Information processing device, information processing system, information processing method, and information processing program

Abstract

To provide an information processing device, an information processing system, an information processing method, and an information processing program capable of determining wearing of a mask.SOLUTION: By registering movement means and a place, and mask necessity information as mask necessity information for each time in a DB in advance, a server deduces a time zone when a mask needs to be worn on the basis of an inputted schedule, and the mask necessity information stored in the DB. More specifically, the necessity of a mask is determined for each breakdown of a schedule from mask necessity information and time when a mask needs to be worn is deduced.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing system, an information processing method, and an information processing program. [Background technology]

[0002] Patent Document 1 discloses an information processing system that acquires transfer search conditions including a departure time of a transportation facility, a departure stop of the transportation facility, and a destination stop of the transportation facility, searches for a transfer route of a transportation facility from the departure stop to the destination stop using network information, departing during a specified time period including the departure time included in the acquired transfer search conditions, generates route information for the transfer route, acquires congestion information regarding congestion on transportation facilities on the searched transfer route, and displays route guidance information including the generated route information and the acquired congestion information on a display unit. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-112565 A Summary of the Invention [Problem to be solved by the invention]

[0004] By the way, due to the spread of coronavirus infection, people have started to wear masks on a daily basis, but recently there has been a movement to return to normal life even amid the coronavirus pandemic, and the decision to wear a mask is left up to individuals.

[0005] The technology in Patent Document 1 makes it possible to check the degree of congestion from route guidance information, but since wearing a mask is a personal decision, there is room for improvement.

[0006] The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide an information processing device, an information processing system, an information processing method, and an information processing program that are capable of determining whether a mask is being worn. [Means for solving the problem]

[0007] The information processing device of the present invention includes an acquisition unit that acquires schedule information including location information representing locations including a departure point and a destination, and time information including a desired time at each location, and a notification unit that notifies the time when a mask needs to be worn based on the schedule information acquired by the acquisition unit and mask necessity information indicating whether or not a mask needs to be worn.

[0008] In addition, an information processing system according to the present invention includes the above-mentioned information processing device, and an information processing terminal that accepts the schedule information and transmits it to the information processing device, and receives and displays notification of the times when a mask needs to be worn.

[0009] In addition, the information processing method of the present invention includes a computer acquiring schedule information including location information indicating locations including a departure point and a destination, and time information including a desired time at each location, and performing processing to notify the time when a mask needs to be worn based on the acquired schedule information and mask necessity information indicating whether or not a mask needs to be worn.

[0010] In addition, the information processing program of the present invention causes a computer to acquire schedule information including location information indicating locations including a departure point and a destination, and time information including a desired time at each location, and executes a process of notifying the time when a mask needs to be worn based on the acquired schedule information and mask necessity information indicating whether or not a mask needs to be worn. Effect of the Invention

[0011] According to the present invention, it is possible to provide an information processing device, an information processing system, an information processing method, and an information processing program capable of determining whether a mask is being worn. [Brief description of the drawings]

[0012] [Figure 1] 1 is a diagram showing a schematic configuration of an information processing system according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram showing the configuration of a main part of a server, a mobile terminal, and a client computer of an information processing system according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram for explaining the flow of operations in the information processing system according to the first embodiment. [Figure 4] FIG. 13 is a diagram showing an example of mask necessity information registered in advance in a DB. [Diagram 5] 10 is a flowchart showing an example of the flow of processing performed in a mobile terminal or a client computer of the information processing system according to the present embodiment. [Figure 6] 10 is a flowchart showing an example of the flow of processing performed when the server receives a schedule from a mobile terminal or a client computer in the information processing system according to the present embodiment. [Figure 7] FIG. 11 is a diagram for explaining the flow of operations in an information processing system according to a second embodiment. [Figure 8] 11 is a diagram showing an example of information on past means of transportation, places, and congestion levels by time, which are registered in a DB. FIG. [Figure 9] FIG. 11 is a diagram showing an example of mask necessity information in the second embodiment. [Figure 10] A diagram showing an example of the processing flow performed when generating a learning model in a server of the information processing system according to this embodiment. [Figure 11] 13 is a flowchart showing an example of the flow of processing performed when an adjustment schedule is received by a server of an information processing system according to a second embodiment. [Figure 12] 13 is a flowchart showing an example of a flow of processes performed when a schedule is adjusted by a mobile terminal or a client computer in an information processing system according to a second embodiment. [Figure 13]A diagram showing an example of the flow of processing performed when generating a learning model that derives whether or not a mask is necessary in a server of the information processing system according to this embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, the present embodiment will be described with reference to the drawings. Fig. 1 is a diagram showing a schematic configuration of an information processing system according to the present embodiment.

