KR20220147246A - Method for providing profile image creating service based on user's personality, propensity and feelings - Google Patents

Abstract

Provided is a method for providing a profile image generation service based on the personality, propensity, and feelings of a user. According to various embodiments of the present invention, the method for providing a profile image generation service based on the personality, propensity, and feelings of a user, which is a method performed by a computing device, may include the steps of: selecting one or more keywords for a user; selecting a background color using the selected one or more keywords; and generating a customized profile image for the user using the selected background color.

Description

{METHOD FOR PROVIDING PROFILE IMAGE CREATING SERVICE BASED ON USER'S PERSONALITY, PROPENSITY AND FEELINGS}

Various embodiments of the present invention relate to a method of providing a service for generating a profile image based on a user's personality, disposition, and mood.

A profile picture refers to a picture taken to introduce and inform oneself, and a typical example is an ID picture.

An ID photo is a photo of a person attached to ID cards and documents for the purpose of judging a person and identifying another person's disguise. It is used for official documents such as passports and driver's licenses, private documents such as resumes, and applications for entrance exams and qualification tests. Basically, it is a photo of the face on the shoulder on a solid background, and it is taken without a hat, mask, or sunglasses.

Due to these regulations, in general, users perceive ID photos as only a photo looking at the front with a white wall in the background, or a photo attached to a document and submitted somewhere.

Accordingly, the majority of users are actually taking ID photos by applying uniform posture, expression, and background color. There is a problem that the user cannot properly express himself or prove himself.

Korea Patent Publication No. 10-2009-0132838 (2009.12.31)

The problem to be solved by the present invention is to provide a background color that can express the personality, disposition and mood of the user within the scope of complying with preset regulations for the purpose of using the profile image (eg, for submission, etc.) as described above. By selecting and using this to create a user-customized profile image, not only can the profile image be used for the purpose, but also the user can use the image not only for simple submission, but also for the purpose of proving and expressing who they really are. It is to provide a method for providing a profile image creation service based on personality, disposition and mood.

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 following description.

According to an embodiment of the present invention for solving the above problems, a method for providing a profile image generation service based on a user's personality, disposition and mood is a method performed by a computing device, wherein one or more keywords for the user are selected. The method may include selecting, selecting a background color using the selected one or more keywords, and generating a customized profile image for the user using the selected background color.

In various embodiments, the selecting of the one or more keywords includes a keyword including a plurality of keywords, wherein the plurality of keywords include an adjective keyword indicating a property or state of the customized profile image that the user wants to create. The method may include providing a user interface (UI) for outputting a list and receiving a selection of one or more keywords from the plurality of keywords from the user through the user interface.

In various embodiments, the selecting of the background color includes determining one background color corresponding to the two or more keywords when the user selects two or more keywords, wherein the background color corresponding to the two or more keywords is In the case of two or more, grouping the two or more keywords into a plurality of keyword groups based on the degree of similarity of each of the two or more keywords, and individually selecting one background color for each of the plurality of keyword groups. .

In various embodiments, the selecting of the one or more keywords includes providing a user interface (UI), providing a plurality of query contents through the user interface, wherein the plurality of query contents are By analyzing a plurality of answers input from the user in response to the steps and the plurality of questions, which include the content of at least one of disposition, personality, mood, taste, and the atmosphere that the user wants to express, It may include extracting one or more keywords for the user.

In various embodiments, the selecting of the one or more keywords may include analyzing the user's face region included in the original image generated by photographing the user to obtain first attribute information about the user - the first attribute information extracting - including information about the user's facial expression and mood - second attribute information about the user by analyzing the user's body region included in the generated original image - The second attribute information is the It may include extracting - including information about the user's posture, and selecting the one or more keywords using the extracted first attribute information and the extracted second attribute information.

In various embodiments, the selecting of the one or more keywords includes: receiving information on the purpose of use of the customized profile image from the user; selecting a plurality of recommended keywords based on the input purpose of use information; The method may include providing a plurality of recommended keywords to the user and receiving one or more recommended keywords from among the selected plurality of recommended keywords from the user.

In various embodiments, the selecting of the one or more keywords may include individually selecting a keyword for each of the plurality of users when a plurality of users want to create a customized profile image photographed together, and the individually selected keywords. The method may include selecting a keyword identically selected from each of the plurality of users from among keywords or selecting top N keywords from a keyword having a high number of selected keywords from among the individually selected keywords.

In various embodiments, the selecting of the one or more keywords may include, when it is desired to create a customized profile image in which the user and one or more objects are photographed together, properties of the one or more objects and and selecting the one or more keywords based on the association.

In various embodiments, the selecting of the one or more keywords may include determining an attribute for the user by analyzing a plurality of social data uploaded to the user's account, and determining the one or more keywords based on the determined attribute for the user. It may include the step of selecting the above keywords.

In various embodiments, the generating of the customized profile image may include generating a temporary profile image for the user based on the selected background color, and feedback on the generated temporary profile image from a plurality of predefined users. receiving information, wherein the feedback information is an adjective keyword indicating a property or state of the generated temporary profile image; Compensating the selected background color by comparing the selected one or more keywords with the feedback information can do.

In various embodiments, the generating of the customized profile image includes generating a customized profile image for the user based on the selected background color, and a customized profile for the user through a user interface (UI). providing the image creation process to the user in real time, receiving request information from the user through the user interface, and providing the user with modified contents in response to the input request information may include

A user-customized profile image generating apparatus according to another embodiment of the present invention for solving the above-described problems, including a processor, a network interface, a memory, and a computer program loaded into the memory and executed by the processor , the computer program, an instruction for selecting one or more keywords for the user, an instruction for selecting a background color using the selected one or more keywords, and a customized profile image for the user using the selected background color It may include instructions to create

A computer program recorded on a computer-readable recording medium according to another embodiment of the present invention for solving the above problems is combined with a computing device, the step of selecting one or more keywords for the user, the selected one or more In order to execute the steps of selecting a background color using a keyword and generating a customized profile image for the user by using the selected background color, it may be stored in a computer-readable recording medium.

