Electronic device and control method thereof

Abstract

An electronic device and a control method thereof are provided. The electronic device includes a camera configured to capture an image, a communication interface, a memory configured to store at least one instruction, and a processor configured to obtain feature information of a user based on the image, identify whether first information corresponding to the feature information of the user is stored in the memory based on the feature information of the user being obtained, generate identification information corresponding to the feature information of the user based on the corresponding first information not being stored in the memory, perform mapping of the feature information of the user and the identification information and store in the memory, and transmit the feature information of the user and the identification information to a second electronic device.

Description

Technical Field

[0001] This disclosure relates to an electronic device and a control method thereof, and more specifically, to an electronic device and a control method thereof for providing services to a user. Background Technology

[0002] With the latest developments in electronic technology, various types of electronic devices are being developed and released.

[0003] In particular, electronic devices that can replace humans (such as self-service kiosks or robots) are being actively supplied to shops, cafes, restaurants, and the like. These electronic devices can process customer orders or perform operations such as delivery to customers.

[0004] Currently, while many electronic devices (such as self-service kiosks or robots) merely replace simple manual labor, there is a growing need for automation to provide consistent or customized customer service by utilizing and sharing relevant customer information.

[0005] Typically, in order to provide consistent or customized services to customers, it is essential to manage sensitive personal information. Consequently, there is a growing concern about information leaks and hacking attacks targeting customer personal information. Summary of the Invention

[0006] Technical issues

[0007] -

[0008] Technical solution

[0009] According to an embodiment, an electronic device is provided, including: a camera configured to capture images; a communication interface; a memory configured to store at least one instruction; and a processor. The processor is configured to: obtain user feature information based on the images; based on the obtained user feature information, identify whether first information corresponding to the feature information is stored in the memory; based on the absence of the corresponding first information in the memory, generate identification information corresponding to the user feature information; perform a mapping between the user's feature information and the identification information and store it in the memory; and control the communication interface to send the user's feature information and the identification information to a second electronic device, wherein the electronic device and the second electronic device are located in a specific space, and each of the electronic device and the second electronic device is configured to perform at least one service provided in the specific space.

[0010] The processor is further configured to: obtain the user's identification information mapped in the corresponding first information from the memory based on the corresponding first information being stored in the memory, and execute at least one service in the service based on the obtained identification information.

[0011] The processor is further configured to: based on the execution of at least one service in the service, control the communication interface to send a second piece of information, other than facial recognition information, from the feature information related to the at least one service in the service to the server.

[0012] The processor is also configured to: based on the fact that at least one of the services is being executed, control the communication interface to send first information related to the executed service and the feature information to a second electronic device via peer-to-peer (P2P) communication, wherein the feature information includes the user's facial recognition information.

[0013] The processor is also configured to: control the communication interface to send second information, other than facial recognition information, from the feature information to the server, receive service execution information for the second information from the server, and provide recommended service information based on the received service execution information.

[0014] The processor is further configured to: provide additional service information about the selected recommended service information based on the user selecting one of the multiple service information included in the recommended service information; and control the communication interface to send a request to the server to update the additional service information for the recommended service information based on the user selecting one of the multiple additional service information.

[0015] The processor is also configured to store in memory the user's identification information and characteristic information received from the second electronic device via a communication interface.

[0016] The processor is further configured to: obtain from the image based on the individual feature information of the multiple users, generate individual identification information corresponding to each of the individual feature information of the multiple users, generate group identification information by grouping the multiple identification information, generate group feature information based on at least one of the individual feature information of the multiple users, and control the communication interface to send the individual identification information, individual feature information, group identification information and group feature information of each of the multiple users to a second electronic device.

[0017] The processor is further configured to: based on the execution of at least one of the services, control the communication interface to send first information and the group feature information related to the at least one of the executed services to the server.

[0018] The processor is further configured to delete the user's feature information and identification information from the memory based on identifying at least one of the following after obtaining the feature information: a predetermined time has elapsed, a predetermined time period has elapsed, or the user has left the specific space.

[0019] The memory is configured to store a learning network model trained to obtain the user's feature information based on an input image, and the processor is further configured to obtain the user's feature information by inputting the image into the learning network model.

[0020] At least one of the electronic device or the second electronic device is a mobile robot that moves within the specific space.

[0021] According to an embodiment, a control method for an electronic device is provided, the method comprising: obtaining user feature information based on an image captured by a camera of the electronic device; identifying whether first information corresponding to the user feature information is stored in the electronic device based on the obtained user feature information; generating identification information corresponding to the user feature information based on the fact that the corresponding first information is not stored in the electronic device; mapping and storing the user feature information and the identification information; and sending the user feature information and the identification information to a second electronic device, wherein the electronic device and the second electronic device are located in a specific space, and each of the electronic device and the second electronic device performs at least one of the services provided in the specific space.

[0022] The method further includes: obtaining the user's identification information mapped in the corresponding first information from the electronic device based on the corresponding first information being stored in the electronic device; and executing the at least one service in the service based on the obtained identification information.

[0023] The method further includes: based on the execution of at least one service in the service, sending to the server second information, other than facial recognition information, from the feature information related to the at least one service in the service.

[0024] The method further includes: based on the execution of at least one service in the service, sending first information related to the executed service and the feature information to a second electronic device via peer-to-peer (P2P) communication, wherein the feature information includes the user's facial recognition information.

[0025] The method further includes: sending second information, other than facial recognition information, from the feature information to a server; receiving service execution information related to the second information from the server; and providing recommended service information based on the received service execution information.

[0026] The method further includes: providing additional service information about the selected recommended service information based on the user's selection of one of the multiple service information included in the recommended service information; and sending a request to the server to update the additional service information about the recommended service information based on the user's selection of one of the multiple additional service information.

[0027] The method further includes storing the user's identification information and feature information received from the second electronic device in the electronic device.

[0028] The method further includes: obtaining individual identifier information corresponding to each of the individual feature information of the multiple users from the image based on the individual feature information of the multiple users; generating group identifier information by grouping the multiple identifier information; generating group feature information based on at least one of the individual feature information of the multiple users; and sending the individual identifier information, individual feature information, group identifier information and group feature information of each of the multiple users to a second electronic device.

[0029] Beneficial effects

[0030] According to various embodiments, customers can be provided with consistent and customized services by using electronic devices, which effectively maintain and manage customers' sensitive personal information while minimizing the risk of leakage and hacking.

