Unmanned store vending machine and unmanned store system

Abstract

To provide an unmanned store sales device and unmanned store sales system that suggest dietary improvements suited to customer's health conditions during shopping in the store to support customer's health promotion.SOLUTION: An unmanned store operation system 1 detects the entry of a customer into an unmanned store with an optical radar 50, authenticates the customer having entered with an authentication display 40, detects a weight change when the authenticated customer takes out a commodity from a commodity shelf 20 and registers the commodity as an unpurchased commodity, generates a radar chart based on five major nutrients of all unpurchased commodities registered at that time, furthermore, generates a radar chart of the five major nutrients based on customer's health check data provided by a health management system 80, and outputs these two types of radar charts to a customer's user terminal 90 connected via a network 200 for display output.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to, for example, an unmanned store sales device and an unmanned store sales system that support health promotion.

Background Art

[0002] In companies, regular health checkups for employees are generally conducted, and systems for collecting and analyzing the health check data and health-related data obtained thereby are widespread. The analysis information thus obtained may be used as guidance and advice to employees through industrial physicians and experts as part of trend promotion.

[0003] In efforts towards health promotion, not only for company employees but also for general individuals, businesses are also making proposals to improve dietary habits. As an example, there is a proposal to input nutritional analysis information into IC tags attached to foods in a convenience store and read that information with a sensor and display it on a display (Patent Document 1).

[0004] As another example, there is a proposal to diagnose the health condition from a dietary habit over a certain period and recommend products (Patent Document 2).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the case of Patent Document 1 mentioned above, the nutritional balance of the food itself placed in the store becomes the criterion for health promotion, and in the case of Patent Document 2 mentioned above, the results of a dietary lifestyle diagnosis become the criterion for health promotion. In both cases, health promotion suggestions are made from a perspective far removed from the customer's own health condition.

[0007] This invention was made in view of the above background, and aims to provide an unmanned store sales device and an unmanned store system that can suggest dietary improvements suitable for the customer's health condition while they are shopping in the store, in order to solve the aforementioned problems and support the promotion of the customer's health. [Means for solving the problem]

[0008] To solve the above-mentioned problems and achieve the above objectives, one embodiment of the present invention is an unmanned store vending machine for selling products containing nutrients in an unmanned store, comprising: a first registration unit that associates and registers nutrient data indicating the health status of each pre-registered customer using multiple nutrients; a second registration unit that associates and registers nutrient data consisting of the same classification as the nutrient data with each product sold in the unmanned store; a first determination unit that determines the entry and exit status of customers in the unmanned store; a recognition unit that recognizes whether a customer whose entry status has been determined by the first determination unit is a pre-registered customer; a second determination unit that determines whether a customer recognized by the recognition unit takes out or returns products sold in the unmanned store; a generation unit that generates graph data based on the nutrient data registered in the first registration unit for the recognized customer when the second determination unit determines whether a customer takes out or returns products sold in the unmanned store, and generates graph data based on the nutrient data for each product taken out by referring to the second registration unit; and a display processing unit that outputs the two types of graph data generated by the generation unit as displayable data.

[0009] Furthermore, another embodiment of the present invention is an unmanned store operation system for operating an unmanned store, comprising: an unmanned store sales device that sells products containing nutrients within the unmanned store; an authentication display that accepts customer photography and operation input; product shelves that display products and detect weight changes related to product removal and return; an optical radar that performs detection for analyzing customer movement patterns; and an authentication infrastructure that authenticates customers, wherein the unmanned store sales device includes: a first registration unit that registers nutrient data indicating health status using multiple nutrients for each pre-registered customer; a second registration unit that registers nutrient data consisting of the same classification as the nutrient data for each product sold in the unmanned store; a first determination unit that determines the entry and exit status of customers in the unmanned store by detection by the optical radar; and the authentication infrastructure The system is characterized by comprising: a recognition unit that recognizes whether a customer whose entry status has been determined by the first determination unit in accordance with the customer's photography and operation input by the authentication display is a customer that has been pre-registered; a second determination unit that determines whether a customer recognized by the recognition unit performs a take-out or return operation of products sold in the unmanned store by detecting a change in weight by the product shelves; a generation unit that generates graph data based on nutrient data registered in the first registration unit for the recognized customer when the second determination unit determines whether a take-out or return operation of products sold in the unmanned store has been performed, and generates graph data based on nutrient data for each product taken out by referring to the second registration unit; and a display processing unit that outputs the two types of graph data generated by the generation unit as displayable data. [Effects of the Invention]

[0010] According to the present invention, it is possible to suggest dietary improvements that are appropriate for the customer's health condition while they are shopping in the store, in order to support the promotion of the customer's health. [Brief explanation of the drawing]

[0011] [Figure 1] This is a diagram showing one example configuration of an unmanned store system according to one embodiment of the present invention. [Figure 2] This is a configuration diagram showing an example of the hardware configuration of an unmanned store vending machine according to one embodiment of the present invention. [Figure 3] Block diagram showing an example of an unmanned store vending machine according to one embodiment of the present invention. [Figure 4A] This is a table diagram illustrating an example of data management using one embodiment of the present invention. [Figure 4B] This is an explanatory diagram illustrating an example of the five major nutrients obtained from health checkup data using one embodiment of the present invention. [Figure 5] This is an explanatory diagram illustrating an example of a single-screen transition during product purchase using one embodiment of the present invention. [Figure 6] This flowchart illustrates an example of the operation of an unmanned store vending machine based on one embodiment of the present invention. [Figure 7] This flowchart illustrates an example of the operation of a display in an unmanned store vending machine according to one embodiment of the present invention. [Figure 8] This flowchart illustrates an example of the payment process for an unmanned store vending machine according to one embodiment of the present invention. [Figure 9] This is a flowchart illustrating an example of the operation of the health judgment mode of an unmanned store vending machine according to another embodiment of the present invention. [Figure 10] This is an explanatory diagram illustrating an example of a single-screen transition during product purchase according to another embodiment of the present invention. [Figure 11] This is a flowchart illustrating an example of the operation of a display in an unmanned store vending machine according to another embodiment of the present invention. [Modes for carrying out the invention]

[0012] Embodiment 1 of the present invention will be described below with reference to the drawings. Note that the following description and drawings are merely illustrative examples for explaining the present invention, and have been omitted or simplified as appropriate for clarity of explanation. Furthermore, the present invention can be implemented in various other forms. Also, unless otherwise specified, each component may be singular or plural.

