, weight, foot size, and recommended clothing and shoe sizes. If the user's sentiment is not positive, the information is presented in a more user-friendly format.

[0342] Specific examples

[0343] Data Entry Example

[0344] The user inputs their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past growth data. For example, one month ago: 98 cm, 14 kg, three months ago: 95 cm, 13 kg, six months ago: 92 cm, 12 kg. The user also inputs the parents' heights (mother: 160 cm, father: 170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[0345] Data reception and growth forecasting

[0346] The server receives the data and emotion data entered by the user, validates them, and then uses the generative AI model to make predictions: specifically, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[0347] Displaying the results

[0348] The device receives the JSON data sent from the server and displays the analysis results to the user. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's emotions are not positive, the device displays the information in a more user-friendly format.

[0349] Prompt Sentence Examples

[0350] "Based on the child's growth data and user emotion data entered by the user, predict the child's growth for the next six months and suggest appropriate clothing and shoe sizes."

[0351] "Based on the child's growth data (1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg) and the parents' height information, predict the child's growth over the next 6 months and determine the recommended size."

[0352] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, and provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1: Data entry and emotion recognition

[0355] The user uses a dedicated input form on their smartphone or computer to input their child's current height, weight, age, shoe size, past time series data, and the parents' height information. At this time, the camera and microphone are activated for emotion recognition. The input data is in a format that includes each item necessary for predicting a child's growth. The data obtained from the input is in the following format:

[0356] Height: 100 cm

[0357] Weight: 15 kg

[0358] Age: 3 years old

[0359] Foot size: 15 cm

[0360] Past data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0361] Parents' height: mother 160 cm, father 170 cm

[0362] The device captures the user's facial expressions and voice in real time using a camera and microphone, and collects this as emotion data. The input data and emotion data are temporarily stored as data entities within the system.

[0363] (Input) The user enters the child's height, weight, age, shoe size, historical time series data, parents' heights, and captured emotion data into the form.

[0364] (Output) A well-formed data entity.

[0365] Step 2: Convert the data to JSON format and send it

[0366] The device converts the collected user data and emotion data into JSON format. The converted data includes user input information and emotion information. The data converted into JSON format has the following format:

[0367] json

[0368] {

[0369] "current_height": 100,

[0370] "current_weight": 15,

[0371] "age": 3,

[0372] "foot_size": 15,

[0373] "past_data": [

[0374] {"height": 98, "weight": 14, "time": "1_month_ago"},

[0375] {"height": 95, "weight": 13, "time": "3_months_ago"},

[0376] {"height": 92, "weight": 12, "time": "6_months_ago"}

[0377] ],

[0378] "parent_heights": {"mother": 160, "father": 170},

[0379] "emotion_data": {"expression": "neutral", "voice_tone": "calm"}

[0380] }

[0381] The converted data is sent to the server using the HTTPS protocol. For security reasons, SSL / TLS is used.

[0382] (Input) A well-formed data entity.

[0383] (Output) The JSON data sent to the server.

[0384] Step 3: Receiving and validating data

[0385] The server receives the JSON data sent from the device. It checks the format of the received data and verifies that all required fields are included. For example, it checks that height, weight, age, etc. are entered in the correct format. The conditions for successful data validation are as follows:

[0386] Height and weight must be positive integers or floating point numbers.

[0387] Age is a positive integer.

[0388] (Input) JSON data sent from the terminal.

[0389] (Output) Flag indicating whether validation was successful.

[0390] Step 4: Perform growth projections

[0391] The server runs a generative AI model based on the validated data. The data is fed into the AI ​​model, which predicts the child's height, weight, and shoe size for the next six months, taking into account past growth data and parental height information. The output of the AI ​​model is as follows:

[0392] Predicted height after 6 months: 105 cm

[0393] Estimated weight after 6 months: 17 kg

[0394] Predicted foot size after 6 months: 16 cm

[0395] (Input) User data that has passed validation.

[0396] (Output) Predicted growth data (height, weight, foot size).

[0397] Step 5: Calculate and format the prediction results

[0398] The server calculates the appropriate clothing and shoe size based on the prediction results from the AI ​​model and the emotion data from the emotion engine. For example, based on a predicted height of 105 cm and foot size of 16 cm, it calculates a clothing size of 110 cm and a shoe size of 17 cm. The server also adjusts the display format of the information depending on the user's emotional state. If the emotion data is determined to be "not positive," the information is presented in a friendly format.

[0399] (Input) Prediction data, user emotion data.

[0400] (Output) Well-formed JSON data (prediction results, recommended size, adjusted display format).

[0401] Step 6: Send and display result data

[0402] The server sends the formatted JSON data to the terminal.

[0403] The device parses this data and displays it to the user, typically displaying something like this:

[0404] Predicted height after 6 months: 105 cm

[0405] Recommended clothing size: 110 cm

[0406] Recommended shoe size: 17 cm

[0407] If the user's emotion is "negative," the information is presented in a more user-friendly format, for example by using different colors and fonts to enhance the visual experience.

[0408] (Input) Well-formed JSON data sent from the server.

[0409] (Output) The predicted result and recommended size displayed to the user.

[0410] These detailed processing steps make the entire process clear, from user input to display of growth prediction results.

[0411] (Application example 2)

[0412] 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."

[0413] Conventional systems have had difficulty predicting appropriate clothing and shoe sizes as a child grows. Furthermore, the system does not display information that takes the user's emotional state into consideration, resulting in a poor user experience. The present invention aims to solve these problems by realizing a system that suggests appropriate clothing and shoe sizes for the user while providing a display format that takes emotions into consideration.

[0414] 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: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for recognizing and analyzing the user's emotional state in real time; and means for adjusting the display format of information according to the emotional state. This not only enables the user to select appropriate clothing and shoe sizes that suit their child's growth, but also provides emotionally sensitive information, enabling a less stressful shopping experience.

[0415] "Child's current height" is data that indicates the child's actual height that is entered into the system.

[0416] "Weight" is data entered into the system indicating the child's actual weight.

[0417] "Age" is data that indicates the actual number of years since birth of a child that is entered into the system.

[0418] "Parent height" is data indicating the height of each parent that is entered into the system for predicting the child's growth.

[0419] "Past time series data" refers to a sequence of data such as past height, weight, and shoe size that is entered into the system to track a child's growth.

[0420] "Foot size" is data indicating the actual foot length that is input into the system to determine a child's shoe size.

[0421] A "growth curve" is a mathematical model based on historical data that is used to predict a child's growth.

[0422] "Means for predicting future height, weight, and shoe size" refers to a function that uses input data to calculate a child's future growth parameters based on AI models and algorithms.

[0423] The "means for calculating appropriate clothing and shoe sizes" is a function for calculating clothing and shoe sizes that fit a child based on predicted growth data.

[0424] "Means for recognizing and analyzing the user's emotional state in real time" refers to a function that uses sensors such as a camera and microphone to capture the user's facial expressions and voice, and analyzes them using an emotion recognition engine.

[0425] The "means for adjusting the information display format" is a function that changes the way prediction results and suggestions are displayed depending on the user's emotional state.

[0426] The present invention aims to provide a system that predicts a child's growth, suggests appropriate clothing and shoe sizes, and improves the user experience by recognizing the user's emotions and adjusting the display format.

[0427] 1. Data Entry and Emotion Recognition

[0428] Users access the system using a smartphone or computer and enter the following information. This information includes the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. When the user enters this information into a dedicated input form, the system uses a camera and microphone to capture the user's emotions in real time and analyzes them using an emotion engine. This is done using a mobile app using React Native or a web application using HTML5, CSS3, and JavaScript (registered trademark).

[0429] 2. Sending and Receiving Data

[0430] The device converts the user-entered data and emotion data into JSON format and sends it to the server using the HTTPS protocol. The Axios library is used to send the data. The server receives the sent data and prepares it for analysis. Server-side technologies used in this process include Node.js.

[0431] 3. Data validation and prediction

[0432] The server validates the received data. This includes checking that the input information is in the correct format and contains all required fields. For example, it checks that height and weight are not invalid values, and that age is not a decimal. If this validation is successful, the data proceeds to the next processing step. The server then runs an AI model based on the validated data. This AI model is built using TensorFlow and uses a growth curve algorithm to predict the child's future height, weight, and shoe size, taking into account past data and the heights of the parents.

[0433] 4. Formatting and displaying prediction results

[0434] The server calculates appropriate clothing and shoe sizes based on prediction data obtained from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. OpenFace and Emotion API are used for emotion recognition. For example, if the user's emotion is not positive, the information is presented in a more understandable and friendly format. The server sends this information to the device in JSON format. The device analyzes the data sent from the server and displays the results to the user. The displayed content includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and the optimal display format based on emotion recognition.

[0435] Specific examples

[0436] For example, if a user enters the following data:

[0437] Child's current height: 110 cm

[0438] Current weight: 18 kg

[0439] Age: 4 years old

[0440] Foot size: 17 cm

[0441] Parents' height: mother 160 cm, father 180 cm

[0442] Past growth data: 1 month ago 108 cm, 16 kg, 3 months ago 105 cm, 15 kg

[0443] The system returns the following prediction:

[0444] Predicted height after 6 months: 115 cm

[0445] Recommended clothing size: 120 cm

[0446] Recommended shoe size: 18 cm

[0447] Additionally, the display format of information based on emotion recognition will be adjusted as follows:

[0448] If the user's emotional state is not positive, provide information in a friendly tone.

[0449] Example prompts for generative AI models

[0450] "Predict a child's height, weight, and shoe size in the next 6 months. Use the parents' height data as well."

[0451] In this way, this invention allows users to easily obtain information for selecting clothes and shoes in sizes that are optimal for their child's growth, and provides information that takes emotions into consideration, thereby providing a better shopping experience.

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] Using a smartphone or computer, users input information about their child's current height, weight, age, shoe size, past time series data, and the heights of their parents. The input information is converted into JSON format. The device's camera and microphone are also used to capture the user's emotional data. This input becomes the basis for analysis by the system.

[0455] Step 2:

[0456] The terminal sends the user input data and captured emotion data to the server using the HTTPS protocol. The Axios library is used for data transmission. The output at this stage is JSON format data that is sent to the server.

[0457] Step 3:

[0458] The server parses and validates the received JSON data, for example, ensuring that the height is within a reasonable range, that the weight is not an invalid value, and that the age is an integer. This validation ensures that the data is in the correct format.

[0459] Step 4:

[0460] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict the child's future height, weight, and shoe size. The AI ​​model is built using TensorFlow. Based on the input data, it outputs predicted data for the next six months.

[0461] Step 5:

[0462] The server calculates appropriate clothing and shoe sizes based on the predicted data obtained from the AI ​​model. It also analyzes the user's emotional data using an emotion recognition engine (OpenFace or EmotionAPI) and determines the display format of information based on the user's emotional state. For example, if the user's emotions are not positive, the server will present information in a more user-friendly format.

[0463] Step 6:

[0464] The server sends formatted JSON data (prediction data, recommended size, display format) to the device, which receives, parses, and displays the data to the user. The display includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and display format information based on emotion recognition.

[0465] Step 7:

[0466] Users can check the information displayed on the device and select appropriate clothes and shoes based on predicted growth data. This information makes it easy for users to find the best products that will suit their child's growth.

[0467] 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.

[0468] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0469] 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.

[0470] [Second embodiment]

[0471] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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."

[0483] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. The system aims to reduce unnecessary replacement purchases and clothing disposal by predicting future growth based on data provided by the user and suggesting appropriate sizes.

[0484] 1. Data Entry

[0485] Users use a smartphone or computer to enter the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. Users enter this information using a dedicated input form.

[0486] 2. Sending and Receiving Data

[0487] The terminal sends the data entered by the user to the server using the HTTPS protocol, structured in JSON format, after which the server receives it and prepares it for analysis.

[0488] 3. Data Validation

[0489] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[0490] 4. Conducting growth forecasts

[0491] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0492] 5. Calculation and format of forecast results

[0493] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[0494] 6. Sending and displaying result data

[0495] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[0496] Specific examples

[0497] Data Entry Example

[0498] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm).

[0499] Data reception and growth forecasting

[0500] The server receives the input data, validates it, and then uses the AI ​​model to make predictions, such as predicting that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[0501] Displaying the results

[0502] The device analyzes the received data and displays the predicted results to the user, such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm."

[0503] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, realizing efficient shopping and reducing environmental impact.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] Users enter their child's current height, weight, age, shoe size, past time series data, and the heights of their parents into a dedicated input form, which is displayed on a smartphone or computer application.

[0507] Step 2:

[0508] The terminal converts the data entered by the user into JSON format, which is then sent to the server using the HTTPS protocol.

[0509] Step 3:

[0510] The server receives the HTTPS request and parses the received JSON data to extract each data item, including height, weight, age, shoe size, past data, and parent's heights.

[0511] Step 4:

[0512] The server validates the received data, checking that it is in the correct format and that all required fields are included (for example, height is greater than 0 cm, age is not a decimal point, etc.).

[0513] Step 5:

[0514] The server passes the validated data to an AI model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents.

[0515] Step 6:

[0516] The server receives the AI ​​model's predictions and calculates the appropriate clothing and shoe size based on them. For example, if the predicted height in six months is 105 cm, weight 17 kg, and shoe size 16 cm, the server calculates the clothing size as 110 cm and shoe size as 17 cm.

[0517] Step 7:

[0518] The server converts the prediction results into JSON format and sends them to the device, which includes the predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[0519] Step 8:

[0520] The device receives and analyzes the JSON data sent from the server, and based on the analysis results, displays the predicted results and recommended sizes to the user.

[0521] Step 9:

[0522] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed.

[0523] Example 1

[0524] 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."

[0525] Currently, there is a lack of methods to predict appropriate clothing and shoe sizes as children grow, resulting in frequent replacements and incorrect size selection, resulting in a lot of waste. Incorrect growth predictions also increase unnecessary waste and increase the environmental burden. Therefore, there is a need for a system that can accurately predict children's growth and recommend appropriate clothing and shoe sizes based on that prediction.

[0526] 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.

[0527] In this invention, the server includes: means for a user to input information about the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for a terminal to transmit the input information to the server; means for the server to check the format of the received information and whether all required fields are included; means for the server to use a generative AI model to predict the child's future height, weight, and shoe size using a growth curve algorithm based on the received information; means for the server to calculate appropriate clothing and shoe sizes based on the predicted information; and means for the terminal to display the calculated clothing and shoe sizes to the user. This allows the user to select appropriate clothing and shoe sizes based on the child's growth, thereby reducing unnecessary replacement purchases and waste.

[0528] "User" refers to a person who utilizes the system to input their child's growth data and obtain growth predictions and size recommendations.

[0529] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to input data.

[0530] "Server" refers to a computer system that receives, analyzes, and processes data sent from a terminal.

[0531] "Information on height, weight, age, parents' height, past time series data, and shoe size" refers to basic data for a child's growth that the user inputs via the terminal.

[0532] "Data validation" refers to the process of checking whether the format and content of entered data are correct and whether all required fields are included.

[0533] A "growth curve algorithm" refers to a mathematical formula or calculation method for predicting a child's future growth based on past growth data.

[0534] A "generative AI model" refers to artificial intelligence that uses growth curve algorithms to predict future height, weight, and shoe size.

[0535] "JSON format" refers to a lightweight data exchange format for structuring and representing data.

[0536] "Method for calculating appropriate clothing and shoe sizes" refers to the process of calculating appropriate clothing and shoe sizes for a child's future growth based on growth projections.

