Method and apparatus for ACQUIRING A CONTENT

Abstract

The present specification describes a method for outputting content, comprising the steps of: generating an image containing hidden information by inserting different content into each of an image of a first image group and an image of a second image group; wherein the first image group and the second image group each include one or more images; capturing an image through a capturing device; extracting hidden information contained in an image from a captured image; and outputting the content of the extracted hidden information to a screen of the capturing device.

Description

Technology Field

[0001] This specification relates to a method for acquiring content, and more specifically, to a method for acquiring content embedded in an image through a capturing device and a device for supporting the same. Background Technology

[0002] Existing cameras only provide the functions of capturing and displaying images; therefore, even when capturing an image with embedded content, the camera cannot extract the embedded content, and a separate processing method was required to extract the content.

[0003] Korean Registered Patent No. 10-1600587 proposes a method for extracting image features from an image and using image feature data.

[0004] However, Korean Registered Patent No. 10-1600587 does not disclose a method for inserting content into an image, extracting the inserted content, and utilizing it.

[0005] Therefore, research is needed on methods to insert content into images and extract and output it using capturing devices such as cameras. Prior art literature

[0006] Korean Registered Patent No. 10-1600587 The problem to be solved

[0007] Accordingly, the purpose of this specification is to provide a method for inserting content as hidden information into an image or object, extracting the inserted hidden information through a capturing device such as a camera, and outputting related information including video and text on the screen of the capturing device.

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

[0009] The present specification describes a method for outputting content, comprising the steps of: generating an image containing hidden information by inserting different content into each of an image of a first image group and an image of a second image group, which are recognized as the same image by a user; wherein the first image group and the second image group each include one or more images; capturing an image through a capturing device; extracting hidden information contained in an image from a captured image; and outputting the content of the extracted hidden information to a screen of the capturing device.

[0010] In addition, the present specification is characterized in that the same content is inserted into the images included in the first image group, and the same content is inserted into the images included in the second image group.

[0011] In addition, the content inserted into the images included in the first image and the content inserted into the images included in the second image in this specification are recognized by the user as the same image, but the inserted content corresponds to different content.

[0012] Additionally, the method described herein is characterized by storing the output content, playing the output content, or deleting the output content depending on the type of the output content.

[0013] Additionally, in this specification, the image is frequency domain data converted into the frequency domain through a Discrete Cosine Transformation (DCT), and the content is characterized by being inserted into the image converted into the frequency domain.

[0014] In addition, the content in this specification is characterized by being inserted into or extracted from the image using an artificial intelligence algorithm such as deep learning.

[0015] Additionally, the step of extracting embedded information included in an image from the captured image in the present specification is characterized by comprising: a step of extracting an image from the captured image; a step of converting the extracted image into the frequency domain; and a step of extracting the hidden information in the converted frequency domain.

[0016] In addition, the content in this specification is characterized as being an invitation, a video, a gift certificate, or text indicating whether it has been won.

[0017] In addition, the present specification is characterized by saving the output content when the content is an invitation or a gift certificate, playing the output content when the content is a video, deleting the output content when the content is text indicating a '꽝' (꽝 means '꽝' - a losing ticket), and playing and saving additional content when the content is text indicating a 'winning ticket'. Effects of the invention

[0018] The present specification has the effect of not only increasing revenue from the sale of images or objects but also obtaining indirect revenue from business operators selling content based on the execution of the output content by inserting content as hidden information into an image or object, extracting the inserted hidden information through a capturing device such as a camera and outputting it on the screen of the capturing device, and performing functions such as playback, saving, or deletion depending on the type of content.

[0019] The effects obtainable from the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below. Brief explanation of the drawing

[0020] The accompanying drawings, which are included as part of the detailed description to aid in understanding the present invention, provide embodiments of the present invention and explain the technical features of the present invention together with the detailed description. FIG. 1 shows an example of a conceptual diagram of a content acquisition system to which the method proposed in this specification can be applied. FIG. 2 is a diagram showing an example of an internal block diagram of a shooting device to which the method proposed in this specification can be applied. FIG. 3 is a flowchart illustrating an example of a content output method proposed in this specification. FIG. 4 is a block diagram of an AI device to which the method proposed in this specification can be applied. Specific details for implementing the invention

[0021] It should be noted that technical terms used in this specification are used merely to describe specific embodiments and are not intended to limit the scope of the technology disclosed herein. Furthermore, unless specifically defined otherwise in this specification, technical terms used in this specification should be interpreted in the sense generally understood by those skilled in the art to which the technology disclosed herein belongs, and should not be interpreted in an overly broad or overly narrow sense. Additionally, if a technical term used in this specification is an incorrect technical term that fails to accurately express the concept of the technology disclosed herein, it should be understood as being replaced by a technical term that can be correctly understood by those skilled in the art to which the technology disclosed herein belongs. Furthermore, general terms used in this specification should be interpreted according to their prior definitions or the context, and should not be interpreted in an overly narrow sense.