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention, as described above, a background color capable of expressing a user's personality, inclination, and mood within the range of complying with preset regulations for the purpose of using the profile image (eg, for submission, etc.) By selecting a profile image and using it to create a user-customized profile image, the advantage of not only being able to use the profile image for its intended purpose, but also being able to use the image to prove and express who you really are, not just for submission There is this.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a user-customized profile image generating system according to an embodiment of the present invention.
2 is a hardware configuration diagram of an apparatus for generating a user-customized profile image according to another embodiment of the present invention.
3 is a flowchart of a method for generating a user-customized profile image according to another embodiment of the present invention.
4 is a flowchart of a method of selecting a keyword using a question and answer, according to various embodiments.
5 is a flowchart of a method of selecting keywords using original image analysis, in various embodiments.
6 is a flowchart of a method of selecting a keyword according to a purpose of using a profile image, according to various embodiments.
7 is a flowchart of a method of selecting a keyword with respect to a group profile image of a plurality of users, according to various embodiments of the present disclosure;
8 is a flowchart of a method of selecting a keyword with respect to a profile image of a user and an object, according to various embodiments of the present disclosure;
9 is a flowchart of a method for correcting a background color based on feedback on a temporary profile image, in various embodiments.
10 is a table exemplarily showing adjective keywords applicable to various embodiments.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform those skilled in the art of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly specifically defined.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “part” or “module” may be configured to reside on an addressable storage medium or may be configured to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. A spatially relative term should be understood as a term that includes different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a diagram illustrating a user-customized profile image generating system according to an embodiment of the present invention.

Referring to FIG. 1 , a user-customized profile image generating system according to an embodiment of the present invention may include a user-customized profile image generating apparatus 100 , a user terminal 200 , and an external server 300 .

Here, the user-customized profile image generating system shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1 , and may be added, changed, or deleted as necessary.

In an embodiment, the user-customized profile image generating apparatus 100 (hereinafter, "computing device 100") may generate a customized profile image for the user.

Here, the customized profile image for the user considers the message that the user wants to convey through the profile image, the feeling that the user wants to express, the image, the concept, etc., and sets a background color that can clearly reveal this information, It may mean a profile image to which the set background color is applied.

The customized profile image described in this specification is described as revealing the user's identity through the background color by determining the background color of the profile image according to the needs of the user as described above, but is not limited thereto, In some cases, the user's identity can be revealed more clearly and effectively by changing the background expression method of the profile image (eg, solid color display, gradation, etc.) or inserting a specific text phrase on the profile image.

In various embodiments, the computing device 100 may select one or more keywords for the user in consideration of the user's needs, select a background color for revealing the user's identity using the selected keyword, and select the selected keyword. You can use the background color to create a custom profile image for the user.

For example, the computing device 100 uses a pre-learned artificial intelligence model using a plurality of profile images in which one or more keywords are labeled and color data for each keyword as learning data, one or more keywords for the user. An operation of extracting and selecting a background color using one or more extracted keywords may be performed.

Here, the AI model is composed of one or more network functions, and the one or more network functions may be composed of a set of interconnected computational units, which may be generally referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. The one or more network functions are configured by including at least one or more nodes. Nodes (or neurons) constituting one or more network functions may be interconnected by one or more ‘links’.

In the AI model, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the AI model to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in the AI model, one or more nodes are interconnected through one or more links to form an input node and an output node relationship within the AI model. The characteristics of the AI model may be determined according to the number of nodes and links in the AI model, the correlation between nodes and links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two AI models having different weight values between the links, the two AI models may be recognized as different from each other.

Some of the nodes constituting the artificial intelligence model may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from the initial input node may constitute n layers. The distance from the initial input node may be defined by the minimum number of links that must be traversed to reach the corresponding node from the initial input node. However, the definition of such a layer is arbitrary for description, and the order of the layer in the AI model may be defined in a different way than the above. For example, a layer of nodes may be defined by a distance from the final output node.

The initial input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the AI model. Alternatively, in the relationship between nodes based on the link in the artificial intelligence model network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the AI model. In addition, the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node. The artificial intelligence model according to an embodiment of the present invention may have more nodes in the input layer than nodes in the hidden layer close to the output layer, and is an artificial intelligence model in which the number of nodes decreases as the input layer progresses to the hidden layer. can

An AI model may include one or more hidden layers. The hidden node of the hidden layer may use the output of the previous layer and the output of the neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be calculated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In various embodiments, the artificial intelligence model may be trained based on supervised learning by using a plurality of profile images in which one or more keywords are labeled and color data for each keyword as learning data.

Here, supervised learning is a method of generating learning data by labeling specific data and information related to specific data, and learning by using this, and generates training data by labeling two data having a causal relationship How to learn from data.

More specifically, the computing device 100 may perform learning of one or more network functions constituting the artificial intelligence model by using the labeled training data. For example, the computing device 100 inputs each of the learning input data to one or more network functions, and compares each of the output data calculated by the one or more network functions with each of the learning output data corresponding to the label of each of the learning input data. error can be derived. That is, in the training of the artificial intelligence model, learning input data may be input to an input layer of one or more network functions, and the training output data may be compared with outputs of one or more network functions.

The computing device 100 may train an artificial intelligence model based on an error between an operation result of one or more network functions on the training input data and an error of the training output data (label).

Also, the computing device 100 may adjust the weights of one or more network functions in a backpropagation method based on the error. That is, the computing device 100 may adjust the weight so that the output of the one or more network functions approaches the learning output data based on an error between the operation result of the one or more network functions on the learning input data and the learning output data.

When the learning of one or more network functions is performed for more than a predetermined epoch, the computing device 100 may determine whether to stop learning by using the verification data. The predetermined epoch may be a part of the overall learning objective epoch.

The verification data may consist of at least a portion of the labeled training data. That is, the computing device 100 performs learning of the artificial intelligence model through the learning data, and after the learning of the artificial intelligence model is repeated more than a predetermined epoch, the learning effect of the artificial intelligence model is a predetermined level using the verification data. It can be determined whether it is abnormal or not. For example, when the computing device 100 performs learning with a target repetition learning number of 10 times using 100 pieces of training data, after performing repeated learning 10 times, which is a predetermined epoch, using 10 verification data By repeating learning three times, if the change in the output of the artificial intelligence model during the three iteration learning is below a predetermined level, it is determined that further learning is meaningless and learning can be terminated.

