Electronic device for recognizing characteristics of person and recommending product, and method for operating electronic device

Abstract

The electronic device for recognizing characteristics of a person and recommending a product may comprise a display, a memory, and a processor. The processor may: capture an image of a user by using a camera sensor on the basis of an object being detected within a specified distance from the electronic device and / or a user input being detected on the display; analyze the captured image of the user to acquire information on the face, height, shoulder frame, and hair length of the user; determine the gender and age of the user on the basis of the information on the face, height, shoulder frame, and hair length of the user; determine a list of products suitable for the user on the basis of product purchase amounts and review scores of other users having the same gender and age as the user; and display same on the display.

Description

Electronic devices and methods of operating electronic devices that recognize human characteristics and recommend products

[0001] This document relates to electronic devices including kiosks, and more specifically to electronic devices that recognize human characteristics and recommend products, and methods of operating the electronic devices.

[0002] A kiosk is a device installed in a public location for easy access by the general public. It uses a touchscreen or other device to provide users with information or display the results of input. These kiosks provide a graphical user interface (GUI) user application through a touchscreen or other screen, allowing users to directly interact with the GUI screen to access various information. For example, there are payment kiosks that allow users to order products or make payments for ordered goods via a touchscreen.

[0003] However, although kiosks can be expected to have various effects in that they can more effectively replace human resources, general kiosks are designed to fit the average height of the general public, making them difficult for people who are excessively tall, children, disabled people in wheelchairs, the elderly, and patients to use.

[0004] Meanwhile, Korean Patent Publication No. 10-2021-0000078 (Patent Document 1) discloses "A kiosk with personalized adjustment and a control method therefor." In the case of Patent Document 1, there is an advantage in that the height and angle of the kiosk display can be individually adjusted by using the surrounding environment in which the kiosk is installed and information about the user. However, since the mechanism for individually adjusting the height and angle of the display is based on environmental information and personalized information of the user received from the user terminal, there is a limitation in that it is difficult for the control unit to individually adjust the height and angle of the display in the case of a user who does not carry a user terminal or has difficulty in skillfully operating the user terminal even if he or she carries one.

[0005] The electronic device according to this document is intended to provide a human body detection user-friendly customized touch display system for a kiosk that automatically adjusts the height and angle of the touch display according to the height (eye) height, shoulder height, etc. of various users (customers) so that the user can comfortably view the touch display screen and operate it easily.

[0006] The electronic device according to this document is intended to determine the user's personal information (e.g. gender and age) and to provide a list of recommended products based on the user's personal information.

[0007] An electronic device that recognizes human characteristics and recommends products may include a display, a memory, and a processor. The processor may capture an image of a user using a camera sensor based on detection of an object within a specified distance from the electronic device and / or detection of a user input on the display, analyze the captured image of the user to obtain information about the user's face, height, shoulder structure, and hair length, determine the user's gender and age based on the information about the user's face, height, shoulder structure, and hair length, and determine a list of products suitable for the user based on the product purchase volume and review scores of other users of the same gender and age as the user and display the list on the display.

[0008] An electronic device according to this document can provide a human body detection user-friendly customized touch display system for a kiosk that automatically adjusts the height and angle of the touch display according to the height (eye height) and shoulder height of various users (customers) so that the user can comfortably view the touch display screen and operate it easily.

[0009] An electronic device according to this document may determine a user's personal information (e.g., gender and age) and provide a list of recommended products based on the user's personal information.

[0010] FIG. 1 is a block diagram illustrating the configuration of an electronic device that recognizes human characteristics and recommends products according to one embodiment.

[0011] FIG. 2A illustrates an appearance of an electronic device according to one embodiment. FIG. 2B illustrates a front view of an electronic device according to one embodiment. FIG. 2C illustrates a side view of an electronic device according to one embodiment. FIG. 2D illustrates an isometric view of an electronic device according to one embodiment.

[0012] FIG. 3A illustrates an embodiment of recognizing a user's face on a camera sensor of an electronic device according to one embodiment.

[0013] FIG. 3b illustrates an embodiment in which an electronic device recognizes a user's face and recommends a product based on age and gender according to one embodiment.

