Information processing device and delivery determination method

Abstract

This information processing device includes: an acquisition unit that acquires a prediction of changes over time in an index indicating the level of excitement of visitors at an event venue where an event is being held; and a determination unit that, on the basis of the changes over time in the index, determines a schedule in which a delivery means for delivering an item related to the event at the event venue delivers the item to a visitor who ordered the item. In addition, the acquisition unit acquires seating information indicating the seat in which the visitor who ordered the item is sitting at the event venue, and the determination unit determines the time required for the delivery means to complete the delivery, from the delivery start, for the visitor sitting in the seat.

Description

Information processing device and delivery decision method

[0001] The present invention relates to an information processing device and a delivery determination method.

[0002] There are known techniques for estimating a user's psychological state. For example, Patent Document 1 discloses an invention for acquiring information on the behavior of attendees at an event and estimating the level of interest of the attendees. Patent Document 2 discloses an invention for estimating the level of enthusiasm for an object from the user's behavior history.

[0003] Patent No. 7381134 Patent No. 7249316

[0004] Neither the invention described in Patent Document 1 nor the invention described in Patent Document 2 estimates the atmosphere of visitors at a venue, nor determines a delivery schedule for items.

[0005] In response to this, the present invention provides a technique for delivering goods based on an estimation of the user's mood.

[0006] One aspect of the present disclosure provides an information processing device having an acquisition unit that acquires a prediction of the change over time of an indicator that indicates the excitement of visitors at an event venue where an event is being held, and a determination unit that determines a delivery schedule for a delivery means at the event venue to deliver items related to the event to the visitors who have ordered the items, based on the change over time of the indicator.

[0007] Another aspect of the present disclosure provides a delivery determination method including the steps of: obtaining a prediction of time-varying changes in an index indicating the excitement of visitors at an event venue where an event is being held; and determining a delivery schedule for a delivery means at the event venue to deliver items related to the event to the visitors who have ordered the items, based on the time-varying changes in the index.

[0008] According to the present invention, it is possible to deliver goods at an appropriate timing based on an estimation of the user's mood.

[0009] A diagram illustrating the system configuration of the information processing system 1. A diagram illustrating the functional configuration of the information processing system 1. A diagram illustrating the hardware configuration of an information processing device 10. A sequence chart illustrating a method for determining a delivery schedule in the information processing system 1. A diagram illustrating an order database. A diagram illustrating an overview of an atmosphere estimation method. A graph illustrating a prediction of time-dependent changes in atmosphere estimation values. A diagram illustrating a delivery schedule. A sequence chart illustrating a delivery method for delivery means in the information processing system 1. A sequence chart illustrating a delivery control method for delivery means in the information processing system 1. A sequence chart illustrating a method for changing the sign of a delivery means in the information processing system 1.

[0010] 1. Configuration FIG. 1 is a diagram illustrating the system configuration of an information processing system 1. In this example, the information processing system 1 (or simply referred to as the system) is a system for delivering items (or simply referred to as "ordered items") ordered by attendees (or simply referred to as "users / audiences") at an event venue to the users' seats using a delivery device. The event venue is a venue where an event such as a concert, a play, or a sports game is held, such as a concert hall, an arena, or a stadium. The ordered items are items ordered by attendees, such as food and beverages or concert goods. The delivery device is a device that automatically delivers the ordered items to the users' seats. The information processing system 1 includes an information processing device 10, a user terminal 20, a delivery device 300, and a measuring device 400. In this example, the components of the system are connected via a network 9 as shown in FIG. 1. In this example, the network 9 is a computer network such as the Internet.

[0011] By utilizing delivery methods using delivery vehicles such as drones, ordered items can be delivered efficiently to the seats of spectators. This solves various problems, such as spectators' transportation to the sales outlet, congestion near the sales outlet, and the hassle of purchasing items. However, at event venues, depending on the excitement and enthusiasm of the spectators, there is a risk of interference with the delivery of items from the drone to the spectators. For example, when spectators are excited, there is a risk of collision with the drone, items falling or tipping over, or users being offended, making it inappropriate to make deliveries in this state.

[0012] Therefore, in the above-described delivery method, it is necessary to time the delivery by drone to coincide with the audience's calm state. Therefore, in this embodiment, a new index indicating the audience's excitement (hereinafter referred to as the "atmosphere estimated value") is defined, and drone delivery is managed based on this atmosphere estimated value. In this example, the atmosphere estimated value is calculated based on the measurement results of the audience's state at the event venue measured using various devices, or a pre-planned event schedule. The pre-planned event schedule includes song data, song order, MC between songs, breaks, and other event information. By predicting the time change of this atmosphere estimated value, the information processing system 1 is able to determine the drone delivery schedule in advance. A specific method for calculating the atmosphere estimated value will be described later.

[0013] The information processing device 10 is an information processing device / server device in the information processing system 1. In this example, the information processing device 10 accepts orders for items from users at an event venue and manages a delivery schedule for delivering the ordered items at a timing appropriate to the atmosphere of the venue. The delivery schedule is, for example, a plan that includes a time period from the delivery start time when a drone starts delivery to the delivery completion time when delivery to spectators is completed. The information processing device 10 determines a delivery schedule for delivery by a delivery means based on the time change in the atmosphere estimation value described above.

