KR20240015783A - Apparatus and method for predicting fit for each part based on lifestyle information, computer readable recording medium and computer program - Google Patents

Abstract

The present invention provides first lifestyle information related to physical body data, which is collected directly from the user through a questionnaire, and first lifestyle information related to cultural taste data, which is collected directly from the user through a questionnaire or indirectly through a social network. 2 a lifestyle data collection unit that collects lifestyle information; and detailed body size prediction that predicts the body size of each part of the user based on the user's basic body information and detailed body information collected by the body data collection unit, and the first lifestyle information collected by the lifestyle data collection unit. wealth; And based on the user's body size for each part predicted in the detailed body size prediction unit, the growth rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and second lifestyle information collected in the lifestyle data collection unit. The present invention relates to a device for predicting a feeling of fit for each part based on lifestyle information, including a fit prediction unit that predicts a feeling of fit for each part, and can more accurately predict the feeling of fit for each part.

Description

Apparatus and method for predicting fit for each part based on lifestyle information, computer readable recording medium and computer program}

The present invention relates to an apparatus and method for predicting fit for each part, and more specifically, to an apparatus and method for predicting fit for each part based on lifestyle information.

In the case of clothing purchasing, clothing manufacturing, etc., the user's partial or overall body measurements are very important. Traditionally, the body size was measured by measuring the length or circumference of each body part by hand using a tape measure, etc. for each necessary part, and using this for clothing manufacturing.

Recently, as the number of online stores such as websites for selling clothing continues to increase, users can directly input the body measurements of each part or the entire body using applications, or calculate the body measurements of other parts from information on at least some of the body measurements. Forecasting methods are being introduced.

Meanwhile, in the case of clothing purchase, clothing manufacturing, etc., users make consumption consistent with their lifestyle. For example, if you do a lot of indoor activities, you tend to prefer types of clothing that complement your body shape even in light clothing, and if you do a lot of outdoor activities, you tend to purchase clothes that are comfortable to move around in.

In this way, the feeling of wearing that an individual feels comfortable with may vary depending on the individual's lifestyle, so there is a need for a device and method that can more accurately predict the feeling of wearing by reflecting the individual's lifestyle.

US 7388492 B2 J.P. 6564246 B2

Accordingly, the purpose of the present invention is to provide an apparatus and method for predicting fit for each part based on lifestyle information, which can more accurately predict fit by reflecting an individual's lifestyle.

Another object of the present invention is to provide an apparatus and method for predicting fit for each part based on lifestyle information, which can reflect an individual's lifestyle and recommend products that match the individual's lifestyle.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

The above object is, according to the first aspect of the present invention,

a physical data collection unit that collects the user's basic physical information and detailed physical information through a questionnaire;

First lifestyle information related to physical body data collected directly from the user through a questionnaire, and second lifestyle information related to cultural preference data collected directly from the user through a questionnaire or indirectly through a social network. a lifestyle data collection unit that collects;

A detailed body size prediction unit that predicts the body size of each part of the user based on the user's basic body information and detailed body information collected by the body data collection unit and the first lifestyle information collected by the lifestyle data collection unit. ;

a product information collection unit that collects first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing;

Detailed product size prediction that predicts the elongation rate of each part of clothing corresponding to the body size of each part of the user predicted by the detailed body size prediction unit based on the first product information and the second product information collected by the product information collection unit. wealth; and

The user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and second lifestyle information collected in the lifestyle data collection unit. It includes a fit prediction unit that predicts the fit of each part based on the fit,

This is achieved by a device that predicts fit for each part based on lifestyle information.

In addition, the user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and second lifestyles collected in the lifestyle data collection unit. It is desirable to further include a product recommendation unit that recommends products based on information.

Furthermore, it further includes a fit display unit that displays the fit for each part predicted by the fit prediction unit, wherein the fit display unit includes one or more products recommended by the product recommendation unit, and the predicted part for each recommended product. It is characterized by indicating a star fit.

At this time, the body data collection unit provides the user's basic body information including gender, age, height, weight, clothing size, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, and body characteristics. It is characterized by providing a questionnaire about the user's detailed physical information, including at least one of information, wrist test, body type, fashion interest, and childbirth information, and receiving response information.