[0031] Other aspects will be set forth in part in the description which follows, and in part will be apparent from the description, or may be learned by practicing the embodiments presented. Attached Figure Description

[0032] The above and other aspects, features, and advantages of specific embodiments will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0033] Figure 1 This is a block diagram illustrating the configuration of an electronic device according to an embodiment;

[0034] Figure 2 This is a diagram illustrating a schematic configuration of an electronic device and other electronic devices according to an embodiment;

[0035] Figure 3 This is a diagram illustrating the user's characteristic information and identification information according to an embodiment;

[0036] Figure 4 This is a diagram illustrating services provided in a specific space according to an embodiment;

[0037] Figure 5 This is a diagram illustrating information sent to a server according to an embodiment;

[0038] Figure 6 This is a diagram illustrating information sent to a server according to an embodiment;

[0039] Figure 7 This is a diagram illustrating recommendation service information according to an embodiment;

[0040] Figure 8 This is a diagram illustrating additional service information according to an embodiment;

[0041] Figure 9 This is a diagram illustrating group identification information and group feature information according to an embodiment;

[0042] Figure 10 This is a diagram illustrating information shared among multiple electronic devices according to an embodiment;

[0043] Figure 11 This is a diagram illustrating a schematic configuration of an electronic device according to an embodiment;

[0044] Figure 12 This is a flowchart illustrating a control method for an electronic device according to an embodiment; and

[0045] Figure 13 This is a sequence diagram illustrating the operation of an electronic device, other electronic devices, and a server according to an embodiment.

[0046] Best mode

[0047] - Detailed Implementation

[0048] The terminology used herein will be briefly described, and this disclosure will be described in more detail below.

[0049] The terms and words used in the following description and claims are not limited to their literal meaning, but are used only to enable a clear and consistent understanding of this disclosure. Accordingly, it will be apparent to those skilled in the art that the following description, which provides various embodiments of this disclosure, is for illustrative purposes only and is not intended to limit the purpose of this disclosure as defined by the appended claims and their equivalents.

[0050] The embodiments of this disclosure can be modified in various ways and may include various embodiments therein. Some embodiments are provided, along with their detailed descriptions, to aid in a comprehensive understanding of this disclosure. However, it should be noted that the various embodiments are not intended to limit the scope of this disclosure to the particular embodiments, but should be construed as including all modifications, combinations, equivalents, and / or substitutions of the embodiments. In describing embodiments, detailed descriptions of related or known techniques will be omitted where it is determined that such detailed descriptions may unnecessarily obscure the spirit of this disclosure.

[0051] Terms (such as "first" and "second") can be used to describe a variety of elements, but elements are not limited by terms. These terms may be used only for the purpose of distinguishing one element from another.

[0052] Unless otherwise expressly stated in the context, singular expressions may include plural expressions. It should be understood that terms such as “comprising” or “consisting of” are used herein to specify the presence of a feature, number, step, operation, element, component, or combination thereof, and do not exclude the presence or possibility of adding one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0053] In this disclosure, terms such as “module” or “part” can be used to perform at least one function or operation and can be implemented as hardware or software, or a combination of hardware and software. Furthermore, except when a “module” or “part” needs to be implemented in specific hardware, multiple “modules” or multiple “parts” can be integrated into at least one module to be implemented as at least one processor.

[0054] Embodiments of this disclosure have been described in detail with reference to the accompanying drawings to aid those skilled in the art in understanding. However, this disclosure can be implemented in various different forms, and it should be noted that this disclosure is not limited to the various embodiments described herein. Furthermore, in the drawings, parts unrelated to the description may be omitted, and the same reference numerals may be used to indicate the same elements.

[0055] Figure 1 This is a block diagram illustrating the configuration of an electronic device according to an embodiment.

[0056] like Figure 1 As shown, the electronic device 100 according to the embodiment can be implemented in various forms of devices (such as, for example, but not limited to, user terminal devices, display devices, set-top boxes, tablet personal computers (PCs), smartphones, e-book readers, desktop PCs, laptop PCs, workstations, servers, personal digital assistants (PDAs), portable multimedia players (PMPs), MP3 players, self-service terminals, etc.). However, the electronic device 100 is not limited to this and can be implemented as various forms of electronic devices (such as, for example, but not limited to, wearable devices corresponding to at least one type of accessory type (e.g., watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs)) or fabric or clothing embedded type (e.g., electronic clothing), robots including drive units, projectors, servers, etc.).

[0057] The electronic device 100 according to the embodiment can be implemented as a robot. A robot can refer to various forms of machines that have the ability to perform functions autonomously. In the example, a robot can refer to an intelligent machine that, in addition to performing simple repetitive functions, detects the surrounding environment in real time, collects information, and operates autonomously based on sensors, cameras, etc.

[0058] A robot may include a drive unit containing actuators or motors. The drive unit can be used to control the movement of a jointed robot. The drive unit may include wheels, brakes, etc., and the robot can use the drive unit to become a mobile robot capable of moving autonomously within a specific space. Additionally, a jointed robot can refer to a component of a robot used to replace the function of a human arm or hand.

[0059] Based on their domain or functional capabilities, robots can be categorized into commercial, medical, domestic, military, and exploratory uses. For example, commercial robots can be further classified as those used in factory product manufacturing processes, or those performing customer reception, order taking, and service tasks in shops or restaurants. However, these are just a few examples, and robots can be classified in various ways according to their application, function, and purpose, and embodiments are not limited to the examples described above.

[0060] For ease of description, electronic device 100 is assumed to be a robot and is described below.

[0061] like Figure 1 As shown, the electronic device 100 may include a camera 110, a communication interface 120, a memory 130, and a processor 140.

[0062] Camera 110 is configured to capture still or moving images. Camera 110 can capture still images at a specific point in time, or it can capture still images continuously. Camera 110 can provide the acquired images to processor 140.

[0063] Camera 110 can acquire an image including the user's face under the control of processor 140. For example, if the user is identified as being near electronic device 100, processor 140 can control camera 110 to move toward the user to acquire an image including the user's face.

[0064] According to an embodiment, multiple cameras 110 may exist. For example, the multiple cameras 110 may include a front surface camera and a rear surface camera.

[0065] Communication interface 120 can receive input of various types of content. For example, communication interface 120 can receive image signals from external devices (e.g., source devices), external storage media (e.g., Universal Serial Bus (USB) memory), external servers (e.g., cloud servers) via streaming or download using communication networks (such as, but not limited to, access point (AP) based Wi-Fi (e.g., Wi-Fi, wireless LAN networks), Bluetooth, ZigBee, wired / wireless local area network (LAN), wide area network (WAN), Ethernet, IEEE 1394, high-definition multimedia interface (HDMI), USB, mobile high-definition link (MHL), Audio Engineering Society / European Broadcasting Union (AES / EBU), optical, coaxial, etc.). The image signal can be a digital image signal of any one of standard definition (SD) image, high definition (HD) image, full HD image, or ultra-HD image, but the embodiment is not limited to this.