[0537] "Means for displaying to the user" refers to the process of displaying the predicted height, weight, foot size, and recommended clothing and shoe sizes based on ... shoe sizes on the device.

[0538] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. Users of the system input their child's current height, weight, age, historical height and weight data, and the heights of their parents using a smartphone or computer.

[0539] Data Entry

[0540] The user uses a dedicated input form to input the child's current height, weight, age, shoe size, historical height and weight data, and the heights of the parents. For example, the user enters the following information:

[0541] Current height: 100 cm

[0542] Current weight: 15 kg

[0543] Age: 3 years old

[0544] Foot size: 15 cm

[0545] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0546] Parents' height: Mother 160 cm, Father 170 cm

[0547] Sending and Receiving Data

[0548] The terminal sends the input data to the server using the HTTPS protocol. The data format is JSON, and the sent data is saved on the server.

[0549] Data Validation

[0550] The server validates the data it receives, ensuring it is in the correct format and that all required fields are included. It checks for invalid values ​​for height and weight, decimals for age, etc. If validation is successful, it proceeds to the next step.

[0551] Conducting growth forecasts

[0552] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, the following predictions can be obtained:

[0553] Height after 6 months: 105 cm

[0554] Weight after 6 months: 17 kg

[0555] Foot size after 6 months: 16 cm

[0556] Calculating and formatting forecast results

[0557] The server calculates the appropriate clothing and shoe size based on the predicted data obtained from the AI ​​model. The prediction results are formatted in JSON format and include the following information:

[0558] Estimated height: 105 cm

[0559] Estimated weight: 17 kg

[0560] Estimated foot size: 16 cm

[0561] Recommended clothing size: 110 cm

[0562] Recommended shoe size: 17 cm

[0563] Sending and displaying result data

[0564] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[0565] Specific examples

[0566] A specific example of data entry would be the following prompt:

[0567] "My child is currently 100 cm tall, weighs 15 kg, is 3 years old, and has a shoe size of 15 cm. One month ago, his height was 98 cm and his weight was 14 kg. His parents' heights are 160 cm and 170 cm, respectively. Given this information, what are his predicted height, weight, shoe size, and recommended clothing and shoe sizes for the next 6 months?"

[0568] This system allows users to select the optimal size of clothes and shoes as their child grows, reducing unnecessary replacements and waste.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1: Data entry

[0571] The user inputs the child's current height, weight, age, past time series data, shoe size, and parents' heights into a dedicated input form. For example, the following information is input:

[0572] Current height: 100 cm

[0573] Current weight: 15 kg

[0574] Age: 3 years old

[0575] Foot size: 15 cm

[0576] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0577] Parents' height: Mother 160 cm, Father 170 cm

[0578] Input data: Multiple data items entered by the user

[0579] Output data: Child growth data entered in the input form

[0580] Step 2: Sending data

[0581] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. For example, the input data is converted into the following JSON format.

[0582] json

[0583] {

[0584] "height": 100,

[0585] "weight": 15,

[0586] "age": 3,

[0587] "footSize": 15,

[0588] "pastData": [

[0589] {"monthsAgo": 1, "height": 98, "weight": 14},

[0590] {"monthsAgo": 3, "height": 95, "weight": 13},

[0591] {"monthsAgo": 6, "height": 92, "weight": 12}

[0592] ],

[0593] "parentHeights": {"mother": 160, "father": 170}

[0594] }

[0595] Input data: Growth data entered by the user

[0596] Output data: Data converted to JSON format

[0597] Step 3: Receiving the data

[0598] The server receives the JSON format data sent from the device using the HTTPS protocol. The data is stored on the server and used for subsequent processing.

[0599] Input data: JSON format data sent via HTTPS protocol

[0600] Output data: Growth data stored on the server

[0601] Step 4: Validate the data

[0602] The server validates the received data. Specifically, it checks whether the data format is correct and whether all required fields are included. For example, it checks whether height and weight are negative values ​​and whether age is a decimal. If validation is successful, it proceeds to the next step.

[0603] Input data: Growth data stored on the server

[0604] Output data: Validation result (pass or fail)

[0605] Step 5: Perform growth projections

[0606] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict future height, weight, and shoe size. For example, the following prediction results can be obtained:

[0607] Height after 6 months: 105 cm

[0608] Weight after 6 months: 17 kg

[0609] Foot size after 6 months: 16 cm

[0610] Input data: Growth data that has passed validation

[0611] Output data: Prediction results (estimated height, weight, and shoe size for the next 6 months)

[0612] Step 6: Calculate and format the forecast results

[0613] The server calculates the appropriate clothing and shoe sizes based on the predicted data obtained from the generative AI model and formats it in JSON format. For example, it generates JSON data containing the following information:

[0614] json

[0615] {

[0616] "predictedHeight": 105,

[0617] "predictedWeight": 17,

[0618] "predictedFootSize": 16,

[0619] "recommendedClothingSize": 110,

[0620] "recommendedShoeSize": 17

[0621] }

[0622] Input data: Prediction data from a generative AI model

[0623] Output data: Result data formatted in JSON format

[0624] Step 7: Send and display result data

[0625] The server sends the resulting data, formatted in JSON, to the device, which receives, analyzes, and displays it to the user. The displayed information includes predicted future height, weight, foot size, and recommended clothing and shoe sizes, allowing the user to choose the optimal size for their next purchase.

[0626] Input data: Result data sent from the server (JSON format)

[0627] Output data: What is displayed to the user (predicted results and recommended sizes)

[0628] Through these steps, the system accurately predicts a child's growth and suggests optimal clothing and shoe sizes, thereby reducing unnecessary replacement purchases and waste.

[0629] (Application example 1)

[0630] 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."

[0631] Traditionally, it has been difficult for many parents to select the appropriate clothing and shoe sizes as their children grow. In particular, when shopping in anticipation of future growth, mistakes in size selection frequently occur, resulting in unnecessary replacements and disposal. Furthermore, the lack of systems that suggest appropriate sized products based on growth predictions is one factor that degrades the online shopping experience. For this reason, there has been a demand for a system that provides accurate size selection, efficient shopping, and reduces environmental impact.

[0632] 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.

[0633] In this invention, the server includes: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and foot size; means for receiving the input information; means for predicting the child's future height, weight, and foot size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for suggesting related product sizes using the calculated information; and means for linking the purchase information of the suggested products to an online shopping site. This allows users to select accurate clothing and shoe sizes that take future growth into consideration, thereby reducing unnecessary replacement and disposal. Furthermore, linking with an online shopping site improves the online shopping experience by suggesting products based on growth predictions.

[0634] "Information on the child's current height, weight, age, parents' height, past time series data, and foot size" refers to the basic data required to predict a child's growth, specifically including the child's current height, weight, age, parents' height, data on changes in height and weight over a certain period of time in the past, and foot size information.

[0635] "Input means" refers to the interface or device that allows the user to provide the system with the necessary information about the child's development.

[0636] "Means for receiving" refers to a mechanism or function that takes information entered by a user into the system and stores it in a processable format.

[0637] "Prediction means" refers to methods or algorithms for estimating future height, weight, and shoe size based on the received information using growth curves or AI models.

[0638] "Means for calculating" refers to the process or method for calculating appropriate clothing and shoe sizes based on predicted future information.

[0639] "Display means" refers to a screen or device for visually presenting the calculated clothing and shoe size information to the user.

[0640] "Means for suggesting" refers to a system and process for selecting sizes of related products based on the calculated size information and providing them to the user.

[0641] "Means of collaboration" refers to a system or interface that shares purchase information for proposed products with the online shopping site and facilitates the purchase process.

[0642] "Online shopping site" refers to a web-based platform that allows customers to search for, purchase, and complete delivery procedures for products online.

[0643] This invention is a system that predicts a child's growth and suggests appropriate clothing and shoe sizes. The system uses input growth data to predict the child's future height, weight, and shoe size, and then calculates and provides the most appropriate clothing and shoe sizes to the user. Furthermore, by linking this information with online shopping sites, the system facilitates the purchase process.

[0644] 1. Data Entry

[0645] The user uses a smartphone or computer to input the following information: current height, weight, age, past time series data (e.g., height and weight from 1 month ago, 3 months ago, and 6 months ago), shoe size, and parents' heights. This information is then provided to the system using a dedicated input form.

[0646] 2. Sending and Receiving Data

[0647] The terminal sends the data entered by the user to the server using the HTTPS protocol. The data is structured in JSON format. The server receives it and prepares it for data analysis.

[0648] 3. Data Validation

[0649] The server validates the data it receives, ensuring that the information entered is in the correct format and that all required fields are included. If this validation is successful, it proceeds to the next step.

[0650] 4. Conducting growth forecasts

[0651] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0652] 5. Calculation and format of forecast results

[0653] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[0654] 6. Sending and displaying result data

[0655] The server sends formatted JSON data to the device, which receives it and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[0656] 7. Collaboration with online shopping sites

[0657] The system uses the predicted size information to suggest appropriate size products from the product database of related online retailers, allowing users to easily purchase clothes and shoes that fit their growing child's size.

[0658] As a specific example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg), mother's height (160 cm), and father's height (170 cm). The server validates this data and makes a prediction using a generative AI model. For example, it predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm over the next 6 months. Based on this information, the device displays the recommended clothing size of 110 cm and shoe size of 17 cm to the user. It also connects to an online shopping site to find products that fit these sizes, making it easy for the user to purchase them.

[0659] An example prompt has the following format:

[0660] "Predict your child's growth. Use the following information: Current height: 100 cm Current weight: 15 kg Age: 3 years Shoe size: 15 cm Additionally, you have the following historical data: 1 month ago height: 98 cm, weight: 14 kg 3 months ago height: 95 cm, weight: 13 kg 6 months ago height: 92 cm, weight: 12 kg Parents' heights: Mother: 160 cm, Father: 170 cm"

[0661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0662] Step 1:

[0663] The user enters information about the child's development.

[0664] Input: Current height, weight, age, shoe size, historical height and weight data, and parents' heights.

[0665] What it does: A user enters data into a special form on their smartphone or computer. The form is designed so that all required fields are filled in.

[0666] Step 2:

[0667] The terminal transmits the input data to the server.

[0668] Input: Growth-related data entered by the user.

[0669] Data processing or data operation: Data is structured in JSON format.

[0670] Output: Structured JSON data.

[0671] Specific operation: The terminal uses the HTTPS protocol to convert the data entered by the user into JSON format and send it to the server.

[0672] Step 3:

[0673] The server receives the submitted data and validates it.

[0674] Input: User data in JSON format.

[0675] Data processing or data calculation: Check the format of input data and check the existence of required fields.

[0676] Output: The data that was successfully validated, or an error message.

[0677] Specific operation: The server parses the JSON data format and checks whether all the required information is included. If the check result is correct, it proceeds to the next step.

[0678] Step 4:

[0679] The server uses an AI model to predict growth.

[0680] Input: User data that passes validation.

[0681] Data processing or data calculation: Input into a generative AI model and apply a growth curve algorithm.

[0682] Output: Predicted future height, weight, and shoe size.

[0683] How it works: The server runs an AI model (e.g., TensorFlow) to predict future height, weight, and shoe size based on the input data.

[0684] Step 5:

[0685] The server calculates appropriate clothing and shoe sizes based on the predicted data.

[0686] Input: Predicted growth data.

[0687] Data processing or data calculation: calculation of recommended size and formatting of data.

[0688] Output: Recommended clothing and shoe sizes, formatted JSON data.

[0689] What it does: The server analyzes the predicted data, calculates appropriate clothing and shoe sizes based on growth predictions, and formats the information into JSON format.

[0690] Step 6:

[0691] The server sends the formatted data to the terminal.

[0692] Input: Well-formed JSON data.

[0693] Data processing or data calculation: None (simple transfer of data).

[0694] Output: Sending data to the user's terminal.

[0695] Specific operation: The server again uses the HTTPS protocol to send the formatted JSON data to the terminal.

[0696] Step 7:

[0697] The terminal displays the received data to the user.

[0698] Input: Formatted JSON data received from the server.

[0699] Data processing or data operation: Parsing JSON data and converting the display format.

[0700] Output: Growth prediction results and recommended size displayed to the user.

[0701] Specific operation: The device parses the received JSON data and displays it on the user's screen as specific values ​​(estimated height, weight, foot size, and recommended clothing and shoe sizes).

[0702] Step 8:

[0703] The system will suggest products of appropriate sizes from related online shopping sites and link purchasing information.

[0704] Input: Recommended size information.

[0705] Data processing or data calculation: Matching with the product database of the online shopping site.

[0706] Output: Right-sized product suggestions and purchasing options.

[0707] Specific operation: Based on the predicted size information, the system searches the product database of related online shopping sites, suggests products of appropriate sizes to the user, and assists in the purchase process of the suggested products.

[0708] 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.

[0709] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[0710] 1. Data Entry and Emotion Recognition

[0711] Users use their smartphones or computers to input the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the parents' heights. Users enter this information using a dedicated input form. At the same time, the system recognizes the user's emotions in real time through the camera and microphone and analyzes them using an emotion engine.

[0712] 2. Sending and Receiving Data

[0713] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives it and prepares it for data analysis.

[0714] 3. Data Validation

[0715] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[0716] 4. Conducting growth forecasts

[0717] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size, taking into account past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0718] 5. Calculation and format of forecast results

[0719] The server calculates appropriate clothing and shoe sizes based on prediction data from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format and recommendations based on the user's emotional state. For example, if the user's emotion is not positive, it presents information in a more understandable and friendly format.

[0720] 6. Sending and displaying result data

[0721] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, recommended clothing and shoe sizes, and optimal display formatting based on emotion recognition.

[0722] Specific examples

[0723] Data Entry Example

[0724] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[0725] Data reception and growth forecasting

[0726] The server receives the input data and emotion data from the emotion engine, validates them, and then uses the AI ​​model to make predictions. For example, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months. Emotion data is also analyzed at the same time.

[0727] Displaying the results

[0728] The device receives the JSON data sent from the server and displays it to the user based on the prediction results and sentiment analysis data. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's sentiment is not positive, the information is displayed in a more friendly format.

[0729] This embodiment allows the user to select clothes and shoes of appropriate sizes according to the child's growth, and further provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] The user launches the application and opens the "Child Growth Prediction" screen. They enter their child's current height, weight, age, shoe size, past time series data, and the parents' heights into the form on the screen. The camera and microphone are then activated, capturing the user's facial expressions and voice in real time and sending them to the emotion engine.

[0733] Step 2:

[0734] The device converts the input data and captured emotion data into JSON format and sends it to the server using the HTTPS protocol. The transmitted data includes specific numerical values ​​for each item and the emotion analysis results.

[0735] Step 3:

[0736] The server receives the HTTPS request and parses the JSON data to extract each data item, including height, weight, age, shoe size, historical time series data, parents' heights, and emotion data.

[0737] Step 4:

[0738] The server validates the received data, specifically checking that the height and weight are within a reasonable range, that the age is not a decimal, that the shoe size is valid, and that all required information is present. If validation is successful, the server proceeds to the next step.

[0739] Step 5:

[0740] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents. For example, it predicts the next six months based on growth data from the past six months.

[0741] Step 6:

[0742] The server receives the prediction data from the AI ​​model and calculates the appropriate clothing and shoe size based on that data. At the same time, it also analyzes the received emotional data and adjusts the way the prediction results are presented. For example, if the user's emotional state is not positive, the information will be displayed in a more friendly manner.

[0743] Step 7:

[0744] The server then converts the prediction results and emotional data into the optimal display format in JSON format and sends it to the device. This data includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and emotional analysis results.