[0014] FIG. 4 illustrates a process of recognizing a user's face and displaying user information on a camera sensor of an electronic device according to one embodiment.

[0015] FIG. 5 is a flowchart illustrating a method by which an electronic device recognizes human characteristics and recommends products according to one embodiment.

[0016] The examples may be implemented as a kiosk-type product. However, this is merely an example and the products to which the operations described in this document apply are not limited to this type.

[0017] Artificial Intelligence (AI) systems are computer systems that achieve human-level intelligence. Unlike existing rule-based smart systems, they are machines that learn and make decisions on their own. As AI systems become more widely used, their recognition rates improve and their ability to more accurately understand customer preferences increases. As a result, existing rule-based smart systems are being replaced by AI systems based on deep learning.

[0018] AI technology consists of machine learning and its underlying technologies. Machine learning is an algorithmic technology that autonomously classifies and learns the characteristics of input data. Elementary technologies utilize machine learning algorithms, such as deep learning, to mimic the cognitive and judgmental functions of the human brain. These technologies encompass areas such as linguistic understanding, visual understanding, inference / prediction, knowledge representation, and motion control.

[0019] The various fields in which AI technology is applied are as follows. Linguistic understanding refers to the technology that recognizes, applies, and processes human language / text, including natural language processing, machine translation, dialogue systems, question-answering, and speech recognition / synthesis. Visual understanding refers to the technology that recognizes and processes objects similar to human vision, including object recognition, object tracking, image search, person recognition, scene understanding, spatial understanding, and image enhancement. Inference prediction refers to the technology that logically infers and predicts information by judging it, including knowledge / probability-based inference, optimization prediction, preference-based planning, and recommendations. Knowledge representation refers to the technology that automatically processes human experience information into knowledge data, including knowledge construction (data creation / classification) and knowledge management (data utilization). Motion control refers to the technology that controls the movement of autonomous vehicles and robots, including movement control (navigation, collision, driving), and manipulation control (behavior control).

[0020] Machine learning can refer to the process of training a neural network model using data processing experience. Through machine learning, computer software can improve its data processing capabilities. Neural network models are built by modeling correlations between data, and these correlations can be expressed by multiple parameters. Neural network models extract and analyze features from given data to derive correlations between data. This process of iteratively optimizing the parameters of a neural network model can be defined as machine learning. For example, a neural network model can learn the mapping (correlation) between inputs and outputs for data presented as input-output pairs. Alternatively, a neural network model can learn the relationships between inputs and outputs by deriving regularities between the given data, even when only input data is provided.

[0021] An artificial intelligence learning model or neural network model can be designed to implement the structure of the human brain on a computer, and can include multiple network nodes that simulate the neurons of a human neural network and have weights. The multiple network nodes can have connections with each other by simulating the synaptic activity of neurons that exchange signals through synapses. In the artificial intelligence learning model, the multiple network nodes can be located at layers of different depths and exchange data according to convolutional connections. The artificial intelligence learning model can be, for example, an artificial neural network model, a convolutional neural network (CNN), etc. In one embodiment, the artificial intelligence learning model can be machine-learned according to a method such as supervised learning, unsupervised learning, or reinforcement learning. Machine learning algorithms that can be used to perform machine learning include decision trees, Bayesian networks, support vector machines, artificial neural networks, Ada-boost, perceptrons, genetic programming, and clustering.

[0022] CNNs are a type of multilayer perceptron (MLP) designed to use minimal preprocessing. They consist of one or more convolutional layers stacked on top of regular artificial neural network layers, with additional weight and pooling layers. This structure allows CNNs to fully utilize two-dimensional input data. Compared to other deep learning architectures, CNNs demonstrate excellent performance in both image and audio domains. CNNs can also be trained using standard backpropagation. Compared to other feedforward neural network techniques, CNNs are easier to train and have fewer parameters.

