System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting an arrival time, a staying time, a desired attraction, and a desired event, a means for aggregating past visitor data and real-time data, a means for generating an optimum schedule by performing congestion prediction on the basis of the aggregated data, and a means for presenting the generated schedule to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] At leisure facilities such as theme parks, visitors are faced with crowds and long waiting times, which reduces the amount of time they can enjoy themselves and increases fatigue. This reduces visitor satisfaction and increases the burden on operating staff. This detracts from the enjoyable experience for visitors and has a negative impact on operators, as it increases their workload. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes the following means: means for inputting arrival time, length of stay, desired attractions, and desired events; means for aggregating past visitor data and real-time data; means for predicting congestion based on the aggregated data and generating an optimal schedule; and means for presenting the generated schedule to the user. This not only enables visitors to move efficiently within the facility while avoiding congestion, but also makes it easier for management staff to predict visitor movements, enabling more effective management.

[0006] "Arrival time" refers to the time a visitor plans to arrive at a leisure facility such as a theme park.

[0007] "Duration" refers to the amount of time a visitor plans to spend at a theme park or other leisure facility.

[0008] "Desired Attraction" refers to the name and type of attraction that the visitor would like to ride or experience.

[0009] "Desired Event" refers to the name and type of event, such as a show or parade, that the visitor would like to attend.

[0010] "Past visitor data" refers to data that records the behavioral history, length of stay, congestion status, etc. of visitors who visited the site last time and before.

[0011] "Real-time data" refers to data collected in real time through sensors and terminals, including information on current congestion and waiting times.

[0012] "Aggregated Data" means data obtained by combining and analyzing historical visitor data and real-time data.

[0013] "Crowd prediction" is the process of predicting the crowding levels at specific times or attractions based on collected data.

[0014] An "optimal schedule" refers to a plan that avoids crowding and allows visitors to move around and experience the theme park efficiently and comfortably.

[0015] "Presenting to the user" refers to the act of displaying the generated schedule and other information on the user's terminal. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system for allowing visitors to move around a leisure facility such as a theme park efficiently while avoiding crowds. The system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting crowds based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[0038] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[0039] Program processing

[0040] User Input

[0041] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration of stay, desired attractions, and desired events.

[0042] Sending data

[0043] The device sends the entered information to the server, which receives and stores this data.

[0044] View past data

[0045] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[0046] Real-time data collection

[0047] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[0048] Data aggregation and analysis

[0049] The server combines the collected historical data with real-time data and analyzes it using AI algorithms, which then predicts crowds at each attraction and event.

[0050] Generating an optimal schedule

[0051] Based on the user's input information and congestion forecast data, the server generates an optimal schedule that allows the user to move around the theme park efficiently and comfortably.

[0052] Schedule suggestions

[0053] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[0054] Specific examples

[0055] 1. User input

[0056] Arrival time: 10:00

[0057] Duration: 6 hours

[0058] Preferred Attractions: Roller coaster, horror house

[0059] Preferred event: Parade

[0060] The user enters and submits this information.

[0061] 2. Data collection and analysis

[0062] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[0063] The server collects waiting times and congestion status for each attraction in real time.

[0064] 3. Generating the optimal schedule

[0065] Based on the user's preferences, the AI ​​algorithm generates the optimal schedule: "Arrive at 10:00, ride the roller coaster from 10:15, enjoy the parade from 11:00, and enjoy the horror house at 12:00."

[0066] 4. Schedule proposal

[0067] The server transmits the generated schedule to the user's terminal.

[0068] Users can check the schedule on their terminal and move around the theme park efficiently.

[0069] In this way, the present invention provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, thereby improving visitor satisfaction and reducing the burden on the park staff.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[0073] Step 2:

[0074] The user inputs information about the theme park visit, specifically, the arrival time, the duration of stay, the desired attractions, and the desired events, and clicks the "Submit" button.

[0075] Step 3:

[0076] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0077] Step 4:

[0078] The server retrieves past visitor data from the database, querying information such as past crowd levels and attraction wait times.

[0079] Step 5:

[0080] The server collects data in real time. Information such as current congestion status and waiting times is obtained from sensors and devices within the theme park via an API, and is cached in memory or stored in a database.

[0081] Step 6:

[0082] The server combines the collected historical and real-time data to create aggregated data, which is then prepared as a data set for statistical analysis.

[0083] Step 7:

[0084] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[0085] Step 8:

[0086] The server generates an optimal schedule based on the user's input and analysis results. For example, a specific schedule such as "Arrival time: 10:00, roller coaster at 10:15, parade at 11:00, horror house at 12:30" is generated.

[0087] Step 9:

[0088] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[0089] Step 10:

[0090] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[0091] Step 11:

[0092] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Leisure facilities such as theme parks require methods for visitors to move around the facility efficiently and avoid crowds. In particular, a system that can predict congestion in real time and provide optimal schedules based on that prediction is needed. However, current systems do not fully utilize past or real-time data, making it difficult to provide schedules that allow visitors to enjoy the facility comfortably. This often results in long waiting times and discomfort due to crowds.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting congestion using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal, thereby enabling visitors to move around the theme park efficiently and comfortably.

[0098] "Arrival time" is the time when the user plans to arrive at a leisure facility such as a theme park.

[0099] "Staying time" is the period of time that a user plans to stay at a leisure facility such as a theme park.

[0100] "Desired Attractions" is a list of attractions that the user wishes to experience during their visit.

[0101] "Desired Events" is a list of events that the user wishes to participate in during their visit.

[0102] "Past visit data" refers to data related to the behavioral history, length of stay, and waiting times for attractions of visitors who have visited the theme park in the past.

[0103] "Real-time data" refers to data that shows real-time conditions within the theme park, such as current wait times for attractions and congestion levels.

[0104] "Aggregated Data" is data that combines historical visit data and real-time data and is used for predictions and analysis.

[0105] A "generative AI model" is an artificial intelligence algorithm that uses past visit data and real-time data as input to predict congestion and generate optimal schedules.

[0106] The "optimal schedule" is a visit plan created by the generative AI model to help users move around the theme park efficiently and comfortably.

[0107] A "display terminal" is a device for presenting an optimal schedule to a user, and includes a smartphone, tablet, etc.

[0108] The present invention is a system for enabling visitors to move efficiently and avoid crowds at leisure facilities such as theme parks. The system includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting crowds using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal.

[0109] The system's hardware configuration includes the device used by the user (such as a smartphone or tablet), a server that processes data, and multiple sensors for collecting real-time data, while the software configuration includes an application for users to input information, data aggregation and analysis algorithms that run on the server, and a generative AI model.

[0110] Specifically, the system operates as follows.

[0111] First, a user uses their device to enter information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. For example, a user might enter the following information into the application form:

[0112] Arrival time: 10:00

[0113] Duration: 6 hours

[0114] Desired attractions: Roller coaster, horror house

[0115] Preferred event: Parade

[0116] The device then sends this input data to the server via an HTTP request, packaging the data in JSON format, which the server receives and stores in a database.

[0117] The server then retrieves past visit data from a database and collects real-time data from multiple sensors installed throughout the theme park, including past crowd levels and current wait times.

[0118] The server aggregates this data and uses a generative AI model to predict crowding. It uses past and real-time data to predict future wait times for each attraction and event. For example, the generative AI model outputs the following predictions:

[0119] Roller Coaster: 15 minute wait

[0120] Horror House: 20 minute wait

[0121] Parade: Starts at 11:00

[0122] Based on this prediction data and user input, the server generates an optimal schedule. The generated schedule provides the user with the order and time of visit to efficiently enjoy the theme park. For example, a suggested schedule might be "arrive at 10:00, ride the roller coaster from 10:15, see the parade from 11:00, and enjoy the horror house at 12:00."

[0123] Finally, the server sends this optimal schedule to the terminal. The user can check the schedule on the terminal and avoid congestion by following the instructions.

[0124] Here are some examples of prompts for generative AI models:

[0125] "I'll arrive at the theme park at 10:00 and plan to stay for 6 hours. I'd like to experience the roller coaster, the horror house, and see the parade. What's the best schedule to efficiently enjoy my time there?"