[0013] <Embodiment 1> First, Embodiment 1 will be described using FIGS. 1 to 8. First, the overall system will be described using FIG. 1. FIG. 1 is a configuration diagram showing an example of the configuration of an unmanned store system according to Embodiment 1.

[0014] The unmanned store operation system 1 shown in FIG. 1 includes an unmanned store sales device 10A, a commodity shelf 20 provided with a weight sensor 21, a monitor 30, an authentication display 40 having a camera 41 and a touch panel 42, an optical radar 50, a settlement platform 60, an authentication platform 70, etc., and each is connected to a network 200. This network 200 is a communication network such as the Internet, a VPN (Virtual Private Network), or a wireless communication network on the premise of security.

[0015] This unmanned store operation system 1 connects each component to the network 200, but the present invention is not limited to this, and it may be configured as one system. Also, although the settlement platform 60 and the authentication platform 70 are configured to be included in the unmanned store operation system 1, since they are not components that need to be installed in the unmanned store, they may be connected via the network 200 as external devices without being included in the unmanned store operation system 1.

[0016] The unmanned store sales device 10A is a computer that efficiently configures and operates an unmanned store without a gate, and supports health promotion for users, that is, customers, who are shopping in the unmanned store. As shown in FIG. 1, this unmanned store sales device 10A is further connected to a user terminal 90 such as a smartphone and a health management system 80, which are external terminals, via the network 200, and communicates data, signals, etc. to realize unmanned store operation.

[0017] The authentication display 40 is a device installed in a predetermined entry area within the space that constitutes the unmanned store. The camera 41 of the authentication display 40 is an imaging device that not only detects the entry and exit of customers who visit the entry area of ​​the unmanned store, but also captures images of biological parts, such as faces, and acquires and generates facial image data or its feature data for the customer.

[0018] Furthermore, the touch panel 42 of the authentication display 40 accepts input of identification information (hereinafter referred to as "ID") that identifies a customer from a customer whose face image has been captured by the camera 41 described above.

[0019] Furthermore, it is assumed that customers of the unmanned store have already completed the user registration process with the authentication infrastructure 70 via the unmanned store sales device 10A (or user terminal 90).

[0020] In this case, the unmanned store vending machine 10A captures the customer's face image with the camera 41 and inputs a pre-held ID via the touch panel 42. The unmanned store vending machine 10A then uses the obtained face image data (or its feature data) and the ID to perform user registration processing on the authentication infrastructure 70. This user registration process, which combines biometric information and an ID, can be carried out using existing methods as appropriate.

[0021] Therefore, customers who have completed the user registration described above proceed with the user authentication procedure at the authentication infrastructure 70 via the unmanned store vending machine 10A by taking a facial photograph with the camera 41 and entering their ID using the touch panel 42 in front of the authentication display 40.

[0022] The information defining the entry area as described above shall include the coordinate values ​​of each vertex that constitutes the shape of the area. Furthermore, the origin coordinates of the coordinate system containing these coordinate values ​​shall, for example, indicate a point (predetermined) within the space constituting the unmanned store.

[0023] Next, the optical radar 50 is, for example, a 3D LiDAR, which is a sensor that irradiates infrared laser light in three dimensions and is capable of measuring the shape of the irradiated area, the objects present in that area, and the distance to them.

[0024] In this embodiment, the optical radar 50 is responsible for identifying the space constituting the unmanned store, the entry area and purchasing area within that space, the location of customers, and the location on the shelves where customers are reaching for items (i.e., the products located at that location). The observation values ​​from the optical radar 50 are distributed to the unmanned store sales device 10A via the network 200.

[0025] Furthermore, weight sensors 21 are installed at each product storage location on the product shelves 20, and the measurement results are transmitted to the unmanned store vending machine 10A via the network 200. The unmanned store vending machine 10A maintains and manages information regarding the weight of each product stored, i.e., displayed, in each section of the product shelves 20 in a product management database (not shown).

[0026] Furthermore, the monitor 30 is a display device installed around the aforementioned product shelves 20, i.e., in the purchasing area, and is also equipped with a speaker. The monitor 30 displays information distributed from the unmanned store sales device 10A on the screen or outputs it as audio.

[0027] The information displayed on the screen includes customer-facing advertisements as well as the status of the customer's purchasing behavior (either currently in the store, making a purchase, or in the process of payment).

[0028] Furthermore, the information outputted via voice includes the status of the customer's classified purchasing behavior, whether user authentication for that customer has been completed or succeeded, and guidance messages to adjust the customer's actions accordingly.

[0029] Furthermore, the user terminal 90 is a terminal used by customers of an unmanned store when using the service, and specifically, it is a device such as a smartphone, tablet, or personal computer. This user terminal 90 is just an example of one terminal, and although not shown in the illustration, each customer (with an ID) who visits the unmanned store will have their own terminal.