That is, the verification data may be used to determine the completion of learning based on whether the effect of learning for each epoch is greater than or equal to a certain level in the iterative learning of the artificial intelligence model. The above-described number of training data and verification data and the number of repetitions are merely examples, and the present invention is not limited thereto.

The computing device 100 may generate an artificial intelligence model by testing the performance of one or more network functions using the test data and determining whether to activate the one or more network functions. The test data may be used to verify the performance of the AI model, and may be composed of at least a portion of the training data. For example, 70% of the training data can be utilized for training the AI model (that is, learning to adjust the weights to output results similar to labels), and 30% to verify the performance of the AI model. It can be used as test data for The computing device 100 may determine whether to activate the artificial intelligence model according to whether it is above a predetermined performance by inputting test data into the trained artificial intelligence model and measuring an error.

The computing device 100 verifies the performance of the trained artificial intelligence model by using the test data on the trained artificial intelligence model, and when the performance of the trained artificial intelligence model is greater than or equal to a predetermined criterion, the artificial intelligence model is transferred to another application. You can enable it for use.

Also, when the performance of the trained artificial intelligence model is less than or equal to a predetermined criterion, the computing device 100 may deactivate and discard the corresponding artificial intelligence model. For example, the computing device 100 may determine the performance of the generated artificial intelligence model based on factors such as accuracy, precision, and recall. The above-described performance evaluation criteria are merely examples, and the present invention is not limited thereto. According to an embodiment of the present invention, the computing device 100 may generate a plurality of artificial intelligence model models by independently learning each artificial intelligence model, and only use artificial intelligence models with a certain performance or higher by evaluating the performance. . However, the present invention is not limited thereto.

In an embodiment, the user terminal 200 may be connected to the computing device 100 through the network 400 , and may receive a customized profile image generation service from the computing device 100 . Here, the customized profile image creation service refers to a service that creates a customized profile image by applying a background color that can reveal the user's identity to the original image created by photographing a user (or a plurality of users or users and a specific object) can do.

For example, the user terminal 200 may be a smartphone including a display on at least a portion of the user terminal 200 , and an operating system capable of driving an application, provided by the computing device 100 . By downloading, installing and running the custom profile image creation application, you can use the custom profile image creation service. However, the present invention is not limited thereto, and the user terminal 200 is a wireless communication device that guarantees portability and mobility. (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) It may include all kinds of handheld-based wireless communication devices such as a terminal, a smart pad, and a tablet PC.

Also, here, the network 400 may refer to a connection structure capable of exchanging information between respective nodes such as a plurality of terminals and servers. For example, the network 400 includes a local area network (LAN), a wide area network (WAN), the Internet (WWW), a wired/wireless data communication network, a telephone network, a wired/wireless television communication network, and the like. do.

In addition, here, the wireless data communication network is 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), Wi-Fi (Wi- Fi), Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, Near-Field Communication (NFC) networks, satellite broadcast networks, analog broadcast networks, Digital Multimedia Broadcasting (DMB) networks, and the like are included, but are not limited thereto.

In an embodiment, the external server 300 may be connected to the computing device 100 through the network 400, and various information and data ( For example, user information and customized profile image data) can be stored and managed.

In addition, the external server 300 may store and manage various information and data (eg, a plurality of keyword data, a plurality of color data, etc.) necessary for the computing device 100 to perform a user-customized profile image generation process. . For example, the external server 300 may be a storage server separately provided outside the computing device 100 , but is not limited thereto. Hereinafter, a hardware configuration of the computing device 100 will be described with reference to FIG. 2 .

2 is a hardware configuration diagram of an apparatus for generating a user-customized profile image according to another embodiment of the present invention.

Referring to FIG. 2 , the computing device 100 according to another embodiment of the present invention includes one or more processors 110 and a memory 120 for loading a computer program 151 executed by the processor 110 . , a bus 130 , a communication interface 140 , and a storage 150 for storing a computer program 151 . Here, only the components related to the embodiment of the present invention are shown in FIG. 2 . Accordingly, those skilled in the art to which the present invention pertains can see that other general-purpose components other than the components shown in FIG. 2 may be further included.

The processor 110 controls the overall operation of each component of the computing device 100 . The processor 110 includes a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art. can be

In addition, the processor 110 may perform an operation on at least one application or program for executing the method according to the embodiments of the present invention, and the computing device 100 may include one or more processors.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110 . Random Access Memory (RAM) and ROM (Read -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 120 stores various data, commands and/or information. The memory 120 may load the computer program 151 from the storage 150 to execute methods/operations according to various embodiments of the present disclosure. When the computer program 151 is loaded into the memory 120 , the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151 . The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 130 provides a communication function between components of the computing device 100 . The bus 130 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired/wireless Internet communication of the computing device 100 . Also, the communication interface 140 may support various communication methods other than Internet communication. To this end, the communication interface 140 may be configured to include a communication module well known in the technical field of the present invention. In some embodiments, the communication interface 140 may be omitted.

The storage 150 may non-temporarily store the computer program 151 . When the user-customized profile image generating process is performed through the computing device 100 , the storage 150 may store various types of information necessary to provide the user-customized profile image generating process.

The storage 150 is a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or well in the art to which the present invention pertains. It may be configured to include any known computer-readable recording medium.

The computer program 151 may include one or more instructions that, when loaded into the memory 120 , cause the processor 110 to perform methods/operations according to various embodiments of the present invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present invention by executing the one or more instructions.

In one embodiment, computer program 151 selects one or more keywords for the user, selects a background color using the selected one or more keywords, and uses the selected background color to create a customized profile image for the user. It may include one or more instructions for performing a method for generating a user-customized profile image including the step of generating.

The steps of the method or algorithm described in relation to the embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, a method for generating a user-customized profile image performed by the computing device 100 will be described with reference to FIGS. 3 to 9 .

3 is a flowchart of a method for generating a user-customized profile image according to another embodiment of the present invention.

