System and method for user-customized artwork curation

Abstract

This user-customized artwork curation method stores, in a central server, user data about a user, artwork data about artworks, and hotel data about a hotel. A first preference value of the user is calculated on the basis of the user data. On the basis of the first preference value, a first recommended work is selected from the artworks via an artificial intelligence algorithm. The first recommended work is transmitted to a hotel server of the hotel. The first recommended work is displayed on a display apparatus provided within the hotel. On the basis of the first recommended work, first feedback data generated according to a selection by the user is transmitted from the hotel server to the central server. A second preference value of the user is calculated on the basis of the user data and the first feedback data. On the basis of the second preference value, a second recommended work is selected from the artworks via the artificial intelligence algorithm. The second recommended work is transmitted to the hotel server of the hotel. The second recommended work is displayed on the display apparatus provided within the hotel.

Description

User-customized art curation system and art curation method

[0001] The present invention relates to a user-customized art curation system and an art curation method, and more specifically, to a user-customized art curation system and an art curation method for displaying customized art to a user visiting a commercial space such as a hotel.

[0002] The present application claims priority based on Korean Patent Application No. 10-2024-0184381 filed on December 12, 2024, and all contents described in the specification and drawings of said application are incorporated by reference into the present application.

[0003] Commercial spaces, such as modern hotels, are increasingly attempting to create differentiated experiences to provide customers with value beyond mere accommodation. In particular, there is a growing trend of combining digital technology with art content to build unique brand images or enhance customer satisfaction. Existing art curation systems generally fail to accurately reflect user preferences, and the methods of viewing artwork are often limited to specific spaces. For instance, art exhibitions within hotels are typically presented as static digital content installed in lobbies or common areas, failing to provide personalized art experiences within guest rooms.

[0004] Furthermore, current hotel in-room entertainment systems focus on popular content such as movies and music, and have limitations in providing personalized access to premium content like artwork. Consequently, guests are unable to fully enjoy artistic and sensory experiences during their stay, while hotels struggle to achieve brand differentiation. Although personalized recommendation systems are widely used in e-commerce and streaming services, there are challenges in effectively applying them within a hotel environment.

[0005] One objective of the present invention is to provide a user-customized art curation system capable of providing customized artworks by analyzing the characteristics of individual users.

[0006] Another objective of the present invention is to provide a user-customized art curation method capable of providing customized art works using the system described above.

[0007] A user-customized art curation method according to exemplary embodiments for achieving one objective of the present invention stores user data regarding a user, art data regarding art works, and hotel data regarding a hotel in a central server. Based on the user data, a first preference value of the user is calculated. Based on the first preference value, a first recommended work is selected from among the art works through an artificial intelligence algorithm. The first recommended work is transmitted to the hotel server of the hotel. The first recommended work is displayed on a display device provided within the hotel. Based on the first recommended work, first feedback data generated according to the user's selection is transmitted from the hotel server to the central server. Based on the user data and the first feedback data, a second preference value of the user is calculated. Based on the second preference value, a second recommended work is selected from among the art works through the artificial intelligence algorithm. The second recommended work is transmitted to the hotel server of the hotel. The second recommended work is displayed on the display device provided within the hotel.

[0008] In exemplary embodiments, the method further comprises transmitting second feedback data generated according to the user's selection based on the second recommended work from the hotel server to the central server, calculating a third preference value of the user based on the user data and the first and second feedback data, selecting a third recommended work from among the artworks through the artificial intelligence algorithm based on the third preference value, transmitting the third recommended work to the hotel server of the hotel, and displaying the third recommended work on the display device provided within the hotel.

[0009] In exemplary embodiments, the artificial intelligence algorithm includes at least one selected from machine learning, deep learning, graph-based learning, generative models, reinforcement learning, natural language processing, and statistical learning.

[0010] In exemplary embodiments, displaying the first recommended work on the display device provided within the hotel includes displaying the first recommended work on a first screen of the first display device provided in the hotel lobby for a first predetermined time; displaying the second recommended work on the display device provided within the hotel includes displaying the second recommended work on a second screen of the second display device provided in the guest room where the user is staying for a second predetermined time later than the first predetermined time; and displaying the third recommended work on the display device provided within the hotel includes displaying the third recommended work on the first screen of the first display device provided in the hotel lobby for a third predetermined time later than the second predetermined time.

[0011] In exemplary embodiments, the first recommended work is selected through a graph neural network model, the second recommended work is selected through a reinforcement learning model, and the third recommended work is selected through a deep learning-based model.