[0066] Electronic device 100 can share information by communicating with other electronic devices in a peer-to-peer (P2P) manner via communication interface 120. In the example, electronic device 100 can communicate with other electronic devices in Ad Hoc mode, where information is sent or received between devices in a P2P manner without an access point (AP). Reference will be made below. Figure 2 Provide a detailed description.

[0067] Figure 2 This is a diagram illustrating a schematic configuration of an electronic device and other electronic devices according to an embodiment.

[0068] Reference Figure 2 Electronic device 100 can obtain information about user 10. Electronic device 100 can then send the obtained information to other electronic devices 200. The information about user 10 may include characteristic information of user 10 obtained based on images of the user's face captured by camera 110. For example, the characteristic information of user 10 may include features such as user 10's face, gender, age group, body type, voice, etc.

[0069] In another example, electronic device 100 can also receive information from user 10 from other electronic devices 200. For example, other electronic devices 200 can obtain information about user 10 and send the obtained information to electronic device 100.

[0070] like Figure 2 As shown, electronic device 100 and other electronic devices 200 can share information about user 10 with each other. A detailed description of information sharing by user 10 will be described below.

[0071] Figure 2The electronic device 100 and other electronic devices 200 shown may be located within a specific space and may be devices that perform at least some services provided to user 10 within that specific space. For example, the specific space may include a shop, restaurant, etc., and each of the electronic devices 100 and other electronic devices 200 may perform at least some services provided in the shop, restaurant, etc. (such as receiving orders, serving, customer reception, or payment). User 10 may refer to a user or customer of the shop or restaurant, but for ease of description, it will be collectively referred to as User 10 below.

[0072] Return to reference Figure 1 The memory 130 can be implemented as internal memory (such as read-only memory (ROM) (e.g., electrically erasable programmable read-only memory (EEPROM)) and random access memory (RAM) or memory separate from the processor 140). The memory 130 can be implemented as memory embedded in the electronic device 100 depending on the data storage purpose, or as memory that can be attached to / removed from the electronic device 100. For example, data for driving the electronic device 100 can be stored in memory embedded in the electronic device 100, and data for extended functions of the electronic device 100 can be stored in memory that can be attached to / removed from the electronic device 100. The memory embedded in the electronic device 100 can be implemented as at least one of volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)) or non-volatile memory (e.g., one-time programmable ROM (OTPROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), hard disk drive (HDD), or solid-state drive (SSD)). Where the memory is attachable / removable to the electronic device 100, the memory can be implemented in the form of a memory card (e.g., compact flash (CF), secure digital (SD), micro-SD, mini-SD, extreme digital (xD), multimedia card (MMC), etc.) or external memory (e.g., USB memory) connectable to a USB port.

[0073] The memory 130 can store information about the user 10 obtained based on images or information received from other electronic devices 200, under the control of the processor 140. For example, the information about the user 10 may include feature information, identification information, etc.

[0074] Additionally, the memory 130 can store a trained learning network model to obtain feature information of the user 10 based on images. The learning network model can be an artificial intelligence (AI) model with machine learning based on multiple sample images.

[0075] The AI-related functions according to the embodiments can be operated via a processor and memory. The processor may include one or more processors. The one or more processors may be general-purpose processors (such as CPUs, APs, or digital signal processors (DSPs)), graphics-specific processors (such as GPUs or visual processing units (VPUs)), or AI-specific processors (such as NPUs). The one or more processors may control the input data to be processed based on predefined operations or AI models stored in memory. Optionally, if the one or more processors are AI-specific processors, the AI-specific processors may be designed with a hardware architecture specifically for processing a particular AI model.

[0076] Predefined operational rules or artificial intelligence models can be generated through machine learning. These models can be trained using learning algorithms that learn to create predefined operational rules or artificial intelligence models that perform desired characteristics. Learning can be performed within the machine performing the artificial intelligence itself, or via a separate server and / or system. Examples of learning algorithms can include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but embodiments are not limited thereto.

[0077] Artificial intelligence models can include multiple neural network layers. Each of the multiple neural network layers can include multiple weight values, and neural network processing can be performed by processing the results of previous layers with the multiple weight values. The multiple weight values ​​in the corresponding multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, multiple weight values ​​can be updated to reduce or optimize the loss or cost values ​​obtained by the artificial intelligence model during learning processing. Artificial neural networks can include deep neural networks (DNNs), and examples of them can include convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), deep Q-networks, etc., but implementation methods are not limited to the examples described above.

[0078] The learning network model can obtain feature information of user 10 from images based on the control of processor 140.

[0079] The processor 140 can control the overall operation of the electronic device 100.

[0080] According to embodiments, processor 140 may be implemented as a digital signal processor (DSP), microprocessor, artificial intelligence (AI) processor, and timing controller (T-CON) for processing digital image signals. However, embodiments are not limited thereto, and processor 140 may include one or more of a central processing unit (CPU), microcontroller unit (MCU), microprocessor unit (MPU), controller, application processor (AP), communication processor (CP), or advanced RISC machine (ARM) processor, or as defined by the corresponding terms. Additionally, processor 140 may be implemented as a system-on-a-chip (SoC) and large-scale integration (LSI) with built-in processing algorithms, or as a field-programmable gate array (FPGA).

[0081] The processor 140 can obtain feature information of the user 10 based on the image acquired by the camera 110. (Refer to the following...) Figure 3 Provide a detailed description.

[0082] Figure 3 This is a diagram illustrating feature information and identification information according to an embodiment.

[0083] Reference Figure 3 The processor 140 can obtain feature information 11 of the user 10 by inputting an image including the user 10 into a learning network model stored in the memory 130. The image including the user 10 is not limited to an image including the user 10's face, but can also be an image including the user 10's entire body or a partially captured image of the user.

[0084] User 10's characteristic information 11 can refer to all types of information that can specify user 10. For example, user 10's characteristic information 11 may include facial recognition information (e.g., face ID), gender information, age group information, user's body type information (e.g., height), or information about the clothing worn by user 10, etc. Figure 3 As shown, the facial recognition information (e.g., face ID), age group, height, and gender of user 10's feature information 11 are just one example, and feature information 11 can include various types of information that can specify user 10. For example, feature information 11 can include user 10's voice information (e.g., voice), fingerprint information, etc. In addition, feature information 11 can also include the time when user 10 enters a specific space (e.g., the time of entering a store) and the time of leaving (e.g., the time of leaving a store).