[0014] 1, an information processing system 10 according to this embodiment includes a server 12, a mobile terminal 14, and a client computer 16. The server 12, the mobile terminal 14, and the client computer 16 are connected to each other via a network 18 such as a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, an intranet, or the like. The server 12, the mobile terminal 14, and the client computer 16 are each capable of transmitting and receiving various data to and from each other via the network 18. The server 12 corresponds to an example of an information processing device, and the mobile terminal 14 and the client computer 16 correspond to examples of an information processing terminal.

[0015] The information processing system 10 according to this embodiment registers in advance in a DB (database) 20 information on past means of transportation, locations, and whether a mask is needed for each time as mask need information. The server 12 performs a process of notifying the user of whether a mask is needed for the user's schedule, based on the mask need information registered in advance in the DB 20. The server 12 may generate a learning model for determining whether a mask is needed by learning with AI (Artificial Intelligence) based on the mask need information registered in advance in the DB 20. The server 12 may then use the generated learning model to perform a process of notifying the user of whether a mask is needed for the user's schedule.

[0016] The user executes an application pre-installed on the mobile terminal 14 or the client computer 16, inputs the user's schedule, and transmits the schedule to the server 12. The server 12 then uses the masking necessity information pre-registered in the DB 20 or a learning model to determine whether or not the user's schedule needs to be masked, and transmits the determination result to the mobile terminal 14 or the client computer 16, thereby performing a process of notifying the user whether or not the schedule needs to be masked.

[0017] Next, the configuration of the main parts of the server 12, mobile terminal 14, and client computer 16 of the information processing system 10 according to this embodiment will be described. Fig. 2 is a block diagram showing the configuration of the main parts of the server 12, mobile terminal 14, and client computer 16 of the information processing system 10 according to this embodiment. Note that since the configuration of the main parts of the server 12, mobile terminal 14, and client computer 16 are general computer configurations, the server 12 will be described as a representative example.

[0018] As shown in FIG. 2, the server 12 is composed of a general microcomputer including a CPU (Central Processing Unit) 12A, a ROM (Read Only Memory) 12B, a RAM (Random Access Memory) 12C, a storage 12D, an interface (I / F) 12E, and a bus 12F.

[0019] The CPU 12A is a central processing unit, and controls the overall operation of the device by executing various programs. The ROM 12B stores various control programs such as an information processing program and various parameters in advance. The RAM 12C is used as a work area when the CPU 12A executes various programs. The storage 12D is composed of various storage units such as a hard disk drive (HDD), a solid state drive (SSD), and a flash memory, and stores various data and application programs. The CPU 12A functions as an acquisition unit, a notification unit, and the like by executing the information processing program stored in the ROM 12B. The I / F 12E is connectable to, for example, a network 18, and transmits and receives various data to and from an external device via the network 18. The above-mentioned units of the server 12 are electrically connected to each other by a bus 12F.

[0020] The mobile terminal 14 is further equipped with a GPS (Global Positioning System) device for measuring its position. Even when the client computer 16 is a mobile type such as a notebook computer, a GPS device may be installed.