[0030] Furthermore, in this embodiment 1, the authentication infrastructure 70 is a device that authenticates the user, for example, by biometric authentication, and employs authentication infrastructure technology that balances privacy protection with high security. The authentication infrastructure 70 receives user authentication requests from the unmanned store vending machine 10A via an API (Application Programming Interface) and responds with the authentication result.

[0031] Furthermore, the payment infrastructure 60 is, for example, a credit card payment platform, which also receives payment requests from the unmanned store vending machine 10A (related to products that the customer has taken from the shelves 20 and removed from the purchasing area) via an API (Application Programming Interface), and responds with the payment result.

[0032] The health management system 80 reads and registers health check data from servers managed by businesses such as hospitals, clinics, and companies to which customers with IDs have entrusted the management of their health check data, and shares the health check data with the unmanned store vending machine 10A via the network 200.

[0033] Regarding the sharing of health checkup data, the unmanned store vending machine 10A will not be able to identify IDs or customers to view the health checkup data itself, and its use will be limited to processing for health promotion purposes between the user terminal 90 owned by each customer.

[0034] Furthermore, the unmanned store vending machine 10A may also be configured to conduct simple health checkups within the unmanned store and acquire health checkup data. In any case, since health checkup data will be treated as personal information, it is assumed that its handling will be restricted only after obtaining the individual's permission.

[0035] Next, the unmanned store vending machine of this embodiment 1 will be described using Figures 2 to 4B. Figure 2 is a configuration diagram showing an example of the hardware configuration of the unmanned store vending machine according to this embodiment 1, Figure 3 is a block diagram showing an example of the unmanned store vending machine according to this embodiment 1, Figure 4A is a table diagram showing an example of data management according to this embodiment 1, and Figure 4B is an explanatory diagram illustrating an example of the five major nutrients obtained from health checkup data according to this embodiment 1.

[0036] First, the hardware configuration of this embodiment 1 will be explained using Figure 2. As shown in Figure 2, the unmanned store vending machine 10A consists of a CPU 11 that controls the entire machine, a memory 13 that stores programs 12 such as the processing related to this embodiment 1 for the CPU 11 to execute, and also stores various data being executed, an operating device 14 equipped with a keyboard and a display device, an external storage device 15 that registers and stores various data in the form of tables, a communication IF 16 connected to the network 200 and responsible for communication with external terminals (various devices such as the user terminal 90 shown in Figure 1), and a bus 17 connected to each unit within the machine and responsible for communication of data, signals, etc. within the machine. The external storage device 15 registers and manages tables, etc., as shown in Figures 4A and 4B.

[0037] Next, the functional blocks of this embodiment 1 will be described using Figures 3, 4A, and 4B. As shown in Figure 3, the functional block configuration of the unmanned store sales device 10A consists of a communication control unit 101, a first determination unit which is an entry / exit determination unit 102, a user recognition unit 103, a second determination unit which is an action determination unit 104, a data storage unit 105, a non-purchase determination unit 109, a nutrient analysis unit 110, a graph generation unit 111, a payment processing unit 112, and a display processing unit 113.

[0038] The communication control unit 101 is the function of the communication IF 16, which is responsible for communication with each device in Figure 1 that is connected to the network 200. The store entry / exit determination unit 102, user recognition unit 103, behavior determination unit 104, non-purchase determination unit 109, graph generation unit 111, payment processing unit 112, and display processing unit 113 are functions realized by the program 12 executed by the CPU 11.

[0039] The entry / exit determination unit 102 determines whether a customer has entered or exited the store based on data acquired by the optical radar 50 when a person enters or exits the store. The user recognition unit 103 recognizes the customer's face based on the data captured by the camera 41 and also recognizes the user by acquiring an ID from the customer's operation of the touch panel 42.

[0040] The behavior determination unit 104 determines the customer's movement based on the data acquired by the optical radar 50, and determines whether the customer is taking products from the shelves or returning products to the shelves based on the customer's location and actions within the store. The non-purchase determination unit 109 determines whether the product has not been purchased based on the determination result of the behavior determination unit 104. In this case, the customer is determined to be in a state of not having purchased the product while taking it out of the store.

[0041] The data storage unit 105 is located in the external storage device 15 and includes a health score table (hereinafter referred to as "TBL") 106, a user information TBL 107 for the second registration unit, and a health checkup TBL 108 for the first registration unit.

[0042] The health score TBL106 stores values ​​for health items, such as carbohydrates, lipids, proteins, vitamins, and minerals, which are linked to the IDs of products in store inventory and on shelves, as shown in Figure 4A, for example. For product ID "P0125", for example, as shown in Figure 4A, carbohydrates are "3", lipids are "1", protein is "2", vitamins are "7", and minerals are "4". This is an example for product ID "P0125", and the absolute values ​​for health items are determined for each product.

[0043] User information TBL107 stores authentication result data, in-store location data, purchase behavior status, observation time data, unpurchased product data, and overall rating data for pre-registered customers, linked to the corresponding ID, as shown in Figure 4A, for example. Authentication result data is registered as "OK" if the user recognition unit 103 can authenticate the user upon entering the store, and as "NG" otherwise.

[0044] The location data is coordinate data indicating the customer's position within the store based on data acquired by the optical radar 50 by the behavior determination unit 104. The observation time data indicates the time measured by, for example, the timer control of the CPU 11.

[0045] Unpurchased product data is managed by increasing the number of registered data entries according to the number of unpurchased items when an item is taken from the shelf for purchase, and deleting the data for the corresponding item when it is returned to the shelf. This unpurchased product data is linked to the product ID and registers a copy of the health score (arranged as indicated by arrow AR0 in Figure 4A) and the total points assigned to each health item. The overall evaluation data calculates and registers the total points for each health item for all unpurchased products, and also adds the total points to register as bonus points.