[0014] The user terminal 20 is a terminal operated by a user at the event venue. The user terminal 20 includes, for example, a smartphone, a tablet, or a personal computer. The user can order food, drink, and the like via the user terminal 20. At this time, the user terminal 20 transmits seat information indicating the seat where the user who placed the order is sitting, along with data on the ordered items, to the information processing device 10. This allows the information processing device 10 to calculate the travel time (delivery time) for the drone to reach the user's location (seat) within the event venue.

[0015] The delivery device 300 (an example of a delivery means) is a device / apparatus for delivering ordered items to a user who has placed an order at an event venue. The delivery device 300 includes, for example, a drone, a delivery robot, or an automated guided vehicle. In FIG. 1 , the delivery device 300 delivers the ordered item, food or beverage J, to a user who has placed an order via a user terminal 20. The delivery device 300 also has a CPU (Central Processing Unit) for performing various controls, memory or storage for storing various data, and a communication IF for communicating with each device.

[0016] The measuring device 400 (an example of a measurement unit) is a device / apparatus for measuring the state of spectators at an event venue in real time. The measuring device 400 includes, for example, a camera and / or a microphone. The measuring device 400 is installed in advance at a predetermined location within the event venue. Data acquired by the measuring device 400 (hereinafter referred to as "measurement data") is transmitted to the information processing device 10. The information processing device 10 can acquire, for example, the movements, gestures, or facial expressions of spectators captured by a camera as data. Alternatively, the information processing device 10 can acquire, for example, the cheers, tone of voice, or clapping of spectators recorded (acquired) by a microphone as data. This allows the information processing device 10 to acquire various data for predicting an estimated audience atmosphere value. Note that the measuring device 400 does not measure the state of a specific (individual) user, but rather measures the state of an unspecified audience as a whole.

[0017] 2 is a diagram illustrating an example of the functional configuration of the information processing system 1. In this embodiment, the information processing device 10 has functional blocks (components) including an acquisition unit 11, a determination unit 12, a prediction unit 13, a delivery control unit 14, a notification unit 15, a change unit 16, a storage unit 18, and a control unit 19. In this example, the storage unit 18 stores various types of data including, for example, a database. In this example, the control unit 19 performs various types of control.

[0018] The acquisition unit 11 acquires a prediction of time change in an index (atmosphere estimation value) indicating the excitement of visitors (users) at an event venue where an event is being held. The acquisition unit 11 acquires seat information from a user terminal 20 indicating the seats in which visitors who have ordered items are sitting at the event venue.

[0019] The determination unit 12 determines a delivery schedule for a delivery means that delivers items at an event venue to attendees who have ordered items related to the event, based on time variations in the atmosphere estimation value. The determination unit 12 determines the time required for the delivery means to deliver items to seated attendees from the start of delivery to the completion of delivery. The determination unit 12 determines the schedule so that delivery will be performed during a time period in which, based on the prediction of time variations in the atmosphere estimation value, a calm state continues for more than a threshold value.

[0020] The prediction unit 13 predicts the excitement (estimated atmosphere value) based on the measurement results of the state of visitors at the event venue. The prediction unit 13 also predicts the excitement based on a schedule of events that has been planned in advance for the event.

[0021] The delivery control unit 14 performs various controls on the delivery means that make the deliveries. If the state of the visitors (estimated atmosphere value) indicated by the measurement data measured in real time by the acquisition unit 11 is equal to or greater than a threshold, the delivery control unit 14 controls the delivery means to suspend its schedule during delivery. This makes it possible to avoid delivery problems even if the audience suddenly becomes excited.

[0022] The notification unit 15 notifies the customer who placed the order of the delivery completion time, which indicates the scheduled time when the delivery means will deliver the item to the customer. The notification unit 15 notifies the user via the user terminal 20. This allows the user to know in advance the estimated time when they will receive the ordered item.

[0023] The change unit 16 changes the state of a sign indicating the location of the delivery vehicle during delivery in accordance with the atmosphere estimation value. The sign refers to, for example, a lighting device (also called a light / lighting apparatus) equipped on a drone. This sign is typically used to indicate the location of the drone or to provide lighting that matches the atmosphere of the venue by changing the color scheme (colored light). Therefore, by operating this sign, the change unit 16 can create a performance that matches the progress of the event and the atmosphere of the venue.

[0024] FIG. 3 is a diagram illustrating an example of the hardware configuration of the information processing device 10. Physically, the information processing device 10 is configured as a computer including a processor 101, a memory 102, a storage 103, a communication device 104, an input device (not shown), an output device (not shown), and a bus connecting these. Each of these devices operates using power supplied from a battery (not shown). In the following description, the term "device" can be interpreted as a circuit, device, unit, or the like. The hardware configuration of the information processing device 10 may be configured to include one or more of the devices shown in FIG. 3, or may be configured without including some of the devices. Furthermore, the information processing device 10 may be configured by communicating with multiple devices each having a different housing.

[0025] Each function of the information processing device 10 is realized by loading specified software (programs) onto hardware such as the processor 101, memory 102, etc., so that the processor 101 performs calculations, controls communication via the communication device 104, and controls at least one of reading and writing data in the memory 102 and storage 103.

