Apparatus for color blending and method thereof

Abstract

The present invention relates to an apparatus for color blending, and comprises: a memory for storing computer-executable instructions; and at least one processor for accessing the memory and executing the instructions. The at least one processor: receives an output request for blending information about a target color; expresses the target color in response to the output request to acquire reference blending information about a reference product in which the blending information about the target color is predetermined; and outputs target blending information about the target product on the basis of the reference blending information and a predetermined color ratio model between the reference product and the target product.

Description

Color mixing device and method thereof

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2024-0117188, filed August 29, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The present invention relates to a color mixing device and method thereof, and more particularly, to a technique for obtaining mixing information of a product that implements a color.

[0005] When a consumer orders paint, they must analyze similar colors and undergo a process of color mixing, which requires labor, typically taking two to three hours before they can see the results. To address this issue, technology is needed to quickly and accurately perform color mixing. For example, a technology is needed that analyzes similar colors based on a database, then utilizes a color mixing design and quality verification system to determine the optimal color combination. Such a data-driven system can increase the speed and accuracy of color mixing and facilitate the efficient management of business resources such as labor, materials, and energy.

[0006] Embodiments of the present invention aim to provide a color mixing device and method capable of easily and quickly providing various colors in a market where demand for small quantities of a variety of colors is rapidly increasing by outputting target mixing information of a target product based on a determined color ratio model between a reference product and a target product.

[0007] In addition, embodiments of the present invention provide a color mixing device and method capable of achieving mixing optimization with 50 to 60 pieces of data per product by storing a color ratio model between color values ​​of each product and outputting target mixing information of a new product, which is a target product, through the stored color ratio model.

[0008] The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0009] A color mixing device according to an embodiment of the present invention comprises: a memory storing computer-executable instructions; and at least one processor accessing the memory to execute the instructions, wherein the at least one processor receives a request to output mixing information regarding a target color, and in response to the output request, acquires reference mixing information of a reference product that implements the target color and has predetermined mixing information regarding the target color, and outputs the target mixing information of the target product based on the reference mixing information and a predetermined color ratio model between the reference product and the target product.

[0010] In one embodiment, the at least one processor may receive a color code of the target color from a user who transmits the output request, apply the reference product to a Lab color system to obtain first color values ​​of the reference product, and obtain the reference mixing information based on the color code and the first color values.

[0011] In one embodiment, the at least one processor may obtain second color values ​​of the target product by applying the target product to the Lab color space, and obtain the color ratio model by applying the first color values ​​and the second color values ​​to a color ratio model learned to output a color ratio model between products.

[0012] In one embodiment, the at least one processor may extract, from the first color values, a first_1 color element and a first_2 color element corresponding to elements of the Lab color space, and, from the second color values, extract a second_1 color element and a second_2 color element corresponding to elements of the Lab color space, and apply a difference between the first_1 color element and the second_1 color element and a difference between the first_2 color element and the second_2 color element to the color ratio model, thereby obtaining the color ratio model.

[0013] In one embodiment, the at least one processor may receive information on whether the color ratio model is identified from a server in which the color ratio model between products is previously stored by transmitting the second color values ​​to the server, and may receive the color ratio model from the server.

[0014] In one embodiment, the at least one processor may output the reference blending information in response to the output request, if the output request is a request for outputting blending information regarding the target color implemented based on the reference product.

[0015] In one embodiment, the at least one processor can obtain the target mixing information by applying the reference mixing information and the color ratio model to a color mixing model learned to output mixing information according to color and product.

[0016] In one embodiment, the at least one processor may determine a predicted color implemented based on the target product and the target blending information, and transmit the predicted color to a user transmitting the output request.

[0017] A color mixing method according to an embodiment of the present invention may include: receiving a request for outputting mixing information regarding a target color implemented based on a target product; acquiring, in response to the output request, reference mixing information of a reference product that implements the target color and has predetermined mixing information regarding the target color; and outputting target mixing information of the target product based on the reference mixing information and a predetermined color ratio model between the reference product and the target product.

[0018] In one embodiment, the method may include a computer program stored on a computer-readable medium to execute a color mixing method in combination with hardware.

