KR20220056994A - Device for providing goods information in offline retail environment and control method thereof - Google Patents

Abstract

Provided is a control method of a product information providing device, which includes the steps of: processing product data based on attribute information for each category of a product stored in a database; and providing the processed data to a consumer terminal. Product attribute information for each category of the product stored in the database is generated based on a product information image photographed by a user terminal.

Description

DEVICE FOR PROVIDING GOODS INFORMATION IN OFFLINE RETAIL ENVIRONMENT AND CONTROL METHOD THEREOF

The present invention relates to an apparatus for providing product information and a method for controlling the apparatus for providing product information, which provide data processed on the basis of data collected offline to a consumer.

As various online businesses are revitalized, the traditional distribution business is also under pressure to develop differentiated promotion methods. However, many products are still sold in offline stores or online stores. In addition, 91% of global shopping is still done in offline stores, and information on consumers' purchasing journeys is a key piece of information that can confirm what stimuli and information they have encountered in offline stores.

However, it is difficult to collect data obtained through offline stores compared to online data, and despite the fact that it is attracting attention as very valuable data because it is data containing the offline life of consumers, both offline and online stores are It was difficult to understand the purchase journey, and accordingly, it was difficult to derive products suitable for consumers who purchase offline.

An object of the present invention is to provide a product information providing device and a control method for a product information providing device that can collect log data of consumers found offline and effectively provide service information generated based on the collected log data to a user.

A control method of an apparatus for providing product information according to an aspect includes processing product data based on attribute information for each category of a product stored in a database, and providing the processed data to a consumer terminal, stored in the database The product attribute information for each product category is generated based on the product information image captured by the user terminal.

The processing of the product data may include processing the product data based on attribute information accumulated during a period selected by the user.

The processing of the product data may include generating a user's product search keyword corresponding to the combination result by combining at least two or more of a category of products stored in the database and one or more elements of personal information.

The step of processing the product data includes measuring the number of product hits by category of the product, and the step of providing the processed data to the consumer terminal may include providing the number of product inquiries by category of the product to the consumer terminal. can

The processing of the product data includes processing the product data based on purchase information and attribute information for each category of the product stored in the database, and the purchase information of the product stored in the database is stored in the purchase receipt image taken from the user terminal. It may be generated based on

The attribute information includes promotion information, and the processing of the product data may include calculating a ratio of users who visit a preset store during a promotion period and users who visit a store during a non-promotion period.

The processing of the product data may include generating a relationship diagram for each element by combining elements of a plurality of personal information stored in the database.

The personal information element may include at least one of a user's gender, age, skin type, skin concerns, skin tone, skin color, and taste.

The product information providing apparatus according to another aspect includes a processor, the processor processes product data based on attribute information for each category of a product stored in a database, and provides the processed data to a consumer terminal, The attribute information of the product for each category of the product stored in the database may be generated based on the product information image captured by the user terminal.

The processor may process product data based on attribute information accumulated during a period selected by the user.

The processor may generate a user's product search keyword corresponding to the combination result by combining at least two or more of a category of a product stored in the database and one or more elements of personal information.

In a computer-readable recording medium according to another aspect, a method comprising processing product data based on attribute information for each category of product stored in a database, and providing the processed data to a consumer terminal is recorded and the attribute information of each product category stored in the database is generated based on the product information image captured by the user terminal.

The processing of the product data may include processing the product data based on attribute information accumulated during a period selected by the user.

According to the above-mentioned problem solving means, by collecting information on various offline purchase journeys of consumers and processing data meaningful to product sellers into structured data based on the collected purchase journeys, personalized product planning, etc. can be implemented in business It can be used throughout.

1 is a conceptual diagram of a system for providing product information according to an embodiment.
2 is a view showing various objects included in a product information image.
3 to 7 are exemplary views of processed data displayed on a data consumer terminal.

Like reference numerals refer to like elements throughout. This specification does not describe all elements of the embodiments, and general content in the technical field to which the present invention pertains or content that overlaps between the embodiments is omitted. The term 'part, module, member, block' used in this specification may be implemented in software or hardware, and according to embodiments, a plurality of 'part, module, member, block' may be implemented as one component, It is also possible for one 'part, module, member, block' to include a plurality of components.

Throughout the specification, when a part is "connected" to another part, it includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Also, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated.