[0012] According to exemplary embodiments, a user-customized art curation method may include storing user data regarding a user, art data regarding art works, and hotel data regarding a hotel on a central server, calculating a first preference value of the user based on the user data, selecting a first recommended work among the art works through an artificial intelligence algorithm based on the first preference value, transmitting the first recommended work to a hotel server of the hotel, displaying the first recommended work on a display device provided within the hotel, transmitting first feedback data generated according to the user's selection based on the first recommended work from the hotel server to the central server, calculating a second preference value of the user based on the user data and the first feedback data, selecting a second recommended work among the art works through an artificial intelligence algorithm based on the second preference value, transmitting the second recommended work to a hotel server of the hotel, and displaying the second recommended work on a display device provided within the hotel.

[0013] Accordingly, the user-customized art curation method described above can maximize customer satisfaction by providing hotel guests with a personalized art appreciation experience. By storing the user data, art data, and hotel data on the central server and calculating the first preference value based on this data, and recommending suitable works through the artificial intelligence algorithm, works optimized for the user's individual tastes and appreciation styles can be provided. As the recommended works are displayed on the display device, the user can enjoy a luxurious art appreciation experience in the lobby or guest room; this service, which is differentiated from existing standardized content, can strengthen the hotel's brand image. The first feedback data selected and provided by the user regarding the recommended works is transmitted back to the central server and used to calculate a new second preference value, thereby enabling the user-customized art curation method to continuously learn and improve recommendation quality.

[0014] Furthermore, the aforementioned user-customized art curation method can support the promotion and revenue generation of emerging artists by digitally connecting hotels and artworks. As the recommended artworks are displayed on the aforementioned display device, users can easily access the artworks and obtain information about the artists and their works. This can lead to opportunities for artwork sales or additional promotion, and contribute to the revitalization of the art ecosystem. Real-time data exchange and feedback learning between the central server and the hotel server increase the efficiency of hotel operations, and management costs can be reduced as artwork recommendations and updates are performed through an automated system. This integrated and organic system provides tangible value to the hotel, guests, and artists alike, and can form a model for future smart hotel operations.

[0015] However, the effects of the present invention are not limited to the effects mentioned above and may be extended in various ways without departing from the spirit and scope of the present invention.

[0016] FIG. 1 is a schematic diagram illustrating a user-customized art curation system according to exemplary embodiments.

[0017] Figures 2 and 3 are flowcharts illustrating a user-customized art curation method using the system of Figure 1.

[0018] Figure 4 is a diagram showing the process of performing the user-customized art curation method of Figure 2.

[0019] [Explanation of the symbol]

[0020] 10: User-customized art curation system

[0021] 20: User-Customized Art Curation Methods

[0022] 100: Central Server 110: Database

[0023] 200: User terminal 300: Hotel server

[0024] 310: Display device 312: First display device

[0025] 314: Second display device

[0026] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings.

[0027] In each drawing of the present invention, the dimensions of the structures are depicted enlarged compared to the actual dimensions for the sake of clarity of the present invention.

[0028] In the present invention, terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another.

[0029] The terms used in this invention are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0030] With respect to the embodiments of the present invention disclosed in the text, specific structural or functional descriptions are provided merely for the purpose of explaining the embodiments of the present invention, and the embodiments of the present invention may be implemented in various forms and should not be interpreted as being limited to the embodiments described in the text.

[0031] That is, the present invention is capable of various modifications and may take various forms, and specific embodiments are illustrated in the drawings and described in detail in the text. However, this is not intended to limit the present invention to the specific disclosed forms, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention.

[0032] FIG. 1 is a schematic diagram illustrating a user-customized art curation system according to exemplary embodiments.

[0033] Referring to FIG. 1, first, a user-customized art curation system (10) may include a central server (100), a plurality of user terminals (200), a hotel server (300), and a plurality of display devices (310).

[0034] In exemplary embodiments, the user-customized art curation system (10) may be a service system for providing a personalized art recommendation experience to hotel guests. The user-customized art curation system (10) can analyze the preferences of the hotel guests and recommend optimized artworks. The user-customized art curation system (10) can display optimal artworks in various spaces such as the lobby and guest rooms. Through this, users (guests) can feel artistic value during their stay, and the hotel can strengthen its brand value by providing differentiated services.

[0035] The user-customized art curation system (10) can maximize customer satisfaction by recommending customized artworks that reflect the individual preferences of the hotel guests. The user-customized art curation system (10) can create an artistic and sensory atmosphere in various spaces within the hotel by selecting and displaying artworks suitable for various environments, such as the lobby and guest rooms. The user-customized art curation system (10) can gradually improve the customer experience by continuously enhancing the accuracy and adaptability of the recommendation system through repetitive user feedback learning. The user-customized art curation system (10) revitalizes the art ecosystem by expanding opportunities for emerging artists to be exposed to their works, and in terms of hotel operations, it can significantly increase the efficiency of content management using digital displays.