[0745] Step 8:

[0746] The device receives and analyzes the JSON data sent from the server. Based on the analysis results, it displays predictions and recommended sizes to the user. Displayed information includes "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," "Recommended shoe size: 17 cm," and so on. If the user's emotions are not positive, the display format is adjusted, such as by adding more explanatory text or using gentler colors.

[0747] Step 9:

[0748] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed. The friendly display provides a better user experience.

[0749] Example 2

[0750] 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."

[0751] Conventional child growth prediction systems predict growth curves and suggest appropriate sizes, but they do not provide information that takes user emotions into consideration, resulting in a poor user experience. Furthermore, input information is not properly checked, and prediction results based on incorrect data are sometimes displayed. Furthermore, there is a lack of systems that can make predictions using specific data formats.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0753] In this invention, the server includes: means for inputting information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for capturing and analyzing user emotion data; and means for adjusting the display format based on the emotion data. This allows for the provision of information that takes the user's emotions into consideration, providing a better user experience. Furthermore, by checking the format of the input information and confirming required fields, the accuracy of the prediction results can be improved.

[0754] "Child's current height" is the child's most recent height, which serves as the basis for the prediction.

[0755] "Weight" is the child's most recent weight, which serves as the basis for the prediction.

[0756] "Age" is the age of the child that the prediction is based on, expressed in months or years.

[0757] "Parental height" refers to the heights of both parents that affect the child's growth prediction. This usually includes the heights of the father and mother.

[0758] "Past time-series data" refers to height and weight data that a child has entered in the past, and is obtained at multiple points in time.

[0759] "Foot size" refers to the actual measurement of a child's current foot size, and is data used to determine the appropriate shoe size.

[0760] The "means for inputting" is a means for the user to input information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size.

[0761] The "receiving means" is a means by which the system receives information input by the user.

[0762] A "growth chart" is a graph showing normal growth patterns used to predict a child's growth.

[0763] A "means for predicting" is a means for predicting future height, weight, and shoe size using growth curves.

[0764] The "means for calculating" is a means for calculating the appropriate clothing size and shoe size based on the predicted information.

[0765] The "display means" is a means for displaying the calculated clothing and shoe sizes to the user.

[0766] The "means for capturing and analyzing emotional data" refers to a means for obtaining the user's emotions through a camera or microphone and analyzing the data.

[0767] The "means for adjusting the display format" is a means for optimizing the way information is displayed based on the user's emotional data.

[0768] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[0769] Data Entry and Emotion Recognition

[0770] Users use a smartphone or computer to enter their child's current height, weight, age, shoe size, past height and weight data over time, and the parents' heights into the system. As users enter information using the input form, the system uses a camera and microphone to recognize the user's emotions in real time and analyzes them using the emotion engine. For example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). They also enter the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter information.

[0771] Sending and Receiving Data

[0772] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives the data and prepares it for data analysis.

[0773] Data Validation

[0774] The server checks the format of the received data and verifies that the input information is correct, checking for required fields. For example, it checks that height and weight are numeric, and age is an integer. If all validations are successful, it proceeds to the next processing step.

[0775] Conducting growth forecasts

[0776] The server then launches a generative AI model based on the validated data. This AI model uses a growth curve algorithm and receives historical growth data and the parents' heights as input. Specifically, the AI ​​model predicts the child's height (e.g., 105 cm), weight (e.g., 17 kg), and shoe size (e.g., 16 cm) for the next six months.

[0777] Calculating and formatting forecast results

[0778] The server calculates appropriate clothing and shoe sizes based on the predictions obtained from the AI ​​model and the emotional data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. If the user's emotion is not positive, the server presents information in a more friendly format.

[0779] Sending and displaying result data

[0780] The server sends formatted JSON data to the device, which parses it and displays predictions to the user, including estimated future height, weight, foot size, and recommended clothing and shoe sizes. If the user's sentiment is not positive, the information is presented in a more user-friendly format.

[0781] Specific examples

[0782] Data Entry Example

[0783] The user inputs their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past growth data. For example, one month ago: 98 cm, 14 kg, three months ago: 95 cm, 13 kg, six months ago: 92 cm, 12 kg. The user also inputs the parents' heights (mother: 160 cm, father: 170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[0784] Data reception and growth forecasting

[0785] The server receives the data and emotion data entered by the user, validates them, and then uses the generative AI model to make predictions: specifically, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[0786] Displaying the results

[0787] The device receives the JSON data sent from the server and displays the analysis results to the user. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's emotions are not positive, the device displays the information in a more user-friendly format.

[0788] Prompt Sentence Examples

[0789] "Based on the child's growth data and user emotion data entered by the user, predict the child's growth for the next six months and suggest appropriate clothing and shoe sizes."

[0790] "Based on the child's growth data (1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg) and the parents' height information, predict the child's growth over the next 6 months and determine the recommended size."

[0791] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, and provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[0792] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0793] Step 1: Data entry and emotion recognition

[0794] The user uses a dedicated input form on their smartphone or computer to input their child's current height, weight, age, shoe size, past time series data, and the parents' height information. At this time, the camera and microphone are activated for emotion recognition. The input data is in a format that includes each item necessary for predicting a child's growth. The data obtained from the input is in the following format:

[0795] Height: 100 cm

[0796] Weight: 15 kg

[0797] Age: 3 years old

[0798] Foot size: 15 cm

[0799] Past data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0800] Parents' height: mother 160 cm, father 170 cm

[0801] The device captures the user's facial expressions and voice in real time using a camera and microphone, and collects this as emotion data. The input data and emotion data are temporarily stored as data entities within the system.

[0802] (Input) The user enters the child's height, weight, age, shoe size, historical time series data, parents' heights, and captured emotion data into the form.

[0803] (Output) A well-formed data entity.

[0804] Step 2: Convert the data to JSON format and send it

[0805] The device converts the collected user data and emotion data into JSON format. The converted data includes user input information and emotion information. The data converted into JSON format has the following format:

[0806] json

[0807] {

[0808] "current_height": 100,

[0809] "current_weight": 15,

[0810] "age": 3,

[0811] "foot_size": 15,

[0812] "past_data": [

[0813] {"height": 98, "weight": 14, "time": "1_month_ago"},

[0814] {"height": 95, "weight": 13, "time": "3_months_ago"},

[0815] {"height": 92, "weight": 12, "time": "6_months_ago"}

[0816] ],

[0817] "parent_heights": {"mother": 160, "father": 170},

[0818] "emotion_data": {"expression": "neutral", "voice_tone": "calm"}

[0819] }

[0820] The converted data is sent to the server using the HTTPS protocol. For security reasons, SSL / TLS is used.

[0821] (Input) A well-formed data entity.

[0822] (Output) The JSON data sent to the server.

[0823] Step 3: Receiving and validating data

[0824] The server receives the JSON data sent from the device. It checks the format of the received data and verifies that all required fields are included. For example, it checks that height, weight, age, etc. are entered in the correct format. The conditions for successful data validation are as follows:

[0825] Height and weight must be positive integers or floating point numbers.

[0826] Age is a positive integer.

[0827] (Input) JSON data sent from the terminal.

[0828] (Output) Flag indicating whether validation was successful.

[0829] Step 4: Perform growth projections

[0830] The server runs a generative AI model based on the validated data. The data is fed into the AI ​​model, which predicts the child's height, weight, and shoe size for the next six months, taking into account past growth data and parental height information. The output of the AI ​​model is as follows:

[0831] Predicted height after 6 months: 105 cm

[0832] Estimated weight after 6 months: 17 kg

[0833] Predicted foot size after 6 months: 16 cm

[0834] (Input) User data that has passed validation.

[0835] (Output) Predicted growth data (height, weight, foot size).

[0836] Step 5: Calculate and format the prediction results

[0837] The server calculates the appropriate clothing and shoe size based on the prediction results from the AI ​​model and the emotion data from the emotion engine. For example, based on a predicted height of 105 cm and foot size of 16 cm, it calculates a clothing size of 110 cm and a shoe size of 17 cm. The server also adjusts the display format of the information depending on the user's emotional state. If the emotion data is determined to be "not positive," the information is presented in a friendly format.

[0838] (Input) Prediction data, user emotion data.

[0839] (Output) Well-formed JSON data (prediction results, recommended size, adjusted display format).

[0840] Step 6: Send and display result data

[0841] The server sends the formatted JSON data to the terminal.

[0842] The device parses this data and displays it to the user, typically displaying something like this:

[0843] Predicted height after 6 months: 105 cm

[0844] Recommended clothing size: 110 cm

[0845] Recommended shoe size: 17 cm

[0846] If the user's emotion is "negative," the information is presented in a more user-friendly format, for example by using different colors and fonts to enhance the visual experience.

[0847] (Input) Well-formed JSON data sent from the server.

[0848] (Output) The predicted result and recommended size displayed to the user.

[0849] These detailed processing steps make the entire process clear, from user input to display of growth prediction results.

[0850] (Application example 2)

[0851] 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."

[0852] Conventional systems have had difficulty predicting appropriate clothing and shoe sizes as a child grows. Furthermore, the system does not display information that takes the user's emotional state into consideration, resulting in a poor user experience. The present invention aims to solve these problems by realizing a system that suggests appropriate clothing and shoe sizes for the user while providing a display format that takes emotions into consideration.

[0853] 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: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for recognizing and analyzing the user's emotional state in real time; and means for adjusting the display format of information according to the emotional state. This not only enables the user to select appropriate clothing and shoe sizes that suit their child's growth, but also provides emotionally sensitive information, enabling a less stressful shopping experience.

[0854] "Child's current height" is data that indicates the child's actual height that is entered into the system.

[0855] "Weight" is data entered into the system indicating the child's actual weight.

[0856] "Age" is data that indicates the actual number of years since birth of a child that is entered into the system.

[0857] "Parent height" is data indicating the height of each parent that is entered into the system for predicting the child's growth.

[0858] "Past time series data" refers to a sequence of data such as past height, weight, and shoe size that is entered into the system to track a child's growth.

[0859] "Foot size" is data indicating the actual foot length that is input into the system to determine a child's shoe size.

[0860] A "growth curve" is a mathematical model based on historical data that is used to predict a child's growth.

[0861] "Means for predicting future height, weight, and shoe size" refers to a function that uses input data to calculate a child's future growth parameters based on AI models and algorithms.

[0862] The "means for calculating appropriate clothing and shoe sizes" is a function for calculating clothing and shoe sizes that fit a child based on predicted growth data.

[0863] "Means for recognizing and analyzing the user's emotional state in real time" refers to a function that uses sensors such as a camera and microphone to capture the user's facial expressions and voice, and analyzes them using an emotion recognition engine.

[0864] The "means for adjusting the information display format" is a function that changes the way prediction results and suggestions are displayed depending on the user's emotional state.

[0865] The present invention aims to provide a system that predicts a child's growth, suggests appropriate clothing and shoe sizes, and improves the user experience by recognizing the user's emotions and adjusting the display format.

[0866] 1. Data Entry and Emotion Recognition

[0867] Users access the system using a smartphone or computer and enter the following information: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. When the user enters this information into a dedicated input form, the camera and microphone are used to capture the user's emotions in real time, which are then analyzed by an emotion engine. This is done using a mobile app using React Native or a web application using HTML5, CSS3, and JavaScript.

[0868] 2. Sending and Receiving Data

[0869] The device converts the user-entered data and emotion data into JSON format and sends it to the server using the HTTPS protocol. The Axios library is used to send the data. The server receives the sent data and prepares it for analysis. Server-side technologies used in this process include Node.js.

[0870] 3. Data validation and prediction

[0871] The server validates the received data. This includes checking that the input information is in the correct format and contains all required fields. For example, it checks that height and weight are not invalid values, and that age is not a decimal. If this validation is successful, the data proceeds to the next processing step. The server then runs an AI model based on the validated data. This AI model is built using TensorFlow and uses a growth curve algorithm to predict the child's future height, weight, and shoe size, taking into account past data and the heights of the parents.

[0872] 4. Formatting and displaying prediction results

[0873] The server calculates appropriate clothing and shoe sizes based on prediction data obtained from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. OpenFace and Emotion API are used for emotion recognition. For example, if the user's emotion is not positive, the information is presented in a more understandable and friendly format. The server sends this information to the device in JSON format. The device analyzes the data sent from the server and displays the results to the user. The displayed content includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and the optimal display format based on emotion recognition.

[0874] Specific examples

[0875] For example, if a user enters the following data:

[0876] Child's current height: 110 cm

[0877] Current weight: 18 kg

[0878] Age: 4 years old

[0879] Foot size: 17 cm

[0880] Parents' height: mother 160 cm, father 180 cm

[0881] Past growth data: 1 month ago 108 cm, 16 kg, 3 months ago 105 cm, 15 kg

[0882] The system returns the following prediction:

[0883] Predicted height after 6 months: 115 cm

[0884] Recommended clothing size: 120 cm

[0885] Recommended shoe size: 18 cm

[0886] Additionally, the display format of information based on emotion recognition will be adjusted as follows:

[0887] If the user's emotional state is not positive, provide information in a friendly tone.

[0888] Example prompts for generative AI models

[0889] "Predict a child's height, weight, and shoe size in the next 6 months. Use the parents' height data as well."

[0890] In this way, this invention allows users to easily obtain information for selecting clothes and shoes in sizes that are optimal for their child's growth, and provides information that takes emotions into consideration, thereby providing a better shopping experience.

[0891] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0892] Step 1:

[0893] Using a smartphone or computer, users input information about their child's current height, weight, age, shoe size, past time series data, and the heights of their parents. The input information is converted into JSON format. The device's camera and microphone are also used to capture the user's emotional data. This input becomes the basis for analysis by the system.

[0894] Step 2:

[0895] The terminal sends the user input data and captured emotion data to the server using the HTTPS protocol. The Axios library is used for data transmission. The output at this stage is JSON format data that is sent to the server.

[0896] Step 3:

[0897] The server parses and validates the received JSON data, for example, ensuring that the height is within a reasonable range, that the weight is not an invalid value, and that the age is an integer. This validation ensures that the data is in the correct format.

[0898] Step 4:

[0899] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict the child's future height, weight, and shoe size. The AI ​​model is built using TensorFlow. Based on the input data, it outputs predicted data for the next six months.

[0900] Step 5:

[0901] The server calculates appropriate clothing and shoe sizes based on the predicted data obtained from the AI ​​model. It also analyzes the user's emotional data using an emotion recognition engine (OpenFace or EmotionAPI) and determines the display format of information based on the user's emotional state. For example, if the user's emotions are not positive, the server will present information in a more user-friendly format.

[0902] Step 6:

[0903] The server sends formatted JSON data (prediction data, recommended size, display format) to the device, which receives, parses, and displays the data to the user. The display includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and display format information based on emotion recognition.

[0904] Step 7:

[0905] Users can check the information displayed on the device and select appropriate clothes and shoes based on predicted growth data. This information makes it easy for users to find the best products that will suit their child's growth.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] [Third embodiment]

[0910] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0911] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0912] 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).

[0913] 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.

[0914] 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.

[0915] 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).

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. The system aims to reduce unnecessary replacement purchases and clothing disposal by predicting future growth based on data provided by the user and suggesting appropriate sizes.

[0923] 1. Data Entry

[0924] Users use a smartphone or computer to enter the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. Users enter this information using a dedicated input form.

[0925] 2. Sending and Receiving Data

[0926] The terminal sends the data entered by the user to the server using the HTTPS protocol, structured in JSON format, after which the server receives it and prepares it for analysis.