[0085] Processor 140 can identify whether the user's feature information 11 obtained based on the image is stored in memory 130. If the information corresponding to feature information 11 is not stored in memory 130, processor 140 can generate identification information 12 corresponding to the user 10's feature information 11. (Refer to...) Figure 3 The identification information 12 of user 10 can be guest #1. Figure 3 The form of the identification information 12 shown is merely an example and is not necessarily limited to it. For example, the processor 140 can generate various forms of ID that can indicate user 10 and user 10's characteristic information 11.

[0086] The processor 140 can perform the mapping of user 10's characteristic information 11 and user identification information 12 and store them in the memory 130. Then, the processor 140 can control the communication interface 120 to send the characteristic information 11 and identification information 12 to other electronic devices 200.

[0087] The processor can obtain user identification information 12 mapped in the corresponding information from the memory 130 based on information corresponding to feature information 11 obtained from an image stored in the memory. In an example, the processor 140 can identify feature information 11 based on a predetermined threshold related to the similarity of multiple feature information 11 stored in the memory 130. Specifically, for example, the processor 140 can identify facial recognition information from the feature information 11 obtained from an image. That is, based on an image captured by the camera 110 and received by the processor 140, the processor 140 can identify the feature information 11 of an image by comparing the image with each of the multiple feature information 11 stored in the memory 130 and determining a correspondence between the image and one of the multiple feature information 11 based on a predetermined threshold. In another example, the processor 140 can add different weight values ​​to each of the facial recognition information, age group information, body type information, or gender information included in the feature information 11, and then identify feature information 11 among the multiple feature information stored in the memory 130 with a similarity of a predetermined threshold or greater. According to an embodiment, the processor 140 may also add a relatively higher weight value to the facial recognition information than to other information (e.g., age group information, body shape information, or gender information).

[0088] Processor 140 can obtain identification information 12 mapped in the identified feature information 11. For example, processor 140 can obtain information corresponding to the feature information 11 of user 10 among multiple feature information 11 stored in memory 130, and obtain the identification information 12 mapped in the obtained information. See reference. Figure 3 Instead of generating identification information 12 for instructing the user 10 and the obtained feature information 11, processor 140 can load identification information 12 mapped in the pre-stored feature information 11 based on the information corresponding to feature information 11 obtained from the image pre-stored in memory 130.

[0089] The processor 140 can control the electronic device 100 to perform at least some services provided in a specific space based on the obtained identification information. (Refer to the following...) Figure 4 Provide a detailed description.

[0090] Figure 4 This is a diagram illustrating services provided in a specific space according to an embodiment.

[0091] Reference Figure 4 The specific space according to the embodiment may be a shop or restaurant, and the electronic device 100 may be a robot that receives the order history of user 10 and sends it to other electronic devices 200. In this case, in addition to the characteristic information 11 and identification information 12 of user 10, the processor 140 may also send the order history received from user 10 to other electronic devices 200.

[0092] For example, such as Figure 4 As shown, electronic device 100 can send user 10's order history (e.g., "pizza") along with user characteristic information 11 and identification information 12 to other electronic devices 200. Processor 140 can also send information about the time when the order history was received from user 10 to other electronic devices 200.

[0093] The order history received from user 10 is shown in information 13 (e.g., “pizza”) related to the service performed by electronic device 100.

[0094] In other words, the processor 140 can perform specific services according to the application field, function, and purpose of the electronic device 100, and can send the information 13 related to the performed service, along with the user 10's characteristic information 11 and identification information 12, to other electronic devices 200.

[0095] In another example, electronic device 100 can be set up in a store and can receive orders from user 10, execute payment transactions, serve food, etc. In this case, processor 140 can obtain information 13 related to the service being performed, such as whether payment has been made, the amount of payment, whether food needs to be served, and the seating position of the user serving the food.

[0096] In yet another example, electronic device 100 may obtain the speech history of user 10, including other requests from user 10 (such as whether the corresponding request has been resolved), as information 13 related to the service being performed.

[0097] According to an embodiment, the other electronic device 200 may be a device for receiving the order history of user 10, and the electronic device 100 may be a device for serving cooked food based on the order history of user 10. In this case, the electronic device 100 may obtain the feature information 11 of user 10 based on the image obtained by camera 110. Then, the electronic device 100 may perform the service based on the service-related information 13 received from the other electronic device 200, based on the obtained feature information 11 being identified as corresponding to the feature information received from the other electronic device 200. The service-related information 13 may be information generated in the other electronic device 200 and may include information about user 10's order, user 10's seat location, etc.

[0098] Furthermore, the processor 140 can control the communication interface 120 to send information other than the facial recognition information of the user 10 and information 13 related to the executed service to the server. (Refer to the following...) Figure 5 Provide a detailed description.

[0099] Figure 5 This is a diagram illustrating information sent to a server according to an embodiment.

[0100] Reference Figure 5 The processor 140 can send information to the server 300 excluding facial recognition information (e.g., face ID) 14 from the feature information 11 and the information 13 related to the performed service. Facial recognition information may be information obtained from an image of the user 10 captured by the camera 110. Facial recognition information, along with fingerprint, voice, iris recognition, etc., constitutes the user's personal information corresponding to biometric information. However, if the user 10's sensitive personal information is stored or retained without the user 10's consent, security issues may arise regarding the retention and protection of such personal information.

[0101] Accordingly, the processor 140 according to the embodiment may send only the remaining information 14 in the feature information 11, excluding facial recognition information, iris recognition information, etc., corresponding to personal information, to the server 300. For ease of description, the personal information and biometric information unique to the user 10 will be collectively referred to as facial recognition information.

[0102] like Figure 5 As shown, the electronic device 100 can retain only the user 10's characteristic information, excluding sensitive personal information such as age group, body type, and gender, and send this personal information to the server 300. Additionally, the electronic device 100 can send information to the server 300 other than the facial recognition information 14 and the information 13 related to the performed service.

[0103] exist Figure 5 In this context, the service-related information 13 has been described as information obtained from the user 10's order history (e.g., "pizza"). However, the service-related information 13 is not limited to order history or request history, and may also include the user 10's speech history and usage history related to the electronic device 100. For example, the processor 140 may determine the user 10's intent based on the user 10's speech by using at least one algorithm from either a speech-to-text (STT) engine for converting the user 10's speech into a string or a natural language processing (NLP) engine for obtaining intent information in natural language, and may identify the obtained intent information as the service-related information 13.

[0104] As described above, the processor 140 according to the embodiment can send information from the user's feature information 11, excluding facial recognition information 14, to the server 300. Then, the processor 140 can receive service execution information related to the sent information 14. The following will refer to... Figure 6 Provide a detailed description.

[0105] Figure 6 This is a diagram illustrating information sent to a server according to an embodiment.

