Order support device, order support method, and order support program

Abstract

This invention provides an ordering support device that can easily predict the appropriate quantity of ingredients to be ordered. [Solution] The ordering support device 10 comprises an acquisition unit 11, an estimation unit 12, a prediction unit 13, and an output unit 14. The acquisition unit 11 acquires reservation information regarding the number of customers visiting the restaurant. The estimation unit 12 estimates the amount of food to be served based on the reservation information. The prediction unit 13 predicts the amount of ingredients to be ordered based on the estimated amount of food to be served. The output unit 14 outputs the predicted amount of ingredients to be ordered.

Description

Technical Field

[0007]

[0001] This disclosure relates to an order support device and the like.

Background Art

[0002] In a restaurant, for example, the order quantity of food ingredients is determined by estimating the number of customers who come to the store. On the other hand, the number of customers who come to the store can vary due to various factors. Also, for example, if the tendency of the dishes ordered by customers changes, the food ingredients that need to be ordered can also change. Therefore, the person in charge of ordering food ingredients in a restaurant needs to appropriately determine the types and quantities of food ingredients to be ordered according to the number of customers and the customer base. In addition, for the work of determining the types and quantities of such food ingredients to be ordered, an information processing system that supports the determination of the types and quantities of food ingredients may be used.

[0003] The food ingredient order support system of Patent Document 1 identifies the food ingredients that are in short supply from the number of reservations for each menu. Then, the food ingredient order support system of Patent Document 1 calculates the number of food ingredients to be ordered based on the food ingredients that are in short supply.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the technology described in Patent Document 1, it may be difficult to predict the appropriate quantity of food ingredients to be ordered.

[0006] An object of this disclosure is to provide an order support device and the like that can easily predict the appropriate quantity of food ingredients to be ordered in order to solve the above problems.

Means for Solving the Problems

[0007] To solve the above problems, the ordering support device of this disclosure comprises an acquisition means for acquiring reservation information regarding the number of customers visiting a restaurant, an estimation means for estimating the amount of food to be served based on the reservation information, a prediction means for predicting the amount of ingredients to be ordered based on the estimated amount of food to be served, and an output means for outputting the prediction result of the amount of ingredients to be ordered.

[0008] The ordering support method disclosed herein acquires reservation information regarding the number of customers visiting a restaurant, estimates the amount of food to be served based on the reservation information, predicts the amount of ingredients to be ordered based on the estimated amount of food to be served, and outputs the predicted amount of ingredients to be ordered.

[0009] The ordering support program disclosed herein causes a computer to perform the following processes: acquiring reservation information regarding the number of customers visiting a restaurant; estimating the amount of food to be served based on the reservation information; predicting the amount of ingredients to be ordered based on the estimated amount of food to be served; and outputting the predicted amount of ingredients to be ordered. [Effects of the Invention]

[0010] According to this disclosure, it is possible to easily predict the appropriate amount of ingredients to order. [Brief explanation of the drawing]

[0011] [Figure 1] This figure shows an example of the configuration of the order support system described in this disclosure. [Figure 2] This figure shows an example of the configuration of the ordering support device in this disclosure. [Figure 3] This figure schematically shows an example of a display screen for the estimated amount of food in this disclosure. [Figure 4] This figure schematically shows an example of a display screen for the estimated amount of food in this disclosure. [Figure 5] This figure schematically shows an example of a display screen for the predicted order quantity of food ingredients in this disclosure. [Figure 6] This figure schematically shows an example of a display screen for the predicted order quantity of food ingredients in this disclosure. [Figure 7] This figure schematically shows an example of a display screen for the predicted order quantity of food ingredients in this disclosure. [Figure 8] This figure schematically shows an example of a display screen for the predicted order quantity of food ingredients in this disclosure. [Figure 9] This figure shows an example of the operation flow of the order support device in this disclosure. [Figure 10] This figure shows an example of the configuration of the order support system described in this disclosure. [Figure 11] This figure shows an example of the configuration of the ordering support device in this disclosure. [Figure 12] This figure shows an example of the operation flow of the order support device in this disclosure. [Figure 13] This figure shows an example of the hardware configuration of the ordering support device described in this disclosure. [Modes for carrying out the invention]

[0012] (First Embodiment) A first embodiment of this disclosure will be described in detail with reference to the figures. Figure 1 is a diagram showing an example of the configuration of an order support system. The order support system comprises an order support device 10, a terminal device 20, and a reservation management device 30. The order support device 10 is connected to the terminal device 20, for example, via a network. The order support device 10 is also connected to the reservation management device 30, for example, via a network. There may be multiple terminal devices 20 and reservation management devices 30. The number of terminal devices 20 and reservation management devices 30 can be set as appropriate.

[0013] The order support system predicts, for example, the quantity of food ingredients to be ordered. The food ingredients are, for example, foods used for cooking in a restaurant. For example, the food ingredients to be ordered are materials prepared for use in dishes provided to customers in a restaurant. That is, the food ingredients to be ordered are foods that are pre-ordered for use in dishes provided to customers in a restaurant. They may also be foods provided to customers without cooking in the restaurant. For example, the food ingredients may be beverages or seasonings. Also, the food ingredients may include materials necessary for cooking. The food ingredients are not limited to the above.

[0014] The order support system estimates the quantity of dishes to be provided in a restaurant based on reservation information regarding the number of customers coming to the restaurant. Then, the order support system predicts the quantity of food ingredients to be ordered based on the estimated quantity of dishes to be provided. The quantity of dishes to be provided in a restaurant is, for example, the quantity of dishes cooked by the restaurant in response to the orders of customers who come to the restaurant. Also, when the dishes are provided in a buffet format, the quantity of dishes to be provided in the restaurant may be, for example, the quantity of dishes cooked by the restaurant for providing to customers.

[0015] The reservation information regarding the number of customers coming to the restaurant is, for example, reservation information that can be reflected in the number of customers coming to the restaurant. The number of customers coming to the restaurant affects, for example, the quantity of dishes to be provided in the restaurant. Therefore, the reservation information regarding the number of customers coming to the restaurant is, for example, reservation information that can affect the quantity of food ingredients necessary for providing dishes in the restaurant. The reservation information regarding the number of customers coming to the restaurant is, for example, information indicating the content of reservations to use facilities attached to the restaurant or the content of reservations to come to the restaurant. The content of the reservation is, for example, the number of people using the facilities or the number of people using the restaurant. The content of the reservation is not limited to the above. Also, specific examples of the reservation information regarding the number of customers coming to the restaurant will be described later.

[0016] For example, when a reservation is not made by specifying a dish, it is difficult to estimate the amount of dishes that need to be provided at a restaurant. Therefore, when a reservation is not made by specifying a dish, for example, it may be difficult to appropriately determine the amount of ingredients to order at a restaurant. The order support system can easily predict the amount of ingredients to order by estimating the amount of dishes to be provided at the restaurant based on reservation information regarding the number of customers coming to the restaurant and predicting the amount of ingredients to order based on the estimated amount of dishes to be provided.