[0046] For example, for customer ID "001," as shown in Figure 4A, the authentication result is "OK" and the purchase behavior status is "In the store," indicating that the customer is still shopping. As of 12:10 PM on September 12, 2023, there are two unpurchased items, 1 "Product ID: P0125" and 2 "Product ID: P0303," and payment has not yet been made. The overall rating data is a bonus point of "57," which is the sum of the points for all unpurchased items: "17" and "40."

[0047] As shown in Figure 4B, the health checkup TBL108 stores the health checkup results of each customer as data, which are then imported from the health management system 80 via the network 200.

[0048] Furthermore, the health checkup TBL108 evaluates the five essential nutrients needed by each customer based on the data from their health checkup results, and calculates a 10-point scale for each nutrient. As a result, each nutrient is registered in accordance with the corresponding health items.

[0049] The evaluation of the five major nutrients is given on a 10-point scale as an example. For each nutrient, a score of "5" is considered a desirable evaluation value. Scores of 4 or less indicate underconsumption, while scores of 6 or more indicate overconsumption. Of course, the 10-point scale is just an example for evaluating the five major nutrients; any scale with fewer or more points is acceptable, and the appropriate score should be the midpoint.

[0050] Carbohydrate intake is evaluated based on total intake and type. Specifically, carbohydrate metabolism is assessed by measuring blood glucose levels and HbA1c. These values ​​indicate the metabolic state of carbohydrates. For example, on a 10-point scale, a score of "1" represents under-intake, a score of "10" represents over-intake, and an appropriate carbohydrate intake is evaluated at the intermediate score of "5".

[0051] In this first embodiment, although not shown in the figures, it also includes controlling the display on the user terminal 90's screen to indicate that insufficient intake means a risk of hypoglycemia and energy deficiency, and excessive intake means a risk of obesity and diabetes.

[0052] Lipids are evaluated based on total lipid intake, particularly the balance between saturated and unsaturated fatty acids. Specifically, a lipid profile test is performed, and values ​​such as total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides are used as reference for evaluation. For example, if the intake of saturated fatty acids is excessive, the score on the evaluation scale will be close to "1," while if the intake is low, the score will be high.

[0053] It is desirable that appropriate lipid intake be evaluated at an intermediate value, i.e., a score of "5". Since a proper lipid balance is important for cardiovascular health, this information also needs to be displayed on the user terminal 90's display screen.

[0054] Protein is evaluated based on appropriate intake and protein quality. Specifically, albumin levels obtained through blood tests are measured to assess the state of protein.

[0055] Appropriate protein intake is evaluated at an intermediate value, i.e., point "5," while under-intake approaches point "1" and over-intake approaches point "10." Under-intake should indicate risks such as muscle loss, and this information also needs to be displayed on the user terminal 90's display screen.

[0056] For vitamins and minerals, blood test results are referenced to assess deficiencies and excesses. Appropriate intake of vitamins and minerals is evaluated on an average value, i.e., point "5," with under-intake approaching point "1" and excessive intake approaching point "10."

[0057] In the example shown in Figure 4B, for customer ID "100," the health score was "8" for carbohydrates, "5" for lipids, "4" for protein, "5" for vitamins, and "4" for minerals. This indicates an excessive intake of carbohydrates, while the other values ​​are appropriate.

[0058] Furthermore, the function of determining the points for each of the five major nutrients based on the health checkup results mentioned above is handled by the nutrient analysis unit 110.

[0059] Furthermore, the non-purchase determination unit 109 determines, based on the action determination unit 104's determination, that the product has been taken off the shelf and is in a non-purchase state, and adds this to the user information TBL 107. On the other hand, when the non-purchase state product is returned to the shelf, the non-purchase determination unit 109 cancels the non-purchase state and deletes the registration from the user information TBL 107.

[0060] The graph generation unit 111 generates and transmits a radar chart (graph data) while connected to the customer's user terminal 9 via the network 200, as shown in Figure 5, for example. The graph generation unit 111 calculates points for each health item corresponding to the five major nutrients: "carbohydrates," "lipids," "proteins," "vitamins," and "minerals," and generates a pentagonal radar chart (graph data) that can be displayed on the screen of the user terminal 90.

[0061] Furthermore, the basic health items are not limited to five; one or more elements such as total protein (TP), albumin (ALB), transthyretin (TTR), total lymphocyte count (TLC), transferrin (Tf), cholinesterase (CH-E), and total cholesterol (TC) may be added, in which case the radar chart will be a polygon with five or more sides.

[0062] Furthermore, since increasing the number of health items increases the number of points for customers to check, it may be advisable to allow customers to select and discard items themselves through the operation of the user terminal 90 or the touch panel 42 in the store.

[0063] The payment processing unit 112 then acquires data on unpurchased items that the unpurchased item determination unit 109 has determined to be unpurchased, and works in cooperation with the payment infrastructure 60 to execute payment for the unpurchased items. The trigger for executing this payment occurs when the store entry / exit determination unit 102 detects the customer's departure, i.e., movement outside the store. In this embodiment 1, there are no payment gates installed as equipment within the store. Therefore, when authenticated customers leave the store, they do not need to go through the payment procedure at a gate, and can leave the store with unpurchased items in hand.

[0064] The display processing unit 113 outputs the radar chart (graph data) received from the graph generation unit 111 to the communication control unit 101 for transmission to the user terminal 90, and also outputs display data to the communication control unit 101 for notifying the user terminal 90 of the settlement result from the settlement processing unit 112.