[0036] In exemplary embodiments, the central server (100) may store user data, artwork data, and hotel data. The user data, artwork data, and hotel data may include, without limitation, data necessary to select and provide optimal artwork to the users.

[0037] The user data may include the user's characteristics and preferences necessary to implement a personalized recommendation system. The user data may include basic profile information such as the user's age, gender, and nationality. The user data may include previous viewing records of artworks, preferred genres or styles, viewing time, and feedback data regarding selected works. For example, if the user prefers abstract or Impressionist style works or shows high interest in the works of a specific artist, such data may be utilized to calculate the user's preferences.

[0038] The user data may include the user's stay history, display time data used in the room, and data on artworks viewed in a specific space. Based on the user data, the user's preferences and behaviors can be analyzed more precisely. The user data may include tendency data categorized into multiple groups based on the users' artistic tendencies derived from basic profile information such as their age, gender, and nationality. Based on the tendency data, the user's preferences can be rapidly predicted at any time according to the group to which the user belongs.

[0039] The above artwork data may include, without limitation, information regarding all artworks that can be provided through displays within the hotel. The above artwork data may include the title, artist, year of production, genre, style, color combination, theme, size, and media type (painting, sculpture, digital art, etc.) of the artwork.

[0040] The aforementioned artwork data may include visual content, such as digital images or video files of the artwork. The artwork data may include detailed descriptions, background stories, commentary information, and viewing points for each artwork, and can be utilized to enhance the user's artistic understanding. The artwork data may include metadata such as public evaluations, recommendation scores, related user reviews, and the history of the artwork's exhibition, which can support a recommendation algorithm in precisely matching the characteristics of the artwork with user preferences.

[0041] The aforementioned artwork data may include, without limitation, a diverse range of artworks that allow many people to encounter various forms of art. The artworks may include a variety of works without geographical, financial, or cultural barriers. The artwork data may include works by emerging artists. For example, the works by these emerging artists may be set to a high priority to provide data preferentially over other works.

[0042] The above hotel data may include information related to the physical and digital infrastructure of the hotel. The above hotel data may include the location, screen size, displayable time zone, and resolution of display devices (310) installed in each space (lobby, guest rooms, restaurant, etc.) of the hotel. The above hotel data may include information on the room where the user is staying (room number, duration of stay, check-in / check-out time), guest type (family, couple, business customer, etc.). The above hotel data may be used to select artwork suitable for a specific environment and to optimize the display time and location. The above hotel data may include analysis data on the hotel's brand concept, interior design style, and customer base, thereby supporting the recommended artwork to harmonize with the overall atmosphere of the hotel.

[0043] The above hotel data may include the hotel's identity, design concept, and suitable artwork attributes provided by the hotel operator. The above hotel data may include the hotel's brand policy. In accordance with the brand policy, the above hotel data may be stored after filtering out artwork themes, colors, styles, etc., that do not match the hotel's image.

[0044] In exemplary embodiments, a plurality of user terminals (200) can transmit the user data to a central server (100). The plurality of user terminals (200) can transmit data to a central server (100), a hotel server (300), etc., via a wireless communication network. The central server (100) and the hotel server (300) can exchange data with the plurality of user terminals (200) via the wireless communication network. The wireless communication network may utilize a global system for mobile communications.

[0045] For example, wireless communication networks can utilize wireless communication technologies such as WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), and 5G (5th generation).

[0046] The user terminals (200) owned by the above users may include a communication module capable of wirelessly communicating with a central server (100) and a hotel server (300). The forms of the user terminals (200) may vary, such as mobile phones, smartphones, smartpads, laptop computers, navigation devices, wearable devices, etc. Wearable devices may vary, such as watch-type terminals, glass-type terminals, HMDs (Head Mounted Displays), etc.

[0047] The above users can log in to the application through user terminals (200). At this time, user information is provided to a central server (100) or a hotel server (300), and the central server (100) or the hotel server (300) can retrieve user storage information based on the user information provided by the users from a database (110). The user storage information may include user identification information, user authorization information, current location information, etc.

[0048] Multiple user terminals (200) can transmit the user's location data to a central server (100) or a hotel server (300). Based on the location data, the user-customized art curation system (10) can determine the user's location and provide the artwork according to the user's location. For example, if the user is located in the lobby of the hotel, the artwork can be provided to the user from a display device (310) provided within the lobby. The location data can be collected using GPS (Global Positioning System), BLE (Bluetooth Low Energy) beacons, Wi-Fi-based location tracking, UWB (Ultra-Wideband), RFID (Radio Frequency Identification), IMU (Inertial Measurement Unit), etc.