[0017] Here, a specific example of the configuration of the order support device 10 will be described. FIG. 2 shows an example of the configuration of the order support device 10. The order support device 10 basically includes an acquisition unit 11, an estimation unit 12, a prediction unit 13, and an output unit 14. Further, the order support device 10 includes, for example, a storage unit 15.

[0018] The acquisition unit 11 acquires reservation information regarding the number of customers coming to the restaurant. The reservation information is, for example, the number of reservations at the facility where the restaurant is co-located or the number of customers of the restaurant. The number of reservations is, for example, information indicating how many people have made a reservation to use the facility or the restaurant. For example, when the restaurant is co-located with a facility, the reservation information is, for example, the number of people who have reserved and are using the facility where the restaurant is co-located. Also, when the reservation information is the number of reservations at the restaurant, the reservation information is, for example, the number of people who have made a reservation to dine at the restaurant.

[0019] The reservation information may be information regarding the reservation customers at the facility where the restaurant is co-located or the restaurant. The information regarding the reservation customers at the restaurant is, for example, the attributes of the reservation customers. The attributes of the reservation customers are, for example, information that can affect the content of the dishes that the reservation customers will order at the restaurant.

[0020] The attributes of the person making the reservation are, for example, information that may influence the type of food the person orders at the restaurant. For example, if the restaurant is attached to a facility, the reservation information may be, for example, information about the attributes of the person who made the reservation for the facility where the restaurant is located. The attributes of the person making the reservation may be, for example, information about one or more of the following items: age, gender, nationality, occupation, annual income, and place of residence. If the reservation is made on a group basis, the person making the reservation may be, for example, the person who made the reservation. If the reservation is made on a group basis, the person making the reservation may be, for example, each individual belonging to the group.

[0021] Facilities include, for example, accommodation facilities, bathing facilities, campgrounds, conference halls, exhibition halls, theaters, art galleries, museums, cinemas, sports facilities, amusement parks, or theme parks. Facilities are not limited to those listed above. "Adjacent" means, for example, that a restaurant is located on the same premises as the facility or on a premises adjacent to the facility. Adjacent also means, for example, that the restaurant is located within a range accessible to users of the facility, and that there is a business relationship between the facility and the restaurant.

[0022] If the reservation information is for a facility with an attached restaurant, the reservation information may further include at least one of the following: the number of people in the group, the expected arrival time at the facility, the expected duration of stay at the facility, food preferences, past use of the facility, past use of the restaurant, and physical characteristics. Physical characteristics may, for example, be the physical characteristics of each person in the group. Physical characteristics may also be, for example, the physical characteristics of some of the people in the group. Physical characteristics may, for example, be physical characteristics related to dietary restrictions. Furthermore, if the reservation information is for a restaurant, the reservation information may further include at least one of the following: the number of people in the group, the expected arrival time at the restaurant, food preferences, past use of the restaurant, and physical characteristics.

[0023] If the facility is an accommodation facility, the reservation information may further include at least one of the following: expected arrival date and time, number of nights, reservation route, room type, room grade, accommodation plan, and whether or not meal vouchers are included. The reservation route is information indicating, for example, whether the reservation was made through a business reservation service, a travel agency, or direct booking. For example, there may be differences in meal preferences between business and leisure stays. The accommodation plan is information such as the price, discount rate, ancillary services, and whether or not meals are included. The reservation information may also include whether or not meal vouchers were issued.

[0024] When reservation information is the number of people reserved for a restaurant, the reservation information may further include information indicating the type of seating reserved at the restaurant. This seating type could be, for example, information indicating the distinction between counter seats, table seats, booth seats, and tatami rooms. The seating type could also indicate the area in which the seats are located. For example, the seating type could indicate the distinction between a regular area and a private room. A regular area is, for example, an area on the dining floor where tables and chairs are arranged and there are no partitions between tables. However, the seating type is not limited to the above.

[0025] Furthermore, if the reservation information is for the number of people reserved at a restaurant, the reservation information may also include at least one of the following: the medium used for the reservation, the purpose of the meal, and information about the reserved dishes. The information about the medium used for the reservation may include information about whether or not there is a coupon from the reservation service. The purpose of the meal may indicate, for example, whether the meal is a regular meal, a class reunion, a meal with an acquaintance, a meal with a business partner, a birthday party, a farewell party, or an anniversary celebration. The purpose of the meal is not limited to those listed above.

[0026] The acquisition unit 11 acquires reservation information, for example, from the reservation management device 30, regarding the number of customers visiting the restaurant. The acquisition unit 11 may also acquire reservation information, regarding the number of customers visiting the restaurant, from the terminal device 20. In addition, the reservation information may be based on usage data from the membership service. For example, the reservation information may be the usage history of a member's restaurant recorded in the restaurant introduction service. In this case, the acquisition unit 11 acquires reservation information regarding the person who made the reservation from the information processing device that manages the membership service.

[0027] If the serving size of the dishes used to estimate the amount of ingredients to order can be changed based on the estimation results, the acquisition unit 11 acquires the changed serving size of the dishes. The acquisition unit 11 acquires the changed serving size of the dishes from, for example, the terminal device 20. Also, if the amount of ingredients to order can be changed based on the prediction results, the acquisition unit 11 acquires the changed amount of ingredients to order. The acquisition unit 11 acquires the changed amount of ingredients to order from, for example, the terminal device 20.

[0028] The estimation unit 12 estimates the amount of food to be served based on reservation information. For example, the estimation unit 12 estimates the amount of food to be served in a restaurant based on the number of reservations at a facility that has a restaurant or at the restaurant itself. The estimation unit 12 may also estimate the amount of food to be served based on the number of reservations and the attributes of the reservations.

[0029] If the restaurant is attached to a facility, the estimation unit 12 estimates the amount of food served at the restaurant based on, for example, the facility's reservation information. If the restaurant is attached to an accommodation facility, the estimation unit 12 estimates the amount of food served at the restaurant based on the accommodation facility's reservation information. If the restaurant is attached to a facility, the estimation unit 12 estimates the amount of food served at the restaurant based on reservation information from the facility that relates to the number of people staying at the facility on the date and time for which the amount of food served at the restaurant is to be estimated. For example, the estimation unit 12 estimates the amount of food served at the restaurant based on the number of people who have made reservations for accommodation on the day for which the amount of food served is to be estimated. Furthermore, when estimating the amount of food served at breakfast, the estimation unit 12 estimates the amount of food served at the restaurant based on, for example, the number of people who have made reservations for accommodation on the night before the day for which the amount of food served is to be estimated.

[0030] The estimation unit 12 may estimate the amount of food to be served based on the number of people making the reservation and the attributes of the people making the reservation. For example, the estimation unit 12 may estimate the amount of food to be served based on the number of people making the reservation and one or more of the following items: age, gender, nationality, occupation, annual income, and place of residence. When the reservation is made on a group basis, the estimation unit 12 may, for example, use the attributes of the group representative as the attributes of the people making the reservation to estimate the amount of food to be served. When the reservation is made on a group basis, the estimation unit 12 may also estimate the amount of food to be served using the attributes of each person belonging to the group as the attributes of the people making the reservation.