The above object is also, according to the second aspect of the present invention,

Collecting basic physical information and detailed physical information of the user through a questionnaire by a physical data collection unit;

First lifestyle information related to physical body data, which is collected directly from the user through a questionnaire by the lifestyle data collection unit, and cultural preferences, which are collected directly from the user through a questionnaire or indirectly through a social network collecting second lifestyle information related to the data;

By the detailed body size prediction unit, the user's body size for each part is calculated based on the user's basic body information and detailed body information collected by the body data collection unit, and the first lifestyle information collected by the lifestyle data collection unit. predicting;

Collecting, by the product information collection unit, first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing;

By the detailed product size prediction unit, each part of clothing corresponding to the user's body size predicted by the detailed body size prediction unit based on the first product information and the second product information collected by the product information collection unit. predicting elongation rate; and

By the fit prediction unit, the body size of each part of the user predicted by the detailed body size prediction unit, the elongation rate of each part of the clothing predicted by the detailed product size prediction unit, and the first and Comprising the step of predicting fit for each part based on second lifestyle information,

This is achieved by predicting fit for each part based on lifestyle information.

In addition, by the product recommendation unit, the user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the product collected in the lifestyle data collection unit. It is desirable to further include a step of recommending a product based on the first and second lifestyle information.

Furthermore, it further includes the step of displaying the fit for each part predicted by the fit prediction unit by the fit display unit, wherein the fit display unit includes one or more products recommended by the product recommendation unit, and each recommended product. It is characterized by displaying the predicted fit for each part.

At this time, the user's basic physical information including gender, age, height, weight, clothing size, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, It is characterized by providing a questionnaire about the user's detailed physical information, including at least one of physical characteristic information, wrist test, body type, fashion interest, and childbirth information, and receiving response information.

The above object is also, according to the third aspect of the present invention,

This is achieved by a computer-readable recording medium on which a program for performing the above method is recorded.

Furthermore, the above object is also, according to the fourth aspect of the present invention,

This is achieved by a computer program stored in a medium for executing the above method through combination with hardware.

According to the apparatus and method for predicting fit for each part based on lifestyle information of the present invention as described above, there is an advantage of being able to more accurately predict fit by reflecting an individual's lifestyle.

In addition, according to the present invention, the device and method for predicting fit for each part based on lifestyle information has the advantage of being able to recommend products that fit an individual's lifestyle.

1 is a configuration diagram of a system including a device for predicting fit for each part based on lifestyle information of the present invention.
Figure 2 is a configuration diagram of a device for predicting fit for each part based on lifestyle information of the present invention.
Figure 3 is a flowchart of a method for predicting fit for each part based on lifestyle information of the present invention.
Figure 4 is a diagram showing an example of display by the fit display unit 180 of the device for predicting fit for each part based on lifestyle information of the present invention.

Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the contents depicted in the attached drawings. However, the present invention is not limited or limited by the exemplary embodiments. The same reference numerals in each drawing indicate members that perform substantially the same function.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component without departing from the scope of the present invention, and similarly, the second component may also be named a first component. The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise.

The terms used in the present invention are general terms that are currently widely used as much as possible while considering the function in the present invention, but this may vary depending on the intention or precedent of a person working in the art, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than simply the name of the term.

When it is said that a part "includes" a certain element throughout the specification, this means that, unless specifically stated to the contrary, it does not exclude other elements but may further include other elements.

1 is a configuration diagram of a system including a device for predicting fit for each part based on lifestyle information of the present invention. The apparatus 100 for predicting fit for each part based on lifestyle information of the present invention includes body information including basic body information and detailed body information, and first and second lifestyle information from the user's terminal 10. Life style information is provided, and the first and second product information stored in the DB 20 is provided, and based on this, the comfort of each body part when the user wears the clothing is predicted and provided to the user. At this time, the DB 20 may be a DB operated directly by a specific online clothing shopping mall, or may be DBs operated separately for each clothing brand.

According to the present invention, the user selects clothing that matches his or her lifestyle based on the user's fit information for each part and/or recommended products provided by the apparatus 100 for predicting fit for each part based on lifestyle information of the present invention. You can purchase it. As a result, the user's inconvenience caused by refunding/returning clothing that does not fit the user's body size can be reduced.