Terms such as first, second, etc. are used to distinguish one component from another, and the component is not limited by the above-mentioned terms.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In each step, the identification code is used for convenience of description, and the identification code does not describe the order of each step, and each step may be performed differently from the specified order unless the specific order is clearly stated in the context. there is.

The product information retrieval device may be a user terminal installed with an application that performs a series of processes by photographing a product information image, or receives the product information image taken from the user terminal, performs a series of processes, and exchanges data with the user terminal It could be a server that Hereinafter, for convenience of description, an example in which the product information search apparatus is implemented as a user terminal will be described.

The user terminal may be implemented as a computer or a portable terminal. Here, the computer includes, for example, a laptop equipped with a web browser, a desktop, a laptop, a tablet PC, a slate PC, and the like, and the portable terminal has, for example, portability and mobility. As a covered wireless communication device, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), International Mobile Telecommunication (IMT) -2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, all kinds of handheld based such as smart phone of wireless communication devices and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-devices (HMDs).

The product name or unique identification code is written on the product. Alternatively, a price stack on which product information is written may be located in a portion adjacent to the product.

1 is a conceptual diagram of a system for providing product information according to an embodiment.

Referring to FIG. 1 , the product information providing system includes a product information searching device 100 , a product information providing device 200 , and product information receiving devices 300 - 1 and 300 - 2 .

The product information search apparatus 100 may photograph an adjacent object together with the product. The product information image captured by the product information search apparatus 100 may include a product name, a unique identification code, or other information.

The product information image refers to a photographed image including information for identifying a product or information for distinguishing a specific product from other products. For example, the product information image may include product information. The product information may include information about various objects, such as a product name, price, promotion information, and a unique identification code. That is, the product information image may be an image of a price stack in which product information is written, an image of a product in which product information is written on the surface, or an image of a packaging material in which product information is recorded.

The product information search apparatus 100 may identify product information from the photographed product information image.

Specifically, the product information search apparatus 100 may specify at least one object in the product information image.

2 is a view showing various objects included in a product information image.

For example, in the product information search apparatus 100, the product name (ob1), the price (ob2, 'price before discount', 'price after discount', etc.), promotion information (ob3, '1+1', 'Discount', 'accumulation', 'gift gift', etc.), and at least one of the unique identification code ob4 may be specified. In this case, the product information search apparatus 100 may specify an object using a convolutional neural network (CNN) model. As an example, the product information search apparatus 100 is a deep learning R-CNN (" Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, in CVPR 2014") An object can be specified using an object detection technique based on the model.

Convolution The 'convolution' operation in the neural network model is an operation that extracts image components and can be implemented based on Equation 1 below.

[Equation 1]

By passing the kernel matrix through the digitized image matrix, the result is calculated by calculating the data of the pixel at its location and neighboring pixels, and the data stored as the output matrix has the characteristics of the image.

However, if only the convolution operation is continued, overfitting with lower accuracy occurs from a specific point in time. and dropout can be performed.

Thereafter, the product information search apparatus 100 may extract the position of the object from the product information image using a data classification algorithm (eg, Support Vector Machine (SVM), SoftMax) and may specify the object.

Also, in order to increase the accuracy of the product information image, the product information search apparatus 100 may perform pre-processing on the product information image before specifying an object. The pre-processing may be, for example, image binarization transformation, noise removal, surrounding background removal, and resolution change.

As an example, the product information search apparatus 100 may extract a price stack image from the product information image and convert the price stack image into an image of a uniform size. And, by calculating the coordinates of the objects in the price stack, it is possible to convert the image data for the object into numerical data by numerical data for each coordinate. The product information search apparatus 100 may specify an object based on numerical data of an image according to such coordinates.

Also, the product information search apparatus 100 may identify text in the photographed image and extract text data. In this case, the product information search apparatus 100 may identify the text by reading the entire image area, or may identify the text for each specified object.

As an embodiment, the product information search apparatus 100 may detect text in the product information image using an optical character recognition (OCR) method or the like.

The product information search apparatus 100 may learn a deep learning model by dividing the case where there is a typo among the recognized text results and the case where there is no typo in the recognized text result and matching the misrecognized product name with the corrected product name in order to accurately detect the text.

Also, the product information search apparatus 100 may acquire location information of a place where the product information image is photographed as meta information. According to an embodiment, information on the shooting time may also be acquired as meta information. The location information may be obtained by extracting shooting coordinates from the product information image, or may be obtained by collecting separate GPS and the like.

The product information search apparatus 100 may store attribute information of a product for which a product information image is captured in a database based on the recognized text data for each object.