Referring to FIG. 3 , in step S110 , the computing device 100 may select one or more keywords for the user in order to generate a customized profile image for the user. For example, the computing device 100 may provide a user interface (UI) that outputs a keyword list including a plurality of keywords, and receives one or more keywords selected from the plurality of keywords by the user through the UI. can

Here, the keyword may mean an adjective keyword (eg, FIG. 10 ) indicating the nature or state of the customized profile image that the user wants to create, but is not limited thereto.

In various embodiments, the computing device 100 may analyze a plurality of social data uploaded to the user's account to determine an attribute (eg, personality, disposition, taste, etc.) for the user, and assign the attribute to the determined user. One or more keywords for the user may be selected based on the.

In general, an SNS account is a space of one's own where one can express and reveal one's personality, disposition, taste, values, etc., and posts (eg, posts or images) uploaded on SNS It can be an important element that clearly reveals your inclinations, tastes and values.

In consideration of this, the computing device 100 may collect a plurality of social data uploaded to the user's account from the external server 300 (eg, an SNS operation server) and analyze it, and through this, the user's attribute (eg, personality, inclination, taste and values) can be determined, and by extracting one or more keywords for the user according to the determined attributes, keywords corresponding to the user's personality, inclination, taste, and values are accurately extracted There are advantages to being able to

Here, the method of determining the attribute of the user by analyzing a plurality of social data is based on the operation of extracting one or more attribute keywords from the text-type post uploaded by the user through string analysis and the stored keyword data for each attribute of the user. A method including an operation of determining a user's attribute corresponding to the extracted attribute keyword may be applied, but is not limited thereto. Hereinafter, various methods of selecting one or more keywords for a user will be described with reference to FIGS. 4 to 8 .

4 is a flowchart of a method of selecting a keyword using a question and answer, according to various embodiments.

Referring to FIG. 4 , in various embodiments, the computing device 100 may select one or more keywords for the user through a question and answer for the user.

In step S210 , the computing device 100 may provide a plurality of query contents to the user. For example, the computing device 100 may provide a plurality of query contents to the user through the UI.

Here, the plurality of query contents may include query contents about the user's disposition, personality, mood, taste, and an atmosphere that the user wants to express through the profile image. For example, the plurality of query contents may include the query contents based on the MBTI test, which is a self-reported personality type test devised by Myers and Briggs based on Jung's psychological typology. not limited

In step S220 , the computing device 100 may receive a plurality of answers in response to the plurality of questions provided to the user in step S210 .

In various embodiments, the computing device 100 provides the query content to the user through steps S210 and S220, and does not separately perform an operation of receiving the answer content corresponding thereto, but performs a test conducted by the user in advance from the user Results (eg, MBTI test results) can be directly input, or test result data generated as the test is performed can be uploaded.

In step S230, the computing device 100 may extract one or more keywords by analyzing a plurality of answers input from the user. For example, the computing device 100 responds to the MBTI test-based question content, and the personality type (eg, energy direction (extroversion (E), introversion (I)), recognition function (sensory (S)) for the user , intuition (N)), judgment function (thinking (T), feeling (F)), lifestyle (judgment (J), perception (P))), based on the determined personality type for the user Thus, one or more adjective keywords suitable for the user's personality type can be extracted.

For example, the computing device 100 may generate adjective keywords (eg, daring, provocative, passionate, exposing, etc.) related to actively revealing and expressing the user himself/herself in response to the user having an extroverted personality (E). Adjective keywords (e.g., neat, soft, subtle, natural, calm, calm) that are selectable and related to the user's introverted, non-active, non-active, introverted personality (I). etc.) can be selected.

In this case, the computing device 100 may define and store a plurality of adjective keyword data for each personality in advance in order to extract an adjective keyword suitable for the user's personality according to the user's personality determined through the above question and answer procedure. Here, the plurality of adjective keywords for each personality defined and stored in advance may be set by an administrator who provides a customized profile image creation service, but is not limited thereto, and the personality of each of the plurality of users and each of the plurality of users One or more adjective keyword data selected from may be accumulated and generated.

Here, the computing device 100 is described as automatically selecting one or more keywords for the user through a question and answer, but is not limited thereto, and selects a recommended keyword expected to be suitable for the user through a question and answer, By providing this to the user, the user can directly select a keyword in consideration of the recommended keyword.

In general, if you have high self-esteem and want to actively reveal and express your identity, the need for how you want to create your profile image is clear, but it is not only about revealing yourself, but also about taking pictures. There are many users who feel burdened, and most of them do not even know exactly what they want.

That is, the computing device 100 performs sufficient communication to understand the user's needs through a question and answer before generating a customized profile image for the user, thereby determining what the user wants to reveal through the profile image. It has the advantage of being able to clearly identify and accurately selecting keywords to meet these needs.

5 is a flowchart of a method of selecting keywords using original image analysis, in various embodiments.

Referring to FIG. 5 , in various embodiments, the computing device 100 may select one or more keywords for the user by analyzing an original image generated by photographing the user.

In step S310, the computing device 100 captures the user's face region included in the original image (eg, the original image photographed through the camera or the original image uploaded from the user terminal 200) generated by photographing the user. By analyzing, first attribute information about the user may be extracted.

Here, the first attribute information may mean information on the user's facial expression and mood (eg, joy, sadness, anger, etc.) extracted by analyzing the user's face. However, the present invention is not limited thereto, and the first attribute information may further include information on the user's makeup state (eg, the degree of makeup or the color of the cosmetic).

In operation S320 , the computing device 100 may extract second attribute information about the user by analyzing the user's body region included in the original image generated by photographing the user.

Here, the second attribute information may refer to information about the user's pose (eg, a front facing posture, a side facing posture, folded arms, posture symmetry, etc.). However, the present invention is not limited thereto, and the second attribute information may further include information on the user's clothes (eg, the type, shape and color of clothes, exposure degree, etc.).

Here, the computing device 100 does not individually perform steps S310 and S320, but information about the position and shape of features, facial expressions and mood, and information about the user's posture (eg, looking at the front). Posture, side-facing posture, folded arms, posture symmetry, etc.) of a plurality of labeled user profile images (images containing at least a part of the face and body) as training data, a pre-trained image analysis model ( For example, by analyzing an original image (Input) generated by photographing a user using an artificial intelligence model), the first attribute information and the second attribute information (output) may be extracted together.