[0023] Convolutional networks are neural networks that contain sets of nodes with bounded parameters. The increasing availability of training data and computational power, combined with advances in algorithms such as piecewise linear units and dropout training, have led to significant improvements in many computer vision tasks. With the massive datasets available today for many tasks, overfitting is no longer a concern, and increasing network size improves test accuracy.

[0024]

[0025] FIG. 1 is a block diagram illustrating the configuration of an electronic device that recognizes human characteristics and recommends products according to one embodiment.

[0026] According to FIG. 1, the electronic device (100) may include a display (110), a sensor (120), a processor (130), and a memory (140), and some of the illustrated components may be omitted or replaced. The electronic device (100) may include, for example, a kiosk.

[0027] According to one embodiment, the size and shape of the display (110) may vary depending on the design and intended use of the electronic device (100). The display (110) may have a built-in touch function to receive commands in response to a user touching or swiping the screen with a finger. The resolution of the display (110) may determine the clarity and quality of the content displayed on the screen. A high-resolution display may display text, images, and video content more clearly, thereby improving the user experience. The display (110) may further include a durable protective layer (e.g., tempered glass) for protection from everyday use and the external environment. The protective layer may prevent water droplets, fingerprints, dust, etc. from adhering to the screen, and may extend the life of the display (110).

[0028] According to one embodiment, the sensor (120) may include a fingerprint sensor and a camera sensor. The fingerprint sensor may be used to recognize a user's fingerprint and verify whether the user is registered in a database (DB). The camera sensor may be used to recognize the user's face, height, shoulder structure, hair length, etc. Using the camera sensor, the electronic device (100) may obtain information related to the user's appearance (e.g., face, height, shoulder structure, hair length) and, based on this, determine personal information about the user (e.g., age, gender, height).

[0029] According to one embodiment, the processor (130) is a configuration capable of performing calculations or data processing related to control and / or communication of each component of the electronic device (100), and may be composed of one or more processors. The memory (140) may store information related to the above-described method or store a program in which the above-described method is implemented. The memory (140) may be a volatile memory or a non-volatile memory. The memory (140) may store various file data, and the stored file data may be updated according to the operation of the processor (130).

[0030] According to one embodiment, the processor (130) can execute a program and control the electronic device (100). The code of the program executed by the processor (130) can be stored in the memory (130). The operations of the processor (130) can be performed by loading instructions stored in the memory (140). The electronic device (100) can be connected to an external device (e.g., a personal computer or a network) through an input / output device (not shown in the drawing) and exchange data.

[0031] According to one embodiment, there will be no limitation to the computational and data processing functions that the processor (130) can implement on the electronic device (100), but below, a method for determining personal information (e.g., height, gender, age, etc.) of a user using the electronic device (100) and recommending a product will be described.

[0032]

[0033] FIG. 2A illustrates an appearance of an electronic device according to one embodiment. FIG. 2B illustrates a front view of an electronic device according to one embodiment. FIG. 2C illustrates a side view of an electronic device according to one embodiment. FIG. 2D illustrates an isometric view of an electronic device according to one embodiment.

[0034] An electronic device (e.g., an electronic device (100) of FIG. 1) may include, for example, a kiosk. The electronic device (100) may further include a display (210), a camera sensor (220), and a driving unit (230) for adjusting the position of the display (210).

[0035] The electronic device (100) can determine the type and quantity of menu items selected by the user based on user input on the display (210) and transmit the information to the server. The server manager can use the received information to check the user's order details and provide services tailored to the order details.

[0036] The electronic device (100) can capture a user's image using a camera sensor (220) and determine the user's personal information (e.g., gender, age, height). The electronic device (100) can continuously operate the camera sensor (220) to detect the user's movements, or can operate the camera sensor (220) based on the detection of user input on the display (210).

[0037] The electronic device (100) can adjust the position of the display (210) up and down using the driving unit (230) based on user information (e.g., height).

[0038] According to one embodiment, a processor (e.g., processor (130) of FIG. 1) may capture an image of a user using a camera sensor (220) based on a user input detected on a display (210), analyze the captured image of the user to obtain information about the user's face, height, shoulder structure, and hair length, determine the user's gender and age based on the information about the user's face, height, shoulder structure, and hair length, and determine a list of products suitable for the user based on the product purchase volume and review scores of other users of the same gender and age as the user, and display the list on the display (210).