[0049] In exemplary embodiments, the hotel server (300) may be a main server installed within the hotel for managing the hotel. The hotel server (300) may transmit and receive data with the central server (100) and user terminals (200) via the wireless communication network. The hotel server (300) may be electrically connected to display devices (310) installed within the hotel and may control the display devices (310).

[0050] The hotel server (300) can be operated via a wireless communication network (Wi-Fi, BLE, etc.) capable of transmitting and receiving data with various devices inside and outside the hotel. The hotel server (300) can communicate with the central server (100) to transmit and receive the aforementioned artwork data, the aforementioned user data, preference data, etc., required inside the hotel. The hotel server (300) can transmit the aforementioned artwork suitable according to the user's preference to display devices (310) provided in the lobby or the aforementioned guest room. The hotel server (300) supports bidirectional data communication with the user terminal (200) and can process the aforementioned location data, the aforementioned feedback data, the aforementioned accommodation information, etc., collected from the user in real time.

[0051] The hotel server (300) is electrically connected to all display devices (310) installed within the hotel and can centrally and integrally control them. It can monitor the status of the display devices (310) and, if necessary, perform tasks such as power management, content switching, and screen brightness adjustment remotely. The hotel server (300) can control the display time of the display devices (310) or schedule different content for specific time periods.

[0052] The hotel server (300) can process various data to support the user-customized service. The hotel server (300) can analyze information such as the user's check-in / check-out time, room number, and length of stay, and can provide services tailored to the customer's journey. The hotel server (300) can support the learning of the recommendation system by collecting the user's location data, feedback data, etc., in real time, analyzing them, and transmitting them to the central server (100).

[0053] In exemplary embodiments, display devices (310) may be installed inside the hotel. Display devices (310) may be installed to effectively display works of art in various spaces within the hotel. Display devices (310) may be provided in appropriate sizes and arrangements according to the characteristics and purpose of each space. Display devices (310) may be strategically placed in key spaces of the hotel, such as the lobby, guest rooms, restaurants, and conference rooms, to provide customers with a unique and personalized art appreciation experience.

[0054] A display device (310) may be installed in a hotel lobby. The display device (310) installed in the hotel lobby may be designed to be large in size and installed in a prominent location so that customers can naturally notice it. In this case, the display device (310) may include a large LED panel, a high-resolution OLED display, or a wall-mounted digital canvas. The display device (310) may be fixed to a wall or placed on a stand, and through a multi-panel arrangement, it may display a single artwork in an expanded form or display multiple artworks simultaneously. The display device (310) installed in the hotel lobby may be designed to display comprehensive artworks, taking into account a diverse range of customers.

[0055] A display device (310) may be installed in a hotel room. The display device (310) installed in the hotel room may focus on providing a personalized experience. The display device (310) provided in the hotel room may include a smart TV, a wall-mounted display, or a dedicated digital picture frame. The display device (310) installed in the hotel room may support interactive functions that allow the user to select or view personalized artwork. For example, the user may zoom in on the artwork or switch to another artwork via a touchscreen or a smartphone application.

[0056] Below, a method for curating user-customized artworks using the above-mentioned user-customized artwork curation system will be explained in more detail.

[0057] FIGS. 2 and FIGS. 3 are flowcharts illustrating a user-customized art curation method using the system of FIG. 1. FIG. 4 is a diagram illustrating the process of performing the user-customized art curation method of FIG. 2.

[0058] Referring to FIGS. 1 to 4, first, the user-customized art curation method (20) can store user data about the user, art data about the artworks, and hotel data about the hotel in a central server (100) (S110).

[0059] In exemplary embodiments, the user-customized art curation method (20) may store and manage the user data, the art data, and the hotel data in a central server (100) to provide personalized services to the user. The central server (100) processes the user data, the art data, and the hotel data in real time and can play a central role in the curation algorithm. The central server (100) performs an optimized matching between the user's preferences, artwork attributes, and hotel environment by linking and analyzing the data, and can improve curation performance through continuous feedback learning. Through this, the user-customized art curation method (20) can enhance the customer experience and provide a new form of personalized service that combines art and technology.

[0060] In the process of storing the artwork data in the central server (100), the artwork data may be provided by the artists who created the artworks. In the process of receiving the artwork data, the service provider operating the central server (100) may provide a commission to the artists. The service provider may act as an intermediary connecting the hotel and the artists. The service provider may provide the commission to the artists and acquire the right to use the artworks in a specific area.

[0061] Next, a first preference value of the user is calculated based on the user data (S120), and a first recommended work among the artworks can be selected through an artificial intelligence algorithm based on the first preference value (S130).

[0062] In exemplary embodiments, the first preference value may be calculated by quantifying an individual's artistic taste and preference through the analysis of various data provided by the user. For example, if the user has a history of preferring Impressionist works or primarily viewing works with pastel colors, the user's artistic preference can be expressed as a specific indicator based on such data. If the user has a high preference for a specific art field, the first preference value may have a high value for that specific art field.