In order to increase the effectiveness of services that predict fit, it is necessary to provide accurate fit information for each part of the user's body while minimizing inconveniences associated with using services such as measuring the user's body size. Figure 2 is a configuration diagram of a device for predicting fit for each part based on lifestyle information of the present invention. Referring to Figures 1 and 2, the apparatus 100 for predicting fit for each part based on lifestyle information of the present invention can provide a service that makes it possible to collect body measurements for various parts without measuring each part.

The apparatus 100 for predicting fit for each part based on lifestyle information of the present invention includes a body data collection unit 110, a lifestyle data collection unit 120, a detailed body size prediction unit 130, and a product information collection unit 140. ), a detailed product size prediction unit 150, a fit prediction unit 160, a product recommendation unit 170, and a fit display unit 180.

The physical data collection unit 110 may collect the user's basic physical information and detailed physical information through a questionnaire. The body data collection unit 110 receives body information from the user when an application that predicts the fit of clothing using survey information or at least an application that predicts the user's body size is activated through the user terminal. Here, the application that predicts the user's body size using the mentioned survey information refers to a program that helps perform tasks according to the present invention or a program used to manage a computer system, machinery, or computer network. .

The body data collection unit 110 includes the user's basic body information including gender, age, height, weight, clothing size, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, and body size. A survey is provided regarding the user's detailed physical information, including at least one of characteristic information, wrist test, body type, fashion interest, and childbirth information, and response information is input.

To this end, the body data collection unit 110 includes a basic body information collection unit and a detailed body information collection unit. The basic physical information collection unit collects responses to the user's basic physical information including at least one of the user's age, height, and weight. The detailed physical information collection unit includes the clothing size the user usually buys or wears, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, body characteristic information, wrist test, body type, and fashion interest. , and collect responses to the user's detailed physical information, including at least one of birth information.

Here, the development of the upper and lower body refers to information about the upper body or lower body that gains more weight or has well-developed muscles, and may additionally include information about the upper body or lower body that gains weight first or has well-developed muscles. Additionally, information on the level of confidence in choosing whether to develop the upper or lower body can be additionally collected and reflected in the decision to develop the upper or lower body. For example, if the confidence level is 100/100, the collected information may be reflected as is, and if the confidence level is 0/100, the collected information may not be reflected at all.

Additionally, the upper and lower body ratio is an evaluation of the relative length of the upper and lower body, and skeletal muscle information is generally information about muscle mass and can be entered as a value corresponding to muscle weight relative to body weight.

Additionally, the physique type is information indicating the degree of development of the physique and may be either underdeveloped or dwarfed, and the birth information is information applicable only to women and may include the most recent year of birth. This birth information can be especially useful when collecting physical information from parents.

Additionally, body characteristic information includes belly type (thin belly, chubby belly), hip type (small hip, big hip), pelvis type (wide pelvis, narrow pelvis), thigh type (thick thigh, thin thigh), and shoulder width ( Broad shoulders, narrow shoulders), arm length (long arms, short arms) and leg length (long legs, short legs), chest type (large chest, small chest), arm type (thick arms, thin arms), waist type ( At least one of the following: thick waist, thin waist), head size (big head, small head), neck type (thick neck, thin neck), side type (thin side, chubby side), and calf type (thick calves, thin calves) Includes one. This questionnaire regarding physical characteristic information may be presented as multiple-choice items that can be selected, or as subjective items that can be filled out directly. Additionally, for types that are not presented as multiple-choice items, they may be presented as other types so that they can be entered in a subjective manner.

Furthermore, the wrist test is obtained using the person's fingers, that is, information about whether the thumb and index finger touch when the person holds the wrist with the thumb and index finger, and body type type is information about body type, and muscle specific gravity (muscle proportion) It may be information that takes into account (a lot of muscle, a little muscle, average, no muscle), and it can be any of the following: round, square, triangle, inverted triangle, or hourglass shape.