[0126] The present invention thus provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, not only improving visitor satisfaction but also reducing the burden on the park staff.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] The user uses a terminal to input information about the theme park, including arrival time, duration, desired attractions, and desired events. The user enters the following information into the application:

[0130] Arrival time: 10:00

[0131] Duration: 6 hours

[0132] Desired attractions: Roller coaster, horror house

[0133] Preferred event: Parade

[0134] Input: User's visit information (arrival time, stay time, desired attractions, desired events)

[0135] Output: Data entered into the application

[0136] Specific action: The user enters information using the input form and taps the submit button.

[0137] Step 2:

[0138] The device sends the input information to the server. The device app issues an HTTP request to the server and sends the input information in JSON format.

[0139] Input: Visit information entered by the user

[0140] Output: Visit information data sent to the server

[0141] Specific operation: Data is sent via an HTTP request, received by the server, and stored in the database.

[0142] Step 3:

[0143] The server retrieves past visit data from the database, and uses SQL queries to search and retrieve data on crowding and attraction wait times for the past year.

[0144] Input: SQL query to retrieve past visit data

[0145] Output: Past visit data retrieved from the database

[0146] Specific operation: The server executes the SQL query and retrieves the required data from the database.

[0147] Step 4:

[0148] The server collects real-time data from sensors in the theme park, such as waiting times for each attraction, crowding levels, and scheduled event start times.

[0149] Input: Data streams from sensors to collect real-time data

[0150] Output: Real-time data collected

[0151] Specific operation: The server receives the data stream sent from the sensor and stores it in temporary memory as real-time data.

[0152] Step 5:

[0153] The server integrates the collected data and uses a generative AI model to predict congestion. It uses past and real-time data as input to predict future congestion and waiting times.

[0154] Inputs: Historical and real-time data

[0155] Output: Predicted congestion and waiting time

[0156] Specific operation: The server uses the generated AI model to analyze the data and output the congestion prediction results.

[0157] Step 6:

[0158] The server generates an optimal schedule based on user visit information and congestion forecast data. For example, it creates a schedule that includes "Riding the roller coaster at 10:15, the parade at 11:00, and enjoying the horror house at 12:00."

[0159] Input: User visit information and congestion prediction data

[0160] Output: Optimal schedule

[0161] Specific operation: The generative AI model uses a schedule generation algorithm to create an optimal visiting plan.

[0162] Step 7:

[0163] The server sends the generated schedule to the terminal, which receives the schedule and displays it on the application to notify the user.

[0164] Input: Generated optimal schedule

[0165] Output: Schedule displayed on the user's terminal

[0166] Specific operation: The server sends an HTTP response, and the device displays the received data.

[0167] As described above, this system automatically generates and provides the optimal schedule for users to efficiently enjoy a theme park.

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] In theme parks and virtual stores, it is difficult for visitors to move around the facility efficiently while avoiding crowds. In particular, there is a need to provide an optimal schedule based on real-time congestion status and past data, but current systems have difficulty achieving this. Furthermore, there are issues that need to be resolved to improve the efficiency of visitor movement within virtual stores in order to improve visitor satisfaction.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion and generating an optimal schedule based on the aggregated data, a means for presenting the generated schedule to the user, and a means for streamlining visitor movement within the virtual environment. This allows visitors to move efficiently within the facility based on their arrival time, length of stay, and desired attractions and events, avoiding congestion. Similarly, visitors can move efficiently within virtual stores and smoothly use their desired products and services.

[0173] "Arrival time" is the time a visitor plans to arrive at a facility or virtual environment.

[0174] "Dwell Time" means the expected or desired length of time a visitor will remain at a facility or in a virtual environment.

[0175] "Desired attractions" are attractions or spots that visitors would like to visit or experience.

[0176] A "desired event" is an event or activity that a visitor would like to participate in or observe.

[0177] "Historical Visitor Data" means data about visitors who have previously visited a facility or virtual environment, including information about crowd levels and wait times for attractions.

[0178] "Real-time data" refers to ongoing information such as current crowd levels and waiting times for attractions.

[0179] "Congestion forecasting" is the prediction of future congestion conditions based on past and real-time data.

[0180] An "optimal schedule" is one that allows visitors to move efficiently through the facility and experience the attractions and events they desire.

[0181] "Means for presenting to users" refers to the means by which visitors can view the generated schedule. This includes devices such as smartphones, tablets, and head-mounted displays.

[0182] A "virtual environment" is a digital space that mimics a real-world environment and that visitors can access using digital devices.

[0183] This invention is a system that allows visitors to move around a facility efficiently while avoiding crowds. This system can be applied to theme parks and virtual stores, and is realized by generating and presenting an optimal schedule based on the visitor's arrival time, stay time, desired attractions, desired events, etc.

[0184] First, the user uses a device such as a smartphone or head-mounted display to input information about their visit, such as their estimated time of arrival, length of stay, and desired attractions and events. This information is then sent from the device to the server.

[0185] The server retrieves historical visitor data from a database and collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including crowd levels, wait times, and current visitor locations.

[0186] The collected data is analyzed using Python and AI algorithms (e.g., scikit-learn and TensorFlow). The server combines historical and real-time data to predict congestion. Based on this prediction, an optimal schedule is generated to allow users to move around the facility efficiently.

[0187] The generated schedule is then sent back to the user's device, where the user can check and follow the schedule to avoid crowds.In particular, in virtual stores, users can efficiently use the products and services they desire.

[0188] As a specific example, if a user arrives at a theme park at 10:00, plans to stay for six hours, and wants to ride the roller coaster or the horror house, the server can use AI to generate an optimal schedule based on the past year's congestion data and real-time data: "Arrive at 10:00, ride the roller coaster from 10:15, parade from 11:00, horror house at 12:00." This schedule is immediately displayed on the user's device.

[0189] Example prompt sentence:

[0190] A user visits a virtual store, arrives at 10:00, stays for 4 hours, wants to use the shopping area and try on clothes, and also wants to watch a virtual talk show. Generate the optimal schedule.

[0191] In this way, this invention is expected to improve the visitor experience and also improve management efficiency for operators.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] Users use their smartphones or head-mounted displays to input information about their visit, including arrival time, duration, and desired attractions and events, which is then sent from the device to the server.

[0195] Input: Arrival time, stay time, desired attractions, desired events

[0196] Output: Sending visit information to the server

[0197] Step 2:

[0198] The server accesses a database that stores past visitor data, including the congestion status and waiting times for each attraction, and retrieves the visitor data.

[0199] Input: Visit information

[0200] Output: Past visitor data

[0201] Step 3:

[0202] The server collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including current crowd levels and wait times for attractions.

[0203] Input: Visit information

[0204] Output: Real-time data

[0205] Step 4:

[0206] The server combines collected past visitor data with real-time data and uses AI algorithms to predict congestion, which then predicts future congestion and generates an optimal schedule.

[0207] Input: Past visitor data, real-time data

[0208] Output: Congestion forecast data, optimal schedule

[0209] Step 5:

[0210] The server then sends the generated optimal schedule to the user's device, allowing the user to check the schedule and move around the facility efficiently based on it.

[0211] Input: Optimal schedule

[0212] Output: Schedule display on user's device

[0213] Step 6:

[0214] Users move around the theme park and virtual stores based on the schedule provided, allowing them to avoid crowds and efficiently experience the attractions and events they want.

[0215] Input: Proposed schedule

[0216] Output: Efficient travel and experiences

[0217] The above are the specific processing steps for carrying out the present invention.

[0218] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0219] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds, and further combines an emotion engine that recognizes the user's emotions and adjusts the schedule. This system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[0220] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[0221] Program processing

[0222] User Input

[0223] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. Users are also asked to input their emotional state.

[0224] Sending data

[0225] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0226] View past data

[0227] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[0228] Real-time data collection

[0229] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[0230] Collecting Emotional Data

[0231] The server receives the user's emotional state data sent from the device, and the emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology.

[0232] Data aggregation and analysis

[0233] The server combines collected historical data, real-time data, and user sentiment data to create aggregated data. The aggregated data is then prepared as a data set for statistical analysis. AI algorithms are used to analyze the data and predict crowds at each attraction and event.

[0234] Generating an optimal schedule

[0235] The server generates an optimal schedule based on the user's input information, emotional data, and crowd prediction data to allow the user to move around the theme park efficiently and comfortably. The server also takes into account the user's emotional state, adjusting the schedule according to their emotions, for example, choosing a nearby attraction if they are tired.