[0927] 3. Data Validation

[0928] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[0929] 4. Conducting growth forecasts

[0930] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[0931] 5. Calculation and format of forecast results

[0932] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[0933] 6. Sending and displaying result data

[0934] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[0935] Specific examples

[0936] Data Entry Example

[0937] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm).

[0938] Data reception and growth forecasting

[0939] The server receives the input data, validates it, and then uses the AI ​​model to make predictions, such as predicting that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[0940] Displaying the results

[0941] The device analyzes the received data and displays the predicted results to the user, such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm."

[0942] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, realizing efficient shopping and reducing environmental impact.

[0943] The processing flow will be explained below.

[0944] Step 1:

[0945] Users enter their child's current height, weight, age, shoe size, past time series data, and the heights of their parents into a dedicated input form, which is displayed on a smartphone or computer application.

[0946] Step 2:

[0947] The terminal converts the data entered by the user into JSON format, which is then sent to the server using the HTTPS protocol.

[0948] Step 3:

[0949] The server receives the HTTPS request and parses the received JSON data to extract each data item, including height, weight, age, shoe size, past data, and parent's heights.

[0950] Step 4:

[0951] The server validates the received data, checking that it is in the correct format and that all required fields are included (for example, height is greater than 0 cm, age is not a decimal point, etc.).

[0952] Step 5:

[0953] The server passes the validated data to an AI model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents.

[0954] Step 6:

[0955] The server receives the AI ​​model's predictions and calculates the appropriate clothing and shoe size based on them. For example, if the predicted height in six months is 105 cm, weight 17 kg, and shoe size 16 cm, the server calculates the clothing size as 110 cm and shoe size as 17 cm.

[0956] Step 7:

[0957] The server converts the prediction results into JSON format and sends them to the device, which includes the predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[0958] Step 8:

[0959] The device receives and analyzes the JSON data sent from the server, and based on the analysis results, displays the predicted results and recommended sizes to the user.

[0960] Step 9:

[0961] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed.

[0962] Example 1

[0963] 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."

[0964] Currently, there is a lack of methods to predict appropriate clothing and shoe sizes as children grow, resulting in frequent replacements and incorrect size selection, resulting in a lot of waste. Incorrect growth predictions also increase unnecessary waste and increase the environmental burden. Therefore, there is a need for a system that can accurately predict children's growth and recommend appropriate clothing and shoe sizes based on that prediction.

[0965] 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.

[0966] In this invention, the server includes: means for a user to input information about the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for a terminal to transmit the input information to the server; means for the server to check the format of the received information and whether all required fields are included; means for the server to use a generative AI model to predict the child's future height, weight, and shoe size using a growth curve algorithm based on the received information; means for the server to calculate appropriate clothing and shoe sizes based on the predicted information; and means for the terminal to display the calculated clothing and shoe sizes to the user. This allows the user to select appropriate clothing and shoe sizes based on the child's growth, thereby reducing unnecessary replacement purchases and waste.

[0967] "User" refers to a person who utilizes the system to input their child's growth data and obtain growth predictions and size recommendations.

[0968] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to input data.

[0969] "Server" refers to a computer system that receives, analyzes, and processes data sent from a terminal.

[0970] "Information on height, weight, age, parents' height, past time series data, and shoe size" refers to basic data for a child's growth that the user inputs via the terminal.

[0971] "Data validation" refers to the process of checking whether the format and content of entered data are correct and whether all required fields are included.

[0972] A "growth curve algorithm" refers to a mathematical formula or calculation method for predicting a child's future growth based on past growth data.

[0973] A "generative AI model" refers to artificial intelligence that uses growth curve algorithms to predict future height, weight, and shoe size.

[0974] "JSON format" refers to a lightweight data exchange format for structuring and representing data.

[0975] "Method for calculating appropriate clothing and shoe sizes" refers to the process of calculating appropriate clothing and shoe sizes for a child's future growth based on growth projections.

[0976] "Means for displaying to the user" refers to the process of displaying the predicted height, weight, foot size, and recommended clothing and shoe sizes based on ... shoe sizes on the device.

[0977] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. Users of the system input their child's current height, weight, age, historical height and weight data, and the heights of their parents using a smartphone or computer.

[0978] Data Entry

[0979] The user uses a dedicated input form to input the child's current height, weight, age, shoe size, historical height and weight data, and the heights of the parents. For example, the user enters the following information:

[0980] Current height: 100 cm

[0981] Current weight: 15 kg

[0982] Age: 3 years old

[0983] Foot size: 15 cm

[0984] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[0985] Parents' height: Mother 160 cm, Father 170 cm

[0986] Sending and Receiving Data

[0987] The terminal sends the input data to the server using the HTTPS protocol. The data format is JSON, and the sent data is saved on the server.

[0988] Data Validation

[0989] The server validates the data it receives, ensuring it is in the correct format and that all required fields are included. It checks for invalid values ​​for height and weight, decimals for age, etc. If validation is successful, it proceeds to the next step.

[0990] Conducting growth forecasts

[0991] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, the following predictions can be obtained:

[0992] Height after 6 months: 105 cm

[0993] Weight after 6 months: 17 kg

[0994] Foot size after 6 months: 16 cm

[0995] Calculating and formatting forecast results

[0996] The server calculates the appropriate clothing and shoe size based on the predicted data obtained from the AI ​​model. The prediction results are formatted in JSON format and include the following information:

[0997] Estimated height: 105 cm

[0998] Estimated weight: 17 kg

[0999] Estimated foot size: 16 cm

[1000] Recommended clothing size: 110 cm

[1001] Recommended shoe size: 17 cm

[1002] Sending and displaying result data

[1003] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[1004] Specific examples

[1005] A specific example of data entry would be the following prompt:

[1006] "My child is currently 100 cm tall, weighs 15 kg, is 3 years old, and has a shoe size of 15 cm. One month ago, his height was 98 cm and his weight was 14 kg. His parents' heights are 160 cm and 170 cm, respectively. Given this information, what are his predicted height, weight, shoe size, and recommended clothing and shoe sizes for the next 6 months?"

[1007] This system allows users to select the optimal size of clothes and shoes as their child grows, reducing unnecessary replacements and waste.

[1008] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1009] Step 1: Data entry

[1010] The user inputs the child's current height, weight, age, past time series data, shoe size, and parents' heights into a dedicated input form. For example, the following information is input:

[1011] Current height: 100 cm

[1012] Current weight: 15 kg

[1013] Age: 3 years old

[1014] Foot size: 15 cm

[1015] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[1016] Parents' height: Mother 160 cm, Father 170 cm

[1017] Input data: Multiple data items entered by the user

[1018] Output data: Child growth data entered in the input form

[1019] Step 2: Sending data

[1020] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. For example, the input data is converted into the following JSON format.

[1021] json

[1022] {

[1023] "height": 100,

[1024] "weight": 15,

[1025] "age": 3,

[1026] "footSize": 15,

[1027] "pastData": [

[1028] {"monthsAgo": 1, "height": 98, "weight": 14},

[1029] {"monthsAgo": 3, "height": 95, "weight": 13},

[1030] {"monthsAgo": 6, "height": 92, "weight": 12}

[1031] ],

[1032] "parentHeights": {"mother": 160, "father": 170}

[1033] }

[1034] Input data: Growth data entered by the user

[1035] Output data: Data converted to JSON format

[1036] Step 3: Receiving the data

[1037] The server receives the JSON format data sent from the device using the HTTPS protocol. The data is stored on the server and used for subsequent processing.

[1038] Input data: JSON format data sent via HTTPS protocol

[1039] Output data: Growth data stored on the server

[1040] Step 4: Validate the data

[1041] The server validates the received data. Specifically, it checks whether the data format is correct and whether all required fields are included. For example, it checks whether height and weight are negative values ​​and whether age is a decimal. If validation is successful, it proceeds to the next step.

[1042] Input data: Growth data stored on the server

[1043] Output data: Validation result (pass or fail)

[1044] Step 5: Perform growth projections

[1045] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict future height, weight, and shoe size. For example, the following prediction results can be obtained:

[1046] Height after 6 months: 105 cm

[1047] Weight after 6 months: 17 kg

[1048] Foot size after 6 months: 16 cm

[1049] Input data: Growth data that has passed validation

[1050] Output data: Prediction results (estimated height, weight, and shoe size for the next 6 months)

[1051] Step 6: Calculate and format the forecast results

[1052] The server calculates the appropriate clothing and shoe sizes based on the predicted data obtained from the generative AI model and formats it in JSON format. For example, it generates JSON data containing the following information:

[1053] json

[1054] {

[1055] "predictedHeight": 105,

[1056] "predictedWeight": 17,

[1057] "predictedFootSize": 16,

[1058] "recommendedClothingSize": 110,

[1059] "recommendedShoeSize": 17

[1060] }

[1061] Input data: Prediction data from a generative AI model

[1062] Output data: Result data formatted in JSON format

[1063] Step 7: Send and display result data

[1064] The server sends the resulting data, formatted in JSON, to the device, which receives, analyzes, and displays it to the user. The displayed information includes predicted future height, weight, foot size, and recommended clothing and shoe sizes, allowing the user to choose the optimal size for their next purchase.

[1065] Input data: Result data sent from the server (JSON format)

[1066] Output data: What is displayed to the user (predicted results and recommended sizes)

[1067] Through these steps, the system accurately predicts a child's growth and suggests optimal clothing and shoe sizes, thereby reducing unnecessary replacement purchases and waste.

[1068] (Application example 1)

[1069] 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."

[1070] Traditionally, it has been difficult for many parents to select the appropriate clothing and shoe sizes as their children grow. In particular, when shopping in anticipation of future growth, mistakes in size selection frequently occur, resulting in unnecessary replacements and disposal. Furthermore, the lack of systems that suggest appropriate sized products based on growth predictions is one factor that degrades the online shopping experience. For this reason, there has been a demand for a system that provides accurate size selection, efficient shopping, and reduces environmental impact.

[1071] 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.

[1072] In this invention, the server includes: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and foot size; means for receiving the input information; means for predicting the child's future height, weight, and foot size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for suggesting related product sizes using the calculated information; and means for linking the purchase information of the suggested products to an online shopping site. This allows users to select accurate clothing and shoe sizes that take future growth into consideration, thereby reducing unnecessary replacement and disposal. Furthermore, linking with an online shopping site improves the online shopping experience by suggesting products based on growth predictions.

[1073] "Information on the child's current height, weight, age, parents' height, past time series data, and foot size" refers to the basic data required to predict a child's growth, specifically including the child's current height, weight, age, parents' height, data on changes in height and weight over a certain period of time in the past, and foot size information.

[1074] "Input means" refers to the interface or device that allows the user to provide the system with the necessary information about the child's development.

[1075] "Means for receiving" refers to a mechanism or function that takes information entered by a user into the system and stores it in a processable format.

[1076] "Prediction means" refers to methods or algorithms for estimating future height, weight, and shoe size based on the received information using growth curves or AI models.

[1077] "Means for calculating" refers to the process or method for calculating appropriate clothing and shoe sizes based on predicted future information.

[1078] "Display means" refers to a screen or device for visually presenting the calculated clothing and shoe size information to the user.

[1079] "Means for suggesting" refers to a system and process for selecting sizes of related products based on the calculated size information and providing them to the user.

[1080] "Means of collaboration" refers to a system or interface that shares purchase information for proposed products with the online shopping site and facilitates the purchase process.

[1081] "Online shopping site" refers to a web-based platform that allows customers to search for, purchase, and complete delivery procedures for products online.

[1082] This invention is a system that predicts a child's growth and suggests appropriate clothing and shoe sizes. The system uses input growth data to predict the child's future height, weight, and shoe size, and then calculates and provides the most appropriate clothing and shoe sizes to the user. Furthermore, by linking this information with online shopping sites, the system facilitates the purchase process.

[1083] 1. Data Entry

[1084] The user uses a smartphone or computer to input the following information: current height, weight, age, past time series data (e.g., height and weight from 1 month ago, 3 months ago, and 6 months ago), shoe size, and parents' heights. This information is then provided to the system using a dedicated input form.

[1085] 2. Sending and Receiving Data

[1086] The terminal sends the data entered by the user to the server using the HTTPS protocol. The data is structured in JSON format. The server receives it and prepares it for data analysis.

[1087] 3. Data Validation

[1088] The server validates the data it receives, ensuring that the information entered is in the correct format and that all required fields are included. If this validation is successful, it proceeds to the next step.

[1089] 4. Conducting growth forecasts

[1090] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[1091] 5. Calculation and format of forecast results

[1092] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[1093] 6. Sending and displaying result data

[1094] The server sends formatted JSON data to the device, which receives it and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[1095] 7. Collaboration with online shopping sites

[1096] The system uses the predicted size information to suggest appropriate size products from the product database of related online retailers, allowing users to easily purchase clothes and shoes that fit their growing child's size.

[1097] As a specific example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg), mother's height (160 cm), and father's height (170 cm). The server validates this data and makes a prediction using a generative AI model. For example, it predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm over the next 6 months. Based on this information, the device displays the recommended clothing size of 110 cm and shoe size of 17 cm to the user. It also connects to an online shopping site to find products that fit these sizes, making it easy for the user to purchase them.

[1098] An example prompt has the following format:

[1099] "Predict your child's growth. Use the following information: Current height: 100 cm Current weight: 15 kg Age: 3 years Shoe size: 15 cm Additionally, you have the following historical data: 1 month ago height: 98 cm, weight: 14 kg 3 months ago height: 95 cm, weight: 13 kg 6 months ago height: 92 cm, weight: 12 kg Parents' heights: Mother: 160 cm, Father: 170 cm"

[1100] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1101] Step 1:

[1102] The user enters information about the child's development.

[1103] Input: Current height, weight, age, shoe size, historical height and weight data, and parents' heights.

[1104] What it does: A user enters data into a special form on their smartphone or computer. The form is designed so that all required fields are filled in.

[1105] Step 2:

[1106] The terminal transmits the input data to the server.

[1107] Input: Growth-related data entered by the user.

[1108] Data processing or data operation: Data is structured in JSON format.

[1109] Output: Structured JSON data.

[1110] Specific operation: The terminal uses the HTTPS protocol to convert the data entered by the user into JSON format and send it to the server.

[1111] Step 3:

[1112] The server receives the submitted data and validates it.

[1113] Input: User data in JSON format.

[1114] Data processing or data calculation: Check the format of input data and check the existence of required fields.

[1115] Output: The data that was successfully validated, or an error message.

[1116] Specific operation: The server parses the JSON data format and checks whether all the required information is included. If the check result is correct, it proceeds to the next step.

[1117] Step 4:

[1118] The server uses an AI model to predict growth.

[1119] Input: User data that passes validation.

[1120] Data processing or data calculation: Input into a generative AI model and apply a growth curve algorithm.

[1121] Output: Predicted future height, weight, and shoe size.

[1122] How it works: The server runs an AI model (e.g., TensorFlow) to predict future height, weight, and shoe size based on the input data.

[1123] Step 5:

[1124] The server calculates appropriate clothing and shoe sizes based on the predicted data.

[1125] Input: Predicted growth data.

[1126] Data processing or data calculation: calculation of recommended size and formatting of data.

[1127] Output: Recommended clothing and shoe sizes, formatted JSON data.

[1128] What it does: The server analyzes the predicted data, calculates appropriate clothing and shoe sizes based on growth predictions, and formats the information into JSON format.

[1129] Step 6:

[1130] The server sends the formatted data to the terminal.

[1131] Input: Well-formed JSON data.

[1132] Data processing or data calculation: None (simple transfer of data).

[1133] Output: Sending data to the user's terminal.