[0026] The processor 101 controls the entire computer by running, for example, an operating system. The processor 101 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Furthermore, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 101.

[0027] The processor 101 reads programs (program codes), software modules, data, etc. from at least one of the storage 103 and the communication device 104 into the memory 102 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the information processing device 10 may be implemented by a control program stored in the memory 102 and running on the processor 101. Various processes may be executed by one processor 101, or may be executed simultaneously or sequentially by two or more processors 101. The processor 101 may be implemented by one or more chips. The programs may be transmitted to the information processing device 10 via a telecommunications line.

[0028] The memory 102 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), etc. The memory 102 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 102 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

[0029] Storage 103 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 103 may also be called an auxiliary storage device.

[0030] The communication device 104 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0031] Each device, such as the processor 101 and the memory 102, is connected by a bus for communicating information. The bus may be configured using a single bus, or different buses may be used between each device.

[0032] The information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 101 may be implemented using at least one of these pieces of hardware.

[0033] When the processor 101 is executing the above-mentioned program, the processor 101, memory 102, storage 103, and communication device 104 are examples of functional blocks for operating the information processing device 10. The processor 101 is an example of the acquisition unit 11, the determination unit 12, the prediction unit 13, the delivery control unit 14, the change unit 16, and the control unit 19. At least one of the memory 102 and the storage 103 is an example of the storage unit 18. The communication device 104 is an example of the notification unit 15. The configuration of the information processing system 1 has been described above. Next, the operation of the information processing system 1 will be described.

[0034] 2. Operation 2-1. Method for Determining a Delivery Schedule Figure 4 is a sequence chart illustrating a method for determining a delivery schedule in the information processing system 1. In step S101, the user terminal 20 accepts an order for an item from a user. When placing an order, the user can open a dedicated app or access a website from the user terminal 20 and enter the order details.

[0035] In step S102, the information processing device 10 accepts an order for an item from a user via the user terminal 20. When accepting the order, the information processing device 10 acquires information about the user, particularly the user's seat information, along with information about the ordered item. In this case, for example, the user terminal 20 acquires the seat information and transmits it to the information processing device 10. The user terminal 20 can acquire the seat information by, for example, manually entering the information into the user terminal 20 or by reading a barcode previously affixed to the seat with the terminal. Alternatively, the information processing system 1 may be linked to a ticket sales system (or ticket management system) and simply identify the seat number based on the user ID of the orderer.

[0036] In step S103, the information processing device 10 estimates the delivery time required to deliver the ordered items to the user based on the acquired seat information. Here, a database for managing orders received from users will be described.

[0037] FIG. 5 is a diagram illustrating an example of an order database. In this example, the order database 1001 includes multiple records. Each record corresponds to one order. Each record includes an order ID, a spectator (user) ID, an ordered item (order details), seat information, an estimated delivery time, and a handover time. The order ID and spectator ID are ID information that uniquely identify the order details and user for each user's order. The ordered item is an item ordered by the user. The seat information is the seat number of the seat where the user is located. Note that the seat information may also be user location information. The estimated delivery time is the time required for a drone to start delivering the ordered item from a specified location, such as a waiting location / delivery start point, and arrive at the user's seat (delivery completion). The handover time is the time required for the drone to hand over the item to the user, which is determined for each item. Note that the estimated delivery time may include the handover time (or may be a separate time).

[0038] Here, the estimated delivery time may be calculated simply from, for example, the distance from the delivery start point to the user's seat location and the drone's movement speed. Alternatively, it may be calculated based on the time it takes for the drone to lift off the ground and ascend to a predetermined height, the time required for acceleration and deceleration during flight, or the time it takes for the drone to descend from a predetermined height to the height of the user's hand. Alternatively, the estimated delivery time may include the time from receiving an order to loading the ordered items onto the drone (including cooking time if the ordered items are food or drinks). As described above, the information processing device 10 can manage orders received from users and estimate the time required for delivery.

[0039] Returning to Fig. 4, in step S104, the measuring device 400 measures the state of the audience at the event venue. The measuring device 400 uses cameras or microphones installed at predetermined positions within the venue to record (video / audio) video / image data or audio data of the audience.

[0040] In step S105, the information processing device 10 acquires measurement data from the measuring device 400. The information processing device 10 records the acquired data in a database. Note that measurements by the measuring device 400 or acquisition of measurement data by the information processing device 10 (uploading or transmission of measurement data to the information processing device 10) are performed periodically (periodically). Alternatively, they may be performed at any timing, continuously, or at any frequency.

[0041] In step S106, the information processing device 10 predicts changes over time in the atmosphere estimation value. The calculation and prediction of the atmosphere estimation value by the information processing device 10 is performed by machine learning based on the measurement results acquired from the measuring device 400 or the progress schedule of the event stored in the database. Here, a method for predicting the atmosphere estimation value will be described.

[0042] 6 is a diagram illustrating an example of an atmosphere estimation method. This diagram shows an example of a method for estimating atmosphere based on measurement results of audience members' real-time states measured by various devices or a predetermined schedule for an event. Machine learning models M1 and M2 are pre-installed in the information processing device 10 according to the properties of the data required for atmosphere estimation.