[0021] (First embodiment) Here, a brief description will be given of the flow of operations in the information processing system 10 according to the first embodiment. Fig. 3 is a diagram for explaining the flow of operations in the information processing system 10 according to the first embodiment. Fig. 4 is a diagram showing an example of mask necessity information registered in advance in the DB 20.

[0022] First, the mask necessity information, which is information on the means of transportation, the location, and the necessity of a mask for each time, is registered in advance in the DB20. For example, as shown in Figs. 3 and 4, the means of transportation, the information on the necessity of a mask, and the location and the information on the necessity of a mask are registered as the mask necessity information. When registering, for example, the information on the necessity of a mask for each time is registered for each means of transportation and location. In the example of Fig. 4, when the means of transportation is walking, a mask is not required all day (indicated as "not required" in the figure), when the means of transportation is train, a mask is required (indicated as "required" in the figure) on weekdays from 5:00 to 9:00, a mask is not required from 9:00 to 17:00 on weekdays, a mask is not required from 17:00 to 24:00 on weekdays, and a mask is required all day on Saturdays and Sundays. In addition, when the means of transportation is a bus, a mask is not required all day on weekdays, a mask is required all day on Saturdays and Sundays, and when the means of transportation is a car, a mask is not required all day. The means of transportation, the location, and the information on the necessity of a mask for each time may be obtained from the external server 22 and registered in the DB20. In this case, the information processing system 10 has a configuration in which an external server 22 is further connected to the network 18, as indicated by the dotted line in FIG.

[0023] The user starts an application preinstalled on the mobile terminal 14 or the client computer 16 to register a schedule for one day. For example, the schedule includes location information indicating locations including a departure point and a destination, time information including a desired time at each location, and transportation information indicating a transportation means for traveling between each location. In the example of Fig. 3, the schedule includes leaving home at 9:00, traveling on foot from 9:00 to 9:30, traveling by train from 9:30 to 12:00, going indoors (to a theme park) from 12:00 to 17:00, traveling by train from 17:00 to 19:30, and traveling on foot from 19:30 to 20:00 before returning home.

[0024] The server 12 determines the time periods when it is necessary to wear a mask based on the input schedule and the mask necessity information stored in the DB 20. That is, it determines whether a mask is necessary for each schedule item based on the mask necessity information, and determines the time periods when it is necessary to wear a mask.

[0025] The server 12 then notifies the application user of a list of times when wearing a mask is required. For example, the time periods when wearing a mask is required are notified and displayed on the user's mobile terminal 14 or client computer 16. In the example of Fig. 3, the following times are displayed as times when wearing a mask is required: 9:30-12:00 on trains, 12:00-17:00 indoors (theme parks), and 17:00-19:30 on trains.

[0026] Next, specific processes performed in each unit of the information processing system 10 according to this embodiment configured as described above will be described.

[0027] First, specific processing performed in the mobile terminal 14 or the client computer 16 will be described. Fig. 5 is a flowchart showing an example of the flow of processing performed in the mobile terminal 14 or the client computer 16 of the information processing system 10 according to this embodiment. Note that Fig. 5 starts, for example, when a user starts an application pre-installed in the mobile terminal 14 or the client computer 16. The processing performed in the mobile terminal 14 will be described below as a representative example.

[0028] In step 200, the CPU 14A displays a schedule input screen, and proceeds to step 202. For example, an input screen is displayed for inputting points including a departure point and a destination, a desired time at each point, a means of transportation for moving between each point, and the like as a schedule.

[0029] In step 202, the CPU 14A judges whether or not the input of the schedule is completed.

[0030] In step 204 , the CPU 14 A transmits schedule information representing the input schedule to the server 12 , and the process proceeds to step 206 .

[0031] In step 206, the CPU 14A determines whether or not a mask necessity determination result (a list of times when a mask needs to be worn) has been received from the server 12. The process waits until the determination is affirmative, and then proceeds to step 208.

[0032] In step 208, CPU 14A displays a list of times when the mask needs to be worn, and ends the series of processes.