[1134] Specific operation: The server again uses the HTTPS protocol to send the formatted JSON data to the terminal.

[1135] Step 7:

[1136] The terminal displays the received data to the user.

[1137] Input: Formatted JSON data received from the server.

[1138] Data processing or data operation: Parsing JSON data and converting the display format.

[1139] Output: Growth prediction results and recommended size displayed to the user.

[1140] Specific operation: The device parses the received JSON data and displays it on the user's screen as specific values ​​(estimated height, weight, foot size, and recommended clothing and shoe sizes).

[1141] Step 8:

[1142] The system will suggest products of appropriate sizes from related online shopping sites and link purchasing information.

[1143] Input: Recommended size information.

[1144] Data processing or data calculation: Matching with the product database of the online shopping site.

[1145] Output: Right-sized product suggestions and purchasing options.

[1146] Specific operation: Based on the predicted size information, the system searches the product database of related online shopping sites, suggests products of appropriate sizes to the user, and assists in the purchase process of the suggested products.

[1147] 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.

[1148] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[1149] 1. Data Entry and Emotion Recognition

[1150] Users use their smartphones or computers to input the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the parents' heights. Users enter this information using a dedicated input form. At the same time, the system recognizes the user's emotions in real time through the camera and microphone and analyzes them using an emotion engine.

[1151] 2. Sending and Receiving Data

[1152] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives it and prepares it for data analysis.

[1153] 3. Data Validation

[1154] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[1155] 4. Conducting growth forecasts

[1156] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size, taking into account past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[1157] 5. Calculation and format of forecast results

[1158] The server calculates appropriate clothing and shoe sizes based on prediction data from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format and recommendations based on the user's emotional state. For example, if the user's emotion is not positive, it presents information in a more understandable and friendly format.

[1159] 6. Sending and displaying result data

[1160] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, recommended clothing and shoe sizes, and optimal display formatting based on emotion recognition.

[1161] Specific examples

[1162] Data Entry Example

[1163] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[1164] Data reception and growth forecasting

[1165] The server receives the input data and emotion data from the emotion engine, validates them, and then uses the AI ​​model to make predictions. For example, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months. Emotion data is also analyzed at the same time.

[1166] Displaying the results

[1167] The device receives the JSON data sent from the server and displays it to the user based on the prediction results and sentiment analysis data. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's sentiment is not positive, the information is displayed in a more friendly format.

[1168] This embodiment allows the user to select clothes and shoes of appropriate sizes according to the child's growth, and further provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[1169] The processing flow will be explained below.

[1170] Step 1:

[1171] The user launches the application and opens the "Child Growth Prediction" screen. They enter their child's current height, weight, age, shoe size, past time series data, and the parents' heights into the form on the screen. The camera and microphone are then activated, capturing the user's facial expressions and voice in real time and sending them to the emotion engine.

[1172] Step 2:

[1173] The device converts the input data and captured emotion data into JSON format and sends it to the server using the HTTPS protocol. The transmitted data includes specific numerical values ​​for each item and the emotion analysis results.

[1174] Step 3:

[1175] The server receives the HTTPS request and parses the JSON data to extract each data item, including height, weight, age, shoe size, historical time series data, parents' heights, and emotion data.

[1176] Step 4:

[1177] The server validates the received data, specifically checking that the height and weight are within a reasonable range, that the age is not a decimal, that the shoe size is valid, and that all required information is present. If validation is successful, the server proceeds to the next step.

[1178] Step 5:

[1179] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents. For example, it predicts the next six months based on growth data from the past six months.

[1180] Step 6:

[1181] The server receives the prediction data from the AI ​​model and calculates the appropriate clothing and shoe size based on that data. At the same time, it also analyzes the received emotional data and adjusts the way the prediction results are presented. For example, if the user's emotional state is not positive, the information will be displayed in a more friendly manner.

[1182] Step 7:

[1183] The server then converts the prediction results and emotional data into the optimal display format in JSON format and sends it to the device. This data includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and emotional analysis results.

[1184] Step 8:

[1185] The device receives and analyzes the JSON data sent from the server. Based on the analysis results, it displays predictions and recommended sizes to the user. Displayed information includes "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," "Recommended shoe size: 17 cm," and so on. If the user's emotions are not positive, the display format is adjusted, such as by adding more explanatory text or using gentler colors.

[1186] Step 9:

[1187] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed. The friendly display provides a better user experience.

[1188] Example 2

[1189] 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."

[1190] Conventional child growth prediction systems predict growth curves and suggest appropriate sizes, but they do not provide information that takes user emotions into consideration, resulting in a poor user experience. Furthermore, input information is not properly checked, and prediction results based on incorrect data are sometimes displayed. Furthermore, there is a lack of systems that can make predictions using specific data formats.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1192] In this invention, the server includes: means for inputting information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for capturing and analyzing user emotion data; and means for adjusting the display format based on the emotion data. This allows for the provision of information that takes the user's emotions into consideration, providing a better user experience. Furthermore, by checking the format of the input information and confirming required fields, the accuracy of the prediction results can be improved.

[1193] "Child's current height" is the child's most recent height, which serves as the basis for the prediction.

[1194] "Weight" is the child's most recent weight, which serves as the basis for the prediction.

[1195] "Age" is the age of the child that the prediction is based on, expressed in months or years.

[1196] "Parental height" refers to the heights of both parents that affect the child's growth prediction. This usually includes the heights of the father and mother.

[1197] "Past time-series data" refers to height and weight data that a child has entered in the past, and is obtained at multiple points in time.

[1198] "Foot size" refers to the actual measurement of a child's current foot size, and is data used to determine the appropriate shoe size.

[1199] The "means for inputting" is a means for the user to input information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size.

[1200] The "receiving means" is a means by which the system receives information input by the user.

[1201] A "growth chart" is a graph showing normal growth patterns used to predict a child's growth.

[1202] A "means for predicting" is a means for predicting future height, weight, and shoe size using growth curves.

[1203] The "means for calculating" is a means for calculating the appropriate clothing size and shoe size based on the predicted information.

[1204] The "display means" is a means for displaying the calculated clothing and shoe sizes to the user.

[1205] The "means for capturing and analyzing emotional data" refers to a means for obtaining the user's emotions through a camera or microphone and analyzing the data.

[1206] The "means for adjusting the display format" is a means for optimizing the way information is displayed based on the user's emotional data.

[1207] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[1208] Data Entry and Emotion Recognition

[1209] Users use a smartphone or computer to enter their child's current height, weight, age, shoe size, past height and weight data over time, and the parents' heights into the system. As users enter information using the input form, the system uses a camera and microphone to recognize the user's emotions in real time and analyzes them using the emotion engine. For example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). They also enter the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter information.

[1210] Sending and Receiving Data

[1211] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives the data and prepares it for data analysis.

[1212] Data Validation

[1213] The server checks the format of the received data and verifies that the input information is correct, checking for required fields. For example, it checks that height and weight are numeric, and age is an integer. If all validations are successful, it proceeds to the next processing step.

[1214] Conducting growth forecasts

[1215] The server then launches a generative AI model based on the validated data. This AI model uses a growth curve algorithm and receives historical growth data and the parents' heights as input. Specifically, the AI ​​model predicts the child's height (e.g., 105 cm), weight (e.g., 17 kg), and shoe size (e.g., 16 cm) for the next six months.

[1216] Calculating and formatting forecast results

[1217] The server calculates appropriate clothing and shoe sizes based on the predictions obtained from the AI ​​model and the emotional data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. If the user's emotion is not positive, the server presents information in a more friendly format.

[1218] Sending and displaying result data

[1219] The server sends formatted JSON data to the device, which parses it and displays predictions to the user, including estimated future height, weight, foot size, and recommended clothing and shoe sizes. If the user's sentiment is not positive, the information is presented in a more user-friendly format.

[1220] Specific examples

[1221] Data Entry Example

[1222] The user inputs their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past growth data. For example, one month ago: 98 cm, 14 kg, three months ago: 95 cm, 13 kg, six months ago: 92 cm, 12 kg. The user also inputs the parents' heights (mother: 160 cm, father: 170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[1223] Data reception and growth forecasting

[1224] The server receives the data and emotion data entered by the user, validates them, and then uses the generative AI model to make predictions: specifically, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[1225] Displaying the results

[1226] The device receives the JSON data sent from the server and displays the analysis results to the user. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's emotions are not positive, the device displays the information in a more user-friendly format.

[1227] Prompt Sentence Examples

[1228] "Based on the child's growth data and user emotion data entered by the user, predict the child's growth for the next six months and suggest appropriate clothing and shoe sizes."

[1229] "Based on the child's growth data (1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg) and the parents' height information, predict the child's growth over the next 6 months and determine the recommended size."

[1230] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, and provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[1231] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1232] Step 1: Data entry and emotion recognition

[1233] The user uses a dedicated input form on their smartphone or computer to input their child's current height, weight, age, shoe size, past time series data, and the parents' height information. At this time, the camera and microphone are activated for emotion recognition. The input data is in a format that includes each item necessary for predicting a child's growth. The data obtained from the input is in the following format:

[1234] Height: 100 cm

[1235] Weight: 15 kg

[1236] Age: 3 years old

[1237] Foot size: 15 cm

[1238] Past data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[1239] Parents' height: mother 160 cm, father 170 cm

[1240] The device captures the user's facial expressions and voice in real time using a camera and microphone, and collects this as emotion data. The input data and emotion data are temporarily stored as data entities within the system.

[1241] (Input) The user enters the child's height, weight, age, shoe size, historical time series data, parents' heights, and captured emotion data into the form.

[1242] (Output) A well-formed data entity.

[1243] Step 2: Convert the data to JSON format and send it

[1244] The device converts the collected user data and emotion data into JSON format. The converted data includes user input information and emotion information. The data converted into JSON format has the following format:

[1245] json

[1246] {

[1247] "current_height": 100,

[1248] "current_weight": 15,

[1249] "age": 3,

[1250] "foot_size": 15,

[1251] "past_data": [

[1252] {"height": 98, "weight": 14, "time": "1_month_ago"},

[1253] {"height": 95, "weight": 13, "time": "3_months_ago"},

[1254] {"height": 92, "weight": 12, "time": "6_months_ago"}

[1255] ],

[1256] "parent_heights": {"mother": 160, "father": 170},

[1257] "emotion_data": {"expression": "neutral", "voice_tone": "calm"}

[1258] }

[1259] The converted data is sent to the server using the HTTPS protocol. For security reasons, SSL / TLS is used.

[1260] (Input) A well-formed data entity.

[1261] (Output) The JSON data sent to the server.

[1262] Step 3: Receiving and validating data

[1263] The server receives the JSON data sent from the device. It checks the format of the received data and verifies that all required fields are included. For example, it checks that height, weight, age, etc. are entered in the correct format. The conditions for successful data validation are as follows:

[1264] Height and weight must be positive integers or floating point numbers.

[1265] Age is a positive integer.

[1266] (Input) JSON data sent from the terminal.

[1267] (Output) Flag indicating whether validation was successful.

[1268] Step 4: Perform growth projections

[1269] The server runs a generative AI model based on the validated data. The data is fed into the AI ​​model, which predicts the child's height, weight, and shoe size for the next six months, taking into account past growth data and parental height information. The output of the AI ​​model is as follows:

[1270] Predicted height after 6 months: 105 cm

[1271] Estimated weight after 6 months: 17 kg

[1272] Predicted foot size after 6 months: 16 cm

[1273] (Input) User data that has passed validation.

[1274] (Output) Predicted growth data (height, weight, foot size).

[1275] Step 5: Calculate and format the prediction results

[1276] The server calculates the appropriate clothing and shoe size based on the prediction results from the AI ​​model and the emotion data from the emotion engine. For example, based on a predicted height of 105 cm and foot size of 16 cm, it calculates a clothing size of 110 cm and a shoe size of 17 cm. The server also adjusts the display format of the information depending on the user's emotional state. If the emotion data is determined to be "not positive," the information is presented in a friendly format.

[1277] (Input) Prediction data, user emotion data.

[1278] (Output) Well-formed JSON data (prediction results, recommended size, adjusted display format).

[1279] Step 6: Send and display result data

[1280] The server sends the formatted JSON data to the terminal.

[1281] The device parses this data and displays it to the user, typically displaying something like this:

[1282] Predicted height after 6 months: 105 cm

[1283] Recommended clothing size: 110 cm

[1284] Recommended shoe size: 17 cm

[1285] If the user's emotion is "negative," the information is presented in a more user-friendly format, for example by using different colors and fonts to enhance the visual experience.

[1286] (Input) Well-formed JSON data sent from the server.

[1287] (Output) The predicted result and recommended size displayed to the user.

[1288] These detailed processing steps make the entire process clear, from user input to display of growth prediction results.

[1289] (Application example 2)

[1290] 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."

[1291] Conventional systems have had difficulty predicting appropriate clothing and shoe sizes as a child grows. Furthermore, the system does not display information that takes the user's emotional state into consideration, resulting in a poor user experience. The present invention aims to solve these problems by realizing a system that suggests appropriate clothing and shoe sizes for the user while providing a display format that takes emotions into consideration.

[1292] 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: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for recognizing and analyzing the user's emotional state in real time; and means for adjusting the display format of information according to the emotional state. This not only enables the user to select appropriate clothing and shoe sizes that suit their child's growth, but also provides emotionally sensitive information, enabling a less stressful shopping experience.

[1293] "Child's current height" is data that indicates the child's actual height that is entered into the system.

[1294] "Weight" is data entered into the system indicating the child's actual weight.

[1295] "Age" is data that indicates the actual number of years since birth of a child that is entered into the system.

[1296] "Parent height" is data indicating the height of each parent that is entered into the system for predicting the child's growth.

[1297] "Past time series data" refers to a sequence of data such as past height, weight, and shoe size that is entered into the system to track a child's growth.

[1298] "Foot size" is data indicating the actual foot length that is input into the system to determine a child's shoe size.

[1299] A "growth curve" is a mathematical model based on historical data that is used to predict a child's growth.

[1300] "Means for predicting future height, weight, and shoe size" refers to a function that uses input data to calculate a child's future growth parameters based on AI models and algorithms.

[1301] The "means for calculating appropriate clothing and shoe sizes" is a function for calculating clothing and shoe sizes that fit a child based on predicted growth data.

[1302] "Means for recognizing and analyzing the user's emotional state in real time" refers to a function that uses sensors such as a camera and microphone to capture the user's facial expressions and voice, and analyzes them using an emotion recognition engine.

[1303] The "means for adjusting the information display format" is a function that changes the way prediction results and suggestions are displayed depending on the user's emotional state.

[1304] The present invention aims to provide a system that predicts a child's growth, suggests appropriate clothing and shoe sizes, and improves the user experience by recognizing the user's emotions and adjusting the display format.

[1305] 1. Data Entry and Emotion Recognition

[1306] Users access the system using a smartphone or computer and enter the following information: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. When the user enters this information into a dedicated input form, the camera and microphone are used to capture the user's emotions in real time, which are then analyzed by an emotion engine. This is done using a mobile app using React Native or a web application using HTML5, CSS3, and JavaScript.

[1307] 2. Sending and Receiving Data

[1308] The device converts the user-entered data and emotion data into JSON format and sends it to the server using the HTTPS protocol. The Axios library is used to send the data. The server receives the sent data and prepares it for analysis. Server-side technologies used in this process include Node.js.