However, the present invention is not limited thereto, and the computing device 100 performs a first image analysis model (ie, facial analysis) learned by using a plurality of profile images labeled with information about the location and shape of features, facial expressions, and mood as training data. model) and a second image analysis model (that is, a model specialized for posture analysis) learned using a plurality of profile images labeled with information about the user's posture as training data, respectively, to obtain first attribute information and The second attribute information may be extracted.

Also, in some cases, using two or more image models from which the first attribute information and the second attribute information can be simultaneously extracted, two or more first attribute information and second attribute information are respectively extracted, and the extracted attribute information is compared to derive final attribute information (eg, ensemble).

In operation S330 , the computing device 100 may select one or more keywords for the user by using the first attribute information and the second attribute information extracted by analyzing the original image generated by photographing the user.

For example, the computing device 100 determines that the user's expression is expressionless and the user's mood is depressed based on the user's first attribute information, and the user faces the front based on the user's second attribute information. If it is determined that you are taking an upright stance, you can choose adjective keywords about a rather heavy mood (eg cool, dark, calm, simple, etc.).

In various embodiments, the computing device 100 extracts one or more keywords using the first attribute information and the second attribute information, and extracts separate keywords by considering the first attribute information and the second attribute information respectively. can do.

For example, when the user wants to apply different background colors to the background corresponding to the user's face region and the background corresponding to the user's body region, the background corresponding to the user's face region is displayed using the first attribute information. One or more keywords for selecting a background color may be selected, and one or more keywords for selecting a background color of a background corresponding to the user's body region may be selected using the second attribute information.

Here, the computing device 100 is described as automatically selecting one or more keywords for the user through analysis of the original image generated by photographing the user, but is not limited thereto, and the original generated by photographing the user A recommended keyword that is expected to be suitable for the user is selected through image analysis and provided to the user so that the user can directly select the keyword in consideration of the recommended keyword.

6 is a flowchart of a method of selecting a keyword according to a purpose of using a profile image, according to various embodiments.

Referring to FIG. 6 , in various embodiments, the computing device 100 may extract one or more keywords for the user based on the purpose of using the customized profile image.

In step S410 , the computing device 100 may receive use purpose information for the profile image from the user. For example, the computing device 100 may provide a UI to the user terminal 200, and information on the purpose of use of the profile image from the user through the UI (eg, for resume writing, for public office submission, for publicity, for growth record) etc.) can be entered.

Here, the method of receiving the use purpose information of the profile image from the user may be implemented as a form of directly inputting a keyword indicating the purpose of use information from the user as text, but is not limited thereto, and one of a plurality of preset use purpose items It can be implemented in the form of receiving selection (eg, checkbox-based) of the above use-purpose items.

For example, when the user intends to use the profile image for the purpose of composing a resume, the computing device 100 may include an adjective keyword suitable for writing a resume among a plurality of adjective keywords (eg, natural, calm, calm, simple, bright). etc.) can be selected as a recommended adjective keyword, and the selected recommended adjective keyword can be provided to the user by outputting it through the UI.

Here, the recommended adjective keyword corresponding to the purpose of use information input from the user may be selected based on data on the type of the adjective keyword selected for each of the plurality of users according to the purpose of use, but is not limited thereto.

In step S430 , the computing device 100 may be selected one or more recommended keywords in response to providing a plurality of recommended keywords to the user in step S420 , and use the selected one or more recommended keywords as the customized profile image of the user. It can be selected as a keyword for creation.

7 is a flowchart of a method of selecting a keyword with respect to a group profile image of a plurality of users, according to various embodiments of the present disclosure;

Referring to FIG. 7 , in various embodiments, the computing device 100 individually selects keywords for a plurality of users when a plurality of users want to generate a customized profile image (eg, a family profile image) photographed together. and a final keyword for generating a customized profile image taken together by a plurality of users by synthesizing them.

In step S510 , the computing device 100 may individually select keywords for each of the plurality of users when a plurality of users want to create a customized profile image photographed together.

Here, the method of individually selecting a keyword for each of the plurality of users includes a method of directly inputting one or more keywords from each of the plurality of users through a UI, and a method of extracting a keyword for each of the plurality of users using a question and answer method (eg, steps S210 to S230 of FIG. 4), a method of extracting keywords for each of a plurality of users using original image analysis (eg, steps S310 to S330 of FIG. 5), usage input from each of the plurality of users A method of extracting a keyword based on the target information (eg, steps S410 to S430 of FIG. 6 ) may be selectively applied, but is not limited thereto.

In operation S520 , the computing device 100 may select a final keyword for generating a customized profile image photographed by the plurality of users by using individually selected keywords for each of the plurality of users.

In various embodiments, the computing device 100 may select at least one mutually identically selected keyword among a plurality of keywords individually selected from each of a plurality of users as the final keyword.

In various embodiments, the computing device 100 may count the number of selections for a plurality of keywords by using a plurality of keywords individually selected from each of a plurality of users, and sequentially select the top N keywords from a keyword with a high number of selections. You can select it and select it as the final keyword. However, the present invention is not limited thereto, and in some cases, in the process of selecting a background color, which will be described later, a final keyword is selected and a background color is not selected using the selected final keyword, but individually for each of a plurality of users. By selecting a background color for each of the plurality of users using the selected keyword, it is possible to set the background color corresponding to each of the plurality of users independently of each other.

8 is a flowchart of a method of selecting a keyword with respect to a profile image of a user and an object, according to various embodiments of the present disclosure;

Referring to FIG. 8 , in various embodiments, the computing device 100 selects a keyword based on an attribute of one or more objects and an association with the user when a user and one or more objects want to create a customized profile image photographed together. You can choose.