[0033] Next, a description will be given of a process performed when the server 12 receives a schedule from the mobile terminal 14 or the client computer 16. Fig. 6 is a flowchart showing an example of a process flow performed when the server 12 receives a schedule from the mobile terminal 14 or the client computer 16 in the information processing system 10 according to this embodiment.

[0034] In step 300, the CPU 12A receives a schedule from the mobile terminal 14 or the client computer 16, and proceeds to step 302. That is, in step 204 described above, the schedule information transmitted from the mobile terminal 14 or the client computer 16 is obtained.

[0035] In step 302, the CPU 12A focuses on one item of the schedule and proceeds to step 304.

[0036] In step 304, the CPU 12A determines whether or not a mask is required, and proceeds to step 306. That is, for the item of interest in the input schedule, it is determined whether or not a mask is required based on the mask requirement information stored in the DB 20.

[0037] In step 306, the CPU 12A judges whether or not there is a breakdown of the next schedule. If the judgment is affirmative, the process proceeds to step 308, and if not, the process proceeds to step 310.

[0038] In step 308, the CPU 12A focuses on the next item, and returns to step 304 to repeat the above-mentioned process.

[0039] On the other hand, in step 310, the CPU 12A returns a list of times when a mask is required to be worn to the mobile terminal 14 or the client computer 16 as a mask necessity determination result, and ends the series of processes. As a result, the determination in the above-mentioned step 206 is affirmative, and the list of times when a mask is required to be worn is notified to the user's mobile terminal 14 or the client computer 16, and the list is displayed. Note that the process of step 300 corresponds to the acquisition unit, and step 310 corresponds to the notification unit.

[0040] In this way, in the information processing system 10 according to this embodiment, by simply inputting a schedule, the user is notified of the times when a mask needs to be worn, eliminating the need for the user to make the decision of whether or not to wear a mask, which was previously based on personal judgment, and is expected to have the effect of reducing the psychological burden on users.

[0041] Second embodiment Next, a brief description will be given of the flow of operations in the information processing system 10 according to the second embodiment. Fig. 7 is a diagram for explaining the flow of operations in the information processing system 10 according to the second embodiment. Fig. 8 is a diagram showing an example of information on past means of transportation and places and congestion levels by time, which are registered in the DB.

[0042] In the first embodiment, the means of transportation is also input when inputting a schedule, but in this embodiment, points including the departure point and destination are input, and the server 12 searches for the means of transportation. That is, in this embodiment, the server 12 has an AI that searches for a route from the departure point to the destination, as shown in FIG. 7. The server 12 searches for a route and derives transportation information indicating the means of transportation to be used when moving between each point. The route search may be performed by an external server 22. In addition, since various well-known technologies can be applied to the AI ​​that searches for a route, a detailed description will be omitted.

[0043] In addition, in the first embodiment, the need for a mask was determined for the user's schedule based on mask necessity information pre-registered in DB20, but in this embodiment, the AI ​​learns based on past congestion information for each means of transportation and place by time pre-registered in DB20, and predicts the congestion level for each means of transportation and place, and determines whether a mask is necessary for the user's schedule.

[0044] In the information processing system 10 according to this embodiment, first, information on past means of transportation, places, and congestion levels by time is registered in advance in the DB 20. For example, as shown in Fig. 8, information on congestion levels by time for each means of transportation and place is registered in the DB 20. Then, the server 12 performs learning using AI based on the information stored in the DB 20, thereby generating a learning model that derives the congestion levels by time for the means of transportation and places. In the example of Figure 8, the information to be registered in DB20 is, for example, when the means of transportation is walking, the congestion level from 6:00 to 7:00 on 8 / 1 (Tue) is low, when the means of transportation is train, the congestion level from 7:00 to 8:00 on 8 / 1 (Tue) is high, when the means of transportation is train, the congestion level from 7:00 to 8:00 on 8 / 2 (Wed) is high, when the means of transportation is train, the congestion level from 7:00 to 8:00 on 8 / 3 (Thu) is high, when the means of transportation is train, the congestion level from 10:00 to 11:00 on 8 / 5 (Sat), the congestion level from 12:00 to 13:00 on 8 / 6 (Sun), the congestion level from 10:00 to 11:00 on 8 / 8 (Tue), the congestion level from 17:00 to 18:00 on 8 / 10 (Thu), the congestion level. Also, the example shows that the location is store A and the crowding level was normal from 12:00 to 14:00 on 8 / 5 (Sat), the location is park B and the crowding level was vacant from 16:00 to 18:00 on 8 / 5 (Sat), the location is theme park C and the crowding level was busy from 9:00 to 20:00 on 8 / 6 (Sun), and the location is theme park D and the crowding level was busy from 8:00 to 21:00 on 8 / 6 (Sun).