[0106] According to an embodiment, the processor 140 can identify whether the information corresponding to the obtained feature information 11 is stored in the memory 130 based on the feature information 11 of the user 20 obtained from the image of the user 20. The processor 140 can obtain the identification information 12 mapped in the corresponding information based on the corresponding information pre-stored in the memory 130. As long as the information corresponding to the obtained feature information 11 is not stored in the memory 130, the processor 140 can add the identification information 12 to the obtained feature information 11. In other words, if the identification information 12 already stored in the memory 130 can be identified based on the feature information 11, the processor 140 can omit the process of generating new identification information and adding it to the feature information 11, instead of adding new identification information 12 to the corresponding feature information 11 every time the feature information 11 of the user 20 is obtained.

[0107] Reference Figure 6 If the information corresponding to the obtained feature information 11 is stored in the memory 130, the processor 140 can obtain the identification information 12 (e.g., guest #2) indicating the user 20 and the feature information of the user 20 from the memory 130.

[0108] Then, the processor 140 can send the information 14 in the feature information 11, excluding facial recognition information, to the server 300, and receive service execution information related to the sent information 14.

[0109] Figure 7 This is a diagram illustrating recommended service information according to an embodiment. Reference will be made below. Figure 7 Provide a detailed description.

[0110] Reference Figure 6 and Figure 7 The processor 140 can send information of the feature information 11 other than the facial recognition information 14 (e.g., at least one of age group information, body type information, or gender information) to the server 300, and receive service execution information related to the sent information 14.

[0111] According to an embodiment, server 300 can receive information 13 related to services performed from multiple devices (such as electronic device 100 and other electronic devices 200). For example, processor 140 can send age group information and information 13 related to services performed for user 20 corresponding to that age group information to server 300. Additionally, other electronic devices 200 can also send age group information and information 13 related to services performed for user 20 corresponding to that age group information to server 300. For example, server 300 can receive "twenties" as age group information and "pasta" as information 13 related to the performed service from electronic device 100 and other electronic devices 200, respectively, and "thirties" as age group information and "steak" as information 13 related to the performed service.

[0112] Then, server 300 can identify only the information 14 among the multiple pieces of information 13 related to the service being performed, including the information 14 corresponding to the information sent from electronic device 100. For example, server 300 can obtain "pasta" as service performance information related to the received age group information "twenties" based on the received age group information and send it to electronic device 100.

[0113] If, based on the characteristic information 11 of user 20, user 20 is in their "twenties," then electronic device 100 can provide recommended service information to user 20 based on the service execution information received from server 300. For example, electronic device 100 can provide "pasta" as recommended service information to user 20 when they are in their "twenties."

[0114] For ease of description, the case considering only age group information has been described, but the embodiments are not limited thereto. For example, as Figure 6As shown, processor 140 can send age group information, body type information, and gender information as information excluding facial recognition information 14 from user 20's feature information 11 to server 300, and receive service information to be performed on the user corresponding to the information 14 sent from server 300. For example, processor 140 can send "twenties", "height 160cm", and "female" to server 300 based on user 20's feature information, and receive service information to be performed on the user corresponding to "twenties", "height 160cm", and "female" from server 300.

[0115] The processor 140 can also provide multiple recommended service information based on service execution information received from the server 300. According to the embodiment, the processor 140 can add different priority orders to each of the multiple recommended services based on the execution frequency of services performed on users corresponding to "twenty years old", "height 160cm", and "female".

[0116] like Figure 7 As shown, processor 140 can provide multiple recommended service information based on priority order. For example, processor 140 can provide "pasta" as the primary option and "steak" as the secondary option in the recommended service information based on service execution information related to "twenty-something years old", "height 160cm" and "female" received from server 300.

[0117] The electronic device 100 according to an embodiment may include a display 150, and the display 150 may be controlled to display recommended service information. However, the embodiment is not limited thereto, and the electronic device 100 may also provide recommended service information in an audio signal using an output device (such as a speaker). The display 150 may be implemented as a display of various forms (such as, but not limited to, liquid crystal displays (LCDs), organic light-emitting diode (OLED) displays, plasma display panels (PDPs), etc.). The display 150 may include driving circuitry, backlight units, etc., which can be implemented in the form of, but not limited to, amorphous silicon (A-Si) TFTs, low-temperature polycrystalline silicon (LTPS) TFTs, organic TFTs (OTFTs), etc. The display 150 may be implemented as a touch screen coupled to a touch sensor, a flexible display, a 3D display, etc.

[0118] Additionally, according to an embodiment, the display 150 may include not only a display panel for outputting images, but also a bezel accommodating the display panel. According to an embodiment, the bezel may include a touch sensor 150 for detecting user interaction.

[0119] The processor 140 can send information based on the user 20’s characteristics, identification information and order history input by the user 20, according to the user 20’s input of any of the recommended service information being received.

[0120] In addition, the processor 140 can send information 14, excluding facial recognition information, from the feature information 11 of the user 20 input by the user 20, and the order history to the server 300.

[0121] Furthermore, the processor 140 can control the communication interface 120 to send a request to the server 300 to update the additional service information regarding the selected recommended service information, based on the selection of one of a plurality of service information included in the recommended service information and / or the selection of additional service information by the user 20. The following will refer to... Figure 8 Provide a detailed description.

[0122] Figure 8 This is a diagram illustrating additional service information according to an embodiment.

[0123] Reference Figure 8 According to the embodiment, the processor 140 can provide recommendation service information and send the recommendation service information to the server 300 by updating the recommendation service information based on the input of the user 20.

[0124] In the example, processor 140 can provide multiple recommended service options based on service execution information received from server 300. Then, when user 20 selects one of the recommended service options, processor 140 can send the selected service option to server 300. (See reference...) Figure 8 If user 20 selects "steak" from multiple recommended service options, "pasta" and "steak," then processor 140 can send information 14 from user 20's feature information 11 (excluding facial recognition information) and the user 20's selected "steak" to server 300. In this case, as the frequency of "steak" service execution increases, "steak" can be recommended to the user as the primary choice, and "pasta" as the secondary choice. Here, for example, the execution frequency can be the count of specific orders (e.g., "steak") selected by user 20 based on the recommended service information provided to user 20. However, the execution frequency is not limited to this and can be determined in various ways.

[0125] Additionally, the processor 140 can send the user 20's characteristic information 11, identification information 12, and selected recommended service information to other electronic devices 200. These other electronic devices 200 may be robot chefs, robot servers, robot cashiers, etc., placed in the store.

[0126] According to an embodiment, the robot chef can perform cooking based on information received from the electronic device 100, and the robot server can provide services to the user 20 based on the user 20's seating position in a specific space, which is included in the user 20's feature information.