[0063] The first preference value can be used to select the first recommended work by matching it with the artwork data through the artificial intelligence algorithm. The artificial intelligence algorithm can identify a work in which the user has a high preference by comparing and analyzing the user's preference value and the attributes of the artwork data (e.g., genre, style, color, artist, etc.). For example, the artificial intelligence algorithm may include machine learning, deep learning, graph-based learning, graph neural networks (GNN), generative models, reinforcement learning, natural language processing (NLP), statistical learning, etc.

[0064] In exemplary embodiments, the first recommended artwork may be displayed on a first screen of a first display device (312) provided in a common space, such as the lobby of the hotel. Since the first recommended artwork is displayed on the first screen of the first display device (312) provided in the common space, it may be selected as an artwork that can satisfy the maximum number of users. For example, the first recommended artwork may be selected through a Graph Neural Network model.

[0065] The first recommended artwork may be displayed on the first screen of the first display device (312) installed in the common space, such as the lobby of the hotel. The lobby is the first space encountered by many customers visiting the hotel and may be an important place that forms the brand image and influences the customer's first impression. The first display device (312) installed in the common space is positioned so that many users can naturally view the artwork as they pass by, and its high size and resolution can maximize visual immersion. Therefore, the first recommended artwork may be selected to reflect the tastes of various customer segments visiting the hotel in a balanced manner, rather than focusing solely on the preferences of a specific individual.

[0066] In the process of displaying the first recommended work on the first display device (312), the operator of the hotel may provide a subscription fee to the service provider managing the central server (100). At least a portion of the subscription fee may be used as a commission provided to the artists. The operator of the hotel may provide the subscription fee to the service provider managing the central server (100) and acquire the right to display the first recommended work on the first display device (312).

[0067] The first recommended artwork mentioned above can be selected through the graph neural network model. Based on the user data and the artwork data, the graph neural network model can learn by modeling the complex relationship between the user and the artwork in the form of a graph. The graph neural network model can analyze common preference patterns shared by multiple users and the connectivity between each artwork, thereby effectively recommending artworks that can satisfy many customers in the public space, such as the lobby. For example, the graph neural network model can prioritize the selection of artworks that have a high preference among multiple users while also fitting well with the atmosphere of the hotel lobby.

[0068] The graph neural network model described above can derive results suitable for a multi-user environment by comprehensively learning the similarity between users, the relationship between artwork attributes, and the interaction between the artwork and the user, in addition to simply analyzing individual user data. By considering the characteristics of the common space, the graph neural network model can induce an optimized selection so that multiple customers visiting the hotel can simultaneously appreciate the artwork and be satisfied. Through this, the first recommended artwork displayed on the first display device (312) can leave a strong impression on hotel visitors, create a luxurious atmosphere for the hotel, and contribute to strengthening the brand image.

[0069] Next, the first recommended work can be transmitted to the hotel server (300) of the hotel (S140), and the first recommended work can be displayed on the first display device (312) provided in the hotel (S150).

[0070] In exemplary embodiments, the first recommended artwork may be selected based on data generated by the central server (100), transmitted to the hotel server (300), and displayed on the first display device (312) provided within the hotel. The hotel server (300) acts as a relay between the central server (100) and the first display device (312), and can transmit the recommended artwork to the first display device (312) in a timely manner and control it. In this process, the hotel server (300) checks the status of the first display device (312) installed in the lobby and manages the system to ensure that the transmission and display of the artwork can be carried out smoothly.

[0071] The process of displaying the first recommended artwork on the first display device (312) is carried out in an automated manner under the control of the hotel server (300). The first recommended artwork is displayed on the screen of the first display device (312), which is designed to match the atmosphere and spatial characteristics of the hotel lobby, and in this process, the color tone, resolution, and magnification of the artwork may be optimized according to the specifications of the display device. For example, on a high-resolution display device, the detailed features and colors of the artwork may be set to be more clearly revealed.

[0072] The hotel server (300) can collect user reaction data regarding the first recommended artwork or manage the artwork change schedule. If the user's location or real-time interaction data is provided, the hotel server (300) can dynamically change the display content or provide additional information based on this. Through this automated process, the first display device (312) can provide a unique and sensory art experience to customers in the lobby and contribute to enhancing the luxurious image of the hotel.

[0073] In exemplary embodiments, the first recommended artwork may be displayed on the first screen of the first display device (312) provided in the hotel lobby for a first predetermined time. The first predetermined time may be the time when the user first enters the hotel lobby. The first predetermined time may be the time when the user checks in at the hotel.