[0019] The effects of the color mixing device and method according to the present invention are described as follows.

[0020] According to at least one of the embodiments of the present invention, it can have the effect of easily and quickly providing a variety of colors in a market where demand for small quantity, multi-variety colors is rapidly increasing.

[0021] Additionally, according to at least one of the embodiments of the present invention, it may have the effect of achieving blending optimization with 50 to 60 levels of data per product.

[0022] In addition, various effects may be provided, either directly or indirectly, through this document.

[0023] FIG. 1 is a block diagram illustrating a color mixing device according to an embodiment of the present invention.

[0024] Figure 2 is a flowchart for explaining a color mixing method according to one embodiment of the present invention.

[0025] FIG. 3 is a flowchart for explaining a method for outputting optimal mixing conditions in a color mixing device according to one embodiment of the present invention.

[0026] Figure 4 is a diagram illustrating an example for determining a color ratio model in the Lab color space.

[0027] Figure 5 is a diagram illustrating an example of the Lab color space.

[0028] FIGS. 6 to 8 are diagrams illustrating examples of interfaces for execution results of a program including codes or instructions for performing operations of a color mixing device according to an embodiment of the present invention.

[0029] FIG. 9 is a drawing showing a graph of color difference for explaining the effect of a color mixing device according to one embodiment of the present invention.

[0030] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0031] Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. When adding reference numerals to components in each drawing, it should be noted that the same components are given the same numerals as much as possible even if they are shown in different drawings. In addition, when describing embodiments of the present invention, if a detailed description of a related known configuration or function is judged to hinder the understanding of the embodiments of the present invention, the detailed description thereof will be omitted. In particular, various embodiments of this document are described with reference to the attached drawings. However, this is not intended to limit the technology described in this document to a specific embodiment, and it should be understood that it includes various modifications, equivalents, and / or alternatives of the embodiments of this document. With regard to the description of the drawings, similar reference numerals may be used for similar components.

[0032] When describing components of embodiments of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are only intended to distinguish the components from other components, and do not limit the nature, order, or sequence of the components. Furthermore, unless otherwise defined, all terms, including technical or scientific terms, used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application. For example, expressions such as “first,” “second,” “first,” or “second,” used in this document may modify various components, regardless of order and / or importance, and are only used to distinguish one component from another, without limiting the components. For example, the first user device and the second user device may represent different user devices, regardless of order or priority. For example, without departing from the scope of the rights set forth in this document, the first component may be referred to as the second component, and similarly, the second component may be referred to as the first component.

[0033] In this document, the expressions “has,” “can have,” “includes,” or “may include” indicate the presence of a feature (e.g., a number, function, operation, or component such as a part), but do not exclude the presence of additional features.

[0034] When it is said that a component (e.g., a first component) is “(operatively or communicatively) coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that the component is directly coupled to the other component, or can be connected via another component (e.g., a third component). Conversely, when it is said that a component (e.g., a first component) is “directly coupled to” or “directly connected to” another component (e.g., a second component), it should be understood that no other component (e.g., a third component) exists between the first component and the other component.

[0035] The expression “configured to” as used in this document can be used interchangeably with, for example, “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of.”

[0036] The term “configured (or set) to” may not necessarily mean “specifically designed to” in hardware. Instead, in some situations, the phrase “a device configured to” may mean that the device, in conjunction with other devices or components, is “capable of” doing something. For example, the phrase “a processor configured (or set) to perform A, B, and C” may refer to a dedicated processor (e.g., an embedded processor) for performing the operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform the operations by executing one or more software programs stored in a memory device. The terms used herein are used merely to describe particular embodiments and may not be intended to limit the scope of other embodiments. The singular form “a” or “an” may include the plural form unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art described in this document. Terms used in this document, defined in common dictionaries, may be interpreted as having the same or similar meaning within the context of the relevant technology. Unless explicitly defined herein, they shall not be interpreted in an idealized or overly formal sense. In some cases, even if a term is defined herein, it cannot be interpreted to exclude embodiments of this document.