At this time, fashion interest literally refers to interest in fashion, and includes collected responses, such as clothing size that the user usually buys or wears, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, and physique. It can also be used to calculate response reliability for type, body characteristic information, wrist test, body type, birth information, etc. This will affect the accuracy of the user's body measurements being predicted based on these collected responses.

The lifestyle data collection unit 120 provides first lifestyle information related to physical body data, which is collected directly from the user through a questionnaire, and cultural information, which is collected directly from the user through a questionnaire or indirectly collected through a social network. Collect secondary lifestyle information related to personal taste data. When an application for predicting the fit of clothing using survey information is activated through the user terminal, the lifestyle data collection unit 120 receives at least first lifestyle information and optionally second lifestyle information from the user.

At this time, the first lifestyle information is about body type/posture, which is input from the user through a questionnaire, and includes turtle neck size, round shoulders, upper and lower body asymmetry, left and right asymmetry (arm length, chest, hips, etc.), and hunched posture. Responses are collected on whether or not the waist is straight, whether or not the waist is straight, whether or not corrective underwear is used, and whether or not plastic surgery is performed. For example, the degree of turtle neck can be evaluated as level 2: no turtle neck symptoms, present, or level 3: no turtle neck symptoms, slightly present, present. Likewise, it goes without saying that upper and lower body asymmetry can be assessed in two levels: severe upper and lower body asymmetry, weak asymmetry, or three levels: severe, moderate, and weak upper and lower body asymmetry. This first lifestyle information can not only serve as a factor (weight) to correct prediction results such as shoulder width, arm length, chest circumference, and hip circumference when predicting detailed body measurements, but can also serve as an example when predicting fit. For example, it corresponds to information that can be used as a correction factor.

Secondary lifestyle information is about tastes and/or preferences, and can be input from users through surveys or derived indirectly through SNS linking, and can be static/active, cafe/outdoor activities, office look. /Collect responses about casual wear, etc. For example, in determining whether you are static or active, you can select through a survey or analyze photos and/or hashtags through linking to an Instagram account to determine the appearance of keywords related to “home” and keywords related to “activity.” It can be determined by analyzing the frequency. As another example, in order to find out whether you prefer street fashion or feminine style, you can choose through a survey, or if you are a "public figure" or "company or brand" among Instagram followers, you can choose by account (predefined and stored). You can also analyze your tastes using tags. This second lifestyle information is not used when predicting detailed body measurements, but corresponds to information that can only be used to predict fit.

The detailed body size prediction unit 130 is based on the collected information, that is, the user's basic body information and detailed body information collected by the body data collection unit 110, and the user's basic body information collected by the lifestyle data collection unit 120. Based on the first lifestyle information, the user's body size for each part is predicted. At this time, the predicted body measurements for each part are shoulder width, upper arm circumference, arm length, armpit circumference, chest circumference, waist circumference, waist circumference at navel level, hip circumference, thigh circumference, leg length, calf circumference, breast circumference, and under the breast. It may be at least one of circumference, knee height, stomach circumference, neck circumference, and upper body length.

To this end, the detailed body size prediction unit 130 includes a detailed body size prediction model built by learning information collected from the body data collection unit 110 and the lifestyle data collection unit 120. At this time, the detailed body size prediction model is modeled through learning using the information collected from the body data collection unit and the lifestyle data collection unit as independent variables for one model and the body size for each part to be predicted as the dependent variable. You can.

The product information collection unit 140 collects first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing. At this time, the first product information corresponds to information for specifying the area where the elongation rate is to be predicted using the second product information, and the second product information is considered when predicting the elongation rate in the area specified by the first product information. It may correspond to information about possible factors.

Categories of clothing may include, for example, T-shirts, knits, blouses, pants, skirts, dresses, outerwear, etc., but are not limited thereto. Dimensions for each part of clothing may include, for example, clothing size (Small, Medium, Large, Extra Large), total length, waist size, hip size, thigh size, rise size, hem size, etc., but are limited to these. It doesn't work. The material of clothing may include content information of, for example, polyester, cotton, spandex, etc., but is not limited thereto. The material, thickness, weave, and elasticity of clothing are data on fabric properties and can be collected as factors that can be used to predict the elongation rate of each part of clothing.