[0031] The estimation unit 12 estimates the amount of food served in a restaurant, for example, using an estimation model. The estimation model is, for example, a machine learning model that estimates the amount of food served, taking reservation information as input. The estimation model is generated, for example, by learning the relationship between reservation information and the types of dishes served and the quantities of each dish. For example, if the reservation information is the number of people who made the reservation, the estimation model is generated by learning the relationship between the number of people who made the reservation and the types of dishes served and the quantities of each dish. The estimation model is generated, for example, by deep learning using a neural network.

[0032] For generating the estimation model, a machine learning algorithm that allows for the estimation of the reasons for the estimation may be used. For example, when generating the estimation model using deep learning with a neural network, the estimation model is generated as a machine learning model that extracts items that have a large impact on the estimation result as the reasons for the estimation, based on the change in the amount of food when the data for each item is varied. The estimation unit 12, for example, varies the data for each item included in the input data of the estimation model and extracts items that have a large impact on the estimation result as the reasons for the estimation.

[0033] To generate an estimation model in which the reasons for the estimation can be estimated, a machine learning algorithm based on factorized asymptotic Bayesian inference may be used. When training using a machine learning algorithm based on factorized asymptotic Bayesian inference, reservation information is used as input data, and the dishes and quantities of dishes are used as ground truth data, and the data is divided into cases according to the rules of a decision tree. Then, based on the decision tree, a machine learning model is generated using a linear model that combines different explanatory variables for each case. Subsequently, the machine learning model is generated by sequentially performing processes such as optimizing the data division conditions, generating estimation models by optimizing the combination of explanatory variables, and deleting unnecessary estimation models. Estimation models generated by this method of generating machine learning models with different combinations of explanatory variables can explain the estimation results using the division conditions that have a strong influence on the estimation results, thus improving the explainability of the estimation results.

[0034] Furthermore, if the estimated quantity of food served can be modified, the estimation model may be optimized using the modified quantity. The estimation model may also be generated, for example, by an external device to the ordering support device 10. It may also be generated by a learning means (not shown) within the ordering support device 10. The machine learning algorithm used to generate the estimation model is not limited to the above.

[0035] The estimation unit 12 may estimate the amount of food to be served by using a function that calculates the amount of food to be served from the reservation information. The function that calculates the amount of food to be served from the reservation information is, for example, a function in which the reservation information is the explanatory variable and the amount of food to be served is the dependent variable. The function that calculates the amount of food to be served from the reservation information is set for each dish, for example. The function that calculates the amount of food to be served from the reservation information may also be set for each category of dish.

[0036] The estimation unit 12 may estimate by scoring the reservation information. The estimation unit 12 calculates a score for the reservation information by referring to a table that associates the reservation information with the score. Then, the estimation unit 12 estimates the amount of food to be served from the calculated score by referring to a table that associates the score with the amount of food.

[0037] If a facility has multiple restaurants, the estimation unit 12 estimates, for example, the amount of food served at each of the restaurants located within the facility. For example, suppose there are three restaurants, Restaurant A, Restaurant B, and Restaurant C, within the facility. In this case, the estimation unit 12 estimates, for example, the amount of food served at each of the restaurants, Restaurant A, Restaurant B, and Restaurant C.

[0038] Furthermore, when estimating the amount of food served at each of the restaurants attached to the facility, the estimation unit 12 may estimate the number of customers at each restaurant and then estimate the amount of food served at each restaurant based on the estimated number of customers. For example, if there are three restaurants, Restaurant A, Restaurant B, and Restaurant C, within the facility, the estimation unit 12 may estimate the number of customers at each of Restaurant A, Restaurant B, and Restaurant C. Then, the estimation unit 12 may estimate the amount of food served at each of Restaurant A, Restaurant B, and Restaurant C based on the estimated number of customers.

[0039] Furthermore, if the reservation information includes information on whether or not meal vouchers are available, the estimation unit 12 may estimate the amount of food to be served based on the number of people in the reservation and the number of meal vouchers issued. For example, people who possess meal vouchers are likely to come to the restaurant, but it can be difficult to predict whether or not people who do not possess meal vouchers will come. Therefore, by referring to the number of people in the reservation who possess meal vouchers and those who do not, the accuracy of estimating the amount of food to be served can be improved.

[0040] When reservation information includes information indicating the type of seating reserved at a restaurant, the estimation unit 12 estimates the amount of food to be served based on the number of people in the reservation and the information indicating the type of seating. For example, suppose a restaurant has three types of seating: counter seats, table seats, and private rooms. In this case, the estimation unit 12 estimates the amount of food to be served based on the number of people in the reservation and the information regarding the reserved seats. For example, there may be a difference in the types of food ordered between someone who has reserved counter seats and someone who has reserved a private room. Therefore, the accuracy of the estimation can be improved by using information indicating the type of seating to estimate the amount of food to be served.

[0041] The estimation unit 12 may estimate the quantity of food to be served based on constraints. For example, if there is a limited number of dishes, the estimation unit 12 may estimate the quantity of each dish to be served, with the limited number as the upper limit. For example, if the special Wagyu steak is limited to only 10 servings, and demand is expected to exceed 10 servings, the estimation unit 12 will predict the quantity of other beef steaks to be served in a larger quantity. The estimation unit 12 may also estimate the quantity of food to be served using the budget as a constraint. In this case, the estimation unit 12 will estimate, for example, the number of dishes that can be served to customers within the budget, and the quantity of each dish to be served. When estimating the quantity of food to be served based on constraints, the estimation unit 12 may, for example, use a mathematical optimization algorithm to estimate the quantity of food to be served.

[0042] The estimation unit 12 may further estimate the amount of food served based on events held in the facility where the restaurant is located or in the restaurant. The estimation unit 12 may also further estimate the amount of food served based on events held in the vicinity of the restaurant. For example, the estimation unit 12 estimates the amount of food served using an estimation model that uses the presence or absence of an event as input data. Events held in or around the facility may include, for example, exhibitions, lectures, concerts, film screenings, seminars, sports matches, school events, or community events. Events held in the restaurant may include, for example, campaigns based on seasonal events or campaigns for specific products. Events are not limited to those mentioned above.

[0043] The estimation unit 12 may estimate the amount of food to be served based on the external environment. For example, the estimation unit 12 may estimate the amount of food to be served based on at least one of the following: season, time of day, day of the week, weather, temperature, whether surrounding facilities are open, and traffic conditions. Whether surrounding facilities are open is, for example, information indicating whether nearby schools or businesses are operating. The information regarding the opening status of surrounding facilities is not limited to the above. Traffic conditions are information regarding road closures or suspension of train or bus services. The information regarding traffic conditions is not limited to the above. The external environment is also not limited to the above. The estimation unit 12 estimates the amount of food to be served using, for example, an estimation model that uses the external environment as input data.

[0044] The estimation unit 12 estimates the amount of food to be served, for example, in accordance with the ordering cycle of ingredients. For example, if ingredients are ordered at 3-day intervals, the estimation unit 12 estimates the amount of food to be served over 3 days. The estimation unit 12 may also estimate the amount of food to be served at a frequency corresponding to the period over which the prediction unit 13 predicts the amount of ingredients to be ordered. Alternatively, the estimation unit 12 may estimate the amount of food to be served on a daily basis. For example, the estimation unit 12 estimates the amount of food to be served on a daily basis over a month. The period for which the amount of food to be served is estimated can be set as appropriate.