[0045] Furthermore, in this embodiment, the need for a mask is determined based on the congestion level derived by AI from mask necessity information as shown in Fig. 9. The example of mask necessity information in Fig. 9 shows examples in which a mask is required (required) when the means of transportation is a train and it is crowded, a mask is required when the means of transportation is a train and the congestion level is normal, a mask is not required (not required) when the means of transportation is a train and the congestion level is uncrowded, a mask is required when the means of transportation is a bus and the congestion level is crowded, a mask is required when the means of transportation is a bus and the congestion level is normal, and a mask is not required when the means of transportation is a bus and the congestion level is uncrowded. Other examples include: if the location is store A and it is crowded, a mask is required; if the location is store A and it is normal crowded, a mask is required; if the location is store A and it is empty, a mask is not required; if the location is park B and it is crowded, a mask is required; if the location is park B and it is normal crowded, a mask is not required; if the location is park B and it is empty, a mask is not required; if the location is theme park C (indoor) and it is crowded, a mask is required; if the location is theme park C (indoor) and it is normal crowded, a mask is required; if the location is theme park C (indoor) and it is empty, a mask is not required; if the location is theme park D (outdoors) and it is crowded, a mask is required; if the location is theme park D (outdoors) and it is normal crowded, a mask is not required; if the location is theme park D (outdoors) and it is empty, a mask is not required.

[0046] In this embodiment, first, the user starts an application pre-installed in the mobile terminal 14 or the client computer 16 and registers a schedule for the day. Here, in this embodiment, location information indicating locations including a departure point and a destination, and time information including a desired time at each location are registered as schedule information. In the example of Fig. 7, the schedule is to leave home at 9:00, arrive at the destination at 12:00, do shopping from 12:00 to 17:00, and move from the destination to home at 17:00.

[0047] The server 12 searches for a route from home to a destination using AI, and inputs a schedule including the transportation information obtained as a result of the route search into a learning model, deriving the congestion level using AI, and figuring out the time period when wearing a mask is necessary from the mask necessity information. For example, in the example of FIG. 7, it is determined that 9:00 to 9:30 is walking (no mask), 9:30 to 11:30 is train (mask must be worn because congestion is expected), 11:30 to 12:00 is walking (no mask), 12:00 to 17:00 is shopping (mask must be worn because congestion is expected), 17:00 to 17:30 is walking (no mask), and 17:30 to 19:30 is train (mask must be worn because congestion is expected). Note that the route search may be requested to the external server 22 to obtain the search results.

[0048] Then, the server 12 notifies the application user of a list of times when wearing a mask is required. For example, the time periods when wearing a mask is required are displayed on the user's mobile terminal 14 or client computer 16. In the example of Fig. 7, the following times are displayed as times when wearing a mask is required: 9:30 to 11:30 (wearing on trains), 12:00 to 17:00 (wearing while shopping), and 17:30 to 19:00 (wearing on trains).

[0049] Furthermore, in this embodiment, if a schedule change occurs, rescheduling is performed, and the necessity of masking is re-determined and notified.