[0022] Terms including ordinal numbers, such as first, second, etc., as used in this specification may be used to describe various components, but said components should not be limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0023] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components are given the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted.

[0024] Furthermore, in describing the technology disclosed in this specification, detailed descriptions of related prior art are omitted if it is determined that such descriptions could obscure the essence of the technology disclosed in this specification. Additionally, it should be noted that the attached drawings are intended merely to facilitate an understanding of the concept of the technology disclosed in this specification, and should not be interpreted as limiting the concept of the technology.

[0026] Content Acquisition System

[0027] FIG. 1 shows an example of a conceptual diagram of a content acquisition system to which the method proposed in this specification can be applied.

[0028] Referring to FIG. 1, the content acquisition system (10) may be configured to include an image (100) and a shooting device (200).

[0029] The above image (100) can be represented as an object, and content related to the image is inserted as hidden information in the image.

[0030] The above content acquisition system may include a plurality of images, and the plurality of images may be classified into a first image group, a second image group, a third image group, etc.

[0031] The first image group, the second image group, the third image group, etc., may each include one or more images.

[0032] For example, if there are 10 images, the first image group may include 3 images, and the second image group may include 7 images.

[0033] Different content is inserted as hidden information into the images of the first image group, the second image group, the third image group, etc., respectively. That is, a coupon may be inserted into the image of the first image group, a video may be inserted into the image of the second image group, and an invitation may be inserted into the image of the third image group.

[0034] The images in the first image group have the same content inserted, the images in the second image group have the same content inserted, and the images in the third image group may have the same content inserted.

[0035] That is, the content inserted into the images of the first image group and the content inserted into the images of the second image group are perceived by the user as the same image, but correspond to different content.

[0036] The above-mentioned shooting device (200) is a device capable of implementing the content acquisition method proposed in this specification, and may be a terminal, a camera, an AI camera, etc. The shooting device includes an image sensor or a camera module capable of capturing an image, and may have an application program installed for extracting content inserted in the image, i.e., hidden information. The image sensor or camera module of the shooting device may be activated when the application program is executed.

[0038] FIG. 2 is a diagram showing an example of an internal block diagram of a shooting device to which the method proposed in this specification can be applied.

[0039] The components illustrated in FIG. 2 are not essential for implementing the imaging device, so the imaging device described herein may have more or fewer components than those listed above.

[0040] Referring to FIG. 2, the shooting device (200) may include a camera module (210), a processor (220), a memory (230), an input unit (240), and an output unit (250).

[0041] The camera module (210) is a device capable of implementing the content acquisition method proposed in this specification and captures an image.

[0042] The processor (220) typically controls the overall operation of the imaging device. The processor can provide or process appropriate information or functions to the user by processing signals, data, information, etc. that are input or output through the components described above, or by running applications stored in memory.

[0043] In addition, the processor can control at least some of the components described in conjunction with FIG. 2 to run an application stored in memory. Furthermore, the processor can operate at least two or more of the components included in the imaging device in combination to run the application.

[0044] The memory (230) stores data that supports various functions of the imaging device. The memory can store a number of application programs (or applications) running on the imaging device, data for the operation of the imaging device, and instructions. At least some of these applications can be downloaded from an external server via wireless communication. Meanwhile, the application programs can be stored in the memory, installed on the imaging device, and driven by a processor to perform the operation (or function) of the imaging device.

[0045] The input unit (240) may include a user input unit (e.g., a touch key, a mechanical key, etc.) for receiving information from a user. The input unit may additionally include a microphone for inputting audio signals, or an audio input unit, etc. Voice data collected from the input unit may be analyzed and processed into a user control command.

[0046] The output unit (250) is intended to generate output related to sight, hearing, or touch, and may include a display unit, a sound output unit, etc.