[0045] The prediction unit 13 predicts the amount of ingredients to order based on the estimated amount of food to be served. The prediction unit 13 predicts the amount of ingredients to order by, for example, calculating the amount of ingredients to be used for each dish using an ingredient list set for each dish. The ingredient list is, for example, a list that associates the ingredients used in a dish with the amount used per serving. The amount used may be in units other than for multiple servings. For example, the amount used may be the weight of the dish or the amount of ingredients used per plate. The unit of the amount of ingredients used is not limited to the above. The ingredient list may also include information on one or more items such as the price of the ingredients, the grade of the ingredients, the order unit, the amount per package, the expiration date, and the supplier of the ingredients, associated with each ingredient.

[0046] The prediction unit 13 predicts the quantity of each ingredient by, for example, multiplying the quantity shown in the ingredient list by the quantity of the dish. If the same ingredient is used in multiple dishes, the prediction unit 13 predicts the quantity of the ingredient to order by, for example, summing the quantities of the ingredient used in each dish. The prediction unit 13 may also predict the quantity of the ingredient to order by adding a reserve quantity of ingredient to the quantity of ingredient calculated from the ingredient list and the quantity of the dish. The reserve quantity of ingredient is set, for example, based on at least one of the dish and the ingredient. The reserve quantity of ingredient is set, for example, to prevent shortages of ingredients in a dish. The reserve quantity may also be set to be smaller if the ingredient has a short shelf life or is expensive. The prediction unit 13 may also predict the quantity of ingredient to order based on the difference between the ingredient inventory and the quantity required for the dish.

[0047] If a facility has multiple restaurants, the prediction unit 13 predicts, for example, the amount of ingredients to be ordered for each of the restaurants located within the facility. The prediction unit 13 may also predict the total amount of ingredients used by two or more restaurants.

[0048] The prediction unit 13 predicts the amount of ingredients to be ordered, for example, in accordance with the ordering cycle of the ingredients. For example, if ingredients are ordered at 3-day intervals, the prediction unit 13 predicts the amount of ingredients to be ordered based on the predicted amount of food to be served over those 3 days. The prediction unit 13 may also predict the amount of ingredients for different periods for each ingredient. For example, if the ordering cycle for ingredient A is 3 days and the ordering cycle for ingredient B is 7 days, the prediction unit 13 predicts the amount of ingredient A to be ordered based on the predicted amount of food to be served over those 3 days. The prediction unit 13 also predicts the amount of ingredient B to be ordered based on the predicted amount of food to be served over those 7 days.

[0049] The prediction unit 13 may estimate the basis for predicting the order quantity. For example, the prediction unit 13 estimates the basis for predicting the order quantity when the order quantity is different from the usual. An order quantity that is different from the usual means, for example, that the order quantity for each ingredient is greater than or equal to a predetermined standard from the average order quantity per order for each ingredient. The prediction unit 13 estimates the serving quantity of dishes that have a large impact on the increase or decrease in the order quantity of ingredients as the basis for predicting the order quantity of ingredients. The predetermined standard is set, for example, based on the magnitude of the impact on inventory management. For example, if the estimated serving quantity of sashimi platters is higher than usual, the prediction unit 13 estimates, for example, that the serving quantity of sashimi platters is higher than usual as the basis for predicting the order quantity of tuna. If the estimation model is a machine learning model that can output the reasons for the estimation results, the prediction unit 13 may estimate the reasons for the estimation results of the dishes to be served as the basis for the prediction.

[0050] The prediction unit 13 may change the quantity of ingredients to be ordered based on the change in the predicted quantity of ingredients. For example, if a change in the predicted value is obtained, the prediction unit 13 will determine the obtained change in value as the quantity to be ordered for the ingredients for which a change in value has been obtained. Alternatively, the prediction unit 13 may determine the predicted value as the quantity to be ordered for ingredients for which no change in value has been obtained.

[0051] The adjustment of predicted quantities of ingredients to be ordered is typically made by the person in charge of ordering ingredients in a restaurant. For example, if there is a shortage of staff or skilled staff, the person in charge of ordering ingredients will adjust the prediction to reduce the quantity of dishes that would be difficult to prepare. Conversely, if there is a campaign to promote a particular dish, the person in charge of ordering ingredients will adjust the prediction to increase the quantity of that dish.

[0052] The output unit 14 outputs the predicted quantity of ingredients to be ordered. The output unit 14 may output, for example, the order quantity for each ingredient. The output unit 14 may also output the order quantity for each supplier. For example, when ordering from three companies, A, B, and C, the output unit 14 may output the order quantity for each supplier. For example, it may output the order quantity for A, B, and C. The output unit 14 may also output the order quantity for each order. For example, when orders are placed daily, the output unit 14 outputs the order quantity for each day. The output unit 14 may also output the reason for the predicted order quantity of ingredients. The output unit 14 may also output an input screen for entering changes to the order quantity of ingredients.

[0053] The output unit 14 may output the estimated serving size of the dishes. For example, based on the estimated serving size, the output unit 14 may output the dishes to be made with the ordered ingredients and the quantities of the dishes for each order of ingredients. The output unit 14 may also output the reason for the estimated serving size. The output unit 14 may also output an input screen for entering a change in the serving size of the dishes. Furthermore, if any dish is selected in the estimated serving size, the output unit 14 may output a list of ingredients used in the selected dish.

[0054] The output unit 14 may highlight ingredients whose order quantities differ from the usual. Highlighting means outputting them in a way that improves their visibility compared to other ingredients. For example, the output unit 14 may output ingredients whose order quantities differ from the usual in at least one of the following: color, font size, font thickness, and presence or absence of an underline. The manner in which ingredients whose order quantities differ from the usual is not limited to the above. The output unit 14 may also output ingredients whose order quantities differ from the usual using multiple stages of emphasis depending on the magnitude of the difference from the usual. For example, the output unit 14 may highlight ingredients whose order quantities have increased or decreased by more than a predetermined standard from the average order quantity per order for each ingredient. The output unit 14 may also highlight dishes whose serving quantities differ from the usual.

[0055] The output unit 14 outputs, for example, the quantity of ingredients to be ordered to the terminal device 20. The output unit 14 also outputs, for example, the quantity of food to be served to the terminal device 20. The output unit 14 may also output the quantity of ingredients to be ordered to an ingredient ordering system (not shown).

[0056] Figure 3 shows an example of a display screen that shows the estimated amount of food served. In the example display screen in Figure 3, the target date for which the amount of food served is estimated is shown. The target date is, for example, a day when food is served at a restaurant. Also, in the example display screen in Figure 3, the dishes are classified into "meat-based," "fish-based," and "other." In the example display screen in Figure 3, the names of the dishes belonging to each classification and the estimated amount of food served for each dish are displayed. The person in charge of ordering ingredients can understand the estimated amount of food served by referring to a display screen like the one in Figure 3.