[0043] The machine learning model M1 is a machine learning model for calculating an estimated atmosphere value for the current time (at a point on the time axis) based on measurement data (video data or audio data) acquired from the measuring device 400. The machine learning model M1 is configured with a function for accepting input of video data or audio data as an explanatory variable and a function for outputting an estimated atmosphere value for the current time as a target variable. The machine learning model M1 can perform the above-described processing, for example, by learning features contained in past measurement data in advance as training data. Specifically, the machine learning model M1 learns the audience's level of excitement based on features such as the audience's movements and gestures from past video data. Furthermore, the machine learning model M1 learns the audience's level of excitement based on features such as the audience's voice volume, tone of voice, or loudness from past audio data. As an example, the machine learning model M1 includes a learning model capable of processing video data or audio data using deep learning, such as an RNN or a convolutional neural network (CNN).

[0044] The machine learning model M2 is a machine learning model for predicting the level of excitement in a venue, i.e., the time change of an estimated atmosphere value, based on a predetermined event progress schedule, etc. The machine learning model M2 is configured with a function for accepting input of the current atmosphere estimate (output data of the machine learning model M1), progress schedule data, or the current time as explanatory variables, and a function for outputting a prediction of the time change of the estimated atmosphere value from the current time to the end of the event as a target variable. The machine learning model M2 can perform the above-mentioned processing by, for example, learning in advance the progress of past events or the situation of the venue as training data. As an example, the machine learning model M2 includes a learning model that handles time-series data, such as a recurrent neural network (RNN) or a long short-term memory (LSTM).

[0045] In this embodiment, the processing by machine learning model M1 or machine learning model M2 is intended to predict changes over time in the atmosphere estimation value. As described above, the information processing device 10 can predict changes over time in the atmosphere estimation value by using the above-described machine learning models. Here, the predicted data of the atmosphere estimation value output by the machine learning model will be described.

[0046] FIG. 7 is a graph illustrating a prediction of time-dependent changes in the mood estimation value. In the graph of mood estimation value (F) versus time (t) (hereinafter referred to as the "F-t graph") in FIG. 7, the vertical axis represents the mood estimation value (F), the horizontal axis represents time (t), and the starting point of the horizontal axis where it intersects with the origin represents the current time (t0). A threshold value (f) represents an arbitrary mood estimation value determined in advance for determining the state of the audience (hereinafter referred to as the "mood threshold"). Therefore, if the mood estimation value is equal to or greater than the mood threshold, the state of the audience at that time is determined to be lively (enthusiastic or excited). Conversely, if the mood estimation value is below the mood threshold, the state of the audience at that time is determined to be calm. Note that the determination of the state of the audience based on such mood threshold is performed by the information processing device 10. Period T1 (time t1 to time t2) and period T2 (time t3 to time t4) are both examples of time periods (or periods) during which the mood estimation value is continuously below the mood threshold. The information processing device 10 determines the delivery schedule based on whether the delivery time for each order recorded in the order database 1001 falls within the range of the period T1 or T2 of this Ft graph.

[0047] In this example, consider the case where the length of period T1 is 15 seconds and the length of period T2 is 1 minute and 5 seconds. The determination of whether delivery is possible is made by comparing period T1 or period T2 with the delivery time set for each item (e.g., 30 seconds for one drink, 20 seconds for one T-shirt, etc.). The information processing device 10 references the order database 1001. Because the delivery time for the order with order ID [O1] (simply referred to as "order [O1]") is 30 seconds, the information processing device 10 determines that delivery is not possible during period T1 (if period T1 is long enough for delivery, delivery is determined to be possible). On the other hand, because period T2 is a time period that lasts for at least the delivery time (hereinafter referred to as the "time threshold") for order [O1], the information processing device 10 determines that delivery is possible (determines that delivery of the item is possible during period T2). The time threshold is determined according to the delivery time for each order.

[0048] Note that since it is acceptable for the audience not to be in a lively state when the item is handed over to the user from the drone (handover time), for example, the time period when the venue is lively (time periods other than periods T1 and T2) may include the time when the drone flies over the seats. Furthermore, since the drone will arrive at the user's seat at the current time plus the estimated delivery time, if the time (t2) until the end of period T1 is earlier than this time, delivery will not be considered in the first place. In this way, the information processing device 10 can identify a time period during which delivery is possible based on the predicted atmosphere estimation value.

[0049] Returning to FIG. 4, in step S107, the information processing device 10 determines a delivery schedule for each order based on a prediction of time-dependent changes in the atmosphere estimation value. The information processing device 10 identifies a time slot during which delivery is possible and determines the delivery start time (delivery completion time) using the method described above. The delivery schedule will now be described.

[0050] FIG. 8 is a diagram illustrating a delivery schedule. In this example, delivery schedule 2001 is a delivery schedule by delivery means established for each order. The delivery start time is determined based on the F-t graph described above. The delivery completion time is calculated by adding the estimated delivery time to the delivery start time. For example, the delivery start time (19:00:00) is determined based on period T2, which covers the delivery time of order [O1]. The delivery completion time (19:01:23) is set by adding the estimated delivery time (1 minute 23 seconds) to the delivery start time (19:00:00) of order [O1]. The delivery start time and delivery completion time are set similarly for order [O2] (description omitted). The delivery completion time may also be determined based on the estimated delivery time or delivery time.