[0039] According to one embodiment, the processor (130) may recognize a user's face or a part of a face using a camera sensor (220), determine the user's height based on the recognized body part of the user, and adjust the position of the display (210) of the electronic device (100) up and down in response to the determined user's height.

[0040] The electronic device (100) may further include a distance measurement sensor in addition to the camera sensor (220). The electronic device (100) may use the camera sensor (220) to initiate photographing or recognition of a user who approaches within a specified range from the electronic device using the distance measurement sensor, which may be an ultrasonic sensor or a depth sensor. Alternatively, the electronic device (100) may use the camera sensor (220) to initiate photographing or recognition based on the detection of a user input on the display (210).

[0041] The electronic device (100) can adjust the length of the driving unit (230) up and down based on the user's body part recognized through the camera sensor (220). The electronic device (100) can adjust the position of the display (210) of the electronic device (100) up and down using the driving unit (230).

[0042]

[0043] FIG. 3A illustrates an embodiment of recognizing a user's face on a camera sensor of an electronic device according to one embodiment.

[0044] In FIG. 3A, an electronic device (e.g., the electronic device (100) of FIG. 1) may include a display (310) and a camera sensor (320). The electronic device (100) may recognize a user's face and body parts using the camera sensor (320).

[0045] Figure 330 of FIG. 3a illustrates a situation in which a user's face is recognized and personal information (e.g., gender, age, height) of the user is inferred based on the recognized information.

[0046] According to one embodiment, a processor (e.g., processor (130) of FIG. 1) determines the age of a user based on information about the user's face, height, shoulder bone structure, and hair length, receives product purchase records of other users of the same age as the determined age of the user, and displays products in the order of the cumulative purchase volume of other users over a specified period (e.g., 3 months) as recommended products, and displays products whose current sales volume has increased by a specified level (e.g., 50%) compared to sales volume before a specified period (e.g., 3 months) as rapidly rising products.

[0047] The processor (130) may receive product purchase records of other users of the same age group from an external server. The external server may include information about kiosks of other businesses that sell the same type of products as the electronic device (100). For example, if the electronic device (100) is used in a cafe for the food and beverage industry, the external server may obtain information about user orders from multiple kiosks used in other cafes.

[0048] If the processor (130) recognizes the user as a man in his 30s using the camera sensor (320), it can receive cafe order records corresponding to the man in his 30s from an external server. Based on the received information, the electronic device (100) can determine a list of products that can be recommended to the man in his 30s and display them on the display (310). "Cafe," "30s," and "man" are just examples, and the industry and the user's gender and age may vary depending on the settings. If the external server provides kiosks belonging to the same franchise business, it can determine to provide information about the same industry.

[0049] The processor (130) can display recommended products in order of the cumulative purchase volume of other users over a specified period (e.g., three months). The other users may include not only users recorded on the electronic device (100), but also users from other kiosks with matching industry, gender, and age. The specified period (e.g., three months) is merely an example and may vary depending on the settings.

[0050] The processor (130) may display recommended products in the order of the cumulative purchase volume of other users over a specified period (e.g., 3 months) based on the present, and may display products in which the cumulative purchase volume over a specified period of time based on the present has increased by more than a first level (e.g., 50%) compared to the cumulative purchase volume over a specific period in the past as a product that is rapidly rising in popularity.

[0051] The cumulative purchase volume of other users over a specified period of time, starting from the present, can refer to, for example, the cumulative purchase volume over the past three months or the past one month. The cumulative purchase volume over a specific period of time can refer to, for example, the cumulative purchase volume from three months ago to six months ago, or the cumulative purchase volume from three months ago to four months ago. The current sales volume, which serves as a reference, and the previous sales volume, which serves as a comparison, may vary depending on the period setting. The specified period (e.g., three months) and level (e.g., 50%) are examples and may vary depending on the settings.

[0052] FIG. 3b illustrates an embodiment in which an electronic device recognizes a user's face and recommends a product based on age and gender according to one embodiment.