[1309] 3. Data validation and prediction

[1310] The server validates the received data. This includes checking that the input information is in the correct format and contains all required fields. For example, it checks that height and weight are not invalid values, and that age is not a decimal. If this validation is successful, the data proceeds to the next processing step. The server then runs an AI model based on the validated data. This AI model is built using TensorFlow and uses a growth curve algorithm to predict the child's future height, weight, and shoe size, taking into account past data and the heights of the parents.

[1311] 4. Formatting and displaying prediction results

[1312] The server calculates appropriate clothing and shoe sizes based on prediction data obtained from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. OpenFace and Emotion API are used for emotion recognition. For example, if the user's emotion is not positive, the information is presented in a more understandable and friendly format. The server sends this information to the device in JSON format. The device analyzes the data sent from the server and displays the results to the user. The displayed content includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and the optimal display format based on emotion recognition.

[1313] Specific examples

[1314] For example, if a user enters the following data:

[1315] Child's current height: 110 cm

[1316] Current weight: 18 kg

[1317] Age: 4 years old

[1318] Foot size: 17 cm

[1319] Parents' height: mother 160 cm, father 180 cm

[1320] Past growth data: 1 month ago 108 cm, 16 kg, 3 months ago 105 cm, 15 kg

[1321] The system returns the following prediction:

[1322] Predicted height after 6 months: 115 cm

[1323] Recommended clothing size: 120 cm

[1324] Recommended shoe size: 18 cm

[1325] Additionally, the display format of information based on emotion recognition will be adjusted as follows:

[1326] If the user's emotional state is not positive, provide information in a friendly tone.

[1327] Example prompts for generative AI models

[1328] "Predict a child's height, weight, and shoe size in the next 6 months. Use the parents' height data as well."

[1329] In this way, this invention allows users to easily obtain information for selecting clothes and shoes in sizes that are optimal for their child's growth, and provides information that takes emotions into consideration, thereby providing a better shopping experience.

[1330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1331] Step 1:

[1332] Using a smartphone or computer, users input information about their child's current height, weight, age, shoe size, past time series data, and the heights of their parents. The input information is converted into JSON format. The device's camera and microphone are also used to capture the user's emotional data. This input becomes the basis for analysis by the system.

[1333] Step 2:

[1334] The terminal sends the user input data and captured emotion data to the server using the HTTPS protocol. The Axios library is used for data transmission. The output at this stage is JSON format data that is sent to the server.

[1335] Step 3:

[1336] The server parses and validates the received JSON data, for example, ensuring that the height is within a reasonable range, that the weight is not an invalid value, and that the age is an integer. This validation ensures that the data is in the correct format.

[1337] Step 4:

[1338] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict the child's future height, weight, and shoe size. The AI ​​model is built using TensorFlow. Based on the input data, it outputs predicted data for the next six months.

[1339] Step 5:

[1340] The server calculates appropriate clothing and shoe sizes based on the predicted data obtained from the AI ​​model. It also analyzes the user's emotional data using an emotion recognition engine (OpenFace or EmotionAPI) and determines the display format of information based on the user's emotional state. For example, if the user's emotions are not positive, the server will present information in a more user-friendly format.

[1341] Step 6:

[1342] The server sends formatted JSON data (prediction data, recommended size, display format) to the device, which receives, parses, and displays the data to the user. The display includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and display format information based on emotion recognition.

[1343] Step 7:

[1344] Users can check the information displayed on the device and select appropriate clothes and shoes based on predicted growth data. This information makes it easy for users to find the best products that will suit their child's growth.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] [Fourth embodiment]

[1349] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1350] 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.

[1351] 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).

[1352] 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.

[1353] 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.

[1354] 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).

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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."

[1362] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. The system aims to reduce unnecessary replacement purchases and clothing disposal by predicting future growth based on data provided by the user and suggesting appropriate sizes.

[1363] 1. Data Entry

[1364] Users use a smartphone or computer to enter the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. Users enter this information using a dedicated input form.

[1365] 2. Sending and Receiving Data

[1366] The terminal sends the data entered by the user to the server using the HTTPS protocol, structured in JSON format, after which the server receives it and prepares it for analysis.

[1367] 3. Data Validation

[1368] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[1369] 4. Conducting growth forecasts

[1370] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[1371] 5. Calculation and format of forecast results

[1372] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[1373] 6. Sending and displaying result data

[1374] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[1375] Specific examples

[1376] Data Entry Example

[1377] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm).

[1378] Data reception and growth forecasting

[1379] The server receives the input data, validates it, and then uses the AI ​​model to make predictions, such as predicting that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[1380] Displaying the results

[1381] The device analyzes the received data and displays the predicted results to the user, such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm."

[1382] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, realizing efficient shopping and reducing environmental impact.

[1383] The processing flow will be explained below.

[1384] Step 1:

[1385] Users enter their child's current height, weight, age, shoe size, past time series data, and the heights of their parents into a dedicated input form, which is displayed on a smartphone or computer application.

[1386] Step 2:

[1387] The terminal converts the data entered by the user into JSON format, which is then sent to the server using the HTTPS protocol.

[1388] Step 3:

[1389] The server receives the HTTPS request and parses the received JSON data to extract each data item, including height, weight, age, shoe size, past data, and parent's heights.

[1390] Step 4:

[1391] The server validates the received data, checking that it is in the correct format and that all required fields are included (for example, height is greater than 0 cm, age is not a decimal point, etc.).

[1392] Step 5:

[1393] The server passes the validated data to an AI model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents.

[1394] Step 6:

[1395] The server receives the AI ​​model's predictions and calculates the appropriate clothing and shoe size based on them. For example, if the predicted height in six months is 105 cm, weight 17 kg, and shoe size 16 cm, the server calculates the clothing size as 110 cm and shoe size as 17 cm.

[1396] Step 7:

[1397] The server converts the prediction results into JSON format and sends them to the device, which includes the predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[1398] Step 8:

[1399] The device receives and analyzes the JSON data sent from the server, and based on the analysis results, displays the predicted results and recommended sizes to the user.

[1400] Step 9:

[1401] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed.

[1402] Example 1

[1403] 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."

[1404] Currently, there is a lack of methods to predict appropriate clothing and shoe sizes as children grow, resulting in frequent replacements and incorrect size selection, resulting in a lot of waste. Incorrect growth predictions also increase unnecessary waste and increase the environmental burden. Therefore, there is a need for a system that can accurately predict children's growth and recommend appropriate clothing and shoe sizes based on that prediction.

[1405] 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.

[1406] In this invention, the server includes: means for a user to input information about the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for a terminal to transmit the input information to the server; means for the server to check the format of the received information and whether all required fields are included; means for the server to use a generative AI model to predict the child's future height, weight, and shoe size using a growth curve algorithm based on the received information; means for the server to calculate appropriate clothing and shoe sizes based on the predicted information; and means for the terminal to display the calculated clothing and shoe sizes to the user. This allows the user to select appropriate clothing and shoe sizes based on the child's growth, thereby reducing unnecessary replacement purchases and waste.

[1407] "User" refers to a person who utilizes the system to input their child's growth data and obtain growth predictions and size recommendations.

[1408] "Terminal" refers to an electronic device such as a computer or smartphone that a user uses to input data.

[1409] "Server" refers to a computer system that receives, analyzes, and processes data sent from a terminal.

[1410] "Information on height, weight, age, parents' height, past time series data, and shoe size" refers to basic data for a child's growth that the user inputs via the terminal.

[1411] "Data validation" refers to the process of checking whether the format and content of entered data are correct and whether all required fields are included.

[1412] A "growth curve algorithm" refers to a mathematical formula or calculation method for predicting a child's future growth based on past growth data.

[1413] A "generative AI model" refers to artificial intelligence that uses growth curve algorithms to predict future height, weight, and shoe size.

[1414] "JSON format" refers to a lightweight data exchange format for structuring and representing data.

[1415] "Method for calculating appropriate clothing and shoe sizes" refers to the process of calculating appropriate clothing and shoe sizes for a child's future growth based on growth projections.

[1416] "Means for displaying to the user" refers to the process of displaying the predicted height, weight, foot size, and recommended clothing and shoe sizes based on ... shoe sizes on the device.

[1417] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes. Users of the system input their child's current height, weight, age, historical height and weight data, and the heights of their parents using a smartphone or computer.

[1418] Data Entry

[1419] The user uses a dedicated input form to input the child's current height, weight, age, shoe size, historical height and weight data, and the heights of the parents. For example, the user enters the following information:

[1420] Current height: 100 cm

[1421] Current weight: 15 kg

[1422] Age: 3 years old

[1423] Foot size: 15 cm

[1424] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[1425] Parents' height: Mother 160 cm, Father 170 cm

[1426] Sending and Receiving Data

[1427] The terminal sends the input data to the server using the HTTPS protocol. The data format is JSON, and the sent data is saved on the server.

[1428] Data Validation

[1429] The server validates the data it receives, ensuring it is in the correct format and that all required fields are included. It checks for invalid values ​​for height and weight, decimals for age, etc. If validation is successful, it proceeds to the next step.

[1430] Conducting growth forecasts

[1431] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, the following predictions can be obtained:

[1432] Height after 6 months: 105 cm

[1433] Weight after 6 months: 17 kg

[1434] Foot size after 6 months: 16 cm

[1435] Calculating and formatting forecast results

[1436] The server calculates the appropriate clothing and shoe size based on the predicted data obtained from the AI ​​model. The prediction results are formatted in JSON format and include the following information:

[1437] Estimated height: 105 cm

[1438] Estimated weight: 17 kg

[1439] Estimated foot size: 16 cm

[1440] Recommended clothing size: 110 cm

[1441] Recommended shoe size: 17 cm

[1442] Sending and displaying result data

[1443] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes.

[1444] Specific examples

[1445] A specific example of data entry would be the following prompt:

[1446] "My child is currently 100 cm tall, weighs 15 kg, is 3 years old, and has a shoe size of 15 cm. One month ago, his height was 98 cm and his weight was 14 kg. His parents' heights are 160 cm and 170 cm, respectively. Given this information, what are his predicted height, weight, shoe size, and recommended clothing and shoe sizes for the next 6 months?"

[1447] This system allows users to select the optimal size of clothes and shoes as their child grows, reducing unnecessary replacements and waste.

[1448] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1449] Step 1: Data entry

[1450] The user inputs the child's current height, weight, age, past time series data, shoe size, and parents' heights into a dedicated input form. For example, the following information is input:

[1451] Current height: 100 cm

[1452] Current weight: 15 kg

[1453] Age: 3 years old

[1454] Foot size: 15 cm

[1455] Historical data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[1456] Parents' height: Mother 160 cm, Father 170 cm

[1457] Input data: Multiple data items entered by the user

[1458] Output data: Child growth data entered in the input form

[1459] Step 2: Sending data

[1460] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. For example, the input data is converted into the following JSON format.

[1461] json

[1462] {

[1463] "height": 100,

[1464] "weight": 15,

[1465] "age": 3,

[1466] "footSize": 15,

[1467] "pastData": [

[1468] {"monthsAgo": 1, "height": 98, "weight": 14},

[1469] {"monthsAgo": 3, "height": 95, "weight": 13},

[1470] {"monthsAgo": 6, "height": 92, "weight": 12}

[1471] ],

[1472] "parentHeights": {"mother": 160, "father": 170}

[1473] }

[1474] Input data: Growth data entered by the user

[1475] Output data: Data converted to JSON format

[1476] Step 3: Receiving the data

[1477] The server receives the JSON format data sent from the device using the HTTPS protocol. The data is stored on the server and used for subsequent processing.

[1478] Input data: JSON format data sent via HTTPS protocol

[1479] Output data: Growth data stored on the server

[1480] Step 4: Validate the data

[1481] The server validates the received data. Specifically, it checks whether the data format is correct and whether all required fields are included. For example, it checks whether height and weight are negative values ​​and whether age is a decimal. If validation is successful, it proceeds to the next step.

[1482] Input data: Growth data stored on the server

[1483] Output data: Validation result (pass or fail)

[1484] Step 5: Perform growth projections

[1485] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict future height, weight, and shoe size. For example, the following prediction results can be obtained:

[1486] Height after 6 months: 105 cm

[1487] Weight after 6 months: 17 kg

[1488] Foot size after 6 months: 16 cm

[1489] Input data: Growth data that has passed validation

[1490] Output data: Prediction results (estimated height, weight, and shoe size for the next 6 months)

[1491] Step 6: Calculate and format the forecast results

[1492] The server calculates the appropriate clothing and shoe sizes based on the predicted data obtained from the generative AI model and formats it in JSON format. For example, it generates JSON data containing the following information:

[1493] json

[1494] {

[1495] "predictedHeight": 105,

[1496] "predictedWeight": 17,

[1497] "predictedFootSize": 16,

[1498] "recommendedClothingSize": 110,

[1499] "recommendedShoeSize": 17

[1500] }

[1501] Input data: Prediction data from a generative AI model

[1502] Output data: Result data formatted in JSON format

[1503] Step 7: Send and display result data

[1504] The server sends the resulting data, formatted in JSON, to the device, which receives, analyzes, and displays it to the user. The displayed information includes predicted future height, weight, foot size, and recommended clothing and shoe sizes, allowing the user to choose the optimal size for their next purchase.

[1505] Input data: Result data sent from the server (JSON format)

[1506] Output data: What is displayed to the user (predicted results and recommended sizes)

[1507] Through these steps, the system accurately predicts a child's growth and suggests optimal clothing and shoe sizes, thereby reducing unnecessary replacement purchases and waste.

[1508] (Application example 1)

[1509] 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."

[1510] Traditionally, it has been difficult for many parents to select the appropriate clothing and shoe sizes as their children grow. In particular, when shopping in anticipation of future growth, mistakes in size selection frequently occur, resulting in unnecessary replacements and disposal. Furthermore, the lack of systems that suggest appropriate sized products based on growth predictions is one factor that degrades the online shopping experience. For this reason, there has been a demand for a system that provides accurate size selection, efficient shopping, and reduces environmental impact.

[1511] 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.

[1512] In this invention, the server includes: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and foot size; means for receiving the input information; means for predicting the child's future height, weight, and foot size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for suggesting related product sizes using the calculated information; and means for linking the purchase information of the suggested products to an online shopping site. This allows users to select accurate clothing and shoe sizes that take future growth into consideration, thereby reducing unnecessary replacement and disposal. Furthermore, linking with an online shopping site improves the online shopping experience by suggesting products based on growth predictions.

[1513] "Information on the child's current height, weight, age, parents' height, past time series data, and foot size" refers to the basic data required to predict a child's growth, specifically including the child's current height, weight, age, parents' height, data on changes in height and weight over a certain period of time in the past, and foot size information.

[1514] "Input means" refers to the interface or device that allows the user to provide the system with the necessary information about the child's development.

[1515] "Means for receiving" refers to a mechanism or function that takes information entered by a user into the system and stores it in a processable format.

[1516] "Prediction means" refers to methods or algorithms for estimating future height, weight, and shoe size based on the received information using growth curves or AI models.

[1517] "Means for calculating" refers to the process or method for calculating appropriate clothing and shoe sizes based on predicted future information.

[1518] "Display means" refers to a screen or device for visually presenting the calculated clothing and shoe size information to the user.

[1519] "Means for suggesting" refers to a system and process for selecting sizes of related products based on the calculated size information and providing them to the user.

[1520] "Means of collaboration" refers to a system or interface that shares purchase information for proposed products with the online shopping site and facilitates the purchase process.

[1521] "Online shopping site" refers to a web-based platform that allows customers to search for, purchase, and complete delivery procedures for products online.