[0047] At least some of the above components may operate in cooperation with each other to implement the operation, control, or control method of the imaging device according to various embodiments described below. Additionally, the operation, control, or control method of the imaging device may be implemented on the imaging device by running at least one application program stored in the memory.

[0049] FIG. 3 is a flowchart illustrating an example of a content output method proposed in this specification.

[0050] Referring to FIG. 3, the content acquisition system creates an image containing hidden information by inserting different content into each of the images of the first image group and the images of the second image group (S310).

[0051] Here, the first image group and the second image group may each include one or more images. For example, if there are 10 images, the first image may refer to 3 of the 10 images, and the second image may refer to the remaining 7 images.

[0052] The above image may refer to an object, and the object may be any object capable of inserting hidden information, i.e., the content examined earlier, including images displayed on electronic devices as well as printed images such as photographs, photo cards, clothing containing images, coffee coasters, instruction manuals, digital signage, e-tickets, and artworks.

[0053] The same content may be inserted into the images of the first image group and the same content may be inserted into the images of the second image group. That is, in the previous example, the same content is inserted into each of the three images corresponding to the first image group and the same content is inserted into each of the seven images corresponding to the second image group, but different content is inserted into the images of the first image group and the images of the second image group.

[0054] In addition, the content acquisition system can generate frequency domain data by converting the image into the frequency domain through a Discrete Cosine Transformation (DCT) to insert content, i.e., hidden information, into the image, and then insert the content into the image converted into the frequency domain (S320).

[0055] In addition, the content acquisition system can insert the content into the image or extract it from the image by using an artificial intelligence algorithm, such as deep learning, performed on the AI ​​device of FIG. 4.

[0056] And, the content acquisition system captures the image through a shooting device (S330).

[0057] And, the content acquisition system extracts hidden information included in the image from the captured video (S340).

[0058] The process of extracting embedded information included in an image from a captured video in step S340 can be performed by extracting an image from the captured video, converting the extracted image into the frequency domain, and extracting the hidden information, i.e., the content, from the converted frequency domain.

[0059] In addition, at step S340, the imaging device may use an AI algorithm performed by the AI ​​device of FIG. 4, which will be described later, to extract hidden information or to correct the extracted hidden information. As previously discussed, the AI ​​algorithm performed by the AI ​​device of FIG. 4 can be used not only for extracting hidden information but also for inserting or restoring hidden information.

[0060] The above AI device may be implemented within the shooting device or may be implemented as a separate device other than the shooting device.

[0061] That is, the content acquisition system can enable an AI device to learn about images (or objects) containing various content as training data, and when an image captured by a capturing device is input into the AI ​​device, it can output content, i.e., hidden information, based on the training results.

[0062] And, the content acquisition system outputs the content of the extracted hidden information to the screen of the shooting device (S350).

[0063] Additionally, the content acquisition system may save the output content, play the output content, or delete the output content depending on the type of the output content.

[0064] The above content may be an invitation, video, gift certificate, or text displaying '꽝' (꽝 means '꽝' - a losing ticket).

[0065] For example, if the above content is an invitation or a gift certificate, the content acquisition system stores the output content; if the above content is a video, the content acquisition system plays the output content; and if the above content is text displaying '꽝', the content acquisition system can delete the output content.

[0067] AI device block diagram

[0068] FIG. 4 is a block diagram of an AI device to which the method proposed in this specification can be applied.

[0069] The AI ​​device (20) may be included as part of at least a portion of the configuration of the shooting device (200) shown in FIG. 1, or may be implemented separately and configured to perform at least a portion of the AI ​​processing together.

[0070] The AI ​​device (20) may include an AI processor (21), a memory (25), and / or a communication unit (27). If the AI ​​device is included in a shooting device, the communication unit (27) may be omitted.

[0071] The above AI device (20) is a computing device capable of learning a neural network and can be implemented as various electronic devices such as a server, desktop PC, laptop PC, tablet PC, etc.