[0074] In exemplary embodiments, the hotel server (300) may be accessible through an Application Programming Interface (API) provided by the central server (100). The API of the central server (100) can process requests and responses for various data sets, including the user data, the artwork data, and the hotel data, thereby enabling the hotel server (300) to transmit and receive necessary data in real time. For example, the hotel server (300) can retrieve the recommended artwork data from the central server (100) via an API call and transmit the user feedback data to the central server. Through this structure, data consistency and security are maintained, and the hotel server (300) can be supported to access the required data quickly and reliably. Additionally, the API of the central server (100) may include functions such as access control, data encryption, and optimization of request processing speed, and can support the safe and efficient management of communication between the hotel server (300) and the central server (100).

[0075] Next, based on the first recommended work, the first feedback data generated according to the user's selection can be transmitted from the hotel server (300) to the central server (100) (S160).

[0076] In exemplary embodiments, after the first recommended work is displayed on the first screen of the first display device (312), the first feedback data is generated according to the user's selection or interaction, and the first feedback data can be transmitted to a central server via a hotel server. The first feedback data includes various reactions shown by the user to the recommended work and can be used to more accurately learn and improve user preferences in a curation system.

[0077] The user's choice may appear in various forms. For example, if the first display device (312) supports a touchscreen or voice command function, the user may provide direct feedback, such as liking a specific work or extending the viewing time. Alternatively, the user's level of interest in the work may be determined by analyzing indirect behavioral data, such as the user's viewing time, time spent in front of the work, and eye-tracking data. The first feedback data may be used to further refine the user's preferences and continuously improve the recommendation quality of the curation system.

[0078] Before transmitting the first feedback data collected from the user to the central server (100), the hotel server (300) may organize the data and, if necessary, perform anonymization processing to maintain the accuracy of the data analysis while protecting the user's personal information. The first feedback data is transmitted to the central server (100), and the central server (100) may combine the first feedback data with existing user data and artwork data to recalculate the user's preferences or update the learning model.

[0079] The first feedback data mentioned above serves as a key input value through which the system can learn while continuously interacting with the user. Through this, the curation method can provide an advanced recommendation service that reflects the dynamically changing tastes and interests of the user, rather than merely providing static recommendations. This process can contribute to further personalizing the user experience and making artistic appreciation at the hotel more enriching and satisfying.

[0080] Next, a second preference value of the user is calculated based on the user data and the first feedback data (S170), and a second recommended work among the artworks can be selected through the artificial intelligence algorithm based on the second preference value (S180).

[0081] In exemplary embodiments, the second preference value may be calculated by quantifying an individual's artistic taste and preference through the analysis of various data provided by the user. The second preference value may be matched with the artwork data through the artificial intelligence algorithm and utilized to select the second recommended artwork.

[0082] In exemplary embodiments, the second recommended artwork may be displayed on a second screen of a second display device (314) provided in a private space, such as a hotel room. Since the second recommended artwork is displayed on the second screen of the second display device (314) provided in the private space, it may be selected as an artwork that can satisfy only the user staying in the private space. For example, the second recommended artwork may be selected through a reinforcement learning model.

[0083] The second preference value calculated based on the above user data and first feedback data can provide more sophisticated personalized data by combining the user's initial preference and actual reaction to the work. The user's first feedback data regarding the first recommended work may include not only simple preference but also viewing time, frequency of work selection, and interest in specific styles or themes. The second preference value generated by analyzing this data can more finely reflect the user's taste and improve the accuracy of curation by learning changing interests.

[0084] The reinforcement learning model described above learns to optimize user satisfaction based on first feedback data and can select the second recommended work in a personalized manner. The reinforcement learning model defines the user's selection and feedback as a reward function and can recommend works in a direction that maximizes the reward. For example, if the user selects a work of a specific genre or watches it for a long time, the reinforcement learning model learns from this data and can preferentially recommend works with similar attributes.

[0085] The reinforcement learning model can induce personalized recommendations that consider the user's actual response by reflecting the first feedback data collected from the first recommended work. If the first recommended work comprehensively reflects the tastes of multiple users through the graph neural network model, the reinforcement learning model can learn each individual's preferences more finely based on this and perform recommendations to maximize the user's satisfaction.

[0086] The aforementioned second recommended artwork is displayed on the screen of a second display device installed in a private space, such as a hotel room, and is selected to satisfy only the guest within the room. As the room is a space for the individual user to rest and enjoy leisure, the second recommended artwork is selected by prioritizing the user's personal preferences. The second display device is designed to allow the user to interact directly while viewing the artwork and supports functions such as zooming in, checking background information, and changing the artwork. This personalized curation satisfies the user's personal tastes while providing a special and differentiated experience at the hotel.