The detailed product size prediction unit 150 is based on the user's body size for each part predicted by the detailed body size prediction unit 130 based on the first product information and the second product information collected by the product information collection unit 140. Predict the elongation rate of each part of the corresponding garment.

To this end, the detailed product size prediction unit 150 includes a detailed product size prediction model built by learning the information collected in the product information collection unit 140. At this time, the detailed product size prediction model can be modeled through learning by setting the information collected in the product information collection unit as an independent variable for one model and using the elongation rate of each part of various clothing as a dependent variable. For an example of a specific detailed product size prediction model, please refer to the growth rate prediction model of Patent Application No. 2019-0123767 filed by the company in 2019.

The fit prediction unit 160 includes the user's body size for each part predicted by the detailed body size prediction unit 130, the elongation rate for each part of the clothing predicted by the detailed product size prediction unit 150, and the lifestyle data collection unit 120. ) Predict the fit of each part based on the first and second lifestyle information collected.

Here, the fit can be predicted based on the difference between the clothing size and body size plus the minimum space required for the user's activity according to preset standards, such as 'large', 'fits perfectly', ' It may be displayed as ‘small’, etc. The minimum space required for the user's activity may be set differently for each category and part, as well as the degree of elasticity of the clothing. Furthermore, the feeling of fit may vary for each user depending on the user's body type/posture information and/or taste/preference information.

According to a preset standard, if the clothing is not elastic, the fit prediction unit 160 provides body type/posture information and/or taste/preference information as a result of comparing the size of each part of the clothing with the predicted body size of each part of the user. While the fit can be predicted by reflecting the elasticity of the clothing, body type/posture information and /Or the fit can be predicted by reflecting taste/preference information.

Here, before comparison, the body type/posture information and/or taste preference information include, for example, a clothing category as a factor used to predict fit, a preset allowable clothing pressure for the user, an error limit set for each part of the clothing, and the user's It may be reflected in advance, such as a selected wearing fit, or, as described above, may serve as a factor to correct the predicted fit to suit the user's lifestyle.

To this end, the fit prediction unit 160 uses a fit prediction model built by learning the information collected from the detailed body size prediction unit 130, the detailed product size prediction unit 150, and the lifestyle data collection unit 120. It can be included. At this time, the fit prediction model is learned using the information collected from the detailed body size prediction unit, detailed product size prediction unit, and lifestyle data collection unit as independent variables for one model, and the fit for each part to be predicted as the dependent variable. It can be modeled through .

The product recommendation unit 170 includes the user's body size for each part predicted in the detailed body size prediction unit 130, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit 150, and a lifestyle data collection unit ( 120) A product is recommended based on the first and second lifestyle information collected. According to a preset standard, if the clothing is not elastic, the product recommendation unit 170 provides body type/posture information and/or taste/ By reflecting preference information, it is possible to recommend products that fit the user's body measurements and tastes. If the clothing is elastic, the measurements of each part of the clothing reflect the predicted elongation rate of each part of the clothing and the predicted body size of each part of the user. By reflecting body type/posture information and/or taste/preference information in the results of comparing dimensions, products that fit the user's body measurements and tastes can be recommended.

To this end, the product recommendation unit 170 uses a product recommendation model built by learning the information collected from the detailed body size prediction unit 130, the detailed product size prediction unit 150, and the lifestyle data collection unit 120. It can be included. At this time, the product recommendation model uses the information collected from the detailed body size prediction unit, detailed product size prediction unit, and lifestyle data collection unit as independent variables for one model and selects the product to be recommended (defined by the category and size of the clothing). It can be modeled through learning using the product as a dependent variable.

Here, the fit prediction unit 160 and the product recommendation unit 170 may have a precedence relationship as shown in the drawing, and of course, they may be operated independently without a precedence relationship as described above.

The fit display unit 180 displays the fit for each part predicted by the fit prediction unit 160. At this time, as shown in FIG. 4, the fit display unit 180 may include one or more products recommended by the product recommendation unit 170 and display the predicted fit for each part for each recommended product. Here, the recommended product can be displayed with a photo as the category and size of the clothing, and the predicted fit for each part can be provided as text in an easy-to-understand syntax.