[0037] In this document, phrases such as “A or B,” “at least one of A and / or B,” or “one or more of A or / and B” can include all possible combinations of the items listed together. For example, phrases such as “A or B,” “at least one of A and B,” or “at least one of A or B” can all refer to (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B. Furthermore, in describing components of embodiments of the present invention, phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, or C,” and “at least one of A, B, C, or any combination thereof” can include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. In particular, a phrase such as “at least one of A, B, C, or any combination thereof” may include A or B or C, or any combination thereof, such as AB or ABC.

[0038] Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 9.

[0039] FIG. 1 is a block diagram illustrating a color mixing device according to an embodiment of the present invention.

[0040] A color mixing device (100) according to one embodiment may include a processor (110) and a memory (120) including instructions (122).

[0041] The color mixing device (100) may refer to a device that provides color mixing information. For example, the color mixing device (100) may output target mixing information for a target product based on a predetermined color ratio model between a reference product and a target product. The reference product and the target product may be chemical products that can be used in various fields, such as construction oils, construction water-based products, industrial products, plants, or heavy-duty coatings. The reference product may include at least one of the chemical products described above. When the reference product is determined, the target product may be a novel chemical product that is compared with the reference product. In other words, the target product may include all of the chemical products described above, excluding the reference product. The determination of the reference product and the target product may be determined according to operational conditions.

[0042] The color ratio model can be obtained from a model that defines rules for color changes between the color of a reference product and the color of a target product. For example, the color of the reference product can be expressed as a color value of a Lab chromaticity (i.e., Lab color space). The color of the target product can be expressed as a color value of a Lab chromaticity, similar to the color of the reference product. For reference, the Lab chromaticity can represent at least one of several chromaticities that express color values, such as RGB or HEX. For example, if the color of the reference product is '(L1, a1, b1)' and the color of the target product is '(L2, a2, b2)', the color ratio model can include the color changes between '(L1, a1, b1)' and '(L2, a2, b2)'. For reference, the color ratio model is not limited as it can be obtained by various models such as deep learning or a mathematical model. The number of data required to define the color ratio model can be 50 to 60 for each target product.

[0043] For example, the color mixing device (100) can identify the 50 to 60 most adjacent data based on the color value requested to be output (e.g., a value input by a user). However, the present invention is not limited thereto, and the color mixing device (100) can randomly identify 50 to 60 data in an area including the color value requested to be output by dividing the entire Lab color space based on at least one of the L-axis, the a-axis, and the b-axis. In this way, the data identified by the color mixing device (100) can represent the number of data required to define a color ratio model.

[0044] Development of an optimization algorithm for predicting color combinations using a deep learning prediction model and a color ratio model prediction model based on color trends between products. The format of the data required for learning may vary depending on the training data of the existing deep learning prediction model and the color trends between products. The color combination device (100) described in this specification can provide color combination information by utilizing not only a machine learning or deep learning model, but also a model that can be expressed in various forms such as a logarithmic function, an exponential function, or a polygonal equation. A detailed description of how the color combination device (100) provides color combination information is described in detail later in FIG. 3 below.

[0045] The color mixing device (100) can achieve a commercially viable level of precision by optimizing mixing with only 50 to 60 pieces of data per product, rather than 3,000 or more, through the aforementioned model. This allows the color mixing device (100) to achieve high efficiency for the same performance, material, time, or energy requirements.

[0046] The processor (110) can execute software and control at least one other component (e.g., hardware or software component) connected to the processor (110). The processor (110) can also perform various data processing or calculations. For example, the processor (110) can store color combination information in the memory (120).

[0047] For reference, the processor (110) can perform all operations performed by the color mixing device (100). Therefore, in this specification, for convenience of explanation, the operations performed by the color mixing device (100) are mainly described as operations performed by the processor (110). In addition, in this specification, for convenience of explanation, the processor (110) is mainly described as one processor, but is not limited thereto. For example, the color mixing device (100) can include at least one processor. Each of the at least one processor can perform all operations related to the operation of providing color mixing information.

[0048] The memory (120) can temporarily and / or permanently store various data and / or information required to perform an operation of providing color combination information. For example, the memory (120) can store color combination information.