[0065] Next, the operation of the unmanned store vending machine 10A will be explained using Figures 5 to 8. Figure 5 is an explanatory diagram illustrating an example of a single-screen transition when purchasing a product according to this embodiment 1, and Figure 6 is a flowchart illustrating an example of the operation of the unmanned store vending machine according to this embodiment 1. Figure 7 is a flowchart illustrating an example of the display operation of the unmanned store vending machine according to this embodiment 1, and Figure 8 is a flowchart illustrating an example of the payment operation of the unmanned store vending machine according to this embodiment 1.

[0066] Regarding customer entry, the entry / exit determination unit 102 determines whether a customer has entered the store (step S601). If it is determined that a customer has entered the store (YES route in step S601), the user recognition unit 103 identifies the customer after user authentication from the authentication infrastructure 70 (step S602), and obtains the result that the user, i.e., the customer, has been recognized (step S603). In addition, the determination of whether the customer is already registered is made by comparing it with the user information TBL 107, which contains pre-registered customers.

[0067] If a customer is recognized (YES route in step S603), the analysis of the customer's movement within the store begins, and while the customer is moving around the store, the purchase behavior status in user information TBL107 is set to "Currently in store" (step S604).

[0068] Then, the weight sensor 21 detects when a recognized customer takes items out of or puts items into the product shelf 20, and the non-purchase determination unit 109 identifies the items to be taken out or put into and links them to the customer performing that action, thereby updating the user information TBL 107.

[0069] If a customer takes an item from the shelf 20 and the item remains there for a certain period of time (for example, 10 seconds) or longer, the item becomes a purchase candidate and the item is confirmed to be taken (YES route in step S606). The item is then added to the user information TBL108 as an unpurchased item (stored) (step S607).

[0070] Thus, it is difficult to make an instantaneous judgment as to whether or not a product has been taken out. Therefore, since customers may simply pick up a product, it is preferable to observe the situation for a certain period of time in accordance with human behavior principles before making a judgment, which can reduce the processing load.

[0071] Furthermore, the weight sensor 21 detects when items are taken out or put back into the product shelf 20. If an unpurchased item is returned to the product shelf 20, similar to the removal process described above, if the customer returns the item from the product shelf 20 and this state continues for a certain period of time (for example, 10 seconds) or longer, the return of the item is confirmed (NO route in step S606), and the data of the returned unpurchased item is deleted from the user information TBL108 (step S612). The process then returns to step S605.

[0072] Thus, observing returned products for a certain period of time can help reduce the processing load.

[0073] Step S606 described above is the trigger for determining whether to add or remove products, and at this time the list of products to be purchased changes. At this time, as part of the nutrient analysis process, points for the five major nutrients based on the customer's health status and points for the five major nutrients based on unpurchased products are calculated (step S608).

[0074] First, the health items and points for unpurchased products associated with the customer are read from the user information table TBL107. Next, the customer's health checkup data is read from the health checkup table TBL108, and as mentioned above, the nutrient analysis unit 110 calculates points for each health item.

[0075] For data being processed, data to be temporarily stored is stored in memory 13, while data to be added or updated as table data is appropriately stored in user information TBL107 and health checkup TBL108.

[0076] In this way, once all the points for unpurchased items and health check results are collected, the process moves to step S609. In step S609, the overall summary and graph display shown in Figure 7 are performed.

[0077] First, the system references user information TBL107 and calculates and stores the total score for each health item for all potential purchase candidates that have not yet been purchased (step S701). This yields the total points for "carbohydrates," "lipids," "proteins," "vitamins," and "minerals." Furthermore, the total points calculated for each health item are added together, and this total value is stored as bonus points (step S702).

[0078] Then, a 10-level radar chart (graph data) is generated based on the total points for each health item in the unpurchased product obtained in step S702. Furthermore, a 10-level radar chart (graph data) is generated based on the points for each health item in the health checkup results of the customer in question, by referring to the health checkup TBL108 (step S703). The two types of radar charts described above are generated by the graph generation unit 111.

[0079] The two types of radar charts (graph data) and bonus points generated in this way are output to the communication control unit 101 by the display processing unit 113 in the form of display data so that they can be displayed on the user terminal 90 (step S704).

[0080] The connection with the user terminal 90 can be achieved by logging in using the customer's ID, etc., via a browser or app; however, since this is a general technology, a detailed explanation will be omitted. Furthermore, since this involves handling health data linked to an individual, the premise of protecting personal information remains unchanged.

[0081] Figure 5 will be used to supplement the explanation of the radar chart and bonus point display in step S704. In Figure 5, 90A, 90B, and 90C represent the display screens of the user terminal 90, and for the purposes of this explanation, the screens will transition in the order of 90A, 90B, and 90C.

[0082] In Figure 5, "Nutrients you need" is a graph (radar chart shown as a dashed line) based on the health items points derived from the health checkup results explained earlier, and "Nutrients in the product you picked up" is a graph (radar chart shown as a dotted line) based on the health items points derived from the unpurchased product explained earlier.

[0083] As shown in the display screen 90A of Figure 5, the radar chart may initially display "0" without showing bonus points, and only the two types of radar charts may be displayed on the display screen 90A of the user terminal 90. In this case, when the display processing in step S609 is executed after the weight change in step S605, the bonus points will be displayed along with the radar chart.

[0084] Here, display screens 90B and 90C, shown by the display transitions indicated by arrows AR1 and AR2, are just examples. Display screen 90C, where each point for the health item is "5", will have a higher score.

[0085] At the stage before payment when exchanging or adding unpurchased items, it would be acceptable to display bonus points if the average points for each health item have changed to approach "5" compared to the previous state.

[0086] From now on, bonus points may be updated when it is determined that the average points are closer to "5" than the previous time. Of course, bonus points may also be displayed when it is determined that the average points are "5" or higher from the initial display. This method of displaying bonus points is merely a way to raise customers' health awareness, and various designs are possible.