[1522] This invention is a system that predicts a child's growth and suggests appropriate clothing and shoe sizes. The system uses input growth data to predict the child's future height, weight, and shoe size, and then calculates and provides the most appropriate clothing and shoe sizes to the user. Furthermore, by linking this information with online shopping sites, the system facilitates the purchase process.

[1523] 1. Data Entry

[1524] The user uses a smartphone or computer to input the following information: current height, weight, age, past time series data (e.g., height and weight from 1 month ago, 3 months ago, and 6 months ago), shoe size, and parents' heights. This information is then provided to the system using a dedicated input form.

[1525] 2. Sending and Receiving Data

[1526] The terminal sends the data entered by the user to the server using the HTTPS protocol. The data is structured in JSON format. The server receives it and prepares it for data analysis.

[1527] 3. Data Validation

[1528] The server validates the data it receives, ensuring that the information entered is in the correct format and that all required fields are included. If this validation is successful, it proceeds to the next step.

[1529] 4. Conducting growth forecasts

[1530] The server runs a generative AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size based on past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[1531] 5. Calculation and format of forecast results

[1532] The server calculates the appropriate clothing and shoe size based on the prediction data obtained from the AI ​​model, and formats the clothing and shoe size information along with the prediction results in JSON format, which includes specific estimated height, weight, foot size, and recommended clothing and shoe sizes.

[1533] 6. Sending and displaying result data

[1534] The server sends formatted JSON data to the device, which receives it and displays it to the user, including predicted future height, weight, foot size, and recommended clothing and shoe sizes, so the user can see which sizes to choose the next time they shop.

[1535] 7. Collaboration with online shopping sites

[1536] The system uses the predicted size information to suggest appropriate size products from the product database of related online retailers, allowing users to easily purchase clothes and shoes that fit their growing child's size.

[1537] As a specific example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg), mother's height (160 cm), and father's height (170 cm). The server validates this data and makes a prediction using a generative AI model. For example, it predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm over the next 6 months. Based on this information, the device displays the recommended clothing size of 110 cm and shoe size of 17 cm to the user. It also connects to an online shopping site to find products that fit these sizes, making it easy for the user to purchase them.

[1538] An example prompt has the following format:

[1539] "Predict your child's growth. Use the following information: Current height: 100 cm Current weight: 15 kg Age: 3 years Shoe size: 15 cm Additionally, you have the following historical data: 1 month ago height: 98 cm, weight: 14 kg 3 months ago height: 95 cm, weight: 13 kg 6 months ago height: 92 cm, weight: 12 kg Parents' heights: Mother: 160 cm, Father: 170 cm"

[1540] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1541] Step 1:

[1542] The user enters information about the child's development.

[1543] Input: Current height, weight, age, shoe size, historical height and weight data, and parents' heights.

[1544] What it does: A user enters data into a special form on their smartphone or computer. The form is designed so that all required fields are filled in.

[1545] Step 2:

[1546] The terminal transmits the input data to the server.

[1547] Input: Growth-related data entered by the user.

[1548] Data processing or data operation: Data is structured in JSON format.

[1549] Output: Structured JSON data.

[1550] Specific operation: The terminal uses the HTTPS protocol to convert the data entered by the user into JSON format and send it to the server.

[1551] Step 3:

[1552] The server receives the submitted data and validates it.

[1553] Input: User data in JSON format.

[1554] Data processing or data calculation: Check the format of input data and check the existence of required fields.

[1555] Output: The data that was successfully validated, or an error message.

[1556] Specific operation: The server parses the JSON data format and checks whether all the required information is included. If the check result is correct, it proceeds to the next step.

[1557] Step 4:

[1558] The server uses an AI model to predict growth.

[1559] Input: User data that passes validation.

[1560] Data processing or data calculation: Input into a generative AI model and apply a growth curve algorithm.

[1561] Output: Predicted future height, weight, and shoe size.

[1562] How it works: The server runs an AI model (e.g., TensorFlow) to predict future height, weight, and shoe size based on the input data.

[1563] Step 5:

[1564] The server calculates appropriate clothing and shoe sizes based on the predicted data.

[1565] Input: Predicted growth data.

[1566] Data processing or data calculation: calculation of recommended size and formatting of data.

[1567] Output: Recommended clothing and shoe sizes, formatted JSON data.

[1568] What it does: The server analyzes the predicted data, calculates appropriate clothing and shoe sizes based on growth predictions, and formats the information into JSON format.

[1569] Step 6:

[1570] The server sends the formatted data to the terminal.

[1571] Input: Well-formed JSON data.

[1572] Data processing or data calculation: None (simple transfer of data).

[1573] Output: Sending data to the user's terminal.

[1574] Specific operation: The server again uses the HTTPS protocol to send the formatted JSON data to the terminal.

[1575] Step 7:

[1576] The terminal displays the received data to the user.

[1577] Input: Formatted JSON data received from the server.

[1578] Data processing or data operation: Parsing JSON data and converting the display format.

[1579] Output: Growth prediction results and recommended size displayed to the user.

[1580] Specific operation: The device parses the received JSON data and displays it on the user's screen as specific values ​​(estimated height, weight, foot size, and recommended clothing and shoe sizes).

[1581] Step 8:

[1582] The system will suggest products of appropriate sizes from related online shopping sites and link purchasing information.

[1583] Input: Recommended size information.

[1584] Data processing or data calculation: Matching with the product database of the online shopping site.

[1585] Output: Right-sized product suggestions and purchasing options.

[1586] Specific operation: Based on the predicted size information, the system searches the product database of related online shopping sites, suggests products of appropriate sizes to the user, and assists in the purchase process of the suggested products.

[1587] 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.

[1588] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[1589] 1. Data Entry and Emotion Recognition

[1590] Users use their smartphones or computers to input the following information into the system: the child's current height, weight, age, shoe size, historical data on height and weight, and the parents' heights. Users enter this information using a dedicated input form. At the same time, the system recognizes the user's emotions in real time through the camera and microphone and analyzes them using an emotion engine.

[1591] 2. Sending and Receiving Data

[1592] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives it and prepares it for data analysis.

[1593] 3. Data Validation

[1594] The server validates the received data, specifically ensuring that the information entered is in the correct format and that all required fields are included. For example, it checks that height and weight are not invalid, that age is not a decimal, etc. If this validation is successful, the process proceeds to the next step.

[1595] 4. Conducting growth forecasts

[1596] The server runs an AI model based on the validated data. This AI model uses a growth curve algorithm to predict a child's future height, weight, and shoe size, taking into account past data and the parents' heights. For example, it calculates an estimated height and weight for the next six months based on the child's current height and weight and growth data from the past six months.

[1597] 5. Calculation and format of forecast results

[1598] The server calculates appropriate clothing and shoe sizes based on prediction data from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format and recommendations based on the user's emotional state. For example, if the user's emotion is not positive, it presents information in a more understandable and friendly format.

[1599] 6. Sending and displaying result data

[1600] The server sends formatted JSON data to the device, which receives, parses, and displays it to the user, including predicted future height, weight, foot size, recommended clothing and shoe sizes, and optimal display formatting based on emotion recognition.

[1601] Specific examples

[1602] Data Entry Example

[1603] The user inputs the child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). The user also inputs the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[1604] Data reception and growth forecasting

[1605] The server receives the input data and emotion data from the emotion engine, validates them, and then uses the AI ​​model to make predictions. For example, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months. Emotion data is also analyzed at the same time.

[1606] Displaying the results

[1607] The device receives the JSON data sent from the server and displays it to the user based on the prediction results and sentiment analysis data. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's sentiment is not positive, the information is displayed in a more friendly format.

[1608] This embodiment allows the user to select clothes and shoes of appropriate sizes according to the child's growth, and further provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[1609] The processing flow will be explained below.

[1610] Step 1:

[1611] The user launches the application and opens the "Child Growth Prediction" screen. They enter their child's current height, weight, age, shoe size, past time series data, and the parents' heights into the form on the screen. The camera and microphone are then activated, capturing the user's facial expressions and voice in real time and sending them to the emotion engine.

[1612] Step 2:

[1613] The device converts the input data and captured emotion data into JSON format and sends it to the server using the HTTPS protocol. The transmitted data includes specific numerical values ​​for each item and the emotion analysis results.

[1614] Step 3:

[1615] The server receives the HTTPS request and parses the JSON data to extract each data item, including height, weight, age, shoe size, historical time series data, parents' heights, and emotion data.

[1616] Step 4:

[1617] The server validates the received data, specifically checking that the height and weight are within a reasonable range, that the age is not a decimal, that the shoe size is valid, and that all required information is present. If validation is successful, the server proceeds to the next step.

[1618] Step 5:

[1619] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict future height, weight, and shoe size, taking into account past data and the heights of parents. For example, it predicts the next six months based on growth data from the past six months.

[1620] Step 6:

[1621] The server receives the prediction data from the AI ​​model and calculates the appropriate clothing and shoe size based on that data. At the same time, it also analyzes the received emotional data and adjusts the way the prediction results are presented. For example, if the user's emotional state is not positive, the information will be displayed in a more friendly manner.

[1622] Step 7:

[1623] The server then converts the prediction results and emotional data into the optimal display format in JSON format and sends it to the device. This data includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and emotional analysis results.

[1624] Step 8:

[1625] The device receives and analyzes the JSON data sent from the server. Based on the analysis results, it displays predictions and recommended sizes to the user. Displayed information includes "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," "Recommended shoe size: 17 cm," and so on. If the user's emotions are not positive, the display format is adjusted, such as by adding more explanatory text or using gentler colors.

[1626] Step 9:

[1627] The user can check the displayed prediction results and recommended sizes, which will help them choose the right size clothes and shoes the next time they go shopping. For example, information such as "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm" is displayed. The friendly display provides a better user experience.

[1628] Example 2

[1629] 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."

[1630] Conventional child growth prediction systems predict growth curves and suggest appropriate sizes, but they do not provide information that takes user emotions into consideration, resulting in a poor user experience. Furthermore, input information is not properly checked, and prediction results based on incorrect data are sometimes displayed. Furthermore, there is a lack of systems that can make predictions using specific data formats.

[1631] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1632] In this invention, the server includes: means for inputting information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for capturing and analyzing user emotion data; and means for adjusting the display format based on the emotion data. This allows for the provision of information that takes the user's emotions into consideration, providing a better user experience. Furthermore, by checking the format of the input information and confirming required fields, the accuracy of the prediction results can be improved.

[1633] "Child's current height" is the child's most recent height, which serves as the basis for the prediction.

[1634] "Weight" is the child's most recent weight, which serves as the basis for the prediction.

[1635] "Age" is the age of the child that the prediction is based on, expressed in months or years.

[1636] "Parental height" refers to the heights of both parents that affect the child's growth prediction. This usually includes the heights of the father and mother.

[1637] "Past time-series data" refers to height and weight data that a child has entered in the past, and is obtained at multiple points in time.

[1638] "Foot size" refers to the actual measurement of a child's current foot size, and is data used to determine the appropriate shoe size.

[1639] The "means for inputting" is a means for the user to input information regarding the child's current height, weight, age, parents' heights, past time-series data, and shoe size.

[1640] The "receiving means" is a means by which the system receives information input by the user.

[1641] A "growth chart" is a graph showing normal growth patterns used to predict a child's growth.

[1642] A "means for predicting" is a means for predicting future height, weight, and shoe size using growth curves.

[1643] The "means for calculating" is a means for calculating the appropriate clothing size and shoe size based on the predicted information.

[1644] The "display means" is a means for displaying the calculated clothing and shoe sizes to the user.

[1645] The "means for capturing and analyzing emotional data" refers to a means for obtaining the user's emotions through a camera or microphone and analyzing the data.

[1646] The "means for adjusting the display format" is a means for optimizing the way information is displayed based on the user's emotional data.

[1647] This invention is a system that predicts a child's growth and calculates appropriate clothing and shoe sizes, and also combines it with an emotion engine that recognizes the user's emotions. The system aims to improve the user experience by predicting growth and suggesting sizes based on data and emotion information provided by the user.

[1648] Data Entry and Emotion Recognition

[1649] Users use a smartphone or computer to enter their child's current height, weight, age, shoe size, past height and weight data over time, and the parents' heights into the system. As users enter information using the input form, the system uses a camera and microphone to recognize the user's emotions in real time and analyzes them using the emotion engine. For example, a user enters their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past height and weight data (e.g., 1 month ago: 98 cm, 14 kg; 3 months ago: 95 cm, 13 kg; 6 months ago: 92 cm, 12 kg). They also enter the mother's height (160 cm) and the father's height (170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter information.

[1650] Sending and Receiving Data

[1651] The device converts the data and emotion data entered by the user into JSON format and sends it to the server using the HTTPS protocol. After sending, the server receives the data and prepares it for data analysis.

[1652] Data Validation

[1653] The server checks the format of the received data and verifies that the input information is correct, checking for required fields. For example, it checks that height and weight are numeric, and age is an integer. If all validations are successful, it proceeds to the next processing step.

[1654] Conducting growth forecasts

[1655] The server then launches a generative AI model based on the validated data. This AI model uses a growth curve algorithm and receives historical growth data and the parents' heights as input. Specifically, the AI ​​model predicts the child's height (e.g., 105 cm), weight (e.g., 17 kg), and shoe size (e.g., 16 cm) for the next six months.

[1656] Calculating and formatting forecast results

[1657] The server calculates appropriate clothing and shoe sizes based on the predictions obtained from the AI ​​model and the emotional data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. If the user's emotion is not positive, the server presents information in a more friendly format.

[1658] Sending and displaying result data

[1659] The server sends formatted JSON data to the device, which parses it and displays predictions to the user, including estimated future height, weight, foot size, and recommended clothing and shoe sizes. If the user's sentiment is not positive, the information is presented in a more user-friendly format.

[1660] Specific examples

[1661] Data Entry Example

[1662] The user inputs their child's current height (100 cm), weight (15 kg), age (3 years old), shoe size (15 cm), and past growth data. For example, one month ago: 98 cm, 14 kg, three months ago: 95 cm, 13 kg, six months ago: 92 cm, 12 kg. The user also inputs the parents' heights (mother: 160 cm, father: 170 cm). The emotion engine analyzes the user's facial expressions and voice as they enter their information.

[1663] Data reception and growth forecasting

[1664] The server receives the data and emotion data entered by the user, validates them, and then uses the generative AI model to make predictions: specifically, the AI ​​model predicts that the child's height will be 105 cm, weight 17 kg, and shoe size 16 cm in the next six months.

[1665] Displaying the results

[1666] The device receives the JSON data sent from the server and displays the analysis results to the user. Examples of displayed information include "Predicted height in 6 months: 105 cm," "Recommended clothing size: 110 cm," and "Recommended shoe size: 17 cm." If the user's emotions are not positive, the device displays the information in a more user-friendly format.

[1667] Prompt Sentence Examples

[1668] "Based on the child's growth data and user emotion data entered by the user, predict the child's growth for the next six months and suggest appropriate clothing and shoe sizes."

[1669] "Based on the child's growth data (1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg) and the parents' height information, predict the child's growth over the next 6 months and determine the recommended size."

[1670] This embodiment allows the user to select clothes and shoes of appropriate sizes as the child grows, and provides a better user experience by displaying the clothes and shoes in consideration of emotion data.