[0049] The color mixing device (100) may further include a communication unit. For example, the communication unit may support communication between the color mixing device (100) and a server. The communication unit may include one or more components that enable communication between the color mixing device (100) and the server. Specifically, the communication unit may include a short range wireless communication unit, a microphone, etc. At this time, the short range communication technology may include, but is not limited to, wireless LAN (Wi-Fi), Bluetooth, Zigbee, WFD (Wi-Fi Direct), UWB (ultra-wideband), infrared communication (IrDA, infrared Data Association), BLE (Bluetooth Low Energy), NFC (Near Field Communication), etc.

[0050] Figure 2 is a flowchart for explaining a color mixing method according to one embodiment of the present invention.

[0051] In one embodiment, a processor (e.g., processor (110) of FIG. 1) may, at operation 210, receive a request to output formulation information regarding a target color implemented based on a target product. For example, the target color may represent a color that a user transmitting the output request wants to be implemented and / or is to be implemented by the target product. For example, the target color may be implemented and / or generated by a target product (e.g., a chemical product that can be used in various fields such as building oils, building water-based paints, industrial paints, plants, or heavy-duty paints that generate colors). The user may transmit the output request via a server (e.g., a server connected via the communication unit described in FIG. 1). Specifically, the user may transmit the output request via an interface described in FIG. 6 below.

[0052] In operation 220, the processor, in response to an output request, may obtain reference mixing information of a reference product that implements a target color and has previously determined mixing information regarding the target color. For example, the processor may identify the target color included in the output request. If the processor identifies the target color, it may obtain reference mixing information, which is mixing information regarding the target color. Specifically, the reference mixing information may include information regarding a predicted color, a type of material, and a type and amount of a colorant when implementing the target color with a reference product. The reference mixing information may be stored in a memory (e.g., memory (120) of FIG. 1) or may be stored on a server. The reference mixing information may be determined by a user.

[0053] In operation 230, the processor may output target combination information of the target product based on the determined color ratio model between the reference product and the target product. For example, the processor may obtain a color ratio model, which is the difference between the color information of the reference product and the color information of the target product. When the color ratio model is obtained, the processor may output target combination information based on the reference combination information and the color ratio model. The target combination information may include information regarding the predicted color, the type of material, and the type and amount of the colorant when the target color is implemented by the target product. The target combination information may be stored in memory or may be stored on a server.

[0054] FIG. 3 is a flowchart for explaining a method for outputting optimal mixing conditions in a color mixing device according to one embodiment of the present invention.

[0055] A processor according to one embodiment (e.g., processor (110) of FIG. 1) may, in operation 310, identify a color code input. For example, a user may input a color code through an interface (e.g., an interface described later in FIG. 6).

[0056] The processor, at operation 320, may retrieve a color value. For example, the color value may represent color values ​​according to the Lab color space, paired with a color code input by a user. Specifically, the processor may obtain first color values ​​of the reference product by applying the reference product to the Lab color space based on the color code of the target color received from the user sending the output request. A detailed description of the color values ​​according to the Lab color space is provided in detail below in FIG. 5.

[0057] The processor may generate a colorant blend sample at operation 330. For example, the processor may generate the colorant blend sample using a random function. The colorant blend sample may be used to train a color blend model and may be used in an operation of determining a color ratio model between a reference product and a target product. Additionally, the colorant blend sample may include reference blend information. The processor may obtain the reference blend information based on the color code and the first color values.

[0058] At operation 340, the processor can predict a color from a color combination model. For example, the color combination model can be generated by analyzing color trends among products from learning data by the processor. The processor can obtain target combination information by applying reference combination information and a color ratio model to the learned color combination model to output combination information based on color and product. The processor can predict a color based on the target combination information.

[0059] The processor can obtain second color values ​​of the target product by applying the target product to the Lab color space. The processor can obtain a color ratio model by applying the first color values ​​and the second color values ​​to a color ratio model trained to output a color ratio model between products. However, the operation of the processor to obtain the color ratio model is not limited thereto. For example, the processor can receive information on whether the color ratio model is identified from the server by transmitting the second color values ​​to a server in which the color ratio model between products is previously stored. The processor can receive the color ratio model from the server based on the color ratio model being stored on the server.