[0072] The AI ​​processor (21) can train a neural network using a program stored in memory (25). In particular, the AI ​​processor (21) can train a neural network for recognizing image-related data. Here, the neural network for recognizing image-related data can be designed to simulate the structure of the human brain on a computer and may include multiple network nodes having weights that simulate the neurons of a human neural network. The multiple network modes can exchange data according to their respective connection relationships to simulate the synaptic activity of neurons that exchange signals through synapses. Here, the neural network may include a deep learning model that has evolved from a neural network model. In the deep learning model, multiple network nodes can be located in different layers and exchange data according to convolutional connection relationships. Examples of neural network models include various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent Boltzmann machines (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), and deep Q-networks, and can be applied in fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0073] Meanwhile, a processor that performs the functions described above may be a general-purpose processor (e.g., CPU), but may also be an AI-dedicated processor for artificial intelligence learning (e.g., GPU).

[0074] The memory (25) can store various programs and data required for the operation of the AI ​​device (20). The memory (25) can be implemented as non-volatile memory, volatile memory, flash memory, hard disk drive (HDD), or solid-state drive (SDD). The memory (25) is accessed by the AI ​​processor (21), and the AI ​​processor (21) can perform reading / writing / modifying / deleting / updating of data. Additionally, the memory (25) can store a neural network model (e.g., deep learning model (26)) generated through a learning algorithm for image analysis according to an embodiment of the present invention.

[0075] Meanwhile, the AI ​​processor (21) may include a data learning unit (22) that learns a neural network for image analysis. The data learning unit (22) can learn criteria regarding which training data to use to determine image analysis, and how to classify and recognize data using the training data. The data learning unit (22) can learn a deep learning model by acquiring training data to be used for learning and applying the acquired training data to a deep learning model.

[0076] The data learning unit (22) may be manufactured in the form of at least one hardware chip and mounted on the AI ​​device (20). For example, the data learning unit (22) may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or may be manufactured as part of a general-purpose processor (CPU) or a dedicated graphics processor (GPU) and mounted on the AI ​​device (20). Additionally, the data learning unit (22) may be implemented as a software module. If implemented as a software module (or a program module containing instructions), the software module may be stored on a non-transitory computer-readable media. In this case, at least one software module may be provided by an operating system (OS) or by an application.

[0077] The data learning unit (22) may include a learning data acquisition unit (23) and a model learning unit (24).

[0078] The training data acquisition unit (23) can acquire training data required for a neural network model for image analysis. For example, the training data acquisition unit (23) can acquire image data and / or sample data to be input into a neural network model as training data.

[0079] The model learning unit (24) can learn to have a judgment criterion regarding how a neural network model classifies a predetermined data using the acquired learning data. At this time, the model learning unit (24) can train the neural network model through supervised learning, which uses at least a portion of the learning data as a judgment criterion. Alternatively, the model learning unit (24) can train the neural network model through unsupervised learning, which discovers the judgment criterion by learning on its own using the learning data without supervision. In addition, the model learning unit (24) can train the neural network model through reinforcement learning using feedback on whether the result of the situation judgment based on learning is correct. Furthermore, the model learning unit (24) can train the neural network model using a learning algorithm including error back-propagation or gradient descent.

[0080] When the neural network model is trained, the model training unit (24) can store the trained neural network model in memory. The model training unit (24) can also store the trained neural network model in the memory of a server connected to the AI ​​device (20) via a wired or wireless network.

[0081] The data learning unit (22) may further include a learning data preprocessing unit (not shown) and a learning data selection unit (not shown) to improve the analysis results of the recognition model or to save resources or time required for the creation of the recognition model.

[0082] The learning data preprocessing unit can preprocess the acquired data so that the acquired data can be used for learning to make situational judgments. For example, the learning data preprocessing unit can process the acquired data into a pre-set format so that the model learning unit (24) can use the acquired learning data for learning to recognize images.

[0083] Additionally, the training data selection unit may select data necessary for training from among the training data acquired by the training data acquisition unit (23) or the training data preprocessed by the preprocessing unit. The selected training data may be provided to the model training unit (24). For example, the training data selection unit may select only the data regarding objects included in a specific area as training data by detecting a specific area among the images acquired through the camera.

[0084] Additionally, the data learning unit (22) may further include a model evaluation unit (not shown) to improve the analysis results of the neural network model.

[0085] The model evaluation unit inputs evaluation data into a neural network model, and if the analysis result output from the evaluation data does not satisfy a predetermined standard, it may cause the model learning unit (22) to learn again. In this case, the evaluation data may be predefined data for evaluating a recognition model. For example, the model evaluation unit may evaluate that the predetermined standard is not satisfied if the number or ratio of evaluation data for which the analysis result is inaccurate among the analysis results of the recognition model learned for the evaluation data exceeds a pre-set threshold.