[0087] By selecting the second recommended artwork through the reinforcement learning model, the curation system can evolve from generalized recommendations in the initial stage to increasingly personalized recommendations. This enriches the user experience, and the hotel can provide guests with a unique and moving art experience. This process can improve the adaptability and efficiency of the curation system by continuously learning from and reflecting the user's preference data.

[0088] Next, the second recommended work can be transmitted to the hotel server (300) of the hotel (S190), and the second recommended work can be displayed on a second display device (314) provided within the hotel (S200).

[0089] In exemplary embodiments, the second recommended artwork may be selected based on data generated by the central server (100), transmitted to the hotel server (300), and displayed on a second display device (314) provided within the hotel. The hotel server (300) acts as a relay between the central server (100) and the second display device (314), and can transmit the recommended artwork to the second display device (314) in a timely manner and control it. In this process, the hotel server (300) checks the status of the second display device (314) installed in the guest room and manages the system to ensure that the transmission and display of the artwork can be carried out smoothly.

[0090] The process of displaying the second recommended artwork on the second display device (314) is carried out in an automated manner under the control of the hotel server (300). The second recommended artwork is displayed on the screen of the second display device (314), which is designed to match the atmosphere and spatial characteristics of the room, and in this process, the color, resolution, magnification, etc. of the artwork can be optimized according to the specifications of the display device.

[0091] The hotel server (300) can collect user reaction data regarding the second recommended artwork or manage the artwork change schedule. When user reaction or real-time interaction data is provided, the hotel server (300) can dynamically change the display content or provide additional information based on this. Through this automated process, the second display device (314) can provide a unique and sensory art experience to the guest in the room and contribute to enhancing the luxurious image of the hotel.

[0092] In exemplary embodiments, the second recommended work may be displayed on the second screen of the second display device (314) provided in the room for a second preset time. The second preset time may be the time when the user first enters the room. Since the user enters the hotel lobby at the first preset time and enters the room at the second preset time, the second preset time may be later than the first preset time. Because the second preset time is later than the first preset time, the second recommended work can efficiently reflect the user's first feedback data regarding the first recommended work.

[0093] Next, based on the second recommended work, the second feedback data generated according to the user's selection can be transmitted from the hotel server (300) to the central server (100) (S210).

[0094] In exemplary embodiments, after the second recommended artwork is displayed on the second screen of the second display device (314), the second feedback data is generated according to the user's selection or interaction, and the second feedback data can be transmitted to a central server via a hotel server. The second feedback data can be used to further refine the user's preferences and continuously improve the quality of recommendations in the curation system. The second feedback data is a key input value that the system can learn while continuously interacting with the user, thereby enabling the curation method to provide an advanced recommendation service that reflects the user's tastes and interests, which are dynamically changing, rather than simply providing static recommendations. This process can contribute to further personalizing the user experience and making the artistic appreciation at the hotel richer and more satisfying.

[0095] Next, a third preference value of the user is calculated based on the user data and the first and second feedback data (S220), and a third recommended work among the artworks can be selected through the artificial intelligence algorithm based on the third preference value (S230).

[0096] In exemplary embodiments, the third preference value may be calculated by quantifying an individual's artistic taste and preference through the analysis of various data provided by the user. The third preference value may be matched with the artwork data through the artificial intelligence algorithm and utilized to select the third recommended artwork.

[0097] In exemplary embodiments, the third recommended artwork is displayed on the first screen of the first display device (312) installed in the lobby of the hotel and can be selected as an artwork that can provide satisfaction to various customers visiting the lobby. The third recommended artwork can be selected by integrally analyzing the first and second feedback data obtained from the first recommended artwork and the second recommended artwork in order to balance the roles of personalized experience and public space.

[0098] A deep learning-based model may be utilized to select the aforementioned third recommended work. The deep learning-based model possesses excellent performance in learning complex data and non-linear relationships, and can perform optimal recommendations by reflecting both the first feedback data and the second feedback data. The first feedback data may reflect the general tastes of multiple users collected from the first recommended work displayed in the lobby through the graph neural network model, and the second feedback data may include personalized preference data collected from the personal space within the guest room through the reinforcement learning model. The deep learning-based model may be designed to reflect both the first and second feedback data to select the most suitable work while maintaining a balance between individual taste and popular taste.

[0099] The deep learning-based model described above can select the third recommended artwork by integrally learning various factors, such as user preferences, characteristics of the artwork, and public popularity indicators. For example, if an artwork of a specific genre or style has recorded high satisfaction in the guest room but requires slight modifications considering its public appeal in the lobby, the deep learning-based model can suggest a more suitable artwork by reflecting these factors. Based on existing recommendation data and real-time feedback data, the deep learning model can be used to select the optimal artwork capable of satisfying a large number of customers in the public space known as the lobby.