[0050] For example, in the example of Fig. 7, when the actual behavior of leaving home at 9:10 is confirmed by the GPS device mounted on the mobile terminal 14 or the mobile client computer 16, rescheduling is performed. That is, the schedule is readjusted based on the actual behavior. In this example, the adjusted schedule is leaving home at 9:10, arriving at the destination at 12:10, and shopping from 12:10 to 17:10.

[0051] The server 12 re-searches for a route from home to the destination using AI based on the adjusted schedule. Note that the server 12 may request a route search from the external server 22 and obtain the search results.

[0052] In addition, the server 12 inputs the adjusted schedule including the transportation information obtained as a result of the route re-search into the learning model, derives the congestion degree by AI, and determines the time period when wearing a mask is necessary from the mask necessity information. For example, in the example of Fig. 7, it is determined that 9:10 to 9:40 is walking (no mask), 9:40 to 11:40 is train (mask must be worn because congestion is expected), 11:40 to 12:10 is walking (no mask), 12:10 to 17:10 is shopping (mask must be worn because congestion is expected), 17:10 to 17:40 is walking (no mask), and 17:40 to 19:40 is train (mask must be worn because congestion is expected).

[0053] After that, once shopping times and train delays have been confirmed based on the GPS location information, the schedule is revised as appropriate.

[0054] Next, specific processing performed in the information processing system 10 according to this embodiment will be described.

[0055] First, a process performed by the server 12 when generating a learning model will be described. Fig. 10 is a diagram showing an example of a flow of a process performed by the server 12 of the information processing system 10 according to this embodiment when generating a learning model.

[0056] In step 100, the CPU 12A acquires congestion information for each time period for the location and the means of transportation, and proceeds to step 102. That is, congestion information for each time period for each past location and means of transportation is acquired. The congestion information may be acquired, for example, by inputting the congestion information by a user or a system administrator, or congestion information provided by the external server 22 may be acquired.

[0057] In step 102, the CPU 12A derives a learning model for deriving the congestion degree, and proceeds to step 104. That is, based on the information stored in the DB 20, learning is performed by the AI ​​to generate a learning model for deriving the congestion degree from the means of transportation, the place, the time, and the like.

[0058] In step 104, the CPU 12A stores the generated learning model in the DB 20, and ends the series of processes.

[0059] The processing performed by the mobile terminal 14 or the client computer 16 is the same as that in the first embodiment, and therefore a detailed description thereof will be omitted.

[0060] Moreover, the part where the server 12 receives the schedule and determines whether or not a mask is required is the same as the first embodiment except for the fact that a route search is performed to which well-known technology can be applied, and the fact that the server 12 determines whether or not a mask is required in step 304 described above. Therefore, only the part where the server 12 determines whether or not a mask is required will be described.

[0061] That is, in this embodiment, in order to determine whether a mask is needed, a congestion degree is derived for each item in the user's schedule using a learning model that derives the congestion degree generated as described above. Then, the mask necessity information shown in FIG. 9 is used to determine whether a mask is needed for each item in the schedule. As a result, as in the first embodiment, by simply inputting the schedule, the time when a mask is needed is notified, so that the decision of whether or not to wear a mask, which was previously made at the user's own discretion, is no longer necessary, and the effect of reducing the psychological burden on the user can be expected.

[0062] In the above, a learning model for deriving the congestion level is generated, the congestion level of each item in the user's schedule is derived using the learning model, and then the need for a mask is determined using the mask necessity information registered in advance, but the learning model may be a learning model that directly derives the need for a mask from the schedule. That is, a learning model that derives the need for a mask from the location, means of transportation, and time may be generated by machine learning using information on the location, means of transportation, time, congestion level, and need for a mask as teacher data, and the need for a mask may be determined. The learning model for deriving the need for a mask is generated, for example, as follows.

[0063] Here, a process performed by the server 12 when generating a learning model for deriving whether or not a mask is necessary will be described. Fig. 11 is a diagram showing an example of a flow of a process performed by the server 12 of the information processing system 10 according to this embodiment when generating a learning model for deriving whether or not a mask is necessary.