[0053] In FIG. 3b, the processor (130) can recognize and display the user's age and gender as shown in FIG. 340. The processor (130) can recommend products with the highest sales in the corresponding age and gender group based on the user's age and gender as shown in FIG. 350.

[0054]

[0055] FIG. 4 illustrates a process of recognizing a user's face and displaying user information on a camera sensor of an electronic device according to one embodiment.

[0056] In FIG. 4, an electronic device (e.g., the electronic device (100) of FIG. 1) can recognize a user's face and body parts (e.g., upper body, shoulder) using a camera sensor (e.g., the camera sensor (220) of FIG. 2). Alternatively, the electronic device (100) can share information among multiple kiosks to determine information about the number of people located within a designated zone and the floating population.

[0057] In FIG. 4, the electronic device (100) can recognize the user's face (410) and body parts using the camera sensor (220). The camera may include, for example, a vision camera or a thermal infrared camera. The electronic device (100) can detect other users passing by behind the user entering an order. The electronic device (100) can distinguish whether the user is currently entering an order on the electronic device (100) or a passerby based on the distance from the user. Alternatively, the electronic device (100) can distinguish whether the user is currently entering an order on the electronic device (100) or a passerby based on the user's body size. The processor (130) can detect multiple users using the vision camera or the thermal infrared camera, measure the distance between the electronic device and the multiple users based on the sensor, and determine whether the user is entering an order on the electronic device or a passerby based on the measured distance.

[0058] Alternatively, the processor (130) may determine that the user is a passerby rather than a user entering an order based on the recognized user's body size being below a specified level. When a user places an order at a kiosk, the user may approach within a specified distance for user input. If the user approaches within a specified distance from the kiosk, the user's body size may be measured to be at least above a certain level. On the other hand, for a person passing by from a distance, the camera on the kiosk may measure the user's body size, but the measurement may be relatively small due to the distance. If the user's body size is measured to be below a specified level, the processor (130) may determine that the user is a passerby without measuring the distance from the user.

[0059] Figure 420 of FIG. 4 illustrates a situation in which an electronic device (100) infers and displays a user's personal information using a camera sensor (220). For example, the electronic device (100) may determine that the user's gender is female and that the user's age is 27 years old, and display the information on a display (e.g., the display (110) of FIG. 1).

[0060] The electronic device (100) can receive photographic information measured by camera sensors included in a plurality of kiosks using a communication circuit. The camera sensors included in the plurality of kiosks can be used to determine the number of people located in a specific area (zone) or the number of floating population passing by.

[0061] In one embodiment, a processor (e.g., processor (130) of FIG. 1) may receive user data related to product sales from other kiosks that match industry information and franchise information, and, based on the received user data, determine the most-selling products by user gender and age group. The processor (130) may assign a designated score (e.g., 3 points) to the most-selling product at a kiosk, assign a relatively lower score (e.g., 1 point) to the second-most-selling product at a kiosk, assign scores to the most-selling and second-most-selling products for each kiosk, and calculate a comprehensive score. The designated score (e.g., 3 points) is merely an example and may vary depending on the configuration. However, the most-selling product may be assigned a relatively higher score than the second-most-selling product.

[0062] The processor (130) determines that the product with the highest overall score is the best recommended product in the region, and can send advertisements and discount coupons for the best recommended product in the region to all user terminals recorded in the database of kiosks located in the region. Here, the region may refer to points within a specified distance (e.g., a 5 km radius) from the location of the electronic device (100). The specified distance (e.g., a 5 km radius) is merely an example and may vary depending on the settings.

[0063] The processor (130) can classify kiosks within a specified distance (e.g., a radius of 5 km) based on the location of the electronic device (100) as kiosks within the same area.

[0064]

[0065] FIG. 5 is a flowchart illustrating a method by which an electronic device recognizes human characteristics and recommends products according to one embodiment.

[0066] In operation 510, a processor (e.g., processor (130) of FIG. 1) may capture an image of a user. The processor (130) may capture an image of the user using a camera sensor (e.g., camera sensor (220) of FIG. 2) based on an object being detected within a specified distance from an electronic device (e.g., electronic device (100) of FIG. 1) and / or a user input being detected on a display (e.g., display (210) of FIG. 2).