[0236] Schedule suggestions

[0237] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[0238] Specific examples

[0239] 1. User input

[0240] Arrival time: 10:00

[0241] Duration: 6 hours

[0242] Preferred Attractions: Roller coaster, horror house

[0243] Preferred event: Parade

[0244] Emotional state: Slightly tired

[0245] The user enters and submits this information.

[0246] 2. Data collection and analysis

[0247] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[0248] The server collects waiting times and congestion status for each attraction in real time.

[0249] The emotion engine analyzes the user's emotional state and determines that they are "slightly tired."

[0250] 3. Generating the optimal schedule

[0251] Based on the user's wishes and emotions, an AI algorithm generates a schedule that might include "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying a horror house."

[0252] 4. Schedule proposal

[0253] The server transmits the generated schedule to the user's terminal.

[0254] Users can check the schedule on their terminal and move around the theme park efficiently.

[0255] In this way, the present invention allows visitors to avoid crowds and maximize their enjoyment of the theme park, and by taking emotional data into consideration, it provides a more comfortable and satisfying experience, thereby improving visitor satisfaction and reducing the burden on the park staff.

[0256] The processing flow will be explained below.

[0257] Step 1:

[0258] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[0259] Step 2:

[0260] The user inputs information about their visit to the theme park, such as arrival time, length of stay, desired attractions, and desired events, and then clicks the "Submit" button. The user also inputs their emotional state. For example, emotional state options such as "Tired," "Excited," and "Relaxed" are displayed.

[0261] Step 3:

[0262] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0263] Step 4:

[0264] The server retrieves past visitor data from a database, querying the database for information such as past crowd levels and attraction wait times.

[0265] Step 5:

[0266] The server collects data in real time. Data such as current congestion status and waiting times is obtained via API from sensors and devices installed within the theme park, and is cached in memory or stored in a database.

[0267] Step 6:

[0268] The device sends the user's emotional state data to the server, and the emotion engine analyzes the emotional state using the user's text input, voice input, and facial recognition technology as needed, and generates specific emotional data.

[0269] Step 7:

[0270] The server combines the collected historical data, real-time data, and user sentiment data to create aggregated data, which is then prepared as a dataset for statistical analysis.

[0271] Step 8:

[0272] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[0273] Step 9:

[0274] The server generates an optimal schedule based on user input, emotional data, and congestion prediction data. If the server determines that the user is tired, it prioritizes nearby rest spots in the schedule. The generated schedule takes into account the user's preferences and current emotional state, ensuring efficient and comfortable travel.

[0275] Step 10:

[0276] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[0277] Step 11:

[0278] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[0279] Step 12:

[0280] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[0281] Example 2

[0282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] Leisure facilities such as theme parks face the challenge of preventing visitors from crowding and moving around the facility efficiently. Furthermore, planning that doesn't take into account the emotional state of visitors can prevent them from experiencing a comfortable and satisfying experience. This can lead to lower visitor satisfaction and increased workloads for operational staff.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0285] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, desired events, and emotional state, a means for aggregating past visitor data and real-time data, and a means for predicting congestion based on the aggregated data and emotional data to generate an optimal schedule, thereby enabling visitors to travel efficiently while avoiding congestion and adjusting schedules that take emotional state into consideration.

[0286] "Arrival time" is the time that a visitor plans to arrive at a leisure facility such as a theme park.

[0287] "Duration" is the length of time a visitor plans to stay at a theme park or other leisure facility.

[0288] A "desired attraction" is an attraction that a visitor wishes to visit at a leisure facility such as a theme park.

[0289] A "desired event" is an event that a visitor wishes to participate in at a leisure facility such as a theme park.

[0290] "Emotional state" refers to the visitor's current emotional state, which may be a psychological or physiological state such as fatigue, excitement, or stress.

[0291] "Past visitor data" is data about visitors who have visited a theme park or other leisure facility in the past, and includes information such as congestion levels and waiting times.

[0292] "Real-time data" refers to current data such as the current congestion situation at leisure facilities such as theme parks and waiting times for attractions.

[0293] "Aggregated Data" means data sets created by combining collected historical visitor data and real-time data.

[0294] "Emotional Data" is data regarding a visitor's emotional state analyzed based on their input.

[0295] "Crowd prediction" is the prediction of future congestion conditions based on past visitor data and real-time data.

[0296] An "optimal schedule" is a schedule designed to allow visitors to move through a theme park or other leisure facility efficiently and comfortably.

[0297] The "means for presenting to the user" is a method for transmitting the generated schedule to the user's terminal and displaying it.

[0298] The present invention is a system for allowing visitors to move around leisure facilities such as theme parks efficiently while avoiding crowds. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the schedule accordingly. The system includes the following main means:

[0299] First, users enter their visit information using a device such as a smartphone or tablet. Specifically, they enter their arrival time, duration of stay, desired attractions, desired events, and emotional state. This data is then sent to the server via the application. The accuracy of the data is ensured by using an API to send the input data to the server as an HTTP POST request.

[0300] The server collects past visitor data and real-time data, and uses this data to predict congestion. Past visitor data is retrieved from a database using SQL queries, and real-time data is collected from various sensors installed within the theme park (e.g., cameras, people counters, Wi-Fi terminals). The received data is temporarily stored in storage and used for subsequent processing.

[0301] The system also uses an emotion engine to analyze the user's emotional state. Based on the emotional data sent from the device, the system uses text input, voice input, and facial recognition technology to determine the user's emotional state. This emotional data is also integrated on the server and prepared as a dataset for statistical analysis along with the congestion forecast data.

[0302] Using AI algorithms, the server analyzes the collected data and predicts crowding at each attraction and event. Based on the user's input, emotional data, and crowding prediction data, the system generates an optimal schedule to allow users to move around the theme park efficiently and comfortably. Since the system also takes into account the user's emotional state, it adjusts the schedule according to the user's condition, for example, choosing a nearby attraction if the user is tired.

[0303] The generated schedule is sent from the server to the device and suggested to the user. By checking the schedule on the device and following it, the user can avoid congestion and have a more comfortable experience.

[0304] For example, consider the case where a user enters the following information:

[0305] Arrival time: 10:00

[0306] Duration: 6 hours

[0307] Preferred Attractions: Roller coaster, horror house

[0308] Preferred event: Parade

[0309] Emotional state: Slightly tired

[0310] Based on this information, the server analyzes a combination of past and real-time data and suggests a schedule to the user, such as "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying the horror house."

[0311] Examples of prompts:

[0312] "You are the developer of a system that efficiently plans theme park visitor schedules. The user has entered the following information:

[0313] Arrival time: 10:00

[0314] Duration: 6 hours

[0315] Preferred Attractions: Roller coaster, horror house

[0316] Preferred event: Parade

[0317] Emotional state: Slightly tired

[0318] Generate optimal schedules by taking into account historical data, real-time congestion information, and the user's emotional state."

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1:

[0321] The user uses a terminal to enter visit information, specifically the arrival time, duration of stay, desired attractions, desired events, and emotional state, into the application form. The input data is checked to prevent omissions and formatting errors.

[0322] Input: Arrival time, Stay time, Preferred attractions, Preferred events, Emotional state

[0323] Output: User data entered in the correct format

[0324] Step 2:

[0325] The device sends the input data to the server. The input data is sent to the server via the API as an HTTP POST request. Error handling is performed and the success / failure status is displayed.

[0326] Input: User data

[0327] Output: User data sent to the server

[0328] Step 3:

[0329] The server retrieves past visitor data from the database, issues SQL queries to retrieve information such as past crowd levels and attraction wait times, and caches the data in memory for subsequent processing.

[0330] Input: User data

[0331] Output: Historical visitor data

[0332] Step 4:

[0333] The server collects real-time data from sensors and devices within the theme park. It receives real-time data from various sensors (cameras, people counters, Wi-Fi devices, etc.) and temporarily stores it in storage.

[0334] Input: Real-time data request

[0335] Output: Real-time congestion data

[0336] Step 5:

[0337] The server receives the emotion data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and facial recognition technology. The analysis results are stored in a database and used to generate subsequent schedules.

[0338] Input: Emotional state data

[0339] Output: Parsed emotion data

[0340] Step 6:

[0341] The server integrates and analyzes the past data, real-time data, and sentiment data collected. It uses AI algorithms to predict crowds at each attraction and event. The results are then prepared as a dataset for statistical analysis.