[0100] The aforementioned third recommended artwork can leave a deep impression on customers in the lobby, reinforce the luxurious image of the hotel, and contribute to enhancing brand identity. Since the lobby is the space where customers first enter and exit the hotel, the aforementioned third recommended artwork, selected through the deep learning-based model, can create a sensory and sophisticated atmosphere to maximize the customer experience.

[0101] Next, the third recommended work can be transmitted to the hotel server (300) of the hotel (S240), and the third recommended work can be displayed on the first display device (312) provided in the hotel (S250).

[0102] In exemplary embodiments, the third recommended work may be selected based on data generated by the central server (100), transmitted to the hotel server (300), and displayed on the first display device (312) provided within the hotel. The hotel server (300) acts as a relay between the central server (100) and the first display device (312), and can transmit the recommended work to the first display device (312) in a timely manner and control it.

[0103] In exemplary embodiments, the third recommended artwork may be displayed on the first screen of the first display device (312) provided in the lobby for a third predetermined time. The third predetermined time may be the time when the user exits the lobby. The third predetermined time may be the time when the user checks out of the hotel.

[0104] Since the user enters the hotel lobby at the first set time, enters the guest room at the second set time, and exits the hotel lobby at the third set time, the third set time may be later than the first and second set times. Because the third set time is later than the first and third set times, the third recommended work can efficiently reflect the user's first and second feedback data regarding the first and second recommended works.

[0105] As described above, the user-customized art curation method (20) can maximize customer satisfaction by providing hotel guests with a personalized art appreciation experience. By storing the user data, the art data, and the hotel data in the central server (100), and calculating the first preference value based on this, and recommending a suitable work through the artificial intelligence algorithm, the user can provide a work optimized for their individual taste and appreciation style. As the recommended work is displayed on the display device (310), the user can enjoy a luxurious art appreciation experience in the lobby or guest room, which can strengthen the hotel's brand image as a service differentiated from existing standardized content. The first and second feedback data selected and provided by the user regarding the recommended work are transmitted back to the central server (100) and used to calculate new second and third preference values, thereby allowing the user-customized art curation method (20) to continuously learn and improve recommendation quality.

[0106] In addition, the user-customized art curation method (20) can support the promotion of emerging artists' works and revenue generation by digitally connecting the hotel and the artwork. As the recommended artwork is displayed on the display device (310), the user can easily access the artwork and obtain information about the artist and the work. This can lead to opportunities for selling the artwork or additional promotion, and contribute to the revitalization of the art ecosystem. Through real-time data exchange and feedback learning between the central server (100) and the hotel server (300), the efficiency of hotel operations is increased, and management costs can be reduced as artwork recommendations and updates are performed through an automated system. This integrated and organic system provides tangible value to the hotel, customers, and artists alike, and can form a future smart hotel operation model.

[0107] Although it has been described above that all components constituting an embodiment of the present invention are combined or operate as a single unit, the present invention is not necessarily limited to such an embodiment. That is, within the scope of the purpose of the present invention, all components may be selectively combined in one or more ways to operate.

[0108] The foregoing description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention. Accordingly, the embodiments disclosed in the present invention are intended to explain, not 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 shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention.

Claims

1. Store user data about users, artwork data about artworks, and hotel data about hotels on a central server; Calculate the first preference value of the user based on the above user data; Based on the above first preference value, a first recommended work is selected from the above artworks through an artificial intelligence algorithm; Transmit the above-mentioned first recommended work to the hotel server of the above-mentioned hotel; Displaying the first recommended work on a display device provided within the hotel; Transmitting first feedback data generated according to the user's selection based on the first recommended work above from the hotel server to the central server; Calculate the second preference value of the user based on the above user data and the above first feedback data; Based on the second preference value above, a second recommended work is selected from the artworks through the artificial intelligence algorithm; Transmit the above second recommended work to the above hotel server of the above hotel; and A user-customized art curation method comprising displaying the second recommended artwork on the display device provided within the hotel.

2. In claim 1, second feedback data generated according to the user's selection based on the second recommended work is transmitted from the hotel server to the central server; Calculate a third preference value of the user based on the above user data and the above first and second feedback data; Based on the above third preference value, a third recommended work is selected from the above artworks through the above artificial intelligence algorithm; Transmit the above-mentioned third recommended work to the above-mentioned hotel server of the above-mentioned hotel; and A user-customized art curation method further comprising displaying the third recommended artwork on the display device provided within the hotel.

3. A user-customized art curation method according to claim 1, wherein the artificial intelligence algorithm comprises at least one selected from machine learning, deep learning, graph-based learning, generative models, reinforcement learning, natural language processing, and statistical learning.

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