The database may be included in the product information retrieval apparatus 100 or may be included in a separate server or product information providing apparatus 200, and a hardware location is not limited. Hereinafter, for convenience of description, the database will be described as included in the product information search apparatus 100 .

The product information search apparatus 100 may store attribute information (eg, product name, price, promotion information, ingredients, and unique identification code, etc.) of a corresponding product for each product category in a database. In addition, the product information search apparatus 100 may specify store information corresponding to the product information image based on location information of a place where the product information image is captured, and store the store information as attribute information together.

As an example, the product information search apparatus 100 may vectorize and store product attribute information using a deep learning-based prediction algorithm.

Specifically, the product information retrieval apparatus 100 may utilize Word2Vec to vectorize ingredients and attribute information for each cosmetic type, and vectorize the distance between cosmetic ingredients by using a continuous bag-of-words (CBOW) model. Since Word2Vec and CBOW are well-known technologies, detailed descriptions thereof will be omitted. This is to enable a product to be recommended in the future, including properties information calculated on the basis of the user's shooting record and search history, etc. and a component that is close to the vector.

The product information search apparatus 100 may photograph a purchase receipt image or obtain it from an external device. Likewise in this case, the product information retrieval device 100 may be a user terminal installed with an application that performs a series of processes by photographing a purchase receipt image, or receives a purchase receipt image photographed from the user terminal to perform a series of processes It may be a server that performs and exchanges data with a user terminal, but for convenience of explanation, an example in which the product information search apparatus 100 is implemented as a user terminal to obtain a purchase receipt image will be described.

The purchase receipt contains information related to the purchase of various products. The purchase receipt photographed by the product information retrieval device 100 may contain various purchase-related information, such as store information, product name, price, promotion information, quantity information, purchase time information, purchaser information, and a unique identification code. .

The purchase receipt image refers to an image in which information that can be identified for a purchase history of a consumer is captured. For example, the purchase receipt image may include purchase information. The purchase information may include information on various objects, such as store information, purchaser information (name, phone number, etc.), product name, price, promotion information, quantity information, purchase time information, purchaser information, and a unique identification code. The purchase receipt image may be an image of a paper receipt taken offline, or an image of an online receipt issued online.

The product information search apparatus 100 may identify purchase information from the photographed purchase receipt image.

Specifically, the product information search apparatus 100 may specify at least one object in the purchase receipt image.

For example, the product information search apparatus 100 may specify at least one object among store information, product name, price, promotion information, quantity information, purchase time information, and a unique identification code in the purchase receipt image. In this case, the product information retrieval device 100 may specify an object using a convolutional neural network (CNN) model. Neural Networks for Mobile Vision Applications, Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H, arXiv:1704.04861, 2017.; MobileNetV2: Inverted Residuals and Linear Bottlenecks, Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. arXiv preprint. arXiv:1801.04381, 2018.") Based on the model, an object can be specified using an object detection technique.

The MobileNet model is a model based on a simplified structure using a convolution layer divided in the depth direction. It calculates the convolution of the entire image, but divides it into the convolution for each channel and 1 x 1 convolution, so that it can be applied to the purchase receipt image. Contained objects can be detected. In the case of using the MobileNet model, object detection is possible at a high speed while receiving relatively few restrictions on the size and shape of the image compared to the existing method. In particular, the usability of the model in a user terminal such as a mobile device is increased.

Also, in order to increase the accuracy of the purchase receipt image, the product information search apparatus 100 may perform pre-processing on the purchase receipt image before specifying an object. The pre-processing may be, for example, image binarization transformation, noise removal, surrounding background removal, and resolution change.

Also, the product information search apparatus 100 may identify text in the photographed purchase receipt image and extract text data. In this case, the product information search apparatus 100 may identify the text by reading the entire image area, or may identify the text for each specified object.

As an embodiment, the product information retrieval apparatus 100 may detect text in the purchase receipt image using an optical character recognition (OCR) method or the like.

The product information search apparatus 100 may perform post-processing on text data.

As an embodiment, the product information search apparatus 100 may post-process the text data based on at least one of character unit information and text context information of the purchase receipt image.

For example, the product information search apparatus 100 may infer that a price discount event has occurred through a % (percent) or - (subtract) sign in the purchase receipt image in which the promotion information is damaged as the promotion information.