[0127] In another example, based on user 20 selecting additional service information regarding the service information selected by user 20, processor 140 can update the additional service information for the selected service information and send it to server 300. For example, based on the identification of an additional (or specific) request for the selected service information, processor 140 can update the corresponding request for the selected service information and send it to server 300. See also Figure 8 If user 20 selects "steak" as the service information and selects "cooking level" as additional service information, then processor 140 can perform a mapping from user 20's feature information 11 (excluding facial recognition information 14), the selected service information, and the additional service information, and send it to server 300.

[0128] Based on feature information of multiple users obtained from the acquired images, the processor 140 according to the embodiment can generate identification information corresponding to each of the feature information of the multiple users, and generate group identification information by grouping the generated multiple identification information. Then, the processor 140 can generate group feature information based on at least some feature information of the multiple users, and control the communication interface to send the identification information and feature information of each of the multiple users, as well as the group identification information and group feature information, to other electronic devices. (Referring to the following...) Figure 9 Provide a detailed description.

[0129] Figure 9 This is a diagram illustrating group identification information and group feature information according to an embodiment.

[0130] Reference Figure 9 The processor 140 can generate identification information corresponding to each feature information of the plurality of users 10 and 20 based on feature information of the plurality of users 10 and 20 obtained through one or more images acquired by the camera 110. In the example, the processor 140 can generate first identification information indicating feature information of a first user 10 and second identification information indicating feature information of a second user 20. The processor 140 can then generate group identification information 30 by grouping the plurality of identification information.

[0131] In the example, based on the identification of multiple users 10 and 20 that have entered a specific space simultaneously or within a predetermined time difference from the acquired image, the processor 140 can generate group identification information 30 by grouping the identification information corresponding to each of the multiple users 10 and 20. For example, if the first user 10 and the second user 20 are identified as having entered the store together based on the acquired image, the processor 140 can generate group identification information 30 by grouping the first user 10 and the second user 20 to provide services as group units.

[0132] Then, processor 140 can generate group feature information 31 based on at least some of the feature information of multiple users 10 and 20. For example, processor 140 can generate group feature information 31 based on the facial recognition information of the first user 10 and the second user 20, and can perform a mapping between the generated group feature information 31 and group identification information 30. Then, processor 140 can control communication interface 120 to send the group feature information 31 to other electronic devices 200. In this case, other electronic devices 200 can perform grouping of the first user 10 and the second user 20, and provide services as group units.

[0133] Based on the fact that at least some of the services provided in a specific space are executed to the corresponding group, the processor 140 can control the communication interface 120 to send information related to the executed services and group characteristic information 31 to the server 300.

[0134] According to an embodiment, the group feature information 31 sent to the server 300 may be information other than facial recognition information. For example, the group feature information 31 may include the gender and age group information of each of the multiple users 10 and 20 included in the group.

[0135] Based on the feature information of multiple users 10 and 20 identified from images, processor 140 can send information from the corresponding feature information, excluding facial recognition information, to server 300, and receive service information to be performed for the corresponding group. For example, processor 140 can send gender information to server 300 based on a group of male and female pairs being identified, and receive information from server 300 about the services most frequently performed for the group of male and female pairs. Then, processor 140 can provide recommended service information to the group of male and female pairs based on the information received from server 300.

[0136] Figure 10 This is a diagram illustrating information shared among multiple electronic devices according to an embodiment.

[0137] Reference Figure 10According to an embodiment, multiple electronic devices 100-1, 100-2, and 100-3 may be arranged in a specific space. For ease of description, the specific space has been described as a restaurant, but the embodiment is not necessarily limited to this. For example, the specific space may refer to a store where electronic device 100 identifies random groups of users and provides various forms of services to the identified users.

[0138] Each of the multiple electronic devices 100-1, 100-2, and 100-3 can be a robot performing different functions from each other. For example, the first electronic device 100-1 can be a robot that performs the function of seating user 10 and receiving orders from user 10.

[0139] The first electronic device 100-1 can obtain the feature information 11 of the user 10, and can identify whether the information corresponding to the obtained feature information is stored in the memory 130.

[0140] If the corresponding information is not stored in memory 130, the first electronic device 100-1 can generate identification information 12 (e.g., visitor #1) corresponding to the user 10's characteristic information 11. Then, the first electronic device 100-1 can send the user 10's characteristic information 11, identification information 12, order (or request), and seating location to the second electronic device 100-2.

[0141] The second electronic device 100-2 may be a cooking robot for cooking food according to an order or a robot server for serving cooked food. The second electronic device 100-2 may obtain the feature information of the user 10 through an independently provided camera, or identify the user 10 based on the feature information 11 received from the first electronic device 100-1 with a similarity of a predetermined threshold or greater, and based on the identification information 12 received from the first electronic device 100-1.

[0142] The second electronic device 100-2 can supply food to the user 10 based on the location information in a specific space and the user 10's order. The second electronic device 100-2 can send information to the third electronic device 100-3 about whether food has been supplied to the user 10, whether the user has made any additional requests, and the user 10's characteristic information 11 and identification information 12.

[0143] The third electronic device 100-3 may be a robot that receives the user 10's level of satisfaction with the food or executes a payment transaction. The third electronic device 100-3 may acquire feature information of the user 10 via an independently provided camera, and if the feature information 11 received from the second electronic device 100-2 has a predetermined threshold or greater similarity to the independently acquired feature information, the third electronic device 100-3 may identify the user 10 based on the identification information 12 received from the second electronic device 100-2. According to an embodiment, the third electronic device 100-3 may identify the user 10's level of satisfaction based on another image of the user 10. In the example, a learning network model stored in memory 130 may acquire the user 10's emotions, responses, level of satisfaction, etc., based on the image. The third electronic device 100-3 may identify the level of satisfaction expressed by the user 10 by applying an image of the user 10 taken during the payment phase to the learning network model. The third electronic device 100-3 may then send the service information executed to the user 10, the user 10's order history, and the user 10's level of satisfaction to server 300. The service execution information sent from server 300 to third electronic device 100-3 may include satisfaction results, and third electronic device 100-3 may not only exclude any of the multiple recommended services based on the satisfaction results, but may also add different priority orders among the multiple recommended services.

[0144] The third electronic device 100-3 can execute payment transactions based on the order placed by user 10. The third electronic device 100-3 can send information from user 10's feature information 11 (excluding facial recognition information), user 10's order, and satisfaction level with the order to the server 300. Based on the information 14 from user 10's feature information 11 (excluding facial recognition information) sent by the first electronic device 100-1 to the server 300, the first electronic device 100-1 can subsequently provide order information from multiple users in the same age group, gender, and body type as user 10, as recommended service information for user 10.