In this way, according to an embodiment of the present invention, the user's clothing fit can be accurately predicted for various body parts according to the fit prediction model corresponding to the user's lifestyle and the elasticity index of the clothing, and the fit calculated for each body part. By displaying the results, you can help buyers make decisions, and at the same time recommend products that have the fit that buyers want.

Figure 3 is a flowchart of a method for predicting fit for each part based on lifestyle information of the present invention. The method for predicting a fit for each part based on lifestyle information according to the present invention uses the apparatus for predicting a fit for each part based on lifestyle information according to the present invention described above, and redundant descriptions will be omitted below.

Referring to the drawings, the method for predicting fit for each part based on lifestyle information, which can be implemented in the device for predicting fit for each part based on lifestyle information according to the present invention, first learns a detailed body size prediction model (S100) and determines detailed product dimensions. After learning the prediction model (S200) to create prediction models, collecting the user's basic body information and detailed body information through a questionnaire by the body data collection unit (S300); First lifestyle information related to physical body data, which is collected directly from the user through a questionnaire by the lifestyle data collection unit, and cultural preferences, which are collected directly from the user through a questionnaire or indirectly through a social network Collecting second lifestyle information related to data (S400); By the detailed body size prediction unit, the user's body size for each part is calculated based on the user's basic body information and detailed body information collected by the body data collection unit, and the first lifestyle information collected by the lifestyle data collection unit. Predicting step (S500); A step of collecting, by the product information collection unit, first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing (S600); By the detailed product size prediction unit, each part of clothing corresponding to the user's body size predicted by the detailed body size prediction unit based on the first product information and the second product information collected by the product information collection unit. Predicting elongation rate (S700); And by the fit prediction unit, the body size of each part of the user predicted by the detailed body size prediction unit, the elongation rate of each part of the clothing predicted by the detailed product size prediction unit, and the first collected by the lifestyle data collection unit. and predicting a feeling of fit for each part based on the second lifestyle information (S800).

In addition, the method for predicting fit for each part based on lifestyle information according to the present invention includes, by a product recommendation unit, the user's body size for each part predicted from the detailed body size prediction unit, and clothing predicted from the detailed product size prediction unit. It may further include recommending a product based on the growth rate of each part and the first and second lifestyle information collected by the lifestyle data collection unit.

Furthermore, the method for predicting fit for each part based on lifestyle information according to the present invention further includes the step of displaying the fit for each part predicted by the fit prediction unit using a fit display unit, wherein the fit display unit recommends the product. It is characterized by including one or more products recommended in the section and displaying the predicted fit for each part for each recommended product.

Meanwhile, methods according to embodiments of the present invention may be implemented at least partially in the form of program instructions and recorded on a computer-readable recording medium. For example, implemented with a program product comprised of a computer-readable medium containing program code, which can be executed by a processor to perform any or all steps, operations, or processes described.

The computer may be a computing device, such as a desktop computer, laptop computer, notebook, smart phone, or the like, or any device that may be integrated. A computer is a device that has one or more alternative, special-purpose processors, memory, storage, and networking components (either wireless or wired). The computer may run an operating system such as, for example, Microsoft's Windows-compatible operating system, Apple's OS X or iOS, a Linux distribution, or Google's Android OS.

The program instruction form may be collectively referred to as software, which may include a computer program, code, instructions, or a combination of one or more of these, and may include a processing device to operate as desired. It can configure and command processing units independently or collectively. Software and/or data may be used by any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. It can be embodied permanently or temporarily. Software may be distributed over networked computing devices and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

In general, terms used herein, and particularly in the claims (e.g., in the body of the claims), are generally intended to be “open-ended” terms (e.g., “including” means “including but not limited to”). , “to have” should be construed as “to have at least more,” and “to include” as “including, but not limited to.”) If a specific number is intended for the introduced claim recitation, this intention is to be construed as “having at least more than that.” It is explicitly recited in the claims, and in the absence of such recitation, it is understood that such intent does not exist.

Only certain features of the invention have been shown and described herein, and various modifications and variations will occur to those skilled in the art. It is therefore understood that the claims are intended to cover changes and modifications that fall within the spirit of the invention.