[0057] Figure 4 shows an example of a display screen for changing the amount of food used to predict the amount of ingredients to order in the estimated food serving quantity. In the example display screen of Figure 4, the target date for estimating the food serving quantity is displayed. In the example display screen of Figure 4, the dishes are classified into "meat," "fish," and "other." In the example display screen of Figure 4, the names of the dishes belonging to each classification and the estimated serving quantity for each dish are displayed. In the example display screen of Figure 4, an input field for entering a change value to modify the estimated serving quantity is displayed as "Change Value." In the example display screen of Figure 4, a "Confirm" button is displayed. In the example display screen of Figure 4, the "Confirm" button is used to confirm the estimated serving quantity. In the example display screen of Figure 4, for example, if the "Confirm" button is pressed with a change value entered, the value of the change value will be treated as the estimated result for the dishes with the entered change value. In the example display screen of Figure 4, for dishes without a entered change value, the value estimated by the estimation unit 12 will be treated as the estimated result.

[0058] Figure 5 shows an example of a display screen showing the predicted quantity of ingredients to be ordered. In the example screen in Figure 5, the order date, which is the day the ingredients are ordered, is displayed. In the example screen in Figure 5, the target date for estimating the amount of food to be served is displayed. The order date is, for example, the day on which the ingredients to be used in the dish are ordered. The target date is, for example, the day on which the dish is served in the restaurant. In the example screen in Figure 5, the ingredients to be ordered are classified into "vegetables," "meat," "fish," and "other." In the example screen in Figure 5, the names of the ingredients belonging to each classification and the predicted order quantity for each ingredient are displayed.

[0059] Figure 6 shows an example of a display screen that shows dishes using the ordered ingredients. In the example display screen in Figure 6, "Dish Name," "Number of Servings," and "Amount Used" are displayed. In the example display screen in Figure 6, "Dish Name" is the name of the dish that uses the ordered ingredients. In the example display screen in Figure 6, "Number of Servings" is the estimated amount of each dish to be served. In addition, in the example display screen in Figure 6, "Amount Used" is the amount of ingredients needed to make the estimated number of servings for each dish. That is, in the example display screen in Figure 6, "Amount Used" is the amount of ingredients to order in order to make the estimated number of servings for each dish.

[0060] Figure 7 shows an example of a display screen that shows the predicted quantity of ingredients to be ordered, along with the reason for the prediction. In the example display screen in Figure 7, the order date, which is the day the ingredients are ordered, is displayed. In the example display screen in Figure 7, the target date for estimating the amount of food to be served is displayed. In addition, in the example display screen in Figure 7, the ingredients to be ordered are classified into "vegetables," "meat," "fish," and "other." In the example display screen in Figure 7, the names of the ingredients belonging to each classification and the predicted order quantity for each ingredient are displayed.

[0061] In the example screen in Figure 7, ingredients that are ordered in quantities different from the usual amount are highlighted. In the example screen in Figure 7, "tuna" and "beer" are underlined as they need to be ordered in larger quantities than usual. Also, in the example screen in Figure 7, the reason for the predicted quantity of ingredients to be ordered is displayed. In the example screen in Figure 7, the reason for the predicted quantity of ingredients to be ordered is displayed as, "A seminar for business people is scheduled in the hotel conference room, and customers are expected to visit after it concludes." In the example screen in Figure 7, for example, the reason why "tuna" and "beer" need to be ordered in larger quantities than usual is that there is a seminar for business people in the hotel conference room. In this case, for example, "tuna" and "beer" are ingredients and beverages that are used in dishes that business people tend to like.

[0062] Figure 8 shows an example of a display screen for changing the quantity of ingredients to be ordered based on the predicted quantity of ingredients to be ordered. In the example display screen of Figure 8, the order date, which is the day the ingredients are ordered, is displayed. In the example display screen of Figure 8, the target date for estimating the amount of food to be served is displayed. In the example display screen of Figure 8, the "Dish Name," "Number of Servings," and "Quantity Used" are displayed. In the example display screen of Figure 8, an input field for entering a change value to modify the quantity of ingredients to be ordered is displayed as "Change Value." In the example display screen of Figure 8, a "Confirm" button is displayed. In the example display screen of Figure 8, for example, if the "Confirm" button is pressed after a change value has been entered, the quantity of ingredients for which a change value has been entered will be determined as the quantity of ingredients to be ordered. In the example display screen of Figure 8, for dishes for which no change value has been entered, the predicted value will remain as the quantity of ingredients to be ordered.

[0063] The memory unit 15 stores, for example, information related to the process of predicting the order quantity of ingredients. The memory unit 15 stores, for example, reservation information regarding the number of customers visiting a restaurant. The memory unit 15 stores, for example, the estimated amount of food to be served. The memory unit 15 stores, for example, the predicted amount of ingredients to be ordered. The memory unit 15 stores, for example, the estimation model. The estimation model may be stored in a storage means other than the memory unit 15.

[0064] The terminal device 20 is an information processing device used, for example, by a person in charge of ordering ingredients in a restaurant. The terminal device 20 obtains, for example, the predicted quantity of ingredients to be ordered from the output unit 14 of the ordering support device 10. The terminal device 20 then outputs the predicted quantity of ingredients to a display device (not shown), for example. The predicted quantity of ingredients to be ordered may include the reason for the prediction. If the terminal device 20 obtains a prediction result that includes an input field for the changed quantity of ingredients to be ordered, the terminal device 20 obtains, for example, the changed quantity of ingredients to be ordered entered by the person in charge. The terminal device 20 then outputs the changed quantity of ingredients to the acquisition unit 11 of the ordering support device 10, for example.

[0065] The terminal device 20 obtains, for example, the estimated quantity of food to be served from the output unit 14 of the order support device 10. The terminal device 20 then outputs the estimated quantity of food to a display device (not shown), for example. The estimated quantity of food to be served may include the reason for the estimation. If the terminal device 20 obtains an estimation result that includes an input field for the change in the quantity of food to be served, the terminal device 20 obtains, for example, the change in the quantity of food to be served entered by the person in charge. The terminal device 20 then outputs the change in the quantity of food to the acquisition unit 11 of the order support device 10, for example.

[0066] The terminal device 20 can be, for example, a personal computer, a tablet computer, a smartphone, or a smartwatch. The information processing device used in the terminal device 20 is not limited to those mentioned above.

[0067] The reservation management device 30 stores reservation information, for example, regarding the number of customers at a restaurant. The reservation management device 30 also outputs reservation information, for example, regarding the number of customers at a restaurant to the acquisition unit 11. When the reservation information regarding the number of customers at a restaurant is reservation information for a facility, the reservation management device 30 stores the reservation information for the facility. For example, when the facility is an accommodation facility, the reservation management device 30 stores the information of the accommodation guests as reservation information regarding the number of customers at a restaurant. For example, when the reservation information is information for a restaurant, the reservation management device 30 stores the information of the restaurant guests as reservation information regarding the number of customers at the restaurant.

[0068] This section describes the process by which the ordering support device 10 predicts the quantity of ingredients to be ordered. Figure 9 shows an example of the operation flow in the process by which the ordering support device 10 predicts the quantity of ingredients to be ordered.