[0060] The processor can train a color combination model and a color ratio model. For example, the color combination model and the color ratio model may include a neural network. The neural network may include multiple layers, and each layer may include multiple nodes. The nodes may have node values ​​determined based on an activation function. Nodes in any layer may be connected to nodes in other layers (e.g., other nodes) through links (e.g., connection edges) having connection weights. The node values ​​of a node may be propagated to other nodes through the links. In the inference operation of the neural network, node values ​​may be forward propagated from the previous layer to the next layer.

[0061] For example, in color mixing models and color ratio models, the forward propagation operation may refer to an operation that propagates node values ​​based on input data from the input layer of the model toward the output layer. That is, the node value of a node may be propagated (e.g., forward propagated) to a node in the next layer (e.g., the next node) connected to the node via a connection line. For example, a node may receive a value weighted by the connection weight from a previous node (e.g., multiple nodes) connected to it via a connection line.

[0062] The node value of a node can be determined based on applying an activation function to the sum of weighted values ​​(e.g., a weighted sum) received from previous nodes. The parameters of the neural network may include, for example, the connection weights described above. The parameters of the neural network can be updated so that the objective function value described below changes in a targeted direction (e.g., a direction that minimizes loss).

[0063] The learned color combination model and color ratio model may represent a model learned through machine learning, and may be a learned machine learning model that outputs training outputs from training inputs. The machine learning model (e.g., the learned color combination model and color ratio model) may be generated through machine learning. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above.

[0064] The machine learning model may include multiple artificial neural network layers. The artificial neural network may be at least one of a deep neural network (DNN), a convolutional neural network (CNN), a U-Net for Image Segmentation (U-Net), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination thereof, but is not limited to the examples described above.

[0065] In supervised learning, the machine learning model described above can be trained based on training data that includes pairs of training inputs and training outputs mapped to the training inputs. For example, the machine learning model can be trained to output training outputs from training inputs. During training, the machine learning model can generate temporary outputs in response to the training inputs, and can be trained to minimize the loss between the temporary outputs and the training outputs (e.g., the training target). During the training process, the parameters of the machine learning model (e.g., the connection weights between nodes / layers in a neural network) can be updated according to the loss. This training can be performed, for example, on the color mixing device itself where the machine learning model is executed, or can be performed through a separate server. The machine learning model that has completed training can be stored in memory (e.g., memory (120) of FIG. 1).

[0066] The processor, at operation 350, can output optimal mixing conditions. That is, the optimal mixing conditions can represent target mixing information. If the output request is a request for outputting mixing information regarding a target color implemented based on a reference product, the processor can output the reference mixing information in response to the output request.

[0067] The processor can determine a predicted color based on the target product and target formulation information, and transmit the predicted color to a user who sends an output request. Descriptions of the target formulation information and predicted color are provided below in Figure 8.

[0068] Figure 4 is a diagram illustrating an example for determining a color ratio model in the Lab color space.

[0069] A processor according to an embodiment (e.g., processor (110) of FIG. 1) may calculate and / or determine a color ratio model between products. For example, the processor may extract, from first color values, a first_1 color element and a first_2 color element corresponding to elements of a Lab color space. The processor may extract, from second color values, a second_1 color element and a second_2 color element corresponding to elements of a Lab color space. However, examples of the processor extracting color elements from color values ​​are not limited thereto. For example, the processor may extract three color elements from the color values. In this specification, for convenience of explanation, an operation in which the processor extracts two color elements from color values ​​is mainly described.

[0070] The processor can obtain a color ratio model by applying the difference between the first_1 color element and the second_1 color element, and the difference between the first_2 color element and the second_2 color element to the color ratio model. In other words, the color ratio model can mean the ratio of colors of two different products under the same mixing conditions as a definition of a trend between products. The color ratio model must be able to cover the entire color range, and to model the color ratio model, the results of artificial intelligence learning can be mainly used, and sometimes various mathematical formulas can be used.