In addition, the product information retrieval apparatus 100 may match a specific object to which object information by using context information between surrounding texts in the purchase receipt image in which the specific object information is damaged.

The product information retrieval apparatus 100 may match the user's purchase information obtained from the purchase receipt image and the attribute information of the product photographed by the user terminal to the user and store it in the database.

Specifically, the product information retrieval device 100 acquires the user's personal information from a separate personal information server, and elements constituting the personal information (eg, the user's gender, age, skin type, skin concerns, skin tone) , skin color, taste, etc.) and product attribute information and purchase information can be matched to finally store information on products of interest for each user. Here, the personal information may be obtained or input as membership registration information of an application installed in the user terminal.

In addition, the product information search apparatus 100 may store the user's purchase information obtained from the purchase receipt image with more weight than the product attribute information photographed in the user terminal. This is because, in fact, the product actually purchased by the user more reflects the user's preference than the product photographed and searched by the user.

In this way, when information on products of interest for each user is obtained from a plurality of users, products of interest can be specified based on the personal information of potential consumers, and purchase prediction of potential consumers can be performed.

In order to perform accurate purchase prediction, the product information search apparatus 100 may perform a test based on user-specific interest product information obtained from a plurality of users.

Specifically, the product information search apparatus 100 may perform a test using a binary logistic regression analysis method as in Equation (2).

[Equation 2]

y is the dependent variable, whether product inquiry (including photographing product information images or directly searching for a product through a keyword through a user terminal) or product purchase, X is a matrix composed of independent variables. Independent variables include elements of personal information (eg, gender, age, skin type, skin concerns, skin tone, skin color, taste, etc.) and various user's online activity information secured through the user terminal.

Through such a test, the elements (eg, the user's gender, age, skin type, skin concerns, Skin tone, skin color, taste, etc.), it is possible to determine which factors have a significant influence on whether to inquire or purchase a product, and further select users who are highly likely to inquire or purchase a specific product.

Specifically, the product information retrieval device 100 determines the probability that a user having certain user information inquires or purchases a specific product using a product purchase/inquiry prediction model to which at least one of a deep learning algorithm and a machine learning algorithm is applied, and , it is possible to determine a user to be targeted for advertisement and determine a product to be recommended to the user.

The product purchase and inquiry prediction model may include a Recurrent Neural Network (RNN), for example, may use a Long-Short Term Memory (LSTM).

As such, the product information retrieval device 100 dataizes the entire process of the user's journey offline, so it is possible to derive the factors of decision-making for product purchase and inquiry, and to provide retailers or manufacturers through the purchase prediction of offline users. It can provide actionable insights.

The product information providing device 200 processes product data based on attribute information of products for each category of products stored in the database, and uses the processed data to transmit the processed data to a terminal 300-1 of an offline retail or a terminal 300-2 of a product manufacturer. ) can be provided to a separate data consumer terminal such as

3 to 7 are exemplary views of processed data displayed on a data consumer terminal.

The product information providing apparatus 200 according to an exemplary embodiment may process product data based on attribute information accumulated during a specific period selected by a user (eg, during the second week of October in FIGS. 3 and 4 ).

The product information providing apparatus 200 may process product data based on a specific offline retail store (store A in FIGS. 3 and 4 ).

Specifically, the product information providing apparatus 200 may generate statistical information for each element of personal information based on inquiry/purchase data of products for each category accumulated during a specific period.

For example, as shown in FIG. 3 , the product information providing device 200 determines whether users of any gender or age searched a lot of products belonging to the skin care-essence category based on the product inquiry data accumulated for a week. By judging, the product data can be processed. The processed data may be provided to the consumer terminals 300-1 and 300-2.

Also, the product information providing apparatus 200 may generate a user's product search keyword corresponding to the combination result by combining at least two or more of a category for each product and one or more elements of personal information.

For example, as shown in FIG. 3 , when a product category called cream and women and people in their twenties are selected as elements of personal information, when women and users in their twenties searched for products in the skin care-cream category, they searched the most. A keyword may be determined, and a connection relationship between the keyword and the keyword may be provided to the consumer terminals 300-1 and 300-2.

Also, although not shown, the product information providing apparatus 200 may measure the number of views or purchases for each product category and rank them.

Also, the product information providing apparatus 200 may generate a result of analyzing the behavior of users who visit a specific store based on product inquiry/purchase data for each category accumulated during a specific period.