In step S610 , the computing device 100 may determine properties of one or more objects when a user and one or more objects want to create a customized profile image photographed together. For example, the computing device 100 may determine the type, size, shape, color, and number of one or more objects by a user analyzing an original image in which one or more objects are photographed together using a pre-learned image analysis model. can

Here, the pre-trained image analysis model is a pre-learned model using images of a plurality of objects labeled with property information of the object as training data, and the properties of the objects included in the specific image are obtained as a result by inputting a specific image as an input. It may be a model to be extracted.

In addition, here, the one or more objects include furniture (chairs, lights, tables, etc.) and various shooting props that can be used in profile image shooting, such as musical instruments, as well as companion animals or plants (trees, flowers) such as dogs and cats. can

In operation S620 , the computing device 100 may collect relationship information about the user and one or more objects. For example, the computing device 100 may receive relationship information indicating a relationship between the user and one or more objects from the user through the UI.

Here, the relationship information may refer to information indicating a relationship between a user and one or more objects. For example, when one or more objects are companion animals, the computing device 100 may receive information indicating a relationship, such as a companion animal, a family member, a friend, and the like, from a user.

In operation S630 , the computing device 100 may select one or more keywords based on the determined attributes of the one or more objects and relationship information between the user and the one or more objects. For example, if one or more objects of the computing device 100 are the user's dog, and the relationship information between the user and the dog is a companion animal, family, favorable as described above, adjective keywords such as cute, lovely, sweet, lively, etc. can be extracted.

Referring again to FIG. 3 , in various embodiments, the computing device 100 may set a degree for each of one or more keywords selected for the user. For example, if the adjective keyword selected for the user is "passionate", the computing device 100 may determine the degree of "passionate" (eg, a range of 1 to 10) according to how passionate the user wants to express. expressed as a score of ) can be set.

In this case, the degree of each of the one or more keywords selected for the user may be set in the process of performing the keyword selection method (eg, FIGS. 4 to 8 ). For example, when the user included in the original image wears clothes with some exposure, the computing device 100 may extract a keyword of “exposed” by analyzing it, and the degree of exposure of the clothes worn by the user. The degree of "exposure" can be set according to However, the present invention is not limited thereto, and the computing device 100 may provide the user with one or more keywords selected according to the keyword selection method (eg, FIGS. 4 to 8 ), and may determine the degree of each keyword directly from the user. It can be implemented in the form of receiving input.

In operation S120 , the computing device 100 may select a background color for generating a profile image that may reveal the identity of the user by using one or more keywords selected for the user.

Here, the background color selected using one or more keywords may be a single color such as red, orange, yellow, yellow green, green, cyan, blue, indigo, purple, purple, pink, brown, and achromatic colors, but is not limited thereto. .

In various embodiments, the computing device 100 uses a pre-learned artificial intelligence model using a plurality of color images labeled with one or more adjective keywords as training data, and applies one or more keywords selected for the user to the corresponding AI model. By inputting, a background color corresponding to one or more keywords selected for the user may be derived.

In various embodiments, when two or more keywords are selected according to the keyword selection method (eg, FIGS. 4 to 8 ), the computing device 100 may determine one background color corresponding to the two or more keywords.

In this case, the computing device 100 selects two or more keywords based on the similarity of each of the two or more keywords when there are two or more background colors corresponding to the two or more keywords, that is, when one background color corresponding to the two or more keywords is absent. A plurality of keyword groups may be grouped, and one background color for each of the plurality of keyword groups may be individually selected.

For example, when the computing device 100 receives two adjective keywords of “calm” and “passionate” from the user, since the two adjective keywords express opposite feelings, “calm” and “passionate” There may be cases where it is not possible to select a single background color that can express all

In consideration of this point, the computing device 100 may generate a plurality of keyword groups by grouping similar keywords together based on the degree of similarity of each of the two or more keywords input from the user, and a background corresponding to each of the plurality of keyword groups. Colors can be individually selected.

Thereafter, the computing device 100 generates a plurality of temporary profile images by applying a plurality of background colors individually selected according to each keyword group, and receives a selection of any one temporary profile image from among the plurality of temporary profile images. Accordingly, a background color for generating a customized profile image for the user may be finally determined.

In various embodiments, when there are two or more background colors corresponding to two or more keywords, the computing device 100 may apply two or more background colors to one profile image later by selecting each of two or more background colors, or two or more background colors. You can create more than one profile image by applying color.

In various embodiments, the computing device 100 may determine the brightness and saturation of the background color based on a degree corresponding to one or more adjective keywords. For example, when the keyword selected for the user is “passionate”, the computing device 100 may select red as a background color corresponding to “passionate”. In this case, the computing device 100 may set the brightness and saturation in proportion to the degree of "passionate" set by the user.

In various embodiments, the computing device 100 generates a temporary profile image for the user by using the background color selected through step S120, but the background applied to the temporary profile image based on feedback information on the generated temporary profile image. Color can be corrected. Hereinafter, it will be described with reference to FIG. 9 .

9 is a flowchart of a method for correcting a background color based on feedback on a temporary profile image, in various embodiments.

Referring to FIG. 9 , in operation S710 , the computing device 100 may generate a temporary profile image for the user using a background color selected based on one or more keywords selected for the user. For example, the computing device 100 may generate a temporary profile image for the user by applying a background color selected based on a keyword to the background of the original image generated by photographing the user.

In step S720, the computing device 100 may provide the temporary profile image generated through step S710 to a plurality of predefined users, and may receive feedback information on the corresponding temporary profile image from the plurality of predefined users. .

Here, the plurality of predefined users may mean evaluators set in advance for evaluation and analysis of the temporary profile image. For example, the plurality of predefined users may be professional photographers and students majoring in photography.

Also, here, the feedback information may be a keyword indicating a property or state of the temporary profile image that is felt while viewing the temporary profile image. Here, the form of the keyword included in the feedback information is not limited, but for comparison between the keyword selected for the user and the keyword included in the feedback information, it is preferably implemented in the same form as the keyword selected for the user. For example, when a keyword selected for a user is an adjective keyword, a keyword included in feedback information input from a plurality of predetermined users may also be an adjective keyword.

In operation S730, the computing device 100 may calculate a similarity by comparing one or more keywords selected for the user with the keywords included in the feedback information, and may correct a background color based on the calculated similarity.