[0071] Figure 5 is a diagram illustrating an example of the Lab color space.

[0072] Referring to FIG. 5, an example of a Lab color space is illustrated. For example, the entire color gamut of the Lab color system (or color space) may mean L: 0 to 100, a: -128 to 128, and b: -128 to 128. Since the entire gamut of the Lab color space is equal to L (0 to 100), a (-128 to 128), and b (-128 to 128), a processor (e.g., processor (110) of FIG. 1) can select a color within an effective range (or color development range) for each product and / or base within the entire gamut. The degree of distribution (or density) of the color may vary depending on the product and / or base within the range. 50 to 60 values ​​per product may represent an effective number obtained from experiments. Therefore, the processor can determine or obtain a color ratio model based on 50 to 60 values ​​per product.

[0073] FIGS. 6 to 8 are diagrams illustrating examples of interfaces for execution results of a program including codes or instructions for performing operations of a color mixing device according to an embodiment of the present invention.

[0074] FIG. 6 illustrates an example of an interface for receiving a request for outputting mixing information regarding a target color from a program including code or instructions for performing the operation of a color mixing device according to one embodiment of the present invention.

[0075] Referring to FIG. 6, the color selection bar (610) may include color book search, color value input, and history search. The color book search may include information regarding multiple color groups including the target color.

[0076] The color name input bar (620) may include the code of the target color input by the user. The detailed color input bar (630) may include a more specific code of the code of the target color. The color details (640) may include detailed information of the target color. In other words, the target color includes the color that the user wants to create, and may be selected by color name, color value (L, a, b), or previous history. That is, through the interface of FIG. 6, the processor can identify the target color.

[0077] Figure 7 shows an example of an interface for receiving a target product, of a program including code or instructions for performing the operation of a color mixing device.

[0078] Referring to FIG. 7, the application selection bar (710) may include architectural water-based, architectural oil-based, and industrial / plant applications. The gloss selection bar (720) may include matte, semi-gloss, and glossy applications. The product selection window (730) may include target products selected based on the application selected by the user through the application selection bar (710) and the gloss selected through the gloss selection bar (720). That is, the processor may identify target products through the interface of FIG. 7.

[0079] Figure 8 shows an example of an interface for outputting target mixing information of a target product in a program including code or instructions for performing the operation of a color mixing device.

[0080] Referring to FIG. 8, when a user clicks or activates the blending design window (810), the processor can obtain target blending information. The processor can output the obtained target blending information and predicted color to the user through the interface illustrated in FIG. 8.

[0081] Specifically, when a user clicks or activates the blending design window (810), the processor can obtain reference blending information based on the first color values ​​of the reference product and the color code of the target color. The processor can obtain a color ratio model by applying the first color values ​​and the second color values ​​of the target product to a color ratio model. The processor can obtain target blending information by applying the reference blending information and the color ratio model to a color blending model trained to output blending information according to color and product.

[0082] FIG. 9 is a drawing showing a graph of color difference for explaining the effect of a color mixing device according to one embodiment of the present invention.

[0083] Referring to FIG. 9, a color mixing device (e.g., a color mixing device (100) of FIG. 1) can implement a color adjacent to a good product standard. For example, when the color difference (dE), which is the color difference between the standard color and the predicted color illustrated in FIG. 8, is 1.0, the predicted color is described as good. For example, when the color code of the target color is 'HG961', the color mixing device can output target mixing information having a color difference of 0.98 between the standard color and the predicted color (i.e., the color implemented based on the target mixing information). Additionally, when the color code of the target color is 'BL0793', the color mixing device can output target mixing information having a color difference of 0.92 between the standard color and the predicted color (i.e., the color implemented based on the target mixing information).

[0084] Through this, the color mixing device can achieve cost savings (e.g., time, material costs, etc.) in color mixing operations. With a small amount of training data, the color mixing device can be expected to increase cost efficiency.