[0051] The drone ID is ID information for a delivery means assigned to each order. The allocation of a delivery means to deliver each order is set in advance, for example, based on the drone's delivery availability (whether or not it is waiting). Alternatively, this allocation may be set or updated when an employee (salesperson) at the event venue loads the ordered items onto the drone.

[0052] Returning to Fig. 4, in step S108, the information processing device 10 notifies the user terminal 20 that placed the order of the delivery time of the ordered items (delivery completion time). Based on the notification sent to the user terminal 20, the user who placed the order can know an estimate of the time when the ordered items will be delivered to their seat.

[0053] As a result, the information processing system 1 can deliver goods at appropriate times based on the estimated mood of the audience. This avoids delivery when the audience is excited, making it possible to prevent accidents such as contact with drones, dropping or tipping of goods, or the risk of offending users.

[0054] 9 is a sequence chart illustrating a delivery method of a delivery means in the information processing system 1. In step S201, the information processing device 10 determines a delivery schedule for the drone 301 (transportation device 300) for the accepted order. As in step S107 described above, the delivery schedule is determined based on the predicted atmosphere estimation value.

[0055] In step S202, the information processing device 10 transmits a delivery instruction to the drone 301. More specifically, based on the delivery schedule 2001 described above, the information processing device 10 transmits a delivery instruction to a waiting drone in charge of delivering each order when the delivery start time arrives.

[0056] In step S203, upon receiving a delivery instruction from the information processing device 10, the drone 301 begins delivering the ordered items. More specifically, the drone 301 ascends from a waiting point (delivery start point) while holding the ordered items in a holder or the like, and begins delivering the ordered items to the orderer. When making delivery, the drone 301 acquires information such as the orderer's seat information, air route (airway), and delivery time, along with the delivery instruction, from the information processing device 10. Alternatively, the drone 301 may perform delivery while performing autonomous control based on the information included in the delivery instruction.

[0057] In step S204, the drone 301 delivers the ordered item to the user's seat, completing the delivery. More specifically, when the drone 301 arrives at the orderer's seat, it descends to a position close to the user and hands over the ordered item (in reality, the user removes the ordered item from the drone's holder). When the drone 301 determines that the user has received the ordered item, it notifies the information processing device 10 that the delivery is complete and returns to the original waiting point. Note that when handing over the ordered item, the drone 301 may use lighting equipment to notify the user of its location.

[0058] As a result, the information processing system 1 can deliver goods by drone based on the estimated atmosphere of the audience.

[0059] 2-3. Delivery Control Method of Delivery Vehicles Figure 10 is a sequence chart illustrating a delivery control method of delivery vehicles in the information processing system 1. In step S301, the information processing device 10 transmits a delivery instruction to the drone 301 when the delivery start time arrives (processing similar to step S202). In step S302, upon receiving the delivery instruction from the information processing device 10, the drone 301 starts delivery from a waiting point (processing similar to step S203). The following steps represent processing while the drone 301 is making a delivery.

[0060] In step S303, the measuring device 400 measures the state of the spectators at the event venue in real time (similar to step S104). In step S304, the measuring device 400 transmits the acquired measurement data to the information processing device 10 (similar to step S105).

[0061] In step S305, the information processing device 10 obtains the measurement data from the measuring device 400 and then predicts the atmosphere estimation value using the method described above. Here, the processes from step S303 to step S305 are performed at any timing during delivery by the drone 301. This allows the information processing device 10 to measure the state of the spectators at the event venue in real time and update the predicted atmosphere estimation value over time.

[0062] Here, for example, a process of the information processing system 1 when the mood of the audience at the venue suddenly becomes excited will be described. When the real-time mood estimation value estimated in step S305 reaches or exceeds the mood threshold, the information processing device 10 determines that the state of the audience has suddenly become excited and suspends delivery by the drone 301 in progress. Specifically, the process is as follows.

[0063] In step S306, the information processing device 10 transmits an interruption instruction to the drone 301. This interruption instruction includes an instruction to have the drone 301 wait in hover in the sky until the audience's excitement calms down, or to have the drone 301 temporarily return to a standby position on the ground. If the audience's excitement is temporary and is predicted to subside in a short period of time, such as several seconds, the information processing device 10 may have the drone 301 wait in the sky. Alternatively, if the audience's excitement is predicted to continue for a long period of time, such as several minutes, the information processing device 10 may control the drone 301 to temporarily return to a standby position in consideration of power consumption of the battery, etc.

[0064] In step S307, when the drone 301 receives the interruption instruction from the information processing device 10, it interrupts the delivery in accordance with the instruction. At this time, the drone 301 waits in the sky or returns to the waiting point, as described above. When waiting in the sky, the drone 301 may wait at a position that maintains a certain distance from the user.