[0086] The communication unit (27) can transmit the AI ​​processing results by the AI ​​processor (21) to an external device.

[0087] Although the AI ​​device (20) illustrated in FIG. 4 is described by functionally separating it into an AI processor (21), memory (25), and communication unit (27), it should be noted that the aforementioned components may be integrated into a single module and referred to as an AI module.

[0089] Below, we will briefly examine service scenarios to which the method proposed in this specification is applied.

[0090] <1st Embodiment>

[0091] In the first embodiment, the image (or object) is a celebrity photo card, and by inserting various contents into the celebrity photo card and selling it to consumers, the supplier or business operator can not only increase sales from the sale of the celebrity photo card but also obtain direct sales revenue through the provision of the content inserted in the celebrity photo card or indirect sales revenue by linking with other business operators.

[0092] For example, assume there are 100 images, that is, 100 celebrity photo cards (e.g., BTS photo cards).

[0093] Among 100 BTS photo cards, 10 BTS photo cards have 'BTS fan meeting invitation' inserted as hidden information, another 10 BTS photo cards have a video related to BTS inserted, and another 10 BTS photo cards have text with "꽝" written on it inserted.

[0094] In addition, when the content acquisition system photographs the aforementioned BTS photo cards through a camera device, it can extract hidden information embedded in the BTS photo cards, namely BTS fan meeting invitations, videos related to BTS, and text containing "꽝" (꽝 means "꽝" - a losing ticket), and display the extracted hidden information on the screen of the camera device.

[0095] Through this business model, suppliers or businesses selling celebrity photo cards can generate direct revenue from the sale of celebrity photo cards and indirect revenue by receiving a certain commission from businesses providing BTS fan meeting invitations or by receiving a portion of the copyright royalties generated from the playback of BTS music due to the playing of BTS-related videos.

[0097] <Second Embodiment>

[0098] The second embodiment is a service scenario in which an image (or object) is clothing, and various contents are inserted into a picture imprinted on the clothing and sold to consumers.

[0099] That is, through the second embodiment, the provider of the system in which the method proposed in this specification is implemented can obtain as revenue a portion of the revenue generated from the sale of clothing and a portion of the revenue generated from the execution of content embedded in the clothing by providing services together with clothing retailers who sell clothing, influencers who advertise or promote clothing, etc.

[0100] For example, let us assume that there are 100 pieces of clothing with BTS images on them, and that 10 pieces of clothing have BTS concert invitations inserted, 10 pieces of clothing have BTS goods exchange vouchers inserted, and 10 pieces of clothing have gifticons (or vouchers) inserted.

[0101] And, when the content acquisition system photographs the above clothing items through a camera device, it can extract hidden information embedded in the image of the clothing, namely BTS concert invitations, BTS merchandise exchange vouchers, and gift certificates, and output the extracted hidden information on the screen of the camera device.

[0103] <Third Embodiment>

[0104] A third embodiment is a service scenario in which the image (or object) is a coffee cup (mug or take-out cup), and various contents are inserted into a drawing on the coffee cup to provide to a coffee consumer. Instead of the coffee cup, a coffee saucer, a coffee description sheet, etc., may correspond to the image (or object).

[0105] That is, through the third embodiment, a seller, supplier, or business operator selling coffee can insert various content into the image of a coffee cup and sell it to coffee consumers, thereby increasing revenue from coffee sales as well as obtaining direct revenue through the provision of content inserted in the coffee cup or indirect revenue through linkage with other business operators.

[0106] For example, assuming that hidden information such as a coffee coupon is embedded in an image of a coffee cup, the content acquisition system can extract the hidden information embedded in the image of the coffee cup—namely, the coffee coupon—by photographing the coffee cup through a camera device, and output the extracted hidden information to the screen of the camera device.

[0108] <Fourth Example>

[0109] The fourth embodiment is a service scenario in which an image (or object) is an E-ticket (electronic ticket), and information related to the E-ticket can be obtained by inserting various content (hidden information) into a picture or pattern inserted into the E-ticket and detecting it.

[0110] Here, types of E-tickets may include, for example, movie theater tickets, event admission tickets, and transportation tickets (airline tickets, train tickets, etc.), and these will be collectively referred to as “E-tickets.”