[0145] For ease of description, the first electronic device 100-1 has been described as sending information to the second electronic device 100-2, and the second electronic device 100-2 has been described as sending information to the third electronic device 100-3, but the embodiments are not limited thereto. For example, the first electronic device 100-1 may also send the obtained feature information 11 and identification information 12 to any electronic device or combination of electronic devices located in a specific space. In addition, multiple electronic devices 100-1, 100-2, and 100-3 may also share the feature information 11, the identification information 12, and information related to the services performed between them.

[0146] Since storing and maintaining personal information (such as facial recognition information) and biometric information in the characteristic information of user 10 without user consent may be problematic in terms of security, electronic device 100 may share information with other electronic devices 200 only through P2P communication without sending it to server 300.

[0147] Additionally, if the predetermined time has elapsed, the electronic device 100 according to the embodiment may delete the user 10's feature information 11, or the user 10 may leave the specific space after obtaining the feature information 11.

[0148] For example, electronic device 100 can communicate with multiple sensors placed in the store. Based on the detection by the multiple sensors that user 10 has left the store, electronic device 100 can delete user 10's characteristic information 11.

[0149] In another example, if a predetermined time has elapsed since the user 10's characteristic information 11 was obtained, the electronic device 100 may delete the user 10's characteristic information 11 from the memory 130. In yet another example, the electronic device 100 may also delete multiple pieces of characteristic information 11 stored in the memory 130 at predetermined time intervals.

[0150] In yet another example, electronic device 100 may remove only facial recognition information from user 10's feature information 11, while retaining some biometric information without deletion. For example, electronic device 100 may obtain voice verification information (e.g., vocal cords) based on user 10's spoken voice, while retaining the voice recognition information of feature information 11 without deletion.

[0151] Electronic device 100 can perform the mapping of user identification information 12, voice recognition information, and information related to the services performed on user 10, and store and maintain this information. Electronic device 100 can obtain voice recognition information from user 10's feature information 11, and if user 10 is identified as a frequent visitor who typically visits a specific space during a specific time period based on the obtained voice recognition information being pre-stored in memory 130, electronic device 100 can also provide recommended service information to user 10 based on information related to the services performed on user 10.

[0152] Even if the facial recognition information included in the feature information 11 of user 10 is not sent to server 300, because it may be problematic for electronic device 100 to store and maintain facial recognition information itself in terms of security, such as from hackers, electronic device 100 can delete feature information 11 if a predetermined event occurs. Electronic device 100 and other electronic devices 200 placed in a specific space can delete feature information 11 based on whether a predetermined event occurs. For example, a predetermined event can be triggered by the operator of electronic device 100 manually deleting feature information or by automatically setting a predetermined interval (e.g., the first day of a month) as a trigger for the predetermined event to occur and delete feature information. However, the embodiments are not limited to this. The predetermined event can be any preset event capable of triggering processor 140 to delete feature information 11 stored in memory 130.

[0153] Figure 11 This is a diagram illustrating a schematic configuration of an electronic device according to another embodiment.

[0154] The electronic device 100 according to the embodiment can also be implemented as a self-service terminal.

[0155] Unlike robots, self-service terminals may not include a drive unit and can be installed at a fixed location within a specific space. According to an embodiment, the self-service terminal can obtain user feature information 11 via camera 110, perform mapping of identification information 12 corresponding to the feature information 11, and send it to other electronic devices 200 within the specific space. Other electronic devices 200 may include robots with drive units, electronic devices that perform specific functions based on user 10's order request, robot cashiers, etc.

[0156] According to the embodiment, the electronic device 100 can send information 14, other than facial recognition information, of the feature information 11 obtained by the camera 110 to the server 300, and provide recommended service information based on service execution information related to the sent information 14.

[0157] like Figure 11 As shown, electronic device 100 can provide a recommended menu to user 10, and receive input from user 10 about the recommended menu and send it to other electronic devices 200.

[0158] Figure 12 This is a flowchart illustrating a control method for an electronic device according to an embodiment.

[0159] The control method for an electronic device according to an embodiment may include: identifying whether user feature information is stored in the electronic device based on an image of the user obtained by a camera installed in the electronic device (S1210).

[0160] Based on the determination that the corresponding feature information is not stored in the electronic device, identification information can be generated based on the image obtained by the camera of the electronic device (S1220).

[0161] Then, the mapping of user characteristic information and identification information is executed and stored (S1230).

[0162] Then, the electronic device can send the user's characteristic information and identification information to other electronic devices (S1240).

[0163] Electronic devices and other electronic devices may be located within a specific space, and each electronic device may perform at least one of the services provided within that specific space.

[0164] The control method according to the embodiments may include: obtaining user identification information mapped in the corresponding information from the electronic device based on the corresponding information stored in the electronic device, and performing at least some services in the services based on the obtained identification information.

[0165] The control method according to the embodiments may include: based on the execution of at least some services in the service, sending information related to the executed service and feature information other than facial recognition information to the server.

[0166] The control method according to the embodiments may include: based on at least some services being executed, sending information and feature information related to the executed services to other electronic devices via peer-to-peer (P2P) communication, wherein the feature information may include the user's facial recognition information.

[0167] The control method according to the embodiments may include: sending information other than facial recognition information from the obtained feature information to a server, receiving service execution information related to the information sent from the server, and providing recommended service information based on the received service execution information.

[0168] Additionally, the control method according to the embodiments may include: sending a request to the server to update the additional service information for the selected recommended service information based on the user selecting one of a plurality of service information included in the recommended service information and the user selecting additional service information for the selected recommended service information.

[0169] The control method according to the embodiment may include: storing user identification information and feature information received from other electronic devices into the electronic device, and identifying whether the information is stored in the electronic device may include: obtaining user feature information based on the obtained image, and identifying whether the information corresponding to the obtained feature information is stored in the electronic device.

[0170] The control method according to the embodiment may include: obtaining feature information of multiple users based on the obtained image, generating identification information corresponding to each feature information of the multiple users, generating group identification information by grouping the generated multiple identification information, generating group feature information based on at least some feature information of the multiple users, sending the identification information and feature information of each of the multiple users, and sending the group identification information and group feature information to other electronic devices.

[0171] Figure 13 This is a sequence diagram illustrating the operation of an electronic device, other electronic devices, and a server according to an embodiment.

[0172] Reference Figure 13 According to the embodiment, the electronic device 100 can perform the mapping of the user 10's feature information 11 and identification information 12, and send it to other electronic devices 200 (S1310). In addition, the electronic device 100 can send the information 14 in the feature information 11, excluding facial recognition information, to the server 300 (S1320).