For example, when the similarity between one or more keywords selected for the user and the keywords included in the feedback information is less than a preset reference value, the computing device 100 selects the background color (eg, step S120 of FIG. 3 ) to reselect the background color. Here, the computing device 100 repeatedly performs steps S710 to S730 until the degree of similarity between one or more keywords selected for the user and the keywords included in the feedback information is equal to or greater than a preset reference value, but with respect to the corresponding temporary profile image The same users can repeatedly input feedback information.

Meanwhile, when the similarity between one or more keywords selected for the user and the keywords included in the feedback information is equal to or greater than a preset reference value, the computing device 100 may determine the temporary profile image as the customized profile image for the user.

In various embodiments, the computing device 100 scores each of a plurality of keywords using the keywords included in the feedback information, and a degree of similarity between one or more keywords selected for the user and the keywords included in the feedback information using the scoring result. can be calculated.

First, the computing device 100 calculates the number of selections for each of a plurality of keywords using a plurality of pieces of feedback information input from a plurality of predefined users, and scores a score for each of the plurality of keywords based on the calculated number of times. (Example: 1 point is given for each selected number of times). For example, when the first keyword is selected a total of five times by analyzing the keyword included in the feedback information, the computing device 100 may give 5 points to the first keyword.

In various embodiments, the computing device 100 may give a weight to each of the keywords included in the feedback information based on the attribute of the user who has input the feedback information, and calculate a score for each of the plurality of keywords in consideration of the weight. can be scored.

For example, the computing device 100 assigns a weight of 1.5 to a professional photographer when a user who has input the first feedback information including the first keyword, the second keyword, and the third keyword is a professional photographer. It may be set so that 1.5 points are given instead of 1 point according to the selection of the first keyword.

In addition, the computing device 100 gives a weight of 1.2 when the user who has input the second feedback information including the first keyword, the second keyword, and the third keyword is a photography major, so that the photography major has the first keyword Depending on the selection, it can be set to give 1.2 points instead of 1 point.

In various embodiments, the computing device 100 may perform a verification procedure for feedback information input from a plurality of predefined users, and may score scores for each of a plurality of keywords using only the verified feedback information. have.

When inputting feedback information for a specific temporary profile image, taking advantage of the fact that a predetermined reward is provided as a reward, there is a case where incorrect feedback information is input only for compensation without properly checking the temporary profile image. can occur When such a situation occurs frequently, there is a problem in that accurate feedback information may not be obtained, and thus many errors may occur in selecting a background color optimized for the user.

In consideration of this point, the computing device 100 may filter feedback information having a problem among feedback information input from a plurality of predetermined users. For example, the computing device 100 may provide feedback input within a preset time period after providing the temporary profile image (that is, if feedback is input within a time period that cannot be considered to be accurate) or other users do not select at all. Feedback including only adjective keywords that have not been filtered may be filtered, and each of the plurality of keywords may be scored using only the remaining feedback information.

At this time, the computing device 100 is a penalty (eg, does not provide a reward or, in some cases, the role of the evaluation team to input the feedback information is deprived, etc. ) can be given.

Thereafter, the computing device 100 may sequentially select N keywords from a keyword having a high score based on the scores for each of the plurality of keywords. Here, N may be set to the same number as the keyword selected for the user in order to compare the keyword based on the feedback information with the keyword selected for the user, but is not limited thereto.

Thereafter, the computing device 100 compares the selected N keywords with the keywords selected for the user, calculates the number of identical keywords, and divides the number of identical keywords by the total number of N, so that one or more keywords selected for the user and A degree of similarity between keywords included in the feedback information may be calculated. For example, the computing device 100 compares four keywords selected for the user with the four keywords selected based on the scores for each of the plurality of keywords, and when a total of three keywords are the same, one or more keywords selected for the user and the similarity between the keywords included in the feedback information can be calculated as 0.75.

In this case, the computing device 100 may provide a predetermined reward to a plurality of users who have input feedback information on the temporary profile image. Here, the predetermined reward may be a point that can be used in using the customized profile image generation service, but is not limited thereto.

In various embodiments, the computing device 100 may generate a temporary profile image for the user using a background color selected based on one or more keywords selected for the user, and generate quiz content using the temporary profile image. and can correct the background color based on the result of the quiz content.

First, the computing device 100 may generate quiz content by using a temporary profile image. Here, the quiz content may be a multiple-choice question matching the feeling that the temporary profile image wants to express, and one or more keywords selected for the user and unselected keywords may be set as options for the question.

Thereafter, the computing device 100 may provide the quiz content generated using the temporary profile image to a plurality of users. Here, as a method of providing the quiz content to a plurality of users, a method of providing the quiz content to all users or any user previously subscribed to the user-customized profile image creation service may be applied, but is not limited thereto, and the quiz content is provided through the UI. It can be implemented in a form that allows users to freely participate in content by posting

Thereafter, the computing device 100 may determine whether to correct the background color applied to the temporary profile image based on the correct rate (or incorrect answer rate) for the corresponding quiz content. For example, the computing device 100 determines that the temporary profile image does not accurately express the user's needs when the correct rate for the corresponding quiz content is less than or equal to the preset reference value (or the incorrect rate is greater than or equal to the preset value). , the background color can be corrected so that these needs can be accurately expressed.

In various embodiments, the computing device 100 uses a pre-learned artificial intelligence model using a plurality of color images labeled with one or more adjective keywords as training data, and applies one or more keywords selected for the user to the corresponding AI model. By inputting, a background color corresponding to one or more keywords selected for the user may be derived, but the corresponding AI model may be retrained based on the similarity between the one or more keywords selected for the user and the feedback information.

For example, when the similarity between one or more keywords selected for the user and the keywords included in the feedback information is less than or equal to a preset value, the computing device 100 may use the corresponding background color and the keywords included in the feedback information to create the artificial intelligence model. Learning data for re-learning (eg, labeling a keyword included in the feedback information on a color image corresponding to the background color) can be generated, and the artificial intelligence model can be re-trained using this.

In this case, the computing device 100 may determine the re-learning intensity for the artificial intelligence model according to the degree of similarity between one or more keywords selected for the user and the keywords included in the feedback information. Here, the re-learning intensity may mean at least one of the number of learning data and the re-learning period for re-learning the artificial intelligence model.