[0112] In other words, through this embodiment, a supplier (or business operator) issuing and selling tickets may embed owner information, reservation numbers, validity, and security information, etc., in the form of hidden information within the E-ticket, and provide a service that allows a consumer (user) who has purchased and possesses the ticket to verify or utilize such information by recognizing and photographing the ticket with a camera device (such as a smartphone). Through this, the supplier can expect additional revenue not only from direct sales of tickets but also by providing additional services related to ticket usage (e.g., customer authentication, seat upgrades, promotion notifications, etc.).

[0114] For example, assume there are 100 E-tickets. Among these, 10 E-tickets may have hidden information containing vouchers for special seat upgrades, another 10 E-tickets may have discount coupons usable within the movie theater or event venue (e.g., popcorn discount, event souvenir discount, etc.), and another 10 E-tickets may have vouchers for event-related merchandise. Additionally, the remaining E-tickets may have no separate additional benefits or may have hidden information in the form of a "꽝" (꽝 means "꽝" in Korean, which is a losing ticket).

[0116] Furthermore, when the content acquisition system photographs the aforementioned E-ticket using a camera, hidden information embedded in the E-ticket (owner information, reservation number, validity and security information, exchange vouchers, discount coupons, etc.) is extracted, and the extracted hidden information can be displayed on the screen of the camera. At this time, the extracted information can be utilized for user identity authentication, ticket validity verification, and the automation of the entry process.

[0118] Through this business model, suppliers (or operators) selling and managing E-tickets can generate not only ticket issuance fees and sales revenue, but also indirect revenue, such as promotional income from affiliates through the use of additional information (vouchers, discount coupons, etc.) or commissions received from voucher providers. Furthermore, users can easily check various hidden information included in the E-ticket and enjoy various benefits by utilizing it, thereby further enhancing their satisfaction with the E-ticket.

[0120] <5th Example>

[0121] The fifth embodiment is a service scenario in which an image (or object) is digital signage, and various content (hidden information) is inserted into an advertising image (or screen component) displayed on the digital signage and detected to obtain links related to the advertisement (e.g., product purchase, product information, product specifications, etc.). Digital signage may include, for example, kiosks, outdoor / indoor billboards, large displays installed in public places, etc., and will be collectively referred to as "digital signage" for convenience below.

[0123] That is, through this embodiment, an advertiser (or supplier) executing an advertisement can insert links or additional advertising content in the form of hidden information into images or graphic elements on digital signage. When a consumer (user) who views or is interested in this recognizes and photographs the digital signage using a camera device (such as a smartphone), links related to the advertisement (e.g., product purchase page, detailed specifications page, etc.) are extracted and displayed on the screen of the camera device. This maximizes the advertising effect by allowing the user to immediately check product information or move to a purchase page without a separate web search.

[0125] For example, assume there are 100 digital signage billboards (or kiosks, public displays). Among these, URLs related to new product launches may be inserted as hidden information on 10 billboards, links to product specification documents or demonstration videos may be inserted as hidden information on another 10 billboards, and links to product purchase pages may be inserted on yet another 10 billboards. Additionally, various content such as alternative event links, promotion codes, and simple text information in the form of "꽝" (꽝 means "꽝" in Korean, which is a losing ticket) may be inserted on the remaining digital signage.

[0127] Furthermore, when the content acquisition system captures the aforementioned digital signage billboard using a camera, hidden information embedded in the digital signage (ad links, discount codes, additional video links, etc.) is extracted, and this information is provided to the user through the camera's screen. In this process, advertisers can expect cost savings as advertising rates are determined based on factors such as digital signage exposure time, while users can obtain necessary information while watching natural advertisements without distracting banners or pop-ups.

[0129] Through this business model, operators providing and managing digital signage advertisements can generate direct revenue by receiving billing from advertisers based on ad exposure time, and also obtain indirect revenue, such as attracting additional advertising contracts, by increasing the advertiser's product purchase conversion rate or delivering advertisements to consumers more effectively through hidden information technology. Furthermore, users can experience higher satisfaction as they encounter advertisements that are less visually distracting and more natural than traditional ads, while being able to immediately check product information or purchase links through a camera.