[0064] In step 400, the CPU 12A acquires congestion information for the location and the means of transportation, and proceeds to step 402. That is, congestion information for each time period for each past location and means of transportation is acquired. The congestion information may be acquired, for example, by inputting the congestion information by a user or a system administrator, or congestion information provided by the external server 22 may be acquired.

[0065] In step 402, the CPU 12A sets whether or not a mask is required depending on the acquired congestion information, and proceeds to step 404. For example, the congestion situation is classified into three stages, such as crowded, normal, and empty, and whether or not a mask is required is set depending on the location and the means of transportation. For example, when it is crowded, it is set to wear a mask regardless of the location and the means of transportation. Also, when it is empty, it is set to not wear a mask regardless of the location and the means of transportation. Also, when it is normal, it is set to wear a mask when traveling by train, bus, etc. or indoors, and to not wear a mask when outdoors.

[0066] In step 404 , the CPU 12 A stores the result of the setting as to whether or not masking is required in the DB 20 , and the process proceeds to step 406 .

[0067] In step 406, the CPU 12A derives a learning model and proceeds to step 408. That is, based on the information stored in the DB 20 in step 404, learning is performed by AI to generate a learning model that determines whether or not a mask is necessary based on the means of transportation, location, time, etc.

[0068] In step 408, the CPU 12A stores the generated learning model in the DB 20, and ends the series of processes.

[0069] In this way, by inputting the details of a user's schedule (location, means of transportation, and time) into the generated learning model, it is possible to derive whether or not a mask is required for each schedule detail.

[0070] Next, a process performed when adjusting a schedule on the mobile terminal 14 or the client computer 16 of the information processing system 10 according to this embodiment will be described. Fig. 12 is a flow chart showing an example of a flow of a process performed when adjusting a schedule on the mobile terminal 14 or the client computer 16 of the information processing system 10 according to this embodiment. Note that the process in Fig. 12 may be started, for example, at predetermined time intervals or in response to a user instruction. The process performed on the mobile terminal 14 will be described below as a representative example.

[0071] In step 250, CPU 14A compares the current location information with the schedule and proceeds to step 252. That is, the time information included in the location information is compared with the schedule to confirm whether the location is according to the schedule. The departure from home is confirmed by the GPS device mounted on mobile terminal 14.

[0072] In step 252, the CPU 14A judges whether or not to adjust the schedule. This judges whether or not adjustment is necessary for the already registered schedule. If the judgement is negative, the series of processes is terminated, and if the judgement is positive, the process proceeds to step 254.

[0073] In step 254, the CPU 14A adjusts the schedule and transmits it to the server, and the process proceeds to step 256. Note that it is also possible to transmit only the change in departure time to the server 12, and have the server 12 adjust the schedule itself.

[0074] In step 256, CPU 14A determines whether or not a mask necessity determination result (a list of times when a mask needs to be worn) has been received from server 12. The process waits until the determination is affirmative, and proceeds to step 258.

[0075] In step 258, CPU 14A displays a list of times when the mask needs to be worn, and ends the series of processes.

[0076] Next, a process performed when the server 12 of the information processing system 10 according to this embodiment receives an adjustment schedule will be described. Fig. 13 is a flowchart showing an example of the flow of the process performed when the server 12 of the information processing system 10 according to this embodiment receives an adjustment schedule. In Fig. 13, the same processes as those in Fig. 6 are described with the same reference numerals.

[0077] In step 300, the CPU 12A receives the adjusted schedule from the mobile terminal 14 or the client computer 16, and proceeds to step 302. That is, the CPU 12A acquires the adjusted schedule information from the mobile terminal 14 or the client computer 16.

[0078] In step 301, the CPU 12A performs route search for the adjusted schedule, and proceeds to step 303. For example, if the departure time from home is changed, a route search is performed from home to the destination at the changed time.

[0079] In step 303, the CPU 12A focuses on one of the details of the adjusted schedule including the transportation means information obtained as a result of the route search, and the process proceeds to step 305.