[0173] Other electronic devices 200 can identify user 10 based on feature information 11 and identification information 12 received from electronic device 100, and send service information to be performed on the identified user 100 (S1330).

[0174] Other electronic devices 200 may send information 14, excluding facial recognition information, from the feature information 11 and information about the services performed to the server 300 (S1340).

[0175] Here, each step performed by other electronic devices 200 can also be performed by electronic device 100.

[0176] Additionally, according to the embodiment, the server 300 can send service execution information corresponding to information 14 other than facial recognition information in the feature information 11 received from the electronic device 100 or other electronic devices 200 (S1350). Then, the electronic device 100 can provide recommended service information to the user 10 based on the service execution information received from the server 300.

[0177] According to the embodiment, the electronic device 100 or other electronic device 200 can delete the user 10's characteristic information 11 based on the occurrence of a predetermined event (S1360). For example, if the user is identified as leaving a specific space, the electronic device 100 can delete the user 10's characteristic information 11. In addition, the electronic device 100 according to the embodiment can also send a control signal to other electronic devices 200 requesting the deletion of the user's characteristic information 11.

[0178] The various embodiments described above can be implemented on a recordable medium readable by a computer or a similar device using software, hardware, or a combination of software and hardware. In some cases, the embodiments described herein can be implemented by the processor itself. According to software implementation, embodiments such as the processes and functions described herein can be implemented using separate software modules. Each of the above-described software modules can perform one or more of the functions and operations described herein.

[0179] Furthermore, computer instructions for performing processing operations in the electronic device 100 according to the various embodiments described above may be stored in a non-transitory computer-readable medium. When executed by the processor of the electronic device 100, the computer instructions stored in the non-transitory computer-readable medium can enable the electronic device 100 to execute the above embodiments.

[0180] Non-transitory computer-readable media (such as registers, caches, and memory) can refer to media that store data semi-permanently rather than for a very short period of time and can be read by a device. Specific examples of non-transitory computer-readable media include, but are not limited to, optical discs (CDs), digital universal discs (DVDs), hard disks, Blu-ray discs, universal serial buses (USB), memory cards, read-only memory (ROM), etc.

[0181] While this disclosure has been shown and described with reference to various exemplary embodiments, it is not limited to the specific embodiments described above. Those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of this disclosure.

Claims

1. An electronic device, comprising: The camera is configured to capture images; Communication interface; The memory is configured to store at least one instruction; as well as The processor is configured as follows: Based on the image, the user's feature information is obtained, wherein the feature information includes facial recognition information, gender information, age group information and / or body type information; Based on the user's feature information being obtained, it is determined whether the first information corresponding to the feature information is stored in the memory; Based on the fact that the corresponding first information is not stored in the memory, identification information corresponding to the user's feature information is generated; Perform the mapping of the user's feature information and the identification information and store them in memory; and The control communication interface sends the user's characteristic information and identification information to the second electronic device. The electronic device and the second electronic device are located in a specific space, and each of the electronic device and the second electronic device is configured to perform at least one of the services provided in the specific space. The processor is further configured to: based on the execution of at least one of the services, control the communication interface to send a second piece of information, excluding facial recognition information, from the feature information related to at least one of the services to a server located outside the specific space.

2. The electronic device as claimed in claim 1, wherein, The processor is also configured to: Based on the corresponding first information being stored in the memory, the user's identification information mapped in the corresponding first information is obtained from the memory, and The at least one service in the services is executed based on the obtained identification information.

3. The electronic device as claimed in claim 1, wherein, The processor is further configured to: based on the execution of at least one of the services, control the communication interface to send first information related to the executed service and the feature information to a second electronic device via peer-to-peer (P2P) communication; and The feature information includes the user's facial recognition information.

4. The electronic device as claimed in claim 1, wherein, The processor is also configured to: control the communication interface to receive service execution information for the second information from the server, and provide recommended service information based on the received service execution information.

5. The electronic device as claimed in claim 4, wherein, The processor is further configured to: based on a user selecting one of a plurality of service information included in the recommended service information, provide additional service information about the selected recommended service information, and Based on the user selecting one of multiple additional service options, the control communication interface sends a request to the server to update the additional service options for the recommended service options.

6. The electronic device as claimed in claim 1, wherein, The processor is also configured to store in memory the user's identification information and characteristic information received from the second electronic device via a communication interface.

7. The electronic device as claimed in claim 1, wherein, The processor is also configured to: Based on the feature information of multiple users obtained from the image, individual identifier information corresponding to each of the feature information of the multiple users is generated, and group identifier information is generated by grouping the multiple identifier information. Group feature information is generated based on at least one of the feature information of the multiple users, and The control communication interface sends the individual identification information, individual characteristic information, group identification information, and group characteristic information of each of the multiple users to the second electronic device.

8. The electronic device as claimed in claim 7, wherein, The processor is also configured to, Based on the execution of at least one of the services, the control communication interface sends first information and the group feature information related to the at least one of the executed services to the server.

9. The electronic device as claimed in claim 1, wherein, The processor is further configured to delete the user's feature information and identification information from the memory based on identifying at least one of the following after obtaining the feature information: a predetermined time has elapsed, a predetermined time period has elapsed, or the user has left the specific space.

10. The electronic device of claim 1, wherein, The memory is configured to store a learned network model trained to obtain the user's feature information based on an input image, and The processor is further configured to obtain the user's feature information by inputting the image into a learning network model.

11. The electronic device as claimed in claim 1, wherein, At least one of the electronic device or the second electronic device is a mobile robot that moves within the specific space.

12. A control method for an electronic device, the method comprising: User feature information is obtained based on images captured by the camera of an electronic device, wherein the feature information includes facial recognition information, gender information, age group information and / or body shape information; Based on the user's characteristic information being obtained, it is identified whether the first information corresponding to the characteristic information is stored in the electronic device; Based on the fact that the corresponding first information is not stored in the electronic device, identification information corresponding to the user's feature information is generated; Map and store the user's feature information and identification information; and The user's characteristic information and identification information are sent to the second electronic device. The electronic device and the second electronic device are located in a specific space, and each of the electronic device and the second electronic device performs at least one of the services provided in the specific space. The method further includes: based on the execution of at least one of the services, sending a second piece of information, excluding facial recognition information, from the feature information related to the at least one service in the service to a server located outside the specific space.

13. The control method of claim 12, further comprising: Based on the corresponding first information stored in the electronic device, the user's identification information mapped in the corresponding first information is obtained from the electronic device; as well as The at least one service in the services is executed based on the obtained identification information.

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