[0342] Inputs: Historical visitor data, real-time data, sentiment data

[0343] Output: Congestion forecast data

[0344] Step 7:

[0345] The server generates an optimal schedule based on the user's input, emotional data, and crowd prediction data, allowing the user to move around the theme park efficiently and comfortably. It also takes into account the user's emotional state, adjusting the schedule to select nearby attractions if the user is tired, for example.

[0346] Input: User data, emotion data, congestion prediction data

[0347] Output: Optimal schedule data

[0348] Step 8:

[0349] The server sends the generated schedule to the terminal and proposes it to the user. The optimal schedule data is sent via HTTP response, allowing the user to check and use it on their terminal. It also provides a function to update the schedule according to real-time conditions.

[0350] Input: Optimal schedule data

[0351] Output: The schedule presented to the user

[0352] (Application example 2)

[0353] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0354] Conventional store navigation systems lacked methods for users to move around a store efficiently while avoiding crowds, and methods for suggesting appropriate routes based on the user's emotional state. As a result, users often felt stressed while shopping, and the decrease in satisfaction due to crowding in the store became a problem. The present invention aims to provide a system that suggests a comfortable shopping route while avoiding crowds, taking into account the user's emotional state.

[0355] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting arrival time, stay time, product information, and event information, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal route, and a means for presenting the generated route to the user. This enables the user to avoid congestion and move around the store efficiently and comfortably, improving the shopping experience.

[0356] "Arrival time" is the time when the user plans to arrive at the store or facility.

[0357] "Duration" is the length of time a user plans to stay in a store or facility.

[0358] "Product information" is information about the product that the user wishes to purchase, including the product name, category, etc.

[0359] "Event information" is information about an event that a user wishes to participate in or watch, and includes the event name, start time, and the like.

[0360] "Past visitor data" refers to data such as the behavior history and congestion status of users who have visited the facility in the past, and is stored in a database.

[0361] "Real-time data" refers to data collected in real time, such as current congestion levels and waiting times at each store.

[0362] "Emotion data" is data that indicates the user's current emotional state, and is obtained using text input, voice input, facial recognition technology, or the like.

[0363] "Crowd prediction" refers to predicting future congestion conditions within a facility or store based on past and real-time data.

[0364] An "optimal route" is a travel route proposed for a user to travel efficiently and comfortably within a facility or store.

[0365] The present invention is a shopping navigation system for commercial facilities that allows users to navigate the facility efficiently and comfortably. The system provides optimal routes that avoid crowds, taking into account the user's emotional state.

[0366] System configuration

[0367] User Input

[0368] Users use their smartphones to input arrival time, stay time, product information, and event information into the application. Users are also prompted to input their current emotional state, which can be selected from options such as "I want to relax," "I'm in a hurry," or "I'm tired."

[0369] Sending data

[0370] The device sends the entered information to the server. The API is used to send the input data to the server via an HTTP request. The server receives and stores this data.

[0371] Historical and real-time data collection

[0372] The server accesses a database that stores past visitor data to obtain information such as past congestion levels and waiting times at each store. It also collects real-time data on current congestion levels and waiting times from sensors and terminals installed in the store.

[0373] Collecting Emotional Data

[0374] The server receives the user's emotional state data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology using a camera. An example of a library that can be used is "emotion_engine."

[0375] Data aggregation and analysis

[0376] The server combines the collected historical data, real-time data, and emotion data to create aggregated data. It uses AI algorithms to analyze the data and predict crowding at each store or event. An example of a library used is "route_optimizer."

[0377] Generate optimal routes

[0378] The server generates an optimal route based on the user's input, emotion data, and congestion prediction data to enable the user to move around the facility efficiently and comfortably. For example, if the user inputs that they are "tired," the optimal route will include a stop at a nearby rest spot.

[0379] Route suggestions

[0380] The server then sends the generated optimal route to the device and presents it to the user. The user can then check the proposed route through the app and follow it to avoid congestion and enjoy a comfortable shopping experience.

[0381] Specific examples

[0382] 1. User input

[0383] Arrival time: 13:00

[0384] Visit duration: 3 hours

[0385] Planned purchases: Luxury brand bags and accessories

[0386] Preferred event: Sales event

[0387] Emotional state: I want to relax

[0388] The user enters and submits this information.

[0389] 2. Data collection and analysis

[0390] The server analyzes visitor data from the past year and identifies congestion patterns at the same time of day and on the same day of the week.

[0391] The server collects information on the congestion status and waiting times of each store in real time.

[0392] The emotion engine analyzes the user's emotional state and determines that they "want to relax."

[0393] 3. Generating the optimal route

[0394] Based on the user's wishes and emotions, an AI algorithm generates a route such as "first take a break in the refreshment space, then buy a luxury brand bag, and then stop by an accessory shop."

[0395] 4. Route suggestions

[0396] The server sends the generated route to the user's device.

[0397] The user can check the route on the terminal and move around the shopping facility efficiently.

[0398] Prompt Sentence Examples

[0399] Analyze the user's emotional state.

[0400] Emotional state: "I want to relax"

[0401] User input data:

[0402] Arrival time: 13:00

[0403] Visit duration: 3 hours

[0404] Planned purchases: Luxury brand bags and accessories

[0405] Preferred event: Sales event

[0406] Based on this data, suggest the best shopping route.

[0407] In this way, the present invention is a system that allows users to avoid crowds and enjoy shopping comfortably within the facility.By taking into account the user's emotional state, the system provides a more satisfying shopping experience.

[0408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0409] Step 1:

[0410] The user launches the application using a smartphone and inputs the arrival time, stay time, planned purchases, and desired event information. The user also selects their current emotional state. The input contents are as follows:

[0411] Input: Arrival time (e.g., 13:00), duration (e.g., 3 hours), planned purchase (e.g., luxury brand bags, accessories), desired event (e.g., sale event), emotional state (e.g., wanting to relax)

[0412] Output: User input data

[0413] Step 2:

[0414] The device sends the input information to the server via an HTTP request. Specifically, it uses an API to send the input data to the server in JSON format.

[0415] Input: User-entered data

[0416] Output: Sending to server completed

[0417] Step 3:

[0418] The server stores the received user-entered data, records it in a database, and checks each data for consistency and accuracy.

[0419] Input: User-entered data

[0420] Output: Saved user input data

[0421] Step 4:

[0422] The server retrieves past visitor data from the database, extracting information such as congestion status, waiting times, and store popularity for each data set.

[0423] Input: A request from the database

[0424] Output: Historical data

[0425] Step 5:

[0426] The server collects information on current congestion and waiting times from sensors and real-time data collection terminals installed in the store, and temporarily stores the real-time data in memory for analysis.

[0427] Input: Data from real-time data collection terminal

[0428] Output: Real-time data

[0429] Step 6:

[0430] The server launches an emotion engine to analyze the user's emotional state data, and outputs an evaluation result through text input, voice input, and facial recognition technology.

[0431] Input: User's emotional state data

[0432] Output: Parsed emotion data

[0433] Step 7:

[0434] The server combines historical, real-time, and sentiment data to create an aggregated dataset, which serves as input for statistical analysis and predictive models.

[0435] Input: Historical data, real-time data, analyzed sentiment data

[0436] Output: Aggregated dataset

[0437] Step 8:

[0438] The server uses AI algorithms to analyze the aggregated data set and make crowd predictions, which then predicts future crowding conditions at each store or event.

[0439] Input: Aggregated dataset

[0440] Output: Congestion forecast data

[0441] Step 9:

[0442] The server generates the optimal shopping route based on the user's input information and emotional data. The AI ​​algorithm considers the user's wishes and emotions and suggests an efficient and comfortable route.

[0443] Input: User input data, analyzed emotion data, congestion prediction data

[0444] Output: Optimal route

[0445] Step 10:

[0446] The server sends the generated optimal route to the terminal and presents it to the user, who can then check the proposed route through the application to move efficiently through the shopping facility.

[0447] Input: Optimal Route

[0448] Output: Route suggestions to the user

[0449] The above are the specific processing steps for implementing this invention. The techniques and procedures used in each step are effectively realized by making full use of modern data analysis and AI technology.