[0087] Regarding bonus points, if a customer completes the payment process without changing any unpurchased items, those bonus points are finalized and stored, allowing the customer to use them for future purchases and offering other benefits.

[0088] In this way, customers can identify their nutritional deficiencies from a radar chart based on their health checkup results, allowing the system to encourage them to supplement those deficiencies by exchanging unpurchased products. In particular, by overlaying two types of radar charts to allow for comparison of health status, it becomes visually and intuitively easy to understand which nutrients should be supplemented. Furthermore, since the radar chart changes in real time when an unpurchased product is taken from shelf 20, the user is spared the need to read and understand the nutritional information of each product.

[0089] After the graph display in step S609 is complete, if the customer is still in the store, the process returns to step S605 (the NO route in step S610), and the same process is repeated. On the other hand, if the customer leaves the store, the payment process is executed (step S611).

[0090] Specifically, as shown in Figure 8, the user information TBL108 is referenced, and if there are unpurchased items for the customer (YES route in step S801), the payment process is executed in cooperation with the payment infrastructure 60, and this process is completed (step S802). When the customer leaves the store, the purchase behavior status is updated to "Payment Completed," as is the case with customer ID "002" shown in Figure 4A.

[0091] As explained above, according to this embodiment 1, by suggesting foods that promote the health of customers based on health information of customers visiting the store and nutritional information of the products displayed in the store, i.e., food products, it becomes possible to suggest improvements to the customer's diet that are appropriate for their health condition while they are shopping in the store. As a result, it is possible to support the promotion of the customer's health.

[0092] In particular, customers who visit the store can visually check in real time, in the form of a radar chart, whether a food product contributes to improving their own health simply by picking up the product they wish to buy. This does not disrupt the rhythm of their shopping behavior in the store and does not waste customers' time.

[0093] Furthermore, the process by which the radar chart, optimized for health promotion, changes in conjunction with customers taking and returning items from the shelves allows customers to enjoy shopping in a game-like manner, leading to increased purchasing intent. On the other hand, from the store's perspective, suggesting health promotion to customers allows them to recommend products that benefit customers, which also leads to increased sales.

[0094] Furthermore, since customers only need to view the radar chart on their user terminal screen while shopping, they can enjoy the convenience of the entire process from the moment they enter the store.

[0095] Furthermore, because it utilizes unmanned stores, there is no need for face-to-face interaction with store employees, and it is possible to complete the purchase of goods without interacting with any third parties.

[0096] Furthermore, since customers receive point-based incentives, it is expected that the reach will be enhanced, and it will also lead to an increase in the average customer spending.

[0097] <Embodiment 2> Next, Embodiment 2 of the present invention will be described using Figure 9. Figure 9 is a flowchart illustrating an example of the operation of the health judgment mode of the unmanned store vending machine according to Embodiment 2. Embodiment 2 has the same configuration as Embodiment 1 described above, but with some additional processing, so the additional parts will be explained with reference to the drawings.

[0098] In the previously described Embodiment 1, health checkup results were used as the customer's health status. However, the present invention is not limited to this, and may also take into account the customer's current health status upon entering the store, perform nutrient analysis, and generate a radar chart. This process should be performed after the customer has entered the store and completed user authentication.

[0099] In this second embodiment, a health assessment mode is set, and the customer can optionally activate this mode by operating it from the user terminal 90 or the touch panel 42 of the authentication display 40.

[0100] When the health assessment mode is started by the customer, the authentication display 40 outputs voice and display instructions, guiding the customer to move in front of the camera 41. The camera 41 captures an image of the customer's face, and facial image data is acquired (step S901). The facial expression is analyzed from the facial image data according to a pre-prepared analysis logic, and the customer's current health status is determined based on the analysis results (step S902). This analysis process is performed by the nutrient analysis unit 110.

[0101] Regarding the analytical logic methods described above, one approach is as follows: Facial expression analysis can be performed by using machine learning algorithms to detect facial feature points and identify specific patterns related to facial expressions. Facial expression analysis is achieved by associating these specific patterns of facial expressions with health conditions and emotions. Furthermore, health assessments can be based on the results of facial expression analysis. For example, information such as stress, fatigue, and anxiety can be extracted from facial expression analysis results and used as part of a health checkup.

[0102] Furthermore, for data collection and machine learning, it is necessary to train the learning model using large datasets and improve its performance. The training data should include images representing various health conditions and emotions.

[0103] After the current health status is determined in step S902, health items related to that determined health status are determined (step S903).

[0104] If the results of the facial analysis indicate stress, fatigue, or anxiety, then, for example, B vitamins may be one of the appropriate nutrients. B vitamins include B1 (thiamine), B2 (riboflavin), B3 (niacin), B6 ​​(pyridoxine), B9 (folic acid), and B12 (cobalamin).

[0105] In step S903, when a health item is determined, a process is executed to determine a coefficient to be multiplied by that health item and store it in the data storage unit 105 (step S904). This coefficient will be used for the graph display in step S609.

[0106] In the judgment of step S902, if the coefficient described above is set to correspond to the negative elements of the facial expression, it should be set to a value of 1 or less to reduce the points to less than 1x. On the other hand, if the positive elements of the facial expression are also taken into consideration, the points should be doubled, for example, by setting the value to 2 or less, up to a maximum of 2x.

[0107] As explained above, according to this embodiment 2, since the radar chart is generated by taking into account the customer's current health status in addition to the results of their health checkup, it is possible to support health promotion in a more realistic way than the embodiment 1 described above.

[0108] <Embodiment 3> Next, Embodiment 3 of the present invention will be described using Figures 10 and 11. Figure 10 is an explanatory diagram illustrating an example of a single-screen transition when purchasing a product according to Embodiment 3, and Figure 11 is a flowchart illustrating an example of the operation of the display of an unmanned store sales device according to Embodiment 3. Embodiment 3 has the same configuration as Embodiment 1 described above, but with some additional processing, so these additional parts will be explained using the drawings.