[0067] In operation 520, the processor (130) can analyze the captured image of the user. The processor (130) can analyze the captured image of the user to obtain information about the user's face, height, shoulder structure, and hair length.

[0068] In operation 530, the processor (130) can determine the user's gender and age group. The processor (130) can determine the user's gender and age group based on information about the user's face, height, shoulder bone structure, and hair length.

[0069] In operation 540, the processor (130) may display a list of products suitable for the user. The processor (130) may determine a list of products suitable for the user based on the product purchase volume and review scores of other users of the same gender and age as the user, and display the list of products suitable for the user on the display (210).

[0070] According to one embodiment, the processor (130) may store the types and quantities of products purchased by the user in a memory (e.g., memory (140) of FIG. 1) based on a user input on the display (210), and may determine that a product is popular among the user's gender and age group based on the sales volume exceeding a specified number for a specific period (e.g., one month) based on the current status among the products purchased by the user.

[0071] The processor (130) may determine that a product corresponding to a keyword in which the total number of posts exceeds the first level (e.g., 10,000) when searched on a specific portal site and the number of posts uploaded in the past week exceeds 50% of the first level (e.g., 5,000) is a currently trending product.

[0072] The processor (130) may transmit advertisements and discount coupons for corresponding products to terminals of other users matching the user's gender and age group based on the product being determined to be popular in the user's gender and age group, and may provide a guide recommending increasing search advertisements on a specific portal site based on the product being determined to be currently popular. The electronic device (100) may receive contact information of the user's terminal for the purpose of accumulating points, issuing coupons, etc. upon ordering, and may transmit advertisements and discount coupons for the first product based on such contact information.

[0073]

[0074] According to one embodiment, the processor (130) stores the type and quantity of products purchased by the user in the memory (140) based on the user input on the display (210), and compares the sales volume before a specified period (e.g., one month) to the current sales volume, and determines that the first product, for which the current sales volume has increased by more than a specified level (e.g., 100%), is a popular product among the user's gender and age group. The specified period and current sales volume levels are merely examples and may vary depending on the settings.

[0075] The processor (130) determines that the first product is a popular product across all genders and age groups based on an increase in the current sales volume of the first product exceeding a specified level (e.g., 100%) compared to the sales volume of a specified period (e.g., 1 month) prior to the first product even among users of different genders and age groups, and may transmit advertisements and discount coupons for the first product to all user terminals recorded in the database of the electronic device (100). The electronic device (100) may receive contact information of the user terminal for the purpose of accumulating points, issuing coupons, etc., when ordering, and may transmit advertisements and discount coupons for the first product based on such contact information. Here, the specified period (e.g., 1 month) and the specified level (e.g., 100%) are merely examples and may vary depending on the settings. For example, the processor (130) may determine that the first product is a popular product across all genders and age groups based on the settings even in a situation where the current sales volume of the first product has increased by 50% compared to the sales volume of 3 months ago.

[0076]

[0077] According to one embodiment, the processor (130) may determine the height of the user based on at least one body part of the user recognized using the camera sensor (220), and determine that the user is a woman based on the determined height of the user being less than a specified level (e.g., 165 cm) and the width of the user's shoulder bone being less than a specified level (e.g., 45 cm). Here, the height and width of the user's shoulder bone are merely examples and are not limited thereto, and may vary depending on the settings.

[0078] The processor (130) may determine that the user is male based on the user's height exceeding (or exceeding) a specified level (e.g., 180 cm) and the width of the user's shoulder bone exceeding a specified level (e.g., 70 cm).

[0079] The processor (130) may increase the probability that the user is female by 10% based on the user's head length exceeding a specified level (e.g., 10 cm below the ear), and may decrease the probability that the user is female by 10% based on the user's head length being below a specified level (e.g., 3 cm below the ear).