[0450] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0451] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0452] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0453] [Second embodiment]

[0454] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0455] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0456] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0457] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0458] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0459] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0460] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0461] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0462] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0463] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0464] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0465] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0466] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds. The system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting crowds based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[0467] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[0468] Program processing

[0469] User Input

[0470] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration of stay, desired attractions, and desired events.

[0471] Sending data

[0472] The device sends the entered information to the server, which receives and stores this data.

[0473] View past data

[0474] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[0475] Real-time data collection

[0476] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[0477] Data aggregation and analysis

[0478] The server combines the collected historical data with real-time data and analyzes it using AI algorithms, which then predicts crowds at each attraction and event.

[0479] Generating an optimal schedule

[0480] Based on the user's input information and congestion forecast data, the server generates an optimal schedule that allows the user to move around the theme park efficiently and comfortably.

[0481] Schedule suggestions

[0482] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[0483] Specific examples

[0484] 1. User input

[0485] Arrival time: 10:00

[0486] Duration: 6 hours

[0487] Preferred Attractions: Roller coaster, horror house

[0488] Preferred event: Parade

[0489] The user enters and submits this information.

[0490] 2. Data collection and analysis

[0491] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[0492] The server collects waiting times and congestion status for each attraction in real time.

[0493] 3. Generating the optimal schedule

[0494] Based on the user's preferences, the AI ​​algorithm generates the optimal schedule: "Arrive at 10:00, ride the roller coaster from 10:15, enjoy the parade from 11:00, and enjoy the horror house at 12:00."

[0495] 4. Schedule proposal

[0496] The server transmits the generated schedule to the user's terminal.

[0497] Users can check the schedule on their terminal and move around the theme park efficiently.

[0498] In this way, the present invention provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, thereby improving visitor satisfaction and reducing the burden on the park staff.

[0499] The processing flow will be explained below.

[0500] Step 1:

[0501] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[0502] Step 2:

[0503] The user inputs information about the theme park visit, specifically, the arrival time, the duration of stay, the desired attractions, and the desired events, and clicks the "Submit" button.

[0504] Step 3:

[0505] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0506] Step 4:

[0507] The server retrieves past visitor data from the database, querying information such as past crowd levels and attraction wait times.

[0508] Step 5:

[0509] The server collects data in real time. Information such as current congestion status and waiting times is obtained from sensors and devices within the theme park via an API, and is cached in memory or stored in a database.

[0510] Step 6:

[0511] The server combines the collected historical and real-time data to create aggregated data, which is then prepared as a data set for statistical analysis.

[0512] Step 7:

[0513] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[0514] Step 8:

[0515] The server generates an optimal schedule based on the user's input and analysis results. For example, a specific schedule such as "Arrival time: 10:00, roller coaster at 10:15, parade at 11:00, horror house at 12:30" is generated.

[0516] Step 9:

[0517] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[0518] Step 10:

[0519] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[0520] Step 11:

[0521] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[0522] Example 1

[0523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] Leisure facilities such as theme parks require methods for visitors to move around the facility efficiently and avoid crowds. In particular, a system that can predict congestion in real time and provide optimal schedules based on that prediction is needed. However, current systems do not fully utilize past or real-time data, making it difficult to provide schedules that allow visitors to enjoy the facility comfortably. This often results in long waiting times and discomfort due to crowds.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0526] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting congestion using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal, thereby enabling visitors to move around the theme park efficiently and comfortably.

[0527] "Arrival time" is the time when the user plans to arrive at a leisure facility such as a theme park.

[0528] "Staying time" is the period of time that a user plans to stay at a leisure facility such as a theme park.

[0529] "Desired Attractions" is a list of attractions that the user wishes to experience during their visit.

[0530] "Desired Events" is a list of events that the user wishes to participate in during their visit.

[0531] "Past visit data" refers to data related to the behavioral history, length of stay, and waiting times for attractions of visitors who have visited the theme park in the past.

[0532] "Real-time data" refers to data that shows real-time conditions within the theme park, such as current wait times for attractions and congestion levels.

[0533] "Aggregated Data" is data that combines historical visit data and real-time data and is used for predictions and analysis.

[0534] A "generative AI model" is an artificial intelligence algorithm that uses past visit data and real-time data as input to predict congestion and generate optimal schedules.

[0535] The "optimal schedule" is a visit plan created by the generative AI model to help users move around the theme park efficiently and comfortably.

[0536] A "display terminal" is a device for presenting an optimal schedule to a user, and includes a smartphone, tablet, etc.

[0537] The present invention is a system for enabling visitors to move efficiently and avoid crowds at leisure facilities such as theme parks. The system includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting crowds using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal.

[0538] The system's hardware configuration includes the device used by the user (such as a smartphone or tablet), a server that processes data, and multiple sensors for collecting real-time data, while the software configuration includes an application for users to input information, data aggregation and analysis algorithms that run on the server, and a generative AI model.

[0539] Specifically, the system operates as follows.

[0540] First, a user uses their device to enter information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. For example, a user might enter the following information into the application form:

[0541] Arrival time: 10:00

[0542] Duration: 6 hours

[0543] Desired attractions: Roller coaster, horror house

[0544] Preferred event: Parade

[0545] The device then sends this input data to the server via an HTTP request, packaging the data in JSON format, which the server receives and stores in a database.

[0546] The server then retrieves past visit data from a database and collects real-time data from multiple sensors installed throughout the theme park, including past crowd levels and current wait times.

[0547] The server aggregates this data and uses a generative AI model to predict crowding. It uses past and real-time data to predict future wait times for each attraction and event. For example, the generative AI model outputs the following predictions:

[0548] Roller Coaster: 15 minute wait

[0549] Horror House: 20 minute wait

[0550] Parade: Starts at 11:00

[0551] Based on this prediction data and user input, the server generates an optimal schedule. The generated schedule provides the user with the order and time of visits that will allow them to enjoy the theme park efficiently. For example, a suggested schedule might be, "Arrive at 10:00, ride the roller coaster from 10:15, see the parade from 11:00, and enjoy the horror house at 12:00."

[0552] Finally, the server sends this optimal schedule to the terminal, and the user can check the schedule on the terminal and avoid congestion by following the instructions.

[0553] Here are some examples of prompts for generative AI models:

[0554] "I'll arrive at the theme park at 10:00 and plan to stay for 6 hours. I'd like to experience the roller coaster, the horror house, and see the parade. What's the best schedule to efficiently enjoy my time there?"

[0555] The present invention thus provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, not only improving visitor satisfaction but also reducing the burden on the park staff.

[0556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0557] Step 1:

[0558] The user uses a terminal to input information about the theme park, including arrival time, duration, desired attractions, and desired events. The user enters the following information into the application:

[0559] Arrival time: 10:00

[0560] Duration: 6 hours

[0561] Desired attractions: Roller coaster, horror house

[0562] Preferred event: Parade

[0563] Input: User's visit information (arrival time, stay time, desired attractions, desired events)

[0564] Output: Data entered into the application

[0565] Specific action: The user enters information using the input form and taps the submit button.

[0566] Step 2:

[0567] The device sends the input information to the server. The device app issues an HTTP request to the server and sends the input information in JSON format.

[0568] Input: Visit information entered by the user

[0569] Output: Visit information data sent to the server

[0570] Specific operation: Data is sent via an HTTP request, received by the server, and stored in the database.

[0571] Step 3:

[0572] The server retrieves past visit data from the database, and uses SQL queries to search and retrieve data on crowding and attraction wait times for the past year.

[0573] Input: SQL query to retrieve past visit data

[0574] Output: Past visit data retrieved from the database

[0575] Specific operation: The server executes the SQL query and retrieves the required data from the database.

[0576] Step 4:

[0577] The server collects real-time data from sensors in the theme park, such as waiting times for each attraction, crowding levels, and scheduled event start times.

[0578] Input: Data streams from sensors to collect real-time data

[0579] Output: Real-time data collected

[0580] Specific operation: The server receives the data stream sent from the sensor and stores it in temporary memory as real-time data.

[0581] Step 5:

[0582] The server integrates the collected data and uses a generative AI model to predict congestion. It uses past and real-time data as input to predict future congestion and waiting times.

[0583] Inputs: Historical and real-time data

[0584] Output: Predicted congestion and waiting time

[0585] Specific operation: The server uses the generated AI model to analyze the data and output the congestion prediction results.