For example, referring to FIG. 4 , the product information providing device 200 is a user who has visited a specific store during the promotion period (eg, a user who has acquired a product information image of a specific store or a user determined by a purchase receipt image) ) and the non-promotion period (usual period) can calculate the percentage of users who visited.

Also, the product information providing apparatus 200 may determine the number of re-visits of the user for each promotion period or non-promotion period.

For example, referring to FIG. 4 , the product information providing apparatus 200 may classify a user who has visited only once during a promotion period and a user who has visited a plurality of times, and a user who has visited only once and a user who has visited multiple times during a non-promotion period. Classification may be performed, and the classification result may be provided to the consumer terminals 300-1 and 300-2.

Also, the product information providing apparatus 200 may determine the user's visit rate and product inquiry rate for each promotion performed during a specific period.

For example, referring to FIG. 4 , the product information providing apparatus 200 may map the product inquiry rate (search rate) and the store visit rate of promotion A, promotion B, and promotion C together, and the mapped result is a consumer terminal (300-1, 300-2) can be provided.

Based on this, the data consumer (offline retail manager or manufacturer) can determine which promotion had the most product inquiries or store visits.

In addition, the product information providing apparatus 200 may convert the visitor pattern of each promotion into data for each period, and may provide it to the consumer terminals 300 - 1 and 300 - 2 .

Accordingly, the consumer terminals 300 - 1 and 300 - 2 can determine the store visit rate during the non-promotion period and the store visit rate during the promotion period in chronological order.

Also, the product information providing apparatus 200 may combine a plurality of elements of personal information to generate a relationship diagram for each element.

For example, referring to FIG. 5 , the product information providing device 200 searches for a correlation between the user's skin type (dry, combination, oily) and skin concerns (whitening, wrinkles, trouble, dryness, oiliness). You can make a judgment based on /purchase data, and you can diagram the relationship in a diagram.

Specifically, when it is determined that users with dry skin have the most skin concerns about dryness and oiliness, the product information providing device 200 displays dark spots on dryness and oiliness on the chart of "dry skin" can make it happen In addition, when it is determined that users with combination skin have various skin concerns, the product information providing apparatus 200 may rank each skin concern so that a darker dot is displayed for the skin trouble with a high ranking.

Although not shown, the product information providing apparatus 200 may aggregate the visit rate for each offline retail store, and rank the visit rate of the offline retail store based on the aggregated visit rate and provide it to the consumer terminals 300-1 and 300-2 It is also possible to

Meanwhile, the product information providing apparatus 200 may generate and deliver a report for each product to the consumer terminals 300 - 1 and 300 - 2 .

Referring to FIG. 6 , the product information providing apparatus 200 provides a statistical analysis result of a user's inquiry rate (search amount) for a specific product during a specific period (eg, October), the user information inquiring the specific product, The result of comparison with competitor's products may be reported and periodically provided to the consumer terminals 300-1 and 300-2.

For example, the product information providing device 200 calculates the number of product hits (monthly search volume) for product A during October, and information on stores selling product A and information on stores where product A is most viewed. may be determined, and the determination result may be provided to the consumer terminals 300-1 and 300-2.

Also, the product information providing apparatus 200 may sequentially list the number of product inquiries of product A for each store during October, and provide ranking information to the consumer terminals 300 - 1 and 300 - 2 .

In addition, the product information providing apparatus 200 may arrange the number of views of product A in chronological order and provide it to the consumer terminals 300 - 1 and 300 - 2 . In this case, it is also possible to arrange by store, and it is also possible to arrange by summing the number of views of a plurality of stores.

In addition, the product information providing apparatus 200 may determine a competitive product of the product to be reported, and also provide product information on the competing product to the consumer terminals 300 - 1 and 300 - 2 .

For example, referring to FIG. 7 , the product information providing device 200 determines a product B, which is a competing product having similar attribute information to product A, which is a reporting target, calculates the number of product views of the competing product B, and competes Information on stores selling product B and information on stores in which the most competitive product B is searched may be determined, and the determination result may be provided to the consumer terminals 300 - 1 and 300 - 2 .

In addition, the product information providing device 200 may time-series chart the number of product hits of product A and the number of hits of competing product B, which are the reporting targets, and the reporting target on a social network service (SNS) is The number of times referred to by product A can also be tabulated together as a social index.

By charting the index in a time series like this, data consumers can predict when the promotion effect of a specific product will increase.

Referring back to FIG. 1 , the consumer terminals 300 - 1 and 300 - 2 display the data received from the product information providing apparatus 200 to the data consumer (eg, a manager of an offline retail store, a manufacturer). .