[0080] The processor (130) may decrease the probability that the user is female by 10% based on the user's face size exceeding a specified level (e.g., 25 cm from top to bottom), and may increase the probability that the user is female by 10% based on the user's face size being below a specified level (e.g., 20 cm from top to bottom).

[0081] The processor (130) may increase the probability that the user is female by 10% based on the user's height being below a specified level (e.g., 160 cm), and the processor (130) may decrease the probability that the user is female by 10% based on the user's height being above a specified level (e.g., 175 cm).

[0082] The processor (130) may decrease the probability that the user is female by 10% based on the width of the user's shoulder bone exceeding a specified level (e.g., 60 cm), and may increase the probability that the user is female by 10% based on the width of the user's shoulder bone being below a specified level (e.g., 50 cm). The user's height and shoulder bone width are merely examples and are not limited thereto, and may vary depending on the settings.

[0083] The processor (130) may determine that the user is female based on a total probability that the user is female being greater than or equal to 50%, and may determine that the user is male based on a total probability that the user is female being less than 50%.

[0084]

[0085] According to one embodiment, the processor (130) may receive first information about the product purchase volume of other users of the same gender and age as the user for a specified period (e.g., 3 months) from other kiosks connected to an external server. The processor (130) may receive information about the product purchase volume from other kiosks located within the first area in real time, analyze the information to receive second information organized about the commercial area and product sales volume, determine a list of recommended products corresponding to the user's gender and age group based on the first information and the second information, and display the list of recommended products on the display (210). The specified period (e.g., 3 months) is merely an example and may vary depending on the settings.

[0086] The first information may refer to information selected based on information received from all other kiosks connected to an external server, without any area restrictions. Because the first information receives information from all kiosks, processing may take a relatively long time, and analysis may be performed over a specified period. The first information has a relatively high accuracy due to its large sample size (e.g., data transmitted from kiosks). However, analysis may take time, which may reduce its up-to-dateness.

[0087] Second information may refer to information selected based on information received from other kiosks located within the first area. Because the number of targeted kiosks is small, second information can be analyzed in real time. Second information analyzes sales information from surrounding kiosks based on the location of the electronic device (100), thus possessing a relatively high relevance to the local commercial district and the advantage of high up-to-dateness.

[0088]

[0089] According to one embodiment, the processor (130) may provide a guide to the number of personnel required for starting a business based on information about the time period when the most orders come in and information about the order quantity, and may provide a guide to recommended business types and recommended products for starting a business based on information about the types and industries of products that sell the most within the first zone.

[0090]

[0091] In one embodiment, the processor (130) may determine a zone within a 5 km radius of another kiosk as a second zone if the number of people passing by the other kiosk during a specified period of time (e.g., 10 minutes) exceeds a specified level (e.g., 100 people). The 5 km radius is merely an example, and the scope of the second zone may vary depending on the settings.

[0092] The processor (130) can obtain information on the gender and age of people placing orders within the second zone, information on the types and industries of products that are most sold within the second zone, information on the time zone with the most orders, and information on the order quantity by using other kiosks located within the second zone.

[0093] The processor (130) can select information for the first and second areas based on information regarding the gender and age groups of target customers when starting a business. The processor (130) can compare the best-selling products and industries within the first area with the best-selling products and industries within the second area, and based on the comparison results, provide guidance on recommended industries and products for starting a business.

[0094]

[0095] In one embodiment, the processor (130) may select and recommend a business to be started based on the fact that there is no kiosk in the first area corresponding to the business in the second area that is the most sold business in the second area.

[0096]

[0097] In one embodiment, the processor (130) may select a business type and recommend starting a business based on the fact that the number of kiosks corresponding to the business type with the highest sales within the second region within the first region is less than a specified number (e.g., 3). The specified number (e.g., 3) is merely an example, and the number of kiosks for recommending starting a business may vary depending on the setting.

[0098] The processor (130) may not recommend starting a business in the industry that is the most sold in the second area based on the number of kiosks corresponding to the industry that is the most sold in the second area exceeding a specified number (e.g., 10) in the first area.