[0586] Step 6:

[0587] The server generates an optimal schedule based on user visit information and congestion forecast data. For example, it creates a schedule that includes "Riding the roller coaster at 10:15, the parade at 11:00, and enjoying the horror house at 12:00."

[0588] Input: User visit information and congestion prediction data

[0589] Output: Optimal schedule

[0590] Specific operation: The generative AI model uses a schedule generation algorithm to create an optimal visiting plan.

[0591] Step 7:

[0592] The server sends the generated schedule to the terminal, which receives the schedule and displays it on the application to notify the user.

[0593] Input: Generated optimal schedule

[0594] Output: Schedule displayed on the user's terminal

[0595] Specific operation: The server sends an HTTP response, and the device displays the received data.

[0596] As described above, this system automatically generates and provides the optimal schedule for users to efficiently enjoy a theme park.

[0597] (Application example 1)

[0598] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0599] In theme parks and virtual stores, it is difficult for visitors to move around the facility efficiently while avoiding crowds. In particular, there is a need to provide an optimal schedule based on real-time congestion status and past data, but current systems have difficulty achieving this. Furthermore, there are issues that need to be resolved to improve the efficiency of visitor movement within virtual stores in order to improve visitor satisfaction.

[0600] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0601] In this invention, the server includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and generating an optimal schedule, a means for presenting the generated schedule to the user, and a means for streamlining visitor movement within the virtual environment. This allows visitors to move efficiently within the facility based on their arrival time, length of stay, and desired attractions and events, thereby avoiding congestion. Similarly, visitors can move efficiently within virtual stores and smoothly use their desired products and services.

[0602] "Arrival time" is the time a visitor plans to arrive at a facility or virtual environment.

[0603] "Dwell Time" means the expected or desired length of time a visitor will remain at a facility or in a virtual environment.

[0604] "Desired attractions" are attractions or spots that visitors would like to visit or experience.

[0605] A "desired event" is an event or activity that a visitor would like to participate in or observe.

[0606] "Historical Visitor Data" means data about visitors who have previously visited a facility or virtual environment, including information about crowd levels and wait times for attractions.

[0607] "Real-time data" refers to ongoing information, such as current crowd levels and waiting times for attractions.

[0608] "Congestion forecasting" is the prediction of future congestion conditions based on past and real-time data.

[0609] An "optimal schedule" is one that is planned to allow visitors to move efficiently through the facility and experience the attractions and events they desire.

[0610] "Means for presenting to users" refers to the means by which visitors can view the generated schedule. This includes devices such as smartphones, tablets, and head-mounted displays.

[0611] A "virtual environment" is a digital space that mimics a real-world environment and that visitors can access using digital devices.

[0612] This invention is a system that allows visitors to move around a facility efficiently while avoiding crowds. This system can be applied to theme parks and virtual stores, and is realized by generating and presenting an optimal schedule based on the visitor's arrival time, stay time, desired attractions, desired events, etc.

[0613] First, the user uses a device such as a smartphone or head-mounted display to input information about their visit, such as their estimated time of arrival, length of stay, and desired attractions and events. This information is then sent from the device to the server.

[0614] The server retrieves historical visitor data from a database and collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including crowd levels, wait times, and current visitor locations.

[0615] The collected data is analyzed using Python and AI algorithms (e.g., scikit-learn and TensorFlow). The server combines historical and real-time data to predict congestion. Based on this prediction, an optimal schedule is generated to allow users to move around the facility efficiently.

[0616] The generated schedule is then sent back to the user's device, where the user can check and follow the schedule to avoid crowds.In particular, in virtual stores, users can efficiently use the products and services they desire.

[0617] As a specific example, if a user arrives at a theme park at 10:00, plans to stay for six hours, and wants to ride the roller coaster or the horror house, the server can use AI to generate an optimal schedule based on the past year's congestion data and real-time data: "Arrive at 10:00, ride the roller coaster from 10:15, parade from 11:00, horror house at 12:00." This schedule is immediately displayed on the user's device.

[0618] Example prompt sentence:

[0619] A user visits a virtual store, arrives at 10:00, stays for 4 hours, wants to use the shopping area and try on clothes, and also wants to watch a virtual talk show. Generate the optimal schedule.

[0620] In this way, this invention is expected to improve the visitor experience and also improve management efficiency for operators.

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] Users use their smartphones or head-mounted displays to input information about their visit, including arrival time, duration, and desired attractions and events, which is then sent from the device to the server.

[0624] Input: Arrival time, stay time, desired attractions, desired events

[0625] Output: Sending visit information to the server

[0626] Step 2:

[0627] The server accesses a database that stores past visitor data, including the congestion status and waiting times for each attraction, and retrieves the visitor data.

[0628] Input: Visit information

[0629] Output: Past visitor data

[0630] Step 3:

[0631] The server collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including current crowd levels and wait times for attractions.

[0632] Input: Visit information

[0633] Output: Real-time data

[0634] Step 4:

[0635] The server combines collected past visitor data with real-time data and uses AI algorithms to predict congestion, which then predicts future congestion and generates an optimal schedule.

[0636] Input: Past visitor data, real-time data

[0637] Output: Congestion forecast data, optimal schedule

[0638] Step 5:

[0639] The server then sends the generated optimal schedule to the user's device, allowing the user to check the schedule and move around the facility efficiently based on it.

[0640] Input: Optimal schedule

[0641] Output: Schedule display on user's device

[0642] Step 6:

[0643] Users move around the theme park and virtual stores based on the schedule provided, allowing them to avoid crowds and efficiently experience the attractions and events they want.

[0644] Input: Proposed schedule

[0645] Output: Efficient travel and experiences

[0646] The above are the specific processing steps for carrying out the present invention.

[0647] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0648] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds, and further combines an emotion engine that recognizes the user's emotions and adjusts the schedule. This system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[0649] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[0650] Program processing

[0651] User Input

[0652] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. Users are also asked to input their emotional state.

[0653] Sending data

[0654] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0655] View past data

[0656] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[0657] Real-time data collection

[0658] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[0659] Collecting Emotional Data

[0660] The server receives the user's emotional state data sent from the device, and the emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology.

[0661] Data aggregation and analysis

[0662] The server combines collected historical data, real-time data, and user sentiment data to create aggregated data. The aggregated data is then prepared as a data set for statistical analysis. AI algorithms are used to analyze the data and predict crowds at each attraction and event.

[0663] Generating an optimal schedule

[0664] The server generates an optimal schedule based on the user's input information, emotional data, and crowd prediction data to allow the user to move around the theme park efficiently and comfortably. The server also takes into account the user's emotional state, adjusting the schedule according to their emotions, for example, choosing a nearby attraction if they are tired.

[0665] Schedule suggestions

[0666] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[0667] Specific examples

[0668] 1. User input

[0669] Arrival time: 10:00

[0670] Duration: 6 hours

[0671] Preferred Attractions: Roller coaster, horror house

[0672] Preferred event: Parade

[0673] Emotional state: Slightly tired

[0674] The user enters and submits this information.

[0675] 2. Data collection and analysis

[0676] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[0677] The server collects waiting times and congestion status for each attraction in real time.

[0678] The emotion engine analyzes the user's emotional state and determines that they are "slightly tired."

[0679] 3. Generating the optimal schedule

[0680] Based on the user's wishes and emotions, an AI algorithm generates a schedule that might include "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying a horror house."

[0681] 4. Schedule proposal

[0682] The server transmits the generated schedule to the user's terminal.

[0683] Users can check the schedule on their terminal and move around the theme park efficiently.

[0684] In this way, the present invention allows visitors to avoid crowds and maximize their enjoyment of the theme park, and by taking emotional data into consideration, it provides a more comfortable and satisfying experience, thereby improving visitor satisfaction and reducing the burden on the park staff.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[0688] Step 2:

[0689] The user inputs information about their visit to the theme park, such as arrival time, length of stay, desired attractions, and desired events, and then clicks the "Submit" button. The user also inputs their emotional state. For example, emotional state options such as "Tired," "Excited," and "Relaxed" are displayed.

[0690] Step 3:

[0691] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0692] Step 4:

[0693] The server retrieves past visitor data from a database, querying the database for information such as past crowd levels and attraction wait times.

[0694] Step 5:

[0695] The server collects data in real time. Data such as current congestion status and waiting times is obtained via API from sensors and devices installed within the theme park, and is cached in memory or stored in a database.