10: terminal 100: Fit prediction device

Claims (10)

a physical data collection unit that collects the user's basic physical information and detailed physical information through a questionnaire;
First lifestyle information related to physical body data collected directly from the user through a questionnaire, and second lifestyle information related to cultural preference data collected directly from the user through a questionnaire or indirectly through a social network. a lifestyle data collection unit that collects;
A detailed body size prediction unit that predicts the body size of each part of the user based on the user's basic body information and detailed body information collected by the body data collection unit and the first lifestyle information collected by the lifestyle data collection unit. ;
a product information collection unit that collects first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing;
Detailed product size prediction that predicts the elongation rate of each part of clothing corresponding to the body size of each part of the user predicted by the detailed body size prediction unit based on the first product information and the second product information collected by the product information collection unit. wealth; and
The user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and second lifestyle information collected in the lifestyle data collection unit. It includes a fit prediction unit that predicts the fit of each part based on the fit,
A device that predicts fit for each part based on lifestyle information. According to claim 1,
The user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and second lifestyle information collected in the lifestyle data collection unit. Further comprising a product recommendation unit that recommends products based on
A device that predicts fit for each part based on lifestyle information. According to claim 2,
It further includes a fit display unit that displays the fit for each part predicted by the fit prediction unit, wherein the fit display unit includes one or more products recommended by the product recommendation unit, and the fit for each part predicted for each recommended product. Characterized by displaying,
A device for predicting fit for each part based on lifestyle information. According to claim 3,
The physical data collection unit includes the user's basic physical information including gender, age, height, and weight, clothing size, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, and body characteristic information, Characterized by providing a questionnaire about the user's detailed physical information, including at least one of wrist test, body type, fashion interest, and childbirth information, and receiving response information.
A device that predicts fit for each part based on lifestyle information. Collecting basic physical information and detailed physical information of the user through a questionnaire by a physical data collection unit;
First lifestyle information related to physical body data, which is collected directly from the user through a questionnaire by the lifestyle data collection unit, and cultural preferences, which are collected directly from the user through a questionnaire or indirectly through a social network collecting second lifestyle information related to the data;
By the detailed body size prediction unit, the user's body size for each part is calculated based on the user's basic body information and detailed body information collected by the body data collection unit, and the first lifestyle information collected by the lifestyle data collection unit. predicting;
Collecting, by the product information collection unit, first product information including the category and size of each part of the clothing and second product information including at least one of the material, thickness, texture, and elasticity of the clothing;
By the detailed product size prediction unit, each part of clothing corresponding to the user's body size predicted by the detailed body size prediction unit based on the first product information and the second product information collected by the product information collection unit. predicting elongation rate; and
By the fit prediction unit, the body size of each part of the user predicted by the detailed body size prediction unit, the elongation rate of each part of the clothing predicted by the detailed product size prediction unit, and the first and Comprising the step of predicting fit for each part based on second lifestyle information,
Method for predicting fit for each part based on lifestyle information. According to claim 5,
By the product recommendation unit, the user's body size for each part predicted in the detailed body size prediction unit, the elongation rate for each part of the clothing predicted in the detailed product size prediction unit, and the first and Further comprising recommending a product based on second lifestyle information,
Method for predicting fit for each part based on lifestyle information. According to claim 6,
It further includes the step of displaying, by a fit display unit, the fit for each part predicted by the fit prediction unit, wherein the fit display unit includes one or more products recommended by the product recommendation unit, and predicts for each recommended product. Characterized by indicating the fit for each part,
Method for predicting fit for each part based on lifestyle information. According to claim 7,
By the body data collection unit, the user's basic body information including gender, age, height, weight, clothing size, underwear size, shoe size, upper and lower body development, upper and lower body ratio, skeletal muscle information, physique type, and body characteristics Characterized by providing a survey about the user's detailed physical information, including at least one of information, wrist test, body type, fashion interest, and childbirth information, and receiving response information.
Method for predicting fit for each part based on lifestyle information. A computer-readable recording medium recording a program for performing the method of any one of claims 5 to 8. A computer program stored in a medium for executing the method of any one of claims 5 to 8 through combination with hardware.

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