For example, when the similarity between one or more keywords selected for the user and the keywords included in the feedback information falls within a range of a first value or more and a preset value or less, the computing device 100 gives feedback to a color image corresponding to the background color. By labeling the keywords included in the information, one training data may be generated, and the artificial intelligence model may be retrained using the generated training data.

Meanwhile, when the similarity between one or more keywords selected for the user and the keywords included in the feedback information is less than the first value, the computing device 100 sets the brightness and saturation of the first color image corresponding to the background color and the background color to a predetermined value. A plurality of learning data may be generated by labeling a keyword included in the feedback information on a plurality of second color images arbitrarily within a range, and the artificial intelligence model may be retrained using the generated plurality of learning data.

Referring again to FIG. 3 , in step S130 , the computing device 100 may generate a customized profile image for the user using the background color selected in step S120 .

In various embodiments, the computing device 100 generates a customized profile image for the user based on a background color selected using one or more keywords, and provides the user with a process of generating a customized profile image for the user through the UI in real time. can be provided as

Here, the process of creating a profile image customized for the user may mean not only the process of applying the background color to the original image, but also the process of correcting the user's face or body in the original image (eg, Photoshop), but is limited thereto. doesn't happen

In various embodiments, the computing device 100 may receive request information (eg, a modification request) from the user through the UI, and may provide the modified content to the user in response to the input request information. .

That is, the computing device 100 transparently discloses the process of generating a custom profile image for the user in real time, so that the user can see what process the custom profile image is generated through, and what kind of work is being done, how, and to what extent. At the same time, it is possible to derive a result that can more accurately meet the user's needs by allowing the user to know intuitively and at the same time to immediately converge the user's request and to reflect and modify it.

In various embodiments, the computing device 100 generates a customized profile image for the user, but uploads the created customized profile image for the user to the cloud server, so that the user freely accesses the cloud server to download the customized profile image Can be used. In addition, the user may input a request for modification of his or her customized profile image through the cloud server.

The above-described user-customized profile image generation method has been described with reference to the flowchart shown in the drawings. For the sake of simplicity, the method for generating a user-customized profile image has been described with a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks may be performed in an order different from that shown and operated in this specification, or can be performed simultaneously. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: user-customized profile image generating device (computing device)
200: user terminal
300 : external server
400: network

Claims (13)

A method performed by a computing device, comprising:
selecting one or more keywords for the user;
selecting a background color using the selected one or more keywords; and
using the selected background color to generate a customized profile image for the user,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
A user interface (UI) for outputting a list of keywords including a plurality of keywords - the plurality of keywords includes an adjective keyword indicating a property or state of the customized profile image that the user wants to create - providing a user interface (UI) step; and
Including the step of receiving one or more keywords from the plurality of keywords selected by the user through the user interface,
A method of providing a profile image creation service based on the user's personality, disposition and mood. 3. The method of claim 2,
The step of selecting the background color is,
When two or more keywords are selected by the user, one background color corresponding to the two or more keywords is determined, but when there are two or more background colors corresponding to the two or more keywords, based on the similarity of each of the two or more keywords grouping the two or more keywords into a plurality of keyword groups, and individually selecting one background color for each of the plurality of keyword groups,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
providing a user interface (UI);
A plurality of query contents are provided through the user interface, wherein the plurality of query contents include contents about at least one of the user's disposition, personality, mood, taste, and the atmosphere the user wants to express. ; and
and extracting one or more keywords for the user by analyzing a plurality of answers input from the user in response to the plurality of query contents,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
The user's face region included in the original image generated by photographing the user is analyzed to extract first attribute information about the user, wherein the first attribute information includes information about the user's facial expression and mood. to do;
analyzing the user's body region included in the generated original image to extract second attribute information about the user, wherein the second attribute information includes information about the user's posture; and
selecting the one or more keywords using the extracted first attribute information and the extracted second attribute information;
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
receiving information on the purpose of use of the customized profile image from the user;
selecting a plurality of recommended keywords based on the input purpose of use information and providing the selected plurality of recommended keywords to the user; and
Comprising the step of receiving one or more recommended keywords selected from the selected plurality of recommended keywords from the user,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
individually selecting a keyword for each of the plurality of users when a plurality of users want to create a customized profile image taken together; and
Selecting a keyword identically selected from each of the plurality of users from among the individually selected keywords or selecting the top N keywords from a keyword with a high number of selected keywords from among the individually selected keywords;
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
Selecting the one or more keywords based on properties of the one or more objects and relationship information between the user and the one or more objects when the user and one or more objects want to create a customized profile image photographed together; doing,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of selecting one or more keywords comprises:
determining an attribute for the user by analyzing a plurality of social data uploaded to the user's account, and selecting the one or more keywords based on the determined attribute for the user,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of generating the customized profile image,
generating a temporary profile image for the user based on the selected background color;
receiving feedback information on the generated temporary profile image from a plurality of predefined users, wherein the feedback information is an adjective keyword indicating a property or state of the generated temporary profile image; and
Comprising the step of compensating the selected background color by comparing the selected one or more keywords with the feedback information,
A method of providing a profile image creation service based on the user's personality, disposition and mood. According to claim 1,
The step of generating the customized profile image,
generating a custom profile image for the user based on the selected background color, and providing a process of generating a custom profile image for the user to the user in real time through a user interface (UI); and
receiving request information from the user through the user interface, and providing the user with modified contents in response to the input request information;
A method of providing a profile image creation service based on the user's personality, disposition and mood. processor;
network interface;
Memory; and
a computer program loaded into the memory and executed by the processor;
The computer program is
instructions to select one or more keywords for the user;
instructions for selecting a background color using the selected one or more keywords; and
Including instructions for generating a customized profile image for the user by using the selected background color,
User-customized profile image generator. combined with a computing device,
selecting one or more keywords for the user;
selecting a background color using the selected one or more keywords; and
stored in a computer-readable recording medium to execute the step of generating a customized profile image for the user by using the selected background color;
A computer program recorded on a computer-readable recording medium.

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