[0065] In step S308, the information processing device 10 transmits a delivery resume instruction to the drone 301 based on the predicted atmosphere estimation value. More specifically, if it is predicted that the excitement of the audience will subside in a short time, the information processing device 10 issues an instruction to the drone 301 waiting in the sky to resume the rest of the delivery. Conversely, if it is predicted that the excitement of the audience will continue for a long time, the information processing device 10 creates a new redelivery schedule for the target drone 301 waiting at the waiting position (or on its way back). At this time, the information processing device 10 determines the redelivery schedule using the method described above. Thereafter, the information processing device 10 transmits a redelivery (resume) instruction to the drone 301 when the redelivery start time arrives.

[0066] In step S309, upon receiving a resume instruction from the information processing device 10, the drone 301 resumes delivery in accordance with the instruction. The drone 301 that has been waiting in the sky delivers the item to the seat of the orderer. Meanwhile, the drone 301 that has returned once resumes delivery according to the re-delivery schedule.

[0067] As described above, even if an audience suddenly becomes excited due to an unforeseen event, the information processing system 1 can avoid delivery problems such as a collision with the user or a fall or crash by temporarily suspending delivery by the drone 301. Note that the information processing device 10 may notify the user via the user terminal 20 of a change in the delivery schedule (delivery completion time) due to the suspension or resumption of delivery.

[0068] 11 is a sequence chart illustrating a method for changing the sign of a delivery vehicle in the information processing system 1. In step S401, upon receiving a delivery instruction from the information processing device 10 (not shown), the drone 301 starts delivery from a waiting point (similar processing to step S203). The following steps represent processing while the drone 301 is making a delivery.

[0069] In step S402, the information processing device 10 predicts the estimated mood value using the method described above. The information processing device 10 estimates the current (real-time) mood of the audience based on the estimated mood value. This may simply be a process of determining whether the users are excited or calm (sentimental) based on the estimated mood value.

[0070] In step S403, the information processing device 10 transmits a sign change instruction to the drone 301 during delivery. The sign change instruction includes an instruction to change the light of the lighting device provided on the drone in accordance with the atmosphere of the audience. Specifically, the information processing device 10 selects a hue (or simply "color") of the light that corresponds to the atmosphere of the audience determined from the atmosphere estimation value, and transmits this color information to the drone 301.

[0071] In step S404, the drone 301 changes the color of its own light (sign) based on the acquired sign change instruction (color information). As described above, the information processing system 1 can operate the sign of the drone 301 to perform a performance that matches the progress of the event and the atmosphere of the venue.

[0072] 3. Modifications The present invention is not limited to the above-described embodiment, and various modifications are possible. Some modifications will be described below. Two or more of the following features may be combined and applied.

[0073] (1) Information Processing System 1 The hardware configuration and network configuration of the information processing system 1 are not limited to those illustrated in the embodiments. The information processing system 1 may have any hardware configuration and network configuration as long as the required functions can be realized. For example, multiple physical devices may cooperate to function as the information processing system 1. For example, at least a portion of the measuring device 400 may be implemented in the delivery device 300, and data acquired from a camera or microphone of a drone in flight may be treated as measurement data. Furthermore, the entities, configuration, and system structure shown in FIG. 1 are merely an example and merely represent an overview of the system. Therefore, the information processing system 1 may be a network system in which different devices, such as the information processing device 10 and a business terminal (a terminal of a sales business that sells goods) operating an event venue, can cooperate with each other.

[0074] (2) Information Processing Device 10 Some of the functions of the information processing device 10 may be implemented on another server. This server may be, for example, a physical server or a virtual server (including a so-called cloud). Furthermore, the correspondence between functional elements and hardware is not limited to that illustrated in the embodiment. For example, at least some of the functions described in the embodiment as being implemented on the information processing device 10 may be implemented on another device or system. Conversely, at least some of the functions described as being implemented on another device or system may be implemented on the information processing device 10. In this example, the information processing device 10 may have at least some of the functions of the user terminal 20. In this case, the information processing device 10 may be configured to acquire seat information for the orderer from information pre-registered in a database based on the user ID information of the orderer who accepted the order. Furthermore, the information processing device 10 may be configured to freely change at least some of the control of the delivery device 300. Furthermore, the information processing device 10 may be configured to be directly connected to the camera or microphone of the measuring device 400. In this case, video data or audio data of the event venue can be acquired in real time.

[0075] (3) User Terminal 20 The user terminal 20 is not limited to the example illustrated in the embodiment. The user terminal 20 may perform the above-described processing using any display screen, input device, or various UIs. The user terminal 20 may be equipped with a function for ordering items via an application (or web browser) within the terminal (a so-called mobile order function). When placing an order, the user terminal 20 may transmit location information of its own device to the information processing device 10 in addition to / instead of seat information. This allows the item to be reliably delivered to the orderer's location even if the user places an order away from their seat (while on the move) and wishes to receive the item.

[0076] (4) Delivery Equipment 300 (Delivery Means 30) The delivery equipment 300 is not limited to the example shown in the embodiment. The delivery equipment 300 may have any hardware configuration as long as it can achieve the required functions and operations. The delivery equipment 300 may be equipped with various devices necessary for delivery. For example, the delivery equipment 300 may be equipped with a camera or a microphone. This allows the delivery equipment 300 to verify the identity of the orderer by facial recognition, retinal recognition, voice recognition, or PIN reading authentication when handing over the item to the orderer. This ensures that the item is delivered to the orderer. The delivery equipment 300 may also use the camera or microphone to transmit measurement data necessary for estimating the atmosphere to the information processing device 10. The delivery equipment 300 may notify the information processing device 10 of its own location information. The information processing device 10 may use the acquired location information of the delivery equipment 300, for example, to update the delivery status, instruct the delivery equipment 300 to suspend / resume delivery, or instruct the delivery equipment 300 to change its route.