[0696] Step 6:

[0697] The device sends the user's emotional state data to the server, and the emotion engine analyzes the emotional state using the user's text input, voice input, and facial recognition technology as needed, and generates specific emotional data.

[0698] Step 7:

[0699] The server combines the collected historical data, real-time data, and user sentiment data to create aggregated data, which is then prepared as a dataset for statistical analysis.

[0700] Step 8:

[0701] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[0702] Step 9:

[0703] The server generates an optimal schedule based on user input, emotional data, and congestion prediction data. If the server determines that the user is tired, it prioritizes nearby rest spots in the schedule. The generated schedule takes into account the user's preferences and current emotional state, ensuring efficient and comfortable travel.

[0704] Step 10:

[0705] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[0706] Step 11:

[0707] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[0708] Step 12:

[0709] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[0710] Example 2

[0711] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] Leisure facilities such as theme parks face the challenge of preventing visitors from crowding and moving around the facility efficiently. Furthermore, planning that doesn't take into account the emotional state of visitors can prevent them from experiencing a comfortable and satisfying experience. This can lead to lower visitor satisfaction and increased workloads for operational staff.

[0713] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0714] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, desired events, and emotional state, a means for aggregating past visitor data and real-time data, and a means for predicting congestion based on the aggregated data and emotional data to generate an optimal schedule, thereby enabling visitors to travel efficiently while avoiding congestion and adjusting schedules that take emotional state into consideration.

[0715] "Arrival time" is the time that a visitor plans to arrive at a leisure facility such as a theme park.

[0716] "Duration" is the length of time a visitor plans to stay at a theme park or other leisure facility.

[0717] A "desired attraction" is an attraction that a visitor wishes to visit at a leisure facility such as a theme park.

[0718] A "desired event" is an event that a visitor wishes to participate in at a leisure facility such as a theme park.

[0719] "Emotional state" refers to the visitor's current emotional state, which may be a psychological or physiological state such as fatigue, excitement, or stress.

[0720] "Past visitor data" is data about visitors who have visited a theme park or other leisure facility in the past, and includes information such as congestion levels and waiting times.

[0721] "Real-time data" refers to current data such as the current congestion situation at leisure facilities such as theme parks and waiting times for attractions.

[0722] "Aggregated Data" means data sets created by combining collected historical visitor data and real-time data.

[0723] "Emotional Data" is data regarding a visitor's emotional state analyzed based on their input.

[0724] "Crowd prediction" is the prediction of future congestion conditions based on past visitor data and real-time data.

[0725] An "optimal schedule" is a schedule designed to allow visitors to move through a theme park or other leisure facility efficiently and comfortably.

[0726] The "means for presenting to the user" is a method for transmitting the generated schedule to the user's terminal and displaying it.

[0727] The present invention is a system for allowing visitors to move around leisure facilities such as theme parks efficiently while avoiding crowds. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the schedule accordingly. The system includes the following main means:

[0728] First, users enter their visit information using a device such as a smartphone or tablet. Specifically, they enter their arrival time, duration of stay, desired attractions, desired events, and emotional state. This data is then sent to the server via the application. The accuracy of the data is ensured by using an API to send the input data to the server as an HTTP POST request.

[0729] The server collects past visitor data and real-time data, and uses this data to predict congestion. Past visitor data is retrieved from a database using SQL queries, and real-time data is collected from various sensors installed within the theme park (e.g., cameras, people counters, Wi-Fi terminals). The received data is temporarily stored in storage and used for subsequent processing.

[0730] The system also uses an emotion engine to analyze the user's emotional state. Based on the emotional data sent from the device, the system uses text input, voice input, and facial recognition technology to determine the user's emotional state. This emotional data is also integrated on the server and prepared as a dataset for statistical analysis along with the congestion forecast data.

[0731] Using AI algorithms, the server analyzes the collected data and predicts crowding at each attraction and event. Based on the user's input, emotional data, and crowding prediction data, the system generates an optimal schedule to allow users to move around the theme park efficiently and comfortably. Since the system also takes into account the user's emotional state, it adjusts the schedule according to the user's condition, for example, choosing a nearby attraction if the user is tired.

[0732] The generated schedule is sent from the server to the device and suggested to the user. By checking the schedule on the device and following it, the user can avoid congestion and have a more comfortable experience.

[0733] For example, consider the case where a user enters the following information:

[0734] Arrival time: 10:00

[0735] Duration: 6 hours

[0736] Preferred Attractions: Roller coaster, horror house

[0737] Preferred event: Parade

[0738] Emotional state: Slightly tired

[0739] Based on this information, the server analyzes a combination of past and real-time data and suggests a schedule to the user, such as "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying the horror house."

[0740] Examples of prompts:

[0741] "You are the developer of a system that efficiently plans theme park visitor schedules. The user has entered the following information:

[0742] Arrival time: 10:00

[0743] Duration: 6 hours

[0744] Preferred Attractions: Roller coaster, horror house

[0745] Preferred event: Parade

[0746] Emotional state: Slightly tired

[0747] Generate optimal schedules by taking into account historical data, real-time congestion information, and the user's emotional state."

[0748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] The user uses a terminal to enter visit information, specifically the arrival time, duration of stay, desired attractions, desired events, and emotional state, into the application form. The input data is checked to prevent omissions and formatting errors.

[0751] Input: Arrival time, Stay time, Preferred attractions, Preferred events, Emotional state

[0752] Output: User data entered in the correct format

[0753] Step 2:

[0754] The device sends the input data to the server. The input data is sent to the server via the API as an HTTP POST request. Error handling is performed and the success / failure status is displayed.

[0755] Input: User data

[0756] Output: User data sent to the server

[0757] Step 3:

[0758] The server retrieves past visitor data from the database, issues SQL queries to retrieve information such as past crowd levels and attraction wait times, and caches the data in memory for subsequent processing.

[0759] Input: User data

[0760] Output: Historical visitor data

[0761] Step 4:

[0762] The server collects real-time data from sensors and devices within the theme park. It receives real-time data from various sensors (cameras, people counters, Wi-Fi devices, etc.) and temporarily stores it in storage.

[0763] Input: Real-time data request

[0764] Output: Real-time congestion data

[0765] Step 5:

[0766] The server receives the emotion data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and facial recognition technology. The analysis results are stored in a database and used to generate subsequent schedules.

[0767] Input: Emotional state data

[0768] Output: Parsed emotion data

[0769] Step 6:

[0770] The server integrates and analyzes the past data, real-time data, and sentiment data collected. It uses AI algorithms to predict crowds at each attraction and event. The results are then prepared as a dataset for statistical analysis.

[0771] Inputs: Historical visitor data, real-time data, sentiment data

[0772] Output: Congestion forecast data

[0773] Step 7:

[0774] The server generates an optimal schedule based on the user's input, emotional data, and crowd prediction data, allowing the user to move around the theme park efficiently and comfortably. It also takes into account the user's emotional state, adjusting the schedule to select nearby attractions if the user is tired, for example.

[0775] Input: User data, emotion data, congestion prediction data

[0776] Output: Optimal schedule data

[0777] Step 8:

[0778] The server sends the generated schedule to the terminal and proposes it to the user. The optimal schedule data is sent via HTTP response, allowing the user to check and use it on their terminal. It also provides a function to update the schedule according to real-time conditions.

[0779] Input: Optimal schedule data

[0780] Output: The schedule presented to the user

[0781] (Application example 2)

[0782] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0783] Conventional store navigation systems lacked methods for users to move around a store efficiently while avoiding crowds, and methods for suggesting appropriate routes based on the user's emotional state. As a result, users often felt stressed while shopping, and the decrease in satisfaction due to crowding in the store became a problem. The present invention aims to provide a system that suggests a comfortable shopping route while avoiding crowds, taking into account the user's emotional state.

[0784] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting arrival time, stay time, product information, and event information, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal route, and a means for presenting the generated route to the user. This enables the user to avoid congestion and move around the store efficiently and comfortably, improving the shopping experience.

[0785] "Arrival time" is the time when the user plans to arrive at the store or facility.

[0786] "Duration" is the length of time a user plans to stay in a store or facility.

[0787] "Product information" is information about the product that the user wishes to purchase, including the product name, category, etc.