[0109] In the aforementioned Embodiment 1, any points exceeding the appropriate point "5" (a predetermined value) on the radar chart were simply displayed in 10 steps. However, the present invention is not limited to this, and as in Embodiment 3, the display magnification may be adjusted to fit within the display range of point "5" to generate the radar chart (graph data).

[0110] In Figure 10, 90R and 90S represent the display screens of the user terminal 90, respectively, and AR3 indicates the direction of change from Embodiment 1 to Embodiment 3 described above. For example, in Embodiment 1 described above, as shown in display screen 90R, the radar chart remains displayed even if the nutritional content of the picked-up product exceeds the range of point "5". In Embodiment 3, as shown in display screen 90R, if the nutritional content of the picked-up product exceeds the range of point "5", the display magnification is changed so that the radar chart is displayed within the range of point "5".

[0111] The difference between Embodiment 1 and Embodiment 3 lies in the overall review and graph display in step S609, and this difference will be explained using Figure 11. The content explained in Embodiment 1 above will be omitted here. Once the process up to the calculation of bonus points is completed in step S702, a determination is made in the radar chart graph display to determine whether there are any health items that exceed the standard point value of "5" as a specified size.

[0112] If even one health item is present, the one with the largest points will be used to determine the scaling ratio (YES route in step S1104). In the example in Figure 9, vitamins have the largest points among the "nutrients in the product that were picked up," so a scaling ratio is calculated and applied that brings the vitamin points down to "5." All health items are uniformly reduced in size using the ratio calculated in this way (0.75 is used as an example in Figure 10) (step S1105).

[0113] In this way, as shown in Figure 9, a display screen 90S with a resized radar chart is created and displayed on the user terminal 90 (steps S703 and S704).

[0114] As explained above, according to this embodiment 3, even if there is an excessive intake of one of the five major nutrients (health items), the display size can be reduced to present the information while considering the overall balance of the five major nutrients. This embodiment 3 also makes it possible to support health promotion.

[0115] The above-mentioned arrangement of various functional units, processing units, and databases in the unmanned store vending machine is merely one example. The arrangement of these functional units, processing units, and databases can be changed to the optimal configuration from the perspective of the performance, processing efficiency, and communication efficiency of the hardware and software of these devices.

[0116] Furthermore, the configuration of the database (schema, etc.) that stores the various types of data mentioned above can be flexibly modified from the perspective of efficient resource utilization, improved processing efficiency, improved access efficiency, and improved search efficiency.

[0117] Furthermore, in unmanned stores, customers can complete their purchases without having to wait in line at a checkout, thus protecting their privacy. Based on this premise, it is possible to manage data such as product selection during shopping, movement within the unmanned store, and purchase history in chronological order at each stage, linked to the individual customer. In addition, it is possible to support health promotion by considering the customer's overall dietary habits. In this case, one possible approach is to use a radar chart displayed during shopping to determine whether or not a food purchase was made, and then use that result to encourage customers to further improve their health during their next shopping trip through incentives such as reward points.

[0118] Furthermore, while the embodiments described above supported health promotion for customers by displaying radar charts on the user terminal 90, the present invention is not limited to this, and the radar charts may also be displayed on the monitor 30 to support health promotion for customers. In this case, since the monitor 30 is installed in the store, the behavior determination unit 104 may analyze the movement of other customers in the store and display a warning on the screen to prevent third parties from seeing the radar chart. Alternatively, measures may be taken to prevent the display from being shown when other customers are nearby. Of course, the screen of the monitor 30 may also be provided with a structure to prevent peeping.

[0119] Furthermore, as an application of the embodiments described above, when a health item that is being consumed in excess is identified from the radar chart, a message recommending products (foods) that supplement other nutrients and suppress excessive intake may be displayed on the user terminal screen. In this case, if the user exchanges for a recommended unpurchased product, an incentive may be provided by multiplying the bonus points by a pre-determined coefficient.

[0120] Furthermore, while the embodiments described above used health checkup results directly to generate radar charts of the five major nutrients, the present invention is not limited to this. The invention may also focus on under- and excessive intake of each nutrient and display the number of points lacking or surplus relative to the appropriate point value of "5" in the graph. Of course, the presentation may also focus on either under- or excessive intake.

[0121] Furthermore, in each of the embodiments described above, while the radar chart of the five major nutrients is generated by directly adopting the health checkup results, it is also possible to determine which nutrients should have their intake increased based on the points for the five major nutrients based on the health checkup results, and generate the radar chart with a target amount, such as a 20% increase. In this case, the display method should be such as changing the display color or emphasizing the text for the nutrients that should be increased.

[0122] Furthermore, although radar charts were used as examples of graphs in the embodiments described above, the present invention is not limited to these, and other graphs such as bar graphs may be used. For example, in the case of a stacked bar graph, each nutrient is represented as a percentage of the whole.

[0123] Furthermore, while the embodiments described above illustrate support for health promotion based on health checkup results, the present invention is not limited to this. It can also be applied to areas where the invention supports customers' health promotion by focusing on dietary habits tailored to specific goals such as weight loss or veganism, as part of the health goals customers aim for. In these cases as well, a radar chart will be used to present customers with their health status according to their health items.