[0077] Additionally, delivery equipment 300 may be equipped with an input device for receiving various operations and / or a display device for displaying various data. Delivery equipment 300 may also have at least some of the functions of information processing device 10, and may predict an atmosphere estimation value using a predetermined method based on, for example, measurement data of an arbitrary area acquired via a camera or microphone mounted on delivery equipment 300 (or progress data in a database).

[0078] (5) Measuring Device 400 (Measuring Unit 40) The measuring device 400 is not limited to the example shown in the embodiment. The measuring device 400 may have any hardware configuration as long as it can achieve the required functions and operations. The measuring device 400 may be equipped with a thermography camera. This allows the temperature state of the audience to be measured and the heat of the venue to be estimated. The measuring device 400 may also be equipped with a motion camera (sensor). This allows the movement state of the audience (standing up, putting their arms around each other's shoulders and waving, etc.) to be measured. Data acquired by a microphone or the like equipped in the measuring device 400 may be used as data to identify a time lag (error) from the planned progress of the event.

[0079] (6) Method for Determining a Delivery Schedule The sequence chart shown in FIG. 4 merely illustrates one example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S103, the delivery time may be estimated taking into account buffers such as the preparation time for the ordered items, the cooking time for the food and drink, or the loading time for the items onto the drone 301. In steps S104 and S105, the process of measuring the state of the audience in real time may be performed at any timing. For example, the measurement device 400 may perform measurement and the information processing device 10 may acquire the measurement data when the information processing device 10 accepts an order from a user.

[0080] In step S106, the atmosphere estimation value may be predicted by any method. For example, the atmosphere estimation may be predicted not only by machine learning model M1 and machine learning model M2, but also by multiple machine learning models implemented according to the characteristics of further subdivided data. On the other hand, the configuration of the machine learning model is not limited to the embodiment, and may be a model that inputs video data, audio data, progress schedule data, and the current time into a single model and outputs an atmosphere estimation value at the current time and a prediction of time change in the atmosphere estimation value from the current time to the end of the event (any future time). Furthermore, any data or training data used for the atmosphere estimation prediction or machine learning may be used. For example, attribute data such as gender or age of audience members attending a live concert may be used for atmosphere estimation. Alternatively, learning may be performed by limiting the data on progress for each area where the live concert is held. Atmosphere estimation may also be performed taking into account the distance to the stage (podium) based on the user's seating information.

[0081] In step S107, the delivery schedule may be determined by any method. For example, when the information processing device 10 predicts the atmosphere estimation ( FIG. 7 ), the information processing device 10 may determine the schedule after estimating the preparation time of the ordered items or the loading time onto the delivery vehicle in advance. Alternatively, the delivery schedule may be determined based on the predicted atmosphere estimation value after the preparation of the ordered items or the loading onto the drone is completed. Furthermore, a restriction may be imposed that prohibits drones from flying over seats during times when the venue is lively (times other than periods T1 and T2).

[0082] In step S108, the notification to the user may be of any kind, and the information processing device 10 may notify the user, for example, of the remaining time until the arrival of the drone 301. The information processing device 10 may also notify the user of information necessary for the user to receive the ordered item, such as information for identifying the target drone (such as a number printed on the exterior of the drone), a personal identification number for verifying the orderer (order reference number), or the status of the drone's sign.

[0083] (7) Delivery Method of Delivery Means The sequence chart shown in FIG. 9 merely illustrates one example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be modified or omitted, the order may be changed, or new operations may be added. In step S201, when determining the delivery schedule, the information processing device 10 may select multiple drones 301 for each order. This allows for sharing of delivery responsibilities even when a single order contains more items than the drone's load capacity. Note that the drone responsible for delivery may be selected or determined in any manner. In step S202, the delivery instructions output by the information processing device 10 to the drone 301 may include inquiry information for when the user receives the item. Furthermore, in step S202, the information processing device 10 may output delivery instructions including the delivery start time to the target drone 301 when the drone responsible for delivering the item has been determined in advance (even if the delivery start time has not yet arrived). In this case, in step S203, the drone 301 may automatically start delivery when the delivery start time arrives, triggered by a clock built into the device.

[0084] (8) Delivery Control Method of Delivery Means The sequence chart shown in FIG. 10 merely illustrates one example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In steps S307 and S309, the drone 301 may autonomously suspend / resume delivery based on its own judgment, regardless of whether or not it receives an instruction from the information processing device 10. For example, the drone 301 may measure the state of the audience using a camera or microphone mounted on the drone 301 in a predetermined manner and estimate the atmosphere in real time. This allows the drone 301 to suspend / resume delivery depending on the state of the atmosphere of the audience in the area where the drone 301 flies / waits.