[0131] <6th Example>

[0132] The 6th embodiment is a service scenario in which an image (or object) is digital art or a work of art, and various content (hidden information) is inserted into a digital art work (image) that is mainly displayed through a monitor, TV, digital photo frame, etc., and information related to the work (e.g., artist specifications, ownership of the work, etc.) is obtained by detecting it.

[0134] That is, through this embodiment, an artist (or supplier) exhibiting and selling digital art may insert information regarding artist information (history, portfolio, etc.), a description of the work, and ownership of the work (including authenticity) in the form of hidden information within the digital art image. A visitor (user) interested in the work can immediately check the information inserted in the work by recognizing and photographing the digital art on display using a camera device (e.g., a smartphone).

[0136] For example, assume that 100 digital art pieces are on display. Among these, 10 pieces have artist introductions and social media links inserted as hidden information, and another 10 pieces have ownership information (e.g., proof of ownership, transaction history, etc.) inserted. Detailed descriptions of the works (production intent, techniques used, etc.) may be inserted for another 10 pieces, and various forms of additional content (artist interviews, future exhibition schedules, text corresponding to "꽝", etc.) may be inserted for the remaining pieces depending on their respective characteristics.

[0138] Furthermore, when the content acquisition system captures such digital art works using a camera, hidden information embedded in the work (artist specifications, ownership, description, etc.) is extracted and provided to the user through the camera's screen. In this process, emerging artists can go beyond simply exhibiting their work and effectively promote themselves by embedding hidden information, thereby gaining significant advantages in terms of marketing and branding. Additionally, embedding information that verifies ownership or authenticity can help prevent issues such as digital art duplication or copyright infringement.

[0140] Through this business model, artists or businesses exhibiting, selling, and managing digital art can expect additional revenue related to copyright (utilization of background images, licensing of derivative works, etc.) and promotional effects for the artist (planning of additional exhibitions, collaboration proposals, etc.) through information embedded in the artwork, in addition to simple artwork sales and exhibition revenue. On the other hand, viewers can naturally encounter information about the artist and the artwork through a new method (using a recording device) that did not exist before, and can easily verify the authenticity or ownership of the digital art, thereby further increasing their satisfaction.

[0143] The embodiments described above are combinations of the components and features of the present invention in a specific form. Each component or feature should be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, it is possible to construct embodiments of the present invention by combining some components and / or features. The order of operations described in the embodiments of the present invention may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that embodiments may be constructed by combining claims that do not have an explicit citation relationship in the claims, or that they may be included as new claims through amendments made after filing.

[0144] Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, an embodiment of the present invention may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0145] In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc., that performs the functions or operations described above. The software code may be stored in memory and executed by a processor. The memory may be located inside or outside the processor and may exchange data with the processor by various known means.

[0146] It is obvious to those skilled in the art that the present invention may be embodied in other specific forms without departing from the essential features of the invention. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention. Explanation of the symbols

[0147] 10: Content Acquisition System

Claims

Claim 1 A method for outputting content, comprising the steps of: generating images by inserting hidden information related to each image into each of a plurality of images; wherein the images generated by inserting the hidden information are recognized as identical by a user, and some of the generated images have the same hidden information inserted; capturing an image with the hidden information inserted through a capturing device; extracting the hidden information inserted into the image from the captured image; and outputting content corresponding to the extracted hidden information onto the screen of the capturing device. Claim 2 delete Claim 3 A method according to claim 1, characterized by saving the output content, playing the output content, or deleting the output content depending on the type of the output content. Claim 4 A method according to claim 1, wherein the image before the insertion of the hidden information is frequency domain data converted into the frequency domain through a Discrete Cosine Transformation (DCT), and the hidden information is inserted into the image converted into the frequency domain. Claim 5 A method according to claim 1, wherein the step of extracting hidden information inserted into an image in the captured image comprises: a step of extracting an image from the captured image; a step of converting the extracted image into a frequency domain; and a step of extracting the hidden information in the converted frequency domain. Claim 6 A method according to claim 1, characterized in that the content is an invitation, a video, a voucher, or text displaying a '꽝' (꽝 means '꽝' - a losing ticket). Claim 7 A method according to claim 6, characterized in that if the content is an invitation or a voucher, the output content is saved; if the content is a video, the output content is played; and if the content is text displaying '꽝' (꽝 means '꽝' - a losing ticket), the output content is deleted. Claim 8 A method according to claim 1, characterized in that the hidden information is inserted into an image or extracted from an image using a deep learning algorithm.

Images