Claims

1. In an electronic device that recognizes human characteristics and recommends products, display; memory; and Contains a processor, The above processor Taking a picture of the user using a camera sensor based on an object being detected within a specified distance from the electronic device and / or a user input being detected on the display; By analyzing the captured image of the user, information about the user's face, height, shoulder structure, and hair length is obtained. Determine the user's gender and age based on information about the user's face, height, shoulder bone structure, and hair length. An electronic device that determines a list of products suitable for the user based on the product purchase volume and review scores of other users of the same gender and age as the user and displays the list on the display.

2. In paragraph 1, The above processor Recognize the user's face or any part of the face using the above camera sensor, Determine the user's height based on the recognized user's body parts, An electronic device that adjusts the position of the display of the electronic device up and down in response to a determined user's key.

3. In paragraph 1, The above processor Determine the user's age based on information about the user's face, height, shoulder bone structure, and hair length. Receive product purchase records of other users with the same age as the determined user, Recommended products are displayed in order of the cumulative purchase volume of other users over a specified period (e.g. 3 months) based on the present. An electronic device that displays products as trending products if the cumulative purchase volume of other users has increased by more than the first level (e.g. 50%) over a specified period of time compared to the cumulative purchase volume of a specific period in the past.

4. In paragraph 1, The above processor Store the type and quantity of products purchased by the user in the memory based on the user input on the display, Among the products purchased by the user, products are determined to be popular in the user's gender and age group based on the number of sales exceeding a specified number over a specific period of time (e.g., 1 month). An electronic device that determines that a product corresponding to a keyword, when searched on a specific portal site, has a total number of posts exceeding the first level (e.g., 10,000) and a number of posts uploaded in the past week exceeding 50% of the first level (e.g., 5,000), is a currently trending product.

5. In paragraph 4, The above processor An electronic device that displays a guide recommending increasing search advertising on a particular portal site based on what it determines to be a currently popular product.

6. In paragraph 1, The above processor Store the type and quantity of products purchased by the user in the memory based on the user input on the display, For the first product, if the current sales volume has increased by more than a specified level (e.g. 100%) compared to the sales volume before a specified period (e.g. 1 month), it is determined that the product is popular among the users' gender and age group. It is determined that the product is popular across all genders and age groups based on the fact that the current sales of the first product have increased by a specified level (e.g., 100%) compared to the sales before a specified period (e.g., 1 month) among users of a different gender and age group than the user's gender and age group. An electronic device that transmits advertisements and discount coupons for the first product to all user terminals recorded in the database of the electronic device.

7. In paragraph 1, The above processor Based on the industry information and franchise information registered in the above electronic device, other kiosks with matching industry information and franchise information are determined, Requesting user data entered on other determined kiosks or receiving user data entered on other kiosks from a database on an external server; All received user data is aggregated to determine the ranking of the best-selling products within the franchise. An electronic device that displays a list of popular franchise products based on a determined ranking.

8. In paragraph 7, The above processor Receive user data related to product sales from other kiosks that match industry information and franchise information; Based on the user data received, determine the most popular products by user gender and age group. Assign a designated score (e.g. 3 points) to the product that sells the most at any one kiosk. The second most sold product at any one kiosk is given a relatively low score (e.g., 1 point). We assign scores to the top and second most sold products per kiosk and calculate a composite score. The product with the highest overall score is determined to be the best recommended product in the region. An electronic device that transmits advertisements and discount coupons for the best recommended products in the region to all user terminals recorded in the database of kiosks located in the region.

9. In paragraph 8, The above area It refers to points within a specified distance (e.g., a radius of 5 km) based on the location of the above electronic device. The above processor An electronic device that classifies kiosks within a specified distance (e.g., a 5km radius) from the location of the electronic device as kiosks within the same area.

10. In paragraph 1, The above electronic device further comprises a vision camera or a thermal infrared camera, The above processor Detecting multiple users using the above vision camera or thermal infrared camera, Measure the distance between the electronic device and multiple users based on the sensor, and determine whether the user is entering an order on the electronic device or a passerby based on the measured distance, or An electronic device that determines that a user is a passerby rather than a user entering an order based on the perceived user's body size being below a specified level.

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