[0069] The acquisition unit 11 acquires reservation information regarding the number of customers visiting the restaurant (step S11). The acquisition unit 11 acquires reservation information regarding the number of customers visiting the restaurant from, for example, the terminal device 20.

[0070] Once reservation information is obtained, the estimation unit 12 estimates the amount of food to be served based on the reservation information (step S12).

[0071] Once the amount of food to be served is estimated, the prediction unit 13 predicts the amount of ingredients to order based on the amount of food to be served estimated by the estimation unit 12 (step S13).

[0072] When the quantity of ingredients to be ordered is predicted, the output unit 14 outputs the predicted quantity of ingredients to be ordered (step S14). The output unit 14 outputs the predicted quantity of ingredients to be ordered to, for example, the terminal device 20.

[0073] Each process in the order support device 10 may be distributed and executed across multiple information processing devices connected via a network. For example, the processing in the estimation unit 12 and the processing in the prediction unit 13 may be performed on separate information processing devices. The specific information processing device on which each process in the order support device 10 is performed can be set as appropriate.

[0074] The ordering support device 10 estimates the amount of food to be served based on reservation information regarding the number of customers visiting the restaurant. Then, the ordering support device 10 predicts the amount of ingredients to order based on the estimated amount of food to be served. In this way, by predicting the amount of ingredients to order based on the amount of food to be served predicted from reservation information regarding the number of customers visiting the restaurant, the ordering support device 10 can easily predict the amount of ingredients to order.

[0075] For example, if the reservation information is for a facility that has a restaurant attached, the ordering support device 10 estimates the amount of food to be served based on the facility's reservation information and predicts the amount of ingredients to order based on the estimation result. In this way, by predicting the amount of ingredients to order, the ordering support device 10 can appropriately predict the amount of ingredients to order even without direct information on the number of customers visiting the restaurant. Also, for example, if the reservation information is for the number of customers visiting the restaurant, the ordering support device 10 can appropriately predict the amount of ingredients to order even when customers have not made reservations for food.

[0076] By outputting the reasons for predicting the quantity of ingredients to be ordered, the ordering support device 10 can, for example, make it easier to judge the validity of the prediction results. For this reason, for example, a person in charge of ordering ingredients by referring to the prediction results can judge the validity of the prediction results and decide on the quantity of ingredients to order. Furthermore, by outputting the estimated quantity of food to be served, the ordering support device 10 can, for example, make it easier to judge the validity of the prediction results regarding the quantity of ingredients to be ordered. In addition, by obtaining the change value of the estimated quantity of food to be served and predicting the quantity of ingredients to be ordered based on the change value, the ordering support device 10 can, for example, appropriately predict the quantity of ingredients to be ordered when it is necessary to temporarily increase or decrease the quantity of food to be served depending on the situation.

[0077] (Second embodiment) A second embodiment of this disclosure will be described in detail with reference to the figures. Figure 10 is a diagram showing an example of the configuration of an order support system. The order support system comprises an order support device 40, a terminal device 20, a reservation management device 30, and a camera 50. The order support device 40 connects to the terminal device 20, for example, via a network. The order support device 40 connects to the reservation management device 30, for example, via a network. The order support device 40 connects to the camera 50, for example, via a network. There may also be multiple terminal devices 20, reservation management devices 30, and camera 50. The number of terminal devices 20, reservation management devices 30, and camera 50 can be set as appropriate. The functions of the terminal device 20 and reservation management device 30 are similar to those of the terminal device 20 and reservation management device 30 in the first embodiment, for example.

[0078] The ordering support system of this embodiment estimates the amount of ingredients to order based on the amount of food remaining in a restaurant, for example. The ordering support system detects the amount of food remaining from images of plates of food served in a buffet style, for example, using a camera 50. The ordering support system then estimates the amount of food to be served based on reservation information regarding the number of customers at the restaurant and the detected amount of food remaining. The amount of food remaining may be detected, for example, by a weight sensor installed in the plate's placement area. How the amount of food remaining is detected can be set as appropriate.

[0079] Here, a specific example of the configuration of the order support device 40 will be described. Figure 11 shows an example of the configuration of the order support device 40. The order support device 40 includes, for example, an acquisition unit 41, a data acquisition unit 42, a detection unit 43, an estimation unit 44, a prediction unit 45, an output unit 46, and a storage unit 47. The acquisition unit 41 and the output unit 46 have the same functions as the acquisition unit 11 and the output unit 14 of the first embodiment.

[0080] The data acquisition unit 42 acquires, for example, video footage of plates of food served in a buffet style. The data acquisition unit 42 acquires, for example, video footage of plates of food from the camera 50. The data acquisition unit 42 may also acquire video footage of plates of food via a storage medium that stores the video footage of plates of food. The video may be a moving image or a still image. For example, if a weight sensor is used instead of the camera 50, the data acquisition unit 42 acquires the weight measurement result from the weight sensor.

[0081] The detection unit 43 detects, for example, the remaining amount of food. The detection unit 43 may detect the remaining amount of food based on the amount of food served. The detection unit 43 detects, for example, the remaining amount of each dish. The detection unit 43 detects, for example, the remaining amount when the food plates are replaced or removed. The amount of food served is, for example, the amount of food that customers take from the serving plates at a restaurant. The detection unit 43 detects, for example, the remaining amount of food served in a buffet style.

[0082] The detection unit 43 detects the remaining amount of food using, for example, a detection model. The detection model is a machine learning model that detects the remaining amount of food from images of plates of food, for example, using image recognition technology. The detection model is generated, for example, by learning the relationship between images of plates of food and the remaining amount. A detection model may be generated for each type of dish. The detection model is generated, for example, by deep learning using a neural network. The machine learning algorithm for generating the detection model is not limited to the above. The detection model is generated, for example, by a device outside the ordering support device 40. The detection model may also be generated by a learning means (not shown) within the ordering support device 40.

[0083] When a weight sensor is used instead of the imaging device 50, the detection unit 43 detects the remaining amount of food from the weight sensor's measurement, for example. In this case, the detection unit 43 detects the remaining amount of food based on the weight of the plate alone and the weight measurement. The detection unit 43 detects the remaining amount of food by subtracting the weight of the plate alone from the maximum weight measurement. The weight of the plate alone is input by the person in charge of ordering ingredients, for example, by referring to the prediction results of the ordering support device 40.

[0084] The estimation unit 44 estimates the amount of food to be served based, for example, on reservation information regarding the number of customers at the restaurant and the detection results of the remaining amount of food. The estimation unit 44 estimates the amount of food to be served by taking, for example, the reservation information regarding the number of customers at the restaurant and the detection results of the remaining amount of food as input to an estimation model. In this case, the estimation model is generated by learning the relationship between the reservation information regarding the number of customers at the restaurant, the remaining amount of food, and the amount of food to be served. The estimation model is generated, for example, by deep learning using a neural network. The machine learning algorithm used to generate the estimation model is not limited to the above.