[0085] A computing system for a color mixing device or a color mixing method may include at least one processor, memory, a user interface input device, a user interface output device, storage, and a network interface connected via a bus. For example, the processor may be a central processing unit (CPU) or a semiconductor device that executes processing on instructions stored in the memory and / or storage. The memory and storage may include various types of volatile or non-volatile storage media. For example, the memory may include read-only memory (ROM) and random access memory (RAM). Accordingly, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be directly implemented in hardware, a software module, or a combination of the two executed by the processor. The software module may reside in a storage medium (i.e., memory and / or storage) such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or a CD-ROM. An exemplary storage medium is coupled to the processor, such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integrated with the processor. The processor and storage medium may reside within an application-specific integrated circuit (ASIC). The ASIC may reside within a user terminal. Alternatively, the processor and storage medium may reside as separate components within the user terminal.

[0086] The above description is merely an example of the technical idea of ​​the present invention, and those skilled in the art will appreciate that various modifications and variations can be made without departing from the essential characteristics of the present invention.

[0087] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using a general-purpose computer or a special-purpose computer, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0088] Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may, independently or collectively, command the processing device. The software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on a computer-readable recording medium.

[0089] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination, and the program commands recorded on the medium may be those specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands such as ROMs, RAMs, and flash memories. Examples of program commands include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc.

[0090] The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0091] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the described embodiments. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0092] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

[0093] Accordingly, the embodiments disclosed in the present invention are intended to illustrate, rather than limit, the technical concept of the present invention, and the scope of the technical concept of the present invention is not limited by these embodiments. The scope of protection of the present invention should be interpreted by the following claims, and all technical concepts within the scope equivalent thereto should be construed as being included within the scope of the present invention.

Claims

1. Memory that stores computer-executable instructions; and A processor that accesses the memory and executes the instructions, The above processor, Receive a request to output mixing information about a target color, In response to the above output request, the target color is implemented and the reference mixing information of a reference product for which mixing information regarding the target color is predetermined is obtained, Based on the above standard mixing information and the predetermined color ratio model between the standard product and the target product, output the target mixing information of the target product. Color mixing device.

2. In paragraph 1, The above processor, Receive the color code of the target color from the user who sends the above output request, By applying the above reference product to the Lab color space (Lab color system), the first color values ​​of the above reference product are obtained, Based on the above color code and the first color values, the reference mixing information is obtained. Color mixing device.

3. In paragraph 2, The above processor, By applying the above target product to the Lab color space, the second color values ​​of the above target product are obtained, The color ratio model is obtained by applying the first color values ​​and the second color values ​​to a color ratio model learned to output a color ratio model between products. Color mixing device.

4. In paragraph 3, The above processor, From the above first color values, extract the 1_1 color element and the 1_2 color element corresponding to the elements of the Lab color space, From the above second color values, the second_1 color element and the second_2 color element corresponding to the elements of the Lab color space are extracted, The color ratio model is obtained by applying the difference between the 1_1 color element and the 2_1 color element, and the difference between the 1_2 color element and the 2_2 color element to the color ratio model. Color mixing device.

5. In paragraph 3, The above processor, By transmitting the second color values ​​to a server in which a color ratio model between products is previously stored, information on whether the color ratio model is identified is received from the server, Receiving the color ratio model from the above server Color mixing device.

6. In paragraph 1, The above processor, If the above output request is a request for output of mixing information regarding the target color implemented based on the above reference product, in response to the output request, the reference mixing information is output. Color mixing device.

7. In paragraph 1, The above processor, The above standard mixing information and the color ratio model are applied to a color mixing model learned to output mixing information according to color and product, thereby obtaining the target mixing information. Color mixing device.

8. In paragraph 1, The above processor, Determine the predicted color to be implemented based on the above target product and the above target mixing information, To the user who sends the above output request, the predicted color is sent. Color mixing device.

9. An action of receiving a request for output of formulation information regarding a target color implemented based on a target product; In response to the above output request, an operation of implementing the target color and obtaining reference mixing information of a reference product for which mixing information regarding the target color is predetermined; and An operation of outputting target mixing information of the target product based on the above standard mixing information and a predetermined color ratio model between the standard product and the target product. How to mix colors.

10. A computer program stored on a computer-readable medium to execute the method of claim 9 in combination with hardware.

Images