[0085] (9) Method for Changing Sign of Delivery Vehicle The sequence chart shown in FIG. 11 merely shows one example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S403, when the drone 301 arrives near the seat where the orderer is located, the information processing device 10 may instruct the drone to change the sign to notify the orderer of the drone's location, for example, to change the light color, light intensity, or light pattern (such as flashing).

[0086] (10) Database The database (or the data itself) of the information processing system 1 shown in FIGS. 5 and 8 is not limited to the example shown in the embodiment. In this example, any data may be registered in the database. For example, the data recorded in the order database 1001 may be user attribute information, request information for an order, seat area information, delivery priority, or information indicating whether delivery has been completed. The data recorded in the delivery schedule 2001 may be information indicating the delivery status (delivery status) such as delivery preparation / delivery in progress / delivery completed / suspended / resumed, or information indicating the drone's status (standby / flying / hovering). Furthermore, the layout of the database is not limited to the one shown in the figure, and data may be managed in any layout. The data output by the information processing device 10 to each device may be any data registered in the database.

[0087] (11) Machine Learning The machine learning model or machine learning (AI) function of the information processing system 1 shown in FIG. 6 is not limited to those exemplified in the embodiment. Any machine learning model may be adopted as long as it can realize the required functions and operations. The machine learning model may be configured with any algorithm. For example, the data used as training data may not only be past data, but also video data or audio data measured in real time may be used to repeat reinforcement learning and optimize the prediction of the atmosphere estimation value. This can improve the prediction speed or prediction accuracy when the information processing device 10 predicts the atmosphere estimation value.

[0088] (12) Events Events are not limited to those exemplified in the embodiments. Events may include, for example, sports events, exhibitions, circuses, etc. in addition to the concert events described above. The configurations or operations described in the embodiments may be modified as appropriate depending on the type of event.

[0089] (13) Goods (Ordered Items) Goods are not limited to those exemplified in the embodiments. Goods may be of any form as long as they are related to the event. Furthermore, goods may not be physical objects but may be services related to the event. Services may include, for example, photography using a drone. The configurations or operations described in the embodiments may be modified as appropriate depending on the type of goods or services.

[0090] (14) Others The various programs executed by the processor 101 may be provided by downloading via a network such as the Internet, or may be provided in a state recorded on a computer-readable non-transitory recording medium such as a DVD-ROM. Each processor may be, for example, a CPU, an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit).

[0091] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may also be realized by combining software with the single device or multiple devices.

[0092] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0093] For example, the information processing device 10 according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0094] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other suitable systems, and next-generation systems enhanced based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G) may also be applied.

[0095] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0096] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0097] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0098] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0099] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, should be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0100] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0101] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0102] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0103] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0104] The "unit" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," or the like.

[0105] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0106] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0107] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0108] 1...information processing system, 10...information processing device, 11...acquisition unit, 12...determination unit, 13...prediction unit, 14...delivery control unit, 15...notification unit, 16...change unit, 18...storage unit, 19...control unit, 101...processor, 102...memory, 103...storage, 104...communication IF, 20...user terminal, 30...delivery means, 300...delivery equipment, 301...drone, 40...measurement unit, 400...measuring equipment, 9...network, 1001...order database, 2001...delivery schedule, J...food and beverage, M1, M2...machine learning model

Claims

1. An information processing device having: an acquisition unit that acquires a prediction of time-varying changes in an index that indicates the excitement of visitors at an event venue where an event is being held; and a determination unit that determines a delivery schedule for a delivery means at the event venue to deliver items related to the event to the visitors who have ordered the items, based on the time-varying changes in the index.

2. The information processing device described in claim 1, wherein the acquisition unit acquires seat information indicating the seat in which the visitor who ordered the item is sitting at the event venue, and the determination unit determines the time required for the delivery means to complete delivery from the start of delivery to the visitor sitting at that seat.

3. The information processing device according to claim 1, further comprising a prediction unit that predicts the excitement level based on the measurement results of the state of visitors at the event venue.

4. The information processing device according to claim 1, further comprising a prediction unit that predicts the excitement level based on a predetermined schedule for the event.

5. The information processing device according to claim 1, wherein the determination unit determines the schedule so that the delivery will be carried out during a time period in which the excitement level remains calm for a period of time equal to or greater than a threshold value in predicting the time change of the index.

6. The information processing device according to claim 1, wherein the acquisition unit acquires data indicating the state of the visitors at the event venue from a measurement unit that measures the state of the visitors in real time, and has a delivery control unit that controls the delivery means to suspend the schedule during delivery if the index of real-time excitement estimated from the state of the visitors indicated by the data is equal to or greater than a threshold value.

7. An information processing device according to claim 1, further comprising a notification unit that notifies the visitor of a delivery completion time indicating when the delivery means will deliver the item to the visitor.

8. An information processing device according to claim 1, further comprising a change unit that changes the state of a sign indicating the location of said delivery means during said delivery in accordance with said indicator.

9. The information processing device according to claim 1, wherein the delivery means includes a drone.

10. A delivery determination method comprising the steps of: obtaining a prediction of time-varying changes in an index indicating the excitement of visitors at an event venue where an event is being held; and determining a delivery schedule for a delivery means at the event venue to deliver items related to the event to the visitors who have ordered the items, based on the time-varying changes in the index.