[0085] For example, the remaining amount of food used is the result detected over a predetermined period. The length of the predetermined period is set to be the same as, for example, the length of the period for predicting the amount of ingredients to order. The length of the predetermined period may also be set to be approximately the same as, for example, the length of the period for predicting the amount of ingredients to order. For example, when predicting the amount of ingredients to order on Saturday for use from Monday to Sunday of the following week, the length of the predetermined period is set to one week. The estimation unit 44 estimates the amount of food to be served by using, for example, the amount of food remaining for one week prior to the day the prediction is made as input to the estimation model.

[0086] The estimation unit 44 may correct the estimated amount of food served, which it has estimated based on reservation information regarding the number of customers visiting the restaurant, based on the amount of food remaining. For example, the estimation unit 44 estimates the amount of food served based on reservation information regarding the number of customers visiting the restaurant, similar to the estimation unit 12 in the first embodiment. Then, the estimation unit 44 corrects the estimated amount of food served, for example, based on the amount of food remaining.

[0087] For example, if the estimated amount of food to be served is greater than the baseline value, the estimation unit 44 corrects the estimate so that the greater the difference between the estimated amount of food to be served and the baseline value, the larger the estimated amount of food to be served. Also, for example, if the estimated amount of food to be served is less than the baseline value, the estimation unit 44 corrects the estimate so that the greater the difference between the estimated amount of food to be served and the baseline value, the smaller the estimated amount of food to be served. When the estimation unit 44 corrects the prediction result of the amount of ingredients to be ordered based on the remaining amount of food, the estimation unit 44 estimates the amount of ingredients to be ordered, for example, in the same way as the estimation unit 12 in the first embodiment.

[0088] The prediction unit 45 has a similar function to the prediction unit 13 in the first embodiment, for example. That is, the prediction unit 45 predicts the amount of ingredients to order based on the estimated amount of food to be served estimated by the estimation unit 44. The prediction unit 45 may also predict a correction value for the amount of ingredients to order based on the detection result of the remaining amount of food.

[0089] When the estimation unit 44 estimates the amount of food to be served in the same manner as in the first embodiment, the prediction unit 45 predicts a correction value for the amount of ingredients to be ordered, for example, based on the detection result of the remaining amount of food. Estimating the amount of food to be served in the same manner as in the first embodiment is, for example, an estimation of the amount of food to be served without considering the detection result of the remaining amount of food. For example, the prediction unit 45 estimates the amount of ingredients to be ordered based on the amount of food to be served estimated without considering the remaining amount of food, in the same manner as in the first embodiment. Then, the prediction unit 45 predicts a correction value for the amount of ingredients to be ordered, for example, based on the detection result of the remaining amount of food.

[0090] When prioritizing the reduction of waste, the prediction unit 45 predicts a correction value for the amount of ingredients to be ordered so that the amount of ingredients ordered is reduced when the amount of food remaining is above a certain standard. When prioritizing the avoidance of food shortages, the prediction unit 45 predicts a correction value for the amount of ingredients to be ordered so that the amount of ingredients ordered is increased when the amount of food remaining is below a certain standard.

[0091] The camera 50, for example, photographs the plates of food arranged on a table to be served to customers. The camera 50 then outputs the image of the plates of food to the ordering support device 40, for example. The camera 50 may be installed to photograph the plates of food from the side. The camera 50 may be installed to photograph the plates of food from above. The camera 50 may be installed to photograph all the plates arranged on the table, for example. The camera 50 may be installed to photograph the plates among the plates arranged on the table that are the target for predicting the amount of ingredients to be ordered. Multiple camera 50s may be installed, for example, to photograph each plate of food. Also, multiple camera 50s may be installed, for example, to photograph each of several plates of food.

[0092] The process by which the ordering support device 40 predicts the order quantity of ingredients will be explained. Figure 12 shows an example of the operation flow in the process by which the ordering support device 40 predicts the order quantity of ingredients.

[0093] The acquisition unit 41 acquires, for example, reservation information regarding the number of customers visiting a restaurant (step S21). The acquisition unit 41 acquires, for example, reservation information regarding the number of customers visiting a restaurant from the terminal device 20.

[0094] The data acquisition unit 42 acquires, for example, video footage of a plate of food (step S22). The data acquisition unit 42 acquires, for example, video footage of a plate of food from the camera 50.

[0095] When video footage of a plate of food is acquired, the detection unit 43 detects the remaining amount of food based on the video footage of the plate (step S23).

[0096] When the remaining amount of food is detected, the estimation unit 44 estimates the amount of food to be served based on the reservation information and the detected remaining amount of food (step S24).

[0097] Once the quantity of food to be served is predicted, the prediction unit 45 predicts the amount of ingredients to order based on the quantity of food to be served estimated by the estimation unit 44 (step S25).

[0098] When the quantity of ingredients to be ordered is predicted, the output unit 46 outputs the predicted quantity of ingredients to be ordered (step S26). The output unit 46 outputs the predicted quantity of ingredients to be ordered to, for example, the terminal device 20.

[0099] The ordering support device 40 estimates the amount of food to be served based on reservation information regarding the number of customers at the restaurant and the detection results of the remaining amount of food. Then, the ordering support device 40 predicts the amount of ingredients to order based on the estimated amount of food to be served. In this way, by predicting the amount of ingredients to order based on reservation information regarding the number of customers at the restaurant and the detection results of the remaining amount of food, the ordering support device 40 can easily predict the amount of ingredients to order. Furthermore, with this configuration, the ordering support device 40 can improve the accuracy of predicting the amount of ingredients to order.

[0100] Each process in the order support device 10 and the order support device 40 can be implemented by executing a computer program on a computer. Figure 13 shows an example of the configuration of a computer 100 that executes the computer programs that perform each process in the order support device 10 and the order support device 40. The computer 100 includes a CPU (Central Processing Unit) 101, memory 102, storage device 103, input / output interface (I / F) 104, and communication interface (I / F) 105.

[0101] The CPU 101 reads and executes computer programs that perform various processes from the storage device 103. The CPU 101 may be composed of a combination of multiple CPUs. Alternatively, the CPU 101 may be composed of a combination of a CPU and another type of processor. For example, the CPU 101 may be composed of a combination of a CPU and a GPU (Graphics Processing Unit). The memory 102 is composed of DRAM (Dynamic Random Access Memory) or the like, and temporarily stores computer programs executed by the CPU 101 and data being processed. The storage device 103 stores computer programs executed by the CPU 101. The storage device 103 is composed of, for example, a non-volatile semiconductor storage device. Other storage devices such as hard disk drives may be used for the storage device 103. The input / output I / F 104 is an interface that receives input and outputs display screens, etc. The communication I / F 105 is an interface that sends and receives data between the terminal device 20, the reservation management device 30, the imaging device 50, and other information processing devices. Furthermore, the terminal device 20 and the reservation management device 30 can have the same configuration as the computer 100.

[0102] The computer programs used to execute each process can also be stored and distributed on a computer-readable recording medium that non-temporarily stores data. Examples of recording media include magnetic tapes for data recording and magnetic disks such as hard disks. Optical discs such as CD-ROMs (Compact Disc Read Only Memory) can also be used as recording media. Non-volatile semiconductor memory devices may also be used as recording media.