[0124] Furthermore, each of the above-mentioned configurations, functional units, processing units, processing means, etc., may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above-mentioned configurations, functions, etc., may be implemented in software by having the processor interpret and execute programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in memory, hard disks, SSDs (Solid State Drives), or other recording devices, or in recording media such as IC cards, SD cards, or DVDs. [Explanation of symbols]

[0125] 1. Unmanned store operation system 10A Unmanned Store Vending Machine 10B Unmanned Store Vending Machine 11 CPU 12 Programs 13 memory 14 Operating Devices 15 External storage device 16 Communication IF 17 Bus 20 product shelves 21 Weight Sensor 30 monitors 40 Authentication Display 41 Camera 42 Touch Panel 50 Optical radar 60 Payment infrastructure 70 Authentication Infrastructure 90 User terminals 101 Communication Control Unit 102 Entry / exit judgment department 103 User Recognition Unit 104 Behavior Judgment Department 105 Data Storage Unit 106 Health Score TBL 107 User Information Table 108 Health Checkup TBL 109 Non-purchase determination section 110 Nutrient Analysis Department 111 Graph Generation Unit 112 Payment Processing Unit 113 Display Processing Unit

Claims

1. An unmanned store vending machine that sells products containing nutrients within an unmanned store, A health checkup table that links and registers nutrient data indicating health status using multiple nutrients for each pre-registered customer, A health score table is used to register nutrient data belonging to the same classification as the aforementioned nutrient data for each product sold at the aforementioned unmanned store, A first determination unit that determines the entry and exit status of customers in the aforementioned unmanned store based on data acquired by optical radar, The recognition unit acquires photographic data of customers whose entry status has been determined by the first determination unit, performs facial recognition, and acquires the ID entered by the customer to recognize whether the customer is one of the pre-registered customers. A second determination unit acquires the location and movement of a customer within the store that has been recognized by the recognition unit based on the data acquired by the optical radar, detects changes in the weight of products on the shelves using a weight sensor, and determines whether the customer is taking out or returning products sold in the unmanned store based on the location, movement, and weight change. When the second determination unit determines that a customer has taken out or returned an item sold at the unmanned store, the generation unit generates graph data based on the nutrient data registered in the health checkup table for the recognized customer, and also generates graph data based on the nutrient data for each item taken out by referring to the health score table. A display processing unit that outputs the two types of graph data generated by the generation unit as displayable data, An unmanned store vending machine characterized by being equipped with the following features.

2. An unmanned store vending machine according to claim 1, characterized in that the generation unit generates graph data of a radar chart.

3. An unmanned store vending machine according to claim 1, wherein product shelves displaying the products are installed inside the unmanned store, and the product shelves are provided with weight sensors that detect changes in weight when the products are taken out or returned, and the second determination unit determines whether a product is taken out or returned to be sold in the unmanned store based on the weight change detected by the sensors.

4. An unmanned store vending machine according to claim 3, characterized in that the second determination unit determines whether to take out or return a product to be sold at the unmanned store after a certain period of time has elapsed since the time the weight change was detected.

5. An unmanned store vending machine according to claim 3, further comprising a payment processing unit that processes payment for goods confirmed to have been taken out by the second determination unit when the first determination unit determines that a customer recognized by the recognition unit has left the store.

6. An unmanned store vending machine according to claim 1, further comprising a communication control unit that communicates with an external terminal via a network, wherein the communication control unit transmits data output by the display processing unit to the external terminal via the network.

7. An unmanned store vending machine according to claim 1, further comprising a nutrient analysis unit that inputs health check data for each of the pre-registered customers, performs nutrient analysis based on the health check data, and obtains nutrient data to be registered in the health check table.

8. An unmanned store vending machine according to claim 7, wherein the nutrient analysis unit inputs facial expression data of a customer whose entry status has been determined by the first determination unit, and performs nutrient analysis taking into account the input facial expression data.

9. An unmanned store vending machine according to claim 1, wherein the plurality of nutrients are represented by nutrient data consisting of numerical values, and the generation unit reduces the display size of the entire graph based on the nutrient data when the nutrient data of any of the plurality of nutrients exceeds a predetermined value.

10. An unmanned store operation system for implementing unmanned store operations, An unmanned store vending machine that sells products containing nutrients within an unmanned store, An authentication display that accepts customer photography and operation input, A product shelf that displays products and also detects weight changes related to product removal and return, Optical radar for detection to analyze customer movement patterns, An authentication infrastructure for authenticating customers, Equipped with, The aforementioned unmanned store sales device is A health checkup table that links and registers nutrient data indicating health status using multiple nutrients for each pre-registered customer, A health score table is used to register nutrient data belonging to the same classification as the aforementioned nutrient data for each product sold at the aforementioned unmanned store, A first determination unit that determines the entry and exit status of customers in the unmanned store by detection using the optical radar, A recognition unit that recognizes whether a customer whose entry status has been determined by the first determination unit in accordance with the customer's image capture and operation input by the authentication display using the authentication infrastructure is a customer that has been registered in advance, A second determination unit acquires the location and actions of a customer recognized by the recognition unit based on the data acquired by the optical radar, and determines whether the customer recognized by the recognition unit is taking out or returning goods sold in the unmanned store based on the location and actions, as well as the weight change detected by the product shelf. When the second determination unit determines that a customer has taken out or returned an item sold at the unmanned store, the generation unit generates graph data based on the nutrient data registered in the health checkup table for the recognized customer, and also generates graph data based on the nutrient data for each item taken out by referring to the health score table. A display processing unit that outputs the two types of graph data generated by the generation unit as displayable data, An unmanned store operation system characterized by having the following features.

11. An unmanned store operation system according to claim 10, wherein the unmanned store sales device further has a communication control unit that communicates with an external terminal via a network, and the communication control unit transmits the data output by the display processing unit to the external terminal via the network.

12. An unmanned store operation system according to claim 10, further comprising a monitor, wherein the unmanned store sales device transmits data output by the display processing unit to the monitor, and the monitor displays a graph based on the data.

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