[1671] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1672] Step 1: Data entry and emotion recognition

[1673] The user uses a dedicated input form on their smartphone or computer to input their child's current height, weight, age, shoe size, past time series data, and the parents' height information. At this time, the camera and microphone are activated for emotion recognition. The input data is in a format that includes each item necessary for predicting a child's growth. The data obtained from the input is in the following format:

[1674] Height: 100 cm

[1675] Weight: 15 kg

[1676] Age: 3 years old

[1677] Foot size: 15 cm

[1678] Past data: 1 month ago: 98 cm, 14 kg, 3 months ago: 95 cm, 13 kg, 6 months ago: 92 cm, 12 kg

[1679] Parents' height: mother 160 cm, father 170 cm

[1680] The device captures the user's facial expressions and voice in real time using a camera and microphone, and collects this as emotion data. The input data and emotion data are temporarily stored as data entities within the system.

[1681] (Input) The user enters the child's height, weight, age, shoe size, historical time series data, parents' heights, and captured emotion data into the form.

[1682] (Output) A well-formed data entity.

[1683] Step 2: Convert the data to JSON format and send it

[1684] The device converts the collected user data and emotion data into JSON format. The converted data includes user input information and emotion information. The data converted into JSON format has the following format:

[1685] json

[1686] {

[1687] "current_height": 100,

[1688] "current_weight": 15,

[1689] "age": 3,

[1690] "foot_size": 15,

[1691] "past_data": [

[1692] {"height": 98, "weight": 14, "time": "1_month_ago"},

[1693] {"height": 95, "weight": 13, "time": "3_months_ago"},

[1694] {"height": 92, "weight": 12, "time": "6_months_ago"}

[1695] ],

[1696] "parent_heights": {"mother": 160, "father": 170},

[1697] "emotion_data": {"expression": "neutral", "voice_tone": "calm"}

[1698] }

[1699] The converted data is sent to the server using the HTTPS protocol. For security reasons, SSL / TLS is used.

[1700] (Input) A well-formed data entity.

[1701] (Output) The JSON data sent to the server.

[1702] Step 3: Receiving and validating data

[1703] The server receives the JSON data sent from the device. It checks the format of the received data and verifies that all required fields are included. For example, it checks that height, weight, age, etc. are entered in the correct format. The conditions for successful data validation are as follows:

[1704] Height and weight must be positive integers or floating point numbers.

[1705] Age is a positive integer.

[1706] (Input) JSON data sent from the terminal.

[1707] (Output) Flag indicating whether validation was successful.

[1708] Step 4: Perform growth projections

[1709] The server runs a generative AI model based on the validated data. The data is fed into the AI ​​model, which predicts the child's height, weight, and shoe size for the next six months, taking into account past growth data and parental height information. The output of the AI ​​model is as follows:

[1710] Predicted height after 6 months: 105 cm

[1711] Estimated weight after 6 months: 17 kg

[1712] Predicted foot size after 6 months: 16 cm

[1713] (Input) User data that has passed validation.

[1714] (Output) Predicted growth data (height, weight, foot size).

[1715] Step 5: Calculate and format the prediction results

[1716] The server calculates the appropriate clothing and shoe size based on the prediction results from the AI ​​model and the emotion data from the emotion engine. For example, based on a predicted height of 105 cm and foot size of 16 cm, it calculates a clothing size of 110 cm and a shoe size of 17 cm. The server also adjusts the display format of the information depending on the user's emotional state. If the emotion data is determined to be "not positive," the information is presented in a friendly format.

[1717] (Input) Prediction data, user emotion data.

[1718] (Output) Well-formed JSON data (prediction results, recommended size, adjusted display format).

[1719] Step 6: Send and display result data

[1720] The server sends the formatted JSON data to the terminal.

[1721] The device parses this data and displays it to the user, typically displaying something like this:

[1722] Predicted height after 6 months: 105 cm

[1723] Recommended clothing size: 110 cm

[1724] Recommended shoe size: 17 cm

[1725] If the user's emotion is "negative," the information is presented in a more user-friendly format, for example by using different colors and fonts to enhance the visual experience.

[1726] (Input) Well-formed JSON data sent from the server.

[1727] (Output) The predicted result and recommended size displayed to the user.

[1728] These detailed processing steps make the entire process clear, from user input to display of growth prediction results.

[1729] (Application example 2)

[1730] 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."

[1731] Conventional systems have had difficulty predicting appropriate clothing and shoe sizes as a child grows. Furthermore, the system does not display information that takes the user's emotional state into consideration, resulting in a poor user experience. The present invention aims to solve these problems by realizing a system that suggests appropriate clothing and shoe sizes for the user while providing a display format that takes emotions into consideration.

[1732] 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: means for inputting information regarding a child's current height, weight, age, parents' heights, past time-series data, and shoe size; means for receiving the input information; means for predicting the child's future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; means for recognizing and analyzing the user's emotional state in real time; and means for adjusting the display format of information according to the emotional state. This not only enables the user to select appropriate clothing and shoe sizes that suit their child's growth, but also provides emotionally sensitive information, enabling a less stressful shopping experience.

[1733] "Child's current height" is data that indicates the child's actual height that is entered into the system.

[1734] "Weight" is data entered into the system indicating the child's actual weight.

[1735] "Age" is data that indicates the actual number of years since birth of a child that is entered into the system.

[1736] "Parent height" is data indicating the height of each parent that is entered into the system for predicting the child's growth.

[1737] "Past time series data" refers to a sequence of data such as past height, weight, and shoe size that is entered into the system to track a child's growth.

[1738] "Foot size" is data indicating the actual foot length that is input into the system to determine a child's shoe size.

[1739] A "growth curve" is a mathematical model based on historical data that is used to predict a child's growth.

[1740] "Means for predicting future height, weight, and shoe size" refers to a function that uses input data to calculate a child's future growth parameters based on AI models and algorithms.

[1741] The "means for calculating appropriate clothing and shoe sizes" is a function for calculating clothing and shoe sizes that fit a child based on predicted growth data.

[1742] "Means for recognizing and analyzing the user's emotional state in real time" refers to a function that uses sensors such as a camera and microphone to capture the user's facial expressions and voice, and analyzes them using an emotion recognition engine.

[1743] The "means for adjusting the information display format" is a function that changes the way prediction results and suggestions are displayed depending on the user's emotional state.

[1744] The present invention aims to provide a system that predicts a child's growth, suggests appropriate clothing and shoe sizes, and improves the user experience by recognizing the user's emotions and adjusting the display format.

[1745] 1. Data Entry and Emotion Recognition

[1746] Users access the system using a smartphone or computer and enter the following information: the child's current height, weight, age, shoe size, historical data on height and weight, and the heights of both parents. When the user enters this information into a dedicated input form, the camera and microphone are used to capture the user's emotions in real time, which are then analyzed by an emotion engine. This is done using a mobile app using React Native or a web application using HTML5, CSS3, and JavaScript.

[1747] 2. Sending and Receiving Data

[1748] The device converts the user-entered data and emotion data into JSON format and sends it to the server using the HTTPS protocol. The Axios library is used to send the data. The server receives the sent data and prepares it for analysis. Server-side technologies used in this process include Node.js.

[1749] 3. Data validation and prediction

[1750] The server validates the received data. This includes checking that the input information is in the correct format and contains all required fields. For example, it checks that height and weight are not invalid values, and that age is not a decimal. If this validation is successful, the data proceeds to the next processing step. The server then runs an AI model based on the validated data. This AI model is built using TensorFlow and uses a growth curve algorithm to predict the child's future height, weight, and shoe size, taking into account past data and the heights of the parents.

[1751] 4. Formatting and displaying prediction results

[1752] The server calculates appropriate clothing and shoe sizes based on prediction data obtained from the AI ​​model and emotion data from the emotion engine. It also adjusts the display format of information according to the user's emotional state. OpenFace and Emotion API are used for emotion recognition. For example, if the user's emotion is not positive, the information is presented in a more understandable and friendly format. The server sends this information to the device in JSON format. The device analyzes the data sent from the server and displays the results to the user. The displayed content includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and the optimal display format based on emotion recognition.

[1753] Specific examples

[1754] For example, if a user enters the following data:

[1755] Child's current height: 110 cm

[1756] Current weight: 18 kg

[1757] Age: 4 years old

[1758] Foot size: 17 cm

[1759] Parents' height: mother 160 cm, father 180 cm

[1760] Past growth data: 1 month ago 108 cm, 16 kg, 3 months ago 105 cm, 15 kg

[1761] The system returns the following prediction:

[1762] Predicted height after 6 months: 115 cm

[1763] Recommended clothing size: 120 cm

[1764] Recommended shoe size: 18 cm

[1765] Additionally, the display format of information based on emotion recognition will be adjusted as follows:

[1766] If the user's emotional state is not positive, provide information in a friendly tone.

[1767] Example prompts for generative AI models

[1768] "Predict a child's height, weight, and shoe size in the next 6 months. Use the parents' height data as well."

[1769] In this way, this invention allows users to easily obtain information for selecting clothes and shoes in sizes that are optimal for their child's growth, and provides information that takes emotions into consideration, thereby providing a better shopping experience.

[1770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1771] Step 1:

[1772] Using a smartphone or computer, users input information about their child's current height, weight, age, shoe size, past time series data, and the heights of their parents. The input information is converted into JSON format. The device's camera and microphone are also used to capture the user's emotional data. This input becomes the basis for analysis by the system.

[1773] Step 2:

[1774] The terminal sends the user input data and captured emotion data to the server using the HTTPS protocol. The Axios library is used for data transmission. The output at this stage is JSON format data that is sent to the server.

[1775] Step 3:

[1776] The server parses and validates the received JSON data, for example, ensuring that the height is within a reasonable range, that the weight is not an invalid value, and that the age is an integer. This validation ensures that the data is in the correct format.

[1777] Step 4:

[1778] The server inputs the validated data into the AI ​​model, which uses a growth curve algorithm to predict the child's future height, weight, and shoe size. The AI ​​model is built using TensorFlow. Based on the input data, it outputs predicted data for the next six months.

[1779] Step 5:

[1780] The server calculates appropriate clothing and shoe sizes based on the predicted data obtained from the AI ​​model. It also analyzes the user's emotional data using an emotion recognition engine (OpenFace or EmotionAPI) and determines the display format of information based on the user's emotional state. For example, if the user's emotions are not positive, the server will present information in a more user-friendly format.

[1781] Step 6:

[1782] The server sends formatted JSON data (prediction data, recommended size, display format) to the device, which receives, parses, and displays the data to the user. The display includes predicted future height, weight, foot size, recommended clothing and shoe sizes, and display format information based on emotion recognition.

[1783] Step 7:

[1784] Users can check the information displayed on the device and select appropriate clothes and shoes based on predicted growth data. This information makes it easy for users to find the best products that will suit their child's growth.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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).

[1792] 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.

[1793] 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."

[1794] 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.

[1795] 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).

[1796] 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.

[1797] 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.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] The following is further disclosed regarding the above embodiment.

[1807] (Claim 1)

[1808] A means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size;

[1809] means for receiving the input information;

[1810] means for predicting future height, weight, and shoe size using a growth curve based on the received information;

[1811] means for calculating appropriate clothing and shoe sizes based on the predicted information;

[1812] means for displaying the calculated clothing and shoe sizes;

[1813] A system including:

[1814] (Claim 2)

[1815] 10. The system of claim 1, further comprising means for validating the format of the entered information and checking for inclusion of all required fields.

[1816] (Claim 3)

[1817] 10. The system of claim 1, further comprising means for inputting the received information into a growth curve algorithm and using an AI model to make a prediction.

[1818] (Claim 4)

[1819] The system of claim 1 , further comprising: means for converting the predicted future height, weight, and shoe size into a JSON format and transmitting the JSON format to a terminal.

[1820] (Claim 5)

[1821] The system of claim 1 , further comprising: means for providing an interface through which a user can view the displayed clothing and shoe sizes.

[1822] "Example 1"

[1823] (Claim 1)

[1824] a means for a user to input information about the child's current height, weight, age, parental height, historical time series data, and shoe size;

[1825] means for transmitting the input information to a server by the terminal;

[1826] means for the server to verify the format of the received information and check whether all required fields are included;

[1827] A means for the server to use a generative AI model to predict future height, weight, and shoe size using a growth curve algorithm based on the received information;

[1828] A server calculates appropriate clothing and shoe sizes based on the predicted information;

[1829] a means for displaying the calculated clothing and shoe sizes to a user on a terminal;

[1830] A system including:

[1831] (Claim 2)

[1832] 10. The system of claim 1, further comprising means for the terminal to transmit the input information using the HTTPS protocol.

[1833] (Claim 3)

[1834] 10. The system of claim 1, further comprising means for the server to format the predicted information using the growth curve algorithm in JSON format.

[1835] "Application Example 1"

[1836] (Claim 1)

[1837] A means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size;

[1838] means for receiving the input information;

[1839] means for predicting future height, weight, and shoe size using a growth curve based on the received information;

[1840] means for calculating appropriate clothing and shoe sizes based on the predicted information;

[1841] means for displaying the calculated clothing and shoe sizes;

[1842] A means for suggesting sizes of related products using the calculated information;

[1843] A means for linking the purchase information of the suggested product to an online shopping site;

[1844] A system including:

[1845] (Claim 2)

[1846] 10. The system of claim 1, further comprising means for validating the format of the entered information and checking for inclusion of all required fields.

[1847] (Claim 3)

[1848] 10. The system of claim 1, further comprising means for inputting the received information into a growth curve algorithm and using a generative AI model to make a prediction.

[1849] "Example 2: Combining Emotion Engines"

[1850] (Claim 1)

[1851] A means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size;

[1852] means for receiving the input information;

[1853] means for predicting future height, weight, and shoe size using a growth curve based on the received information;

[1854] means for calculating appropriate clothing and shoe sizes based on the predicted information;

[1855] means for displaying the calculated clothing and shoe sizes;

[1856] a means for capturing and analyzing user emotional data;

[1857] means for adjusting a display format based on the emotion data;

[1858] A system including:

[1859] (Claim 2)

[1860] 10. The system of claim 1, further comprising means for validating the format of the entered information and checking for inclusion of all required fields.

[1861] (Claim 3)

[1862] 10. The system of claim 1, further comprising means for inputting the received information into a growth curve algorithm and using an AI model to make a prediction.

[1863] "Application example 2 when combining emotion engines"

[1864] (Claim 1)

[1865] A means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size;

[1866] means for receiving the input information;

[1867] means for predicting future height, weight, and shoe size using a growth curve based on the received information;

[1868] means for calculating appropriate clothing and shoe sizes based on the predicted information;

[1869] means for displaying the calculated clothing and shoe sizes;

[1870] means for recognizing and analyzing the user's emotional state in real time;

[1871] means for adjusting a display format of information according to said emotional state;

[1872] A system including:

[1873] (Claim 2)

[1874] 10. The system of claim 1, further comprising means for validating the format of the entered information and checking for inclusion of all required fields.

[1875] (Claim 3)

[1876] 10. The system of claim 1, further comprising means for inputting the received information into a growth curve algorithm and using an AI model to make a prediction. [Explanation of symbols]

[1877] 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 means to input information about the child's current height, weight, age, parental heights, historical time series, and shoe size; means for receiving the input information; means for predicting future height, weight, and shoe size using a growth curve based on the received information; means for calculating appropriate clothing and shoe sizes based on the predicted information; means for displaying the calculated clothing and shoe sizes; A system including:

2. 10. The system of claim 1, further comprising means for validating the format of the entered information and checking for inclusion of all required fields.

3. 10. The system of claim 1, further comprising means for inputting the received information into a growth curve algorithm and using an AI model to make a prediction.

4. The system of claim 1 , further comprising: means for converting the predicted future height, weight, and shoe size into a JSON format and transmitting the JSON format to a terminal.

5. The system of claim 1 , further comprising: means for providing an interface through which a user can view the displayed clothing and shoe sizes.

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