[0103] Some or all of the above embodiments may also be described as follows, but are not limited to the following:

[0104] [Note 1] A means of obtaining reservation information regarding the number of customers visiting a restaurant, An estimation means for estimating the amount of food to be served based on the aforementioned reservation information, A prediction means that predicts the amount of ingredients to order based on the estimated amount of the aforementioned dish to be served, An output means for outputting the predicted quantity of ingredients to be ordered. An ordering support device equipped with the following features.

[0105] [Note 2] The aforementioned reservation information is either the number of reservations for the facility where the restaurant is located or the number of reservations for the restaurant itself. The estimation means estimates the amount of food to be served based on the number of reservations. The ordering support device described in Appendix 1.

[0106] [Note 3] The aforementioned reservation information further includes the attributes of the facility where the restaurant is located or the person who made the reservation for the restaurant, The estimation means estimates the amount of food to be served based on the number of reservations and the attributes of the reservations. The ordering support device described in Appendix 2.

[0107] [Note 4] The aforementioned reservation information is the number of people reserved at the facility where the restaurant is located, and further includes at least one of the following: the number of people in each group, the expected arrival time at the facility, the expected duration of stay at the facility, the food preferences, the restaurant's past usage history, and physical characteristics. An ordering support device as described in any of the appendices 1 to 3.

[0108] [Note 5] The estimation means estimates the amount of food to be served at each of the restaurants attached to the facility. The ordering support device described in Appendix 4.

[0109] [Note 6] The prediction means predicts the amount of ingredients to be ordered for each of the restaurants attached to the facility. The ordering support device described in Appendix 5.

[0110] [Note 7] The aforementioned reservation information further includes information indicating the type of seat to be reserved at the restaurant, The estimation means estimates the amount of food to be served based on the number of reservations and information indicating the seating arrangement. Order support device as described in Appendix 2 or 3.

[0111] [Note 8] The estimation means further estimates the amount of food to be served based on the detection result of the remaining amount of food. An ordering support device as described in any of the appendices 1 through 7.

[0112] [Note 9] The estimation means corrects the estimated amount of food served based on the detection result of the remaining amount of food served in a buffet style. An ordering support device as described in any of the appendices 1 through 7.

[0113] [Note 10] The system further includes a detection means for detecting the remaining amount of food served in a buffet style. The estimation means estimates the amount of food to be served based on the detected remaining amount of food. The ordering support device described in Appendix 8.

[0114] [Note 11] The prediction means further predicts the amount of ingredients to be ordered based on the detection results of the remaining amount of ingredients. An ordering support device as described in any of the appendices 1 through 7.

[0115] [Note 12] The estimation means estimates the amount of food to be served based on the facility in which the restaurant is located, events held at the restaurant, or events held in the vicinity of the restaurant. An ordering support device as described in any of the appendices 1 through 11.

[0116] [Note 13] The estimation means estimates the amount of food to be served based on at least one of the following: season, time of day, day of the week, weather, temperature, whether surrounding facilities are open, and traffic conditions. An ordering support device as described in any of the appendices 1 to 12.

[0117] [Note 14] We obtain reservation information regarding the number of customers visiting restaurants. Based on the aforementioned reservation information, the amount of food to be served is estimated. Based on the estimated quantity of the aforementioned dishes to be served, the amount of ingredients to be ordered is predicted. Output the predicted quantity of ingredients to be ordered. Order support methods.

[0118] [Note 15] The process of obtaining reservation information regarding the number of customers visiting a restaurant, Based on the aforementioned reservation information, a process is performed to estimate the amount of food to be served. A process to predict the amount of ingredients to order based on the estimated amount of the aforementioned dish to be served, The process of outputting the predicted quantity of ingredients to be ordered. An order support program that has a computer execute an order.

[0119] Furthermore, some or all of the configurations described in Appendices 2 to 13, which are dependent on Appendice 1 above, may also be dependent on Appendices 14 and 15 in the same way as Appendices 2 to 13. Moreover, not limited to Appendices 1, 14, and 15, some or all of the configurations described as appendices may also be dependent on various hardware, software, various recording means for recording software, or systems, without departing from the embodiments described above.

[0120] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure can be made as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate. [Explanation of Symbols]

[0121] 10. Ordering support device 11 Acquisition Department 12 Estimation part 13 Prediction Section 14 Output section 15 Storage section 20 Terminal devices 30 Reservation Management Device 40 Ordering support device 41 Acquisition Department 42 Data Acquisition Unit 43 Detection unit 44 Estimation part 45 Prediction Section 46 Output section 47 Memory section 100 Computers 101 CPU 102 memory 103 Storage device 104 Input / Output Interfaces 105 Communication I / F

Claims

1. A means of obtaining reservation information regarding the number of customers visiting a restaurant, An estimation means for estimating the amount of food to be served based on the aforementioned reservation information, A prediction means that predicts the amount of ingredients to order based on the estimated amount of the aforementioned dish to be served, An output means for outputting the predicted quantity of ingredients to be ordered. An ordering support device equipped with the following features.

2. The aforementioned reservation information is either the number of reservations for the facility where the restaurant is located or the number of reservations for the restaurant itself. The estimation means estimates the amount of food to be served based on the number of reservations. The ordering support device according to claim 1.

3. The aforementioned reservation information further includes the attributes of the facility where the restaurant is located or the person who made the reservation for the restaurant, The estimation means estimates the amount of food to be served based on the number of reservations and the attributes of the reservations. The ordering support device according to claim 2.

4. The aforementioned reservation information is the number of people reserved at the facility where the restaurant is located, and further includes at least one of the following: the number of people in each group, the expected arrival time at the facility, the expected duration of stay at the facility, the food preferences, the restaurant's past usage history, and physical characteristics. An order support device according to any one of claims 1 to 3.

5. The estimation means estimates the amount of food to be served at each of the restaurants attached to the facility. The ordering support device according to claim 4.

6. The prediction means predicts the amount of ingredients to be ordered for each of the restaurants attached to the facility. The ordering support device according to claim 5.

7. The aforementioned reservation information further includes information indicating the type of seat to be reserved at the restaurant, The estimation means estimates the amount of food to be served based on the number of reservations and information indicating the seating arrangement. The ordering support device according to claim 2 or 3.

8. The estimation means further estimates the amount of the dish to be served based on actual data on the remaining amount of at least one of the dish and ingredients. An order support device according to any one of claims 1 to 3.

9. We obtain reservation information regarding the number of customers visiting restaurants. Based on the aforementioned reservation information, the amount of food to be served is estimated. Based on the estimated quantity of the aforementioned dishes to be served, the amount of ingredients to be ordered is predicted. Output the predicted quantity of ingredients to be ordered. Order support methods.

10. The process of obtaining reservation information regarding the number of customers visiting a restaurant, Based on the aforementioned reservation information, a process is performed to estimate the amount of food to be served. A process to predict the amount of ingredients to order based on the estimated amount of the aforementioned dish to be served, The process of outputting the predicted quantity of ingredients to be ordered. An order support program that has a computer execute an order.

Images