[0080] In step 305, the CPU 12A determines whether or not a mask is needed using AI, and proceeds to step 306. That is, the CPU 12A determines whether or not a mask is needed using AI by inputting a particular item of the input schedule into the learning model.

[0081] In step 306, the CPU 12A judges whether or not there is a breakdown of the next schedule. If the judgment is affirmative, the process proceeds to step 308, and if not, the process proceeds to step 310.

[0082] In step 308, the CPU 12A focuses on the next item, and returns to step 304 to repeat the above-mentioned process.

[0083] Meanwhile, in step 310, CPU 12A returns a list of times when a mask is required to be worn to mobile terminal 14 or client computer 16 as a mask necessity determination result, and ends the series of processes. As a result, the determination in the above-mentioned step 206 is affirmative, and the list of times when a mask is required to be worn is notified to the user's mobile terminal 14 or client computer 16, and the list is displayed.

[0084] In this way, in the information processing system 10 according to the present embodiment, even if the schedule is changed, the change in the schedule can be detected and the user can be notified of the time when the mask needs to be worn.

[0085] In the second embodiment, a route search is performed to search for a means of transportation, but a user may input a means of transportation as in the first embodiment. Also, in the first embodiment, a route search may be performed to search for a means of transportation as in the second embodiment.

[0086] In addition, in each of the above embodiments, a list of times when mask wearing is required is notified to the user's mobile terminal 14 or client computer 16, but this is not limited to the above. For example, a notification that a mask needs to be worn may be sent every time a time when a mask needs to be worn arrives.

[0087] Furthermore, the present invention is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0088] 10. Information Processing Systems 12 Server 14 Mobile devices 16 Client Computers 20 DB

Claims

1. an acquisition unit that acquires schedule information including location information indicating locations including a departure point and a destination and time information including a desired time at each location; A notification unit that notifies a time when a mask needs to be worn based on the schedule information acquired by the acquisition unit and mask necessity information indicating whether or not a mask needs to be worn; An information processing device comprising:

2. The information processing apparatus according to claim 1 , wherein the acquiring unit acquires the schedule information further including transportation means information indicating the transportation means used for moving between the respective locations.

3. The information processing device according to claim 1, wherein the notification unit searches for a means of transportation for traveling between each location based on the location information and the time information, and notifies the user of the time when a mask needs to be worn based on the schedule information, which further includes transportation information representing the searched transportation means, and the mask necessity information.

4. The acquisition unit further acquires adjusted schedule information obtained by adjusting the schedule information, The information processing apparatus according to claim 1 , wherein when the adjustment schedule information is acquired, the notification unit notifies the user of a time period during which a mask is required to be worn based on the adjustment schedule information and the mask necessity information.

5. The information processing device according to claim 1 , wherein the notification unit notifies the user of the time when a mask needs to be worn based on the schedule information, a pre-trained learning model that derives the degree of congestion at each point in the schedule information, and the mask necessity information.

6. The information processing device according to claim 1 , wherein the notification unit notifies the user of the time when a mask needs to be worn based on a pre-trained learning model that derives whether or not a mask needs to be worn at each point in the schedule information, instead of the mask need information.

7. The information processing device according to claim 1 ; an information processing terminal that receives the schedule information, transmits it to the information processing device, and receives and displays a notification of a time when a mask needs to be worn; An information processing system comprising:

8. The computer Obtaining schedule information including location information indicating locations including a departure point and a destination, and time information including a desired time at each location; An information processing method that performs processing to notify the times when a mask needs to be worn based on the acquired schedule information and mask necessity information indicating whether or not a mask needs to be worn.

9. On the computer, Obtaining schedule information including location information indicating locations including a departure point and a destination, and time information including a desired time at each location; An information processing program for executing a process of notifying a time when a mask needs to be worn based on the acquired schedule information and mask necessity information indicating whether or not a mask needs to be worn.

Images