The consumer terminals 300-1 and 300-2 may be implemented as a computer or a portable terminal equipped with a display. Here, the computer includes, for example, a laptop equipped with a web browser, a desktop, a laptop, a tablet PC, a slate PC, and the like, and the portable terminal has, for example, portability and mobility. As a covered wireless communication device, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), International Mobile Telecommunication (IMT) -2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, all kinds of handheld based such as smart phone wireless communication devices and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-devices (HMDs).

The product information search apparatus 100, the product information providing apparatus 200, and the consumer terminals 300-1 and 300-2 are provided with hardware resources and/or software necessary to implement the technical idea of the present invention. and does not necessarily mean a single physical component or a single device. That is, the product information providing system may mean a logical combination of hardware and/or software provided to implement the technical idea of the present invention, and, if necessary, is installed in devices spaced apart from each other to perform each function. It may be implemented as a set of logical configurations for implementing the technical idea of the invention.

In addition, the product information search device 100 , the product information providing device 200 , and the consumer terminals 300 - 1 and 300 - 2 are an algorithm or a program reproducing the algorithm for controlling the operation of components constituting each Alternatively, it may include a memory that stores a database for an application, and a processor that performs the above-described operation using the database stored in the memory. In this case, the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by a computer. Instructions may be stored in the form of program code, and when executed by a processor, may create a program module to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

The computer-readable recording medium includes all types of recording media in which computer-readable instructions are stored. For example, there may be a read only memory (ROM), a random access memory (RAM), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, and the like.

The disclosed embodiments have been described with reference to the accompanying drawings as described above. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention may be practiced in other forms than the disclosed embodiments without changing the technical spirit or essential features of the present invention. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims (13)

A method for controlling a product information providing device, comprising:
processing product data based on attribute information for each category of the product stored in the database; and
Including the step of providing the processed data to the consumer terminal,
The control method of the product information providing apparatus, wherein the attribute information of the product by category of the product stored in the database is generated based on the product information image captured by the user terminal. The method of claim 1,
The step of processing the product data,
A method of controlling a product information providing apparatus, comprising the step of processing product data based on attribute information accumulated during a period selected by a user. The method of claim 1,
The step of processing the product data,
and generating a user's product search keyword corresponding to a combination result by combining at least two or more of a category of products stored in the database and one or more elements of personal information. The method of claim 1,
The step of processing the product data,
Including the step of measuring the number of product hits by category of the product,
The step of providing the processed data to the consumer terminal,
A control method of a product information providing device comprising the step of providing the number of product inquiries for each category of the product to the consumer terminal. The method of claim 1,
The step of processing the product data,
Including the step of processing product data based on the attribute information and purchase information for each category of the product stored in the database,
The product purchase information stored in the database is a control method of a product information providing device that is generated based on the purchase receipt image taken by the user terminal. The method of claim 1,
The attribute information includes promotion information,
The step of processing the product data,
A method of controlling a product information providing device comprising the step of calculating a ratio of a user who visits a preset store during a promotion period and a user who visits the store during a non-promotion period. The method of claim 1,
The step of processing the product data,
and generating a relationship diagram for each element by combining a plurality of elements of personal information stored in the database. 8. The method of claim 7,
The personal information element is a control method of a product information providing device including at least one of a user's gender, age, skin type, skin concerns, skin tone, skin color, and taste. In the product information providing device,
The product information providing device includes a processor,
The processor is
The product data is processed based on the attribute information for each category of the product stored in the database,
The processed data is provided to the consumer's terminal,
The product information providing device in which the attribute information of each product category of the product stored in the database is generated based on the product information image captured by the user terminal. 10. The method of claim 9,
The processor is
A product information providing device for processing product data based on attribute information accumulated during a period selected by a user. 10. The method of claim 9,
The processor is
A product information providing apparatus for generating a user's product search keyword corresponding to a combination result by combining at least two or more of a category of products stored in the database and one or more elements of personal information. processing product data based on attribute information for each category of the product stored in the database; and
There is recorded a method comprising the step of providing the processed data to the consumer terminal,
A computer-readable recording medium that is generated based on the product information image photographed by the user terminal, the attribute information of each category of the product stored in the database. 13. The method of claim 12,
The step of processing the product data,
A recording medium comprising the step of processing product data based on attribute information accumulated during a period selected by a user.

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