[0788] "Event information" is information about an event that a user wishes to participate in or watch, and includes the event name, start time, and the like.

[0789] "Past visitor data" refers to data such as the behavior history and congestion status of users who have visited the facility in the past, and is stored in a database.

[0790] "Real-time data" refers to data collected in real time, such as current congestion levels and waiting times at each store.

[0791] "Emotion data" is data that indicates the user's current emotional state, and is obtained using text input, voice input, facial recognition technology, or the like.

[0792] "Crowd prediction" refers to predicting future congestion conditions within a facility or store based on past and real-time data.

[0793] An "optimal route" is a travel route proposed for a user to travel efficiently and comfortably within a facility or store.

[0794] The present invention is a shopping navigation system for commercial facilities that allows users to navigate the facility efficiently and comfortably. The system provides optimal routes that avoid crowds, taking into account the user's emotional state.

[0795] System configuration

[0796] User Input

[0797] Users use their smartphones to input arrival time, stay time, product information, and event information into the application. Users are also prompted to input their current emotional state, which can be selected from options such as "I want to relax," "I'm in a hurry," or "I'm tired."

[0798] Sending data

[0799] The device sends the entered information to the server. The API is used to send the input data to the server via an HTTP request. The server receives and stores this data.

[0800] Historical and real-time data collection

[0801] The server accesses a database that stores past visitor data to obtain information such as past congestion levels and waiting times at each store. It also collects real-time data on current congestion levels and waiting times from sensors and terminals installed in the store.

[0802] Collecting Emotional Data

[0803] The server receives the user's emotional state data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology from a camera. An example of a library that can be used is "emotion_engine."

[0804] Data aggregation and analysis

[0805] The server combines the collected historical data, real-time data, and emotion data to create aggregated data. It uses AI algorithms to analyze the data and predict crowding at each store or event. An example of a library used is "route_optimizer."

[0806] Generate optimal routes

[0807] The server generates an optimal route based on the user's input, emotion data, and congestion prediction data to enable the user to move around the facility efficiently and comfortably. For example, if the user inputs "I'm tired," the optimal route will include a stop at a nearby rest spot.

[0808] Route suggestions

[0809] The server then sends the generated optimal route to the device and presents it to the user. The user can then check the proposed route through the application and follow it to avoid congestion and enjoy a comfortable shopping experience.

[0810] Specific examples

[0811] 1. User input

[0812] Arrival time: 13:00

[0813] Visit duration: 3 hours

[0814] Planned purchases: Luxury brand bags and accessories

[0815] Preferred event: Sales event

[0816] Emotional state: I want to relax

[0817] The user enters and submits this information.

[0818] 2. Data collection and analysis

[0819] The server analyzes visitor data from the past year and identifies congestion patterns at the same time of day and on the same day of the week.

[0820] The server collects information on the congestion status and waiting times of each store in real time.

[0821] The emotion engine analyzes the user's emotional state and determines that they "want to relax."

[0822] 3. Generating the optimal route

[0823] Based on the user's wishes and emotions, an AI algorithm generates a route such as "first take a break in the refreshment space, then buy a luxury brand bag, and then stop by an accessory shop."

[0824] 4. Route suggestions

[0825] The server sends the generated route to the user's device.

[0826] The user can check the route on the terminal and move around the shopping facility efficiently.

[0827] Prompt Sentence Examples

[0828] Analyze the user's emotional state.

[0829] Emotional state: "I want to relax"

[0830] User input data:

[0831] Arrival time: 13:00

[0832] Visit duration: 3 hours

[0833] Planned purchases: Luxury brand bags and accessories

[0834] Preferred event: Sales event

[0835] Based on this data, suggest the best shopping route.

[0836] In this way, the present invention is a system that allows users to avoid crowds and enjoy shopping comfortably within the facility.By taking into account the user's emotional state, the system provides a more satisfying shopping experience.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] The user launches the application using a smartphone and inputs the arrival time, stay time, planned purchases, and desired event information. The user also selects their current emotional state. The input contents are as follows:

[0840] Input: Arrival time (e.g., 13:00), duration (e.g., 3 hours), planned purchase (e.g., luxury brand bags, accessories), desired event (e.g., sale event), emotional state (e.g., wanting to relax)

[0841] Output: User input data

[0842] Step 2:

[0843] The device sends the input information to the server via an HTTP request. Specifically, it uses an API to send the input data to the server in JSON format.

[0844] Input: User-entered data

[0845] Output: Sending to server completed

[0846] Step 3:

[0847] The server stores the received user-entered data, records it in a database, and checks each data for consistency and accuracy.

[0848] Input: User-entered data

[0849] Output: Saved user-entered data

[0850] Step 4:

[0851] The server retrieves past visitor data from the database, extracting information such as congestion status, waiting times, and store popularity for each data set.

[0852] Input: A request from the database

[0853] Output: Historical data

[0854] Step 5:

[0855] The server collects information on current congestion and waiting times from sensors and real-time data collection terminals installed in the store, and temporarily stores the real-time data in memory for analysis.

[0856] Input: Data from real-time data collection terminal

[0857] Output: Real-time data

[0858] Step 6:

[0859] The server launches an emotion engine to analyze the user's emotional state data, and outputs an evaluation result through text input, voice input, and facial recognition technology.

[0860] Input: User's emotional state data

[0861] Output: Parsed emotion data

[0862] Step 7:

[0863] The server combines historical, real-time, and sentiment data to create an aggregated dataset, which serves as input for statistical analysis and predictive models.

[0864] Input: Historical data, real-time data, analyzed sentiment data

[0865] Output: Aggregated dataset

[0866] Step 8:

[0867] The server uses AI algorithms to analyze the aggregated data set and make crowd predictions, which then predicts future crowding conditions at each store or event.

[0868] Input: Aggregated dataset

[0869] Output: Congestion forecast data

[0870] Step 9:

[0871] The server generates the optimal shopping route based on the user's input information and emotional data. The AI ​​algorithm considers the user's wishes and emotions and suggests an efficient and comfortable route.

[0872] Input: User input data, analyzed emotion data, congestion prediction data

[0873] Output: Optimal route

[0874] Step 10:

[0875] The server sends the generated optimal route to the terminal and presents it to the user, who can then check the proposed route through the application to move efficiently through the shopping facility.

[0876] Input: Optimal Route

[0877] Output: Route suggestions to the user

[0878] The above are the specific processing steps for implementing this invention. The techniques and procedures used in each step are effectively realized by making full use of modern data analysis and AI technology.

[0879] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0880] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0881] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0882] [Third embodiment]

[0883] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0884] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0885] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0886] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0887] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0888] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0889] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0890] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0891] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0892] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0893] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0894] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0895] The present invention is a system for allowing visitors to move around a leisure facility such as a theme park efficiently while avoiding crowds. The system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting crowds based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[0896] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[0897] Program processing

[0898] User Input

[0899] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration of stay, desired attractions, and desired events.

[0900] Sending data

[0901] The device sends the entered information to the server, which receives and stores this data.

[0902] View past data

[0903] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[0904] Real-time data collection

[0905] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[0906] Data aggregation and analysis

[0907] The server combines the collected historical data with real-time data and analyzes it using AI algorithms, which then predicts crowds at each attraction and event.

[0908] Generating an optimal schedule

[0909] Based on the user's input information and congestion forecast data, the server generates an optimal schedule that allows the user to move around the theme park efficiently and comfortably.

[0910] Schedule suggestions

[0911] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[0912] Specific examples

[0913] 1. User input

[0914] Arrival time: 10:00

[0915] Duration: 6 hours

[0916] Preferred Attractions: Roller coaster, horror house

[0917] Preferred event: Parade

[0918] The user enters and submits this information.

[0919] 2. Data collection and analysis

[0920] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[0921] The server collects waiting times and congestion status for each attraction in real time.

[0922] 3. Generating the optimal schedule

[0923] Based on the user's preferences, the AI ​​algorithm generates the optimal schedule: "Arrive at 10:00, ride the roller coaster from 10:15, enjoy the parade from 11:00, and enjoy the horror house at 12:00."

[0924] 4. Schedule proposal

[0925] The server transmits the generated schedule to the user's terminal.

[0926] Users can check the schedule on their terminal and move around the theme park efficiently.

[0927] In this way, the present invention provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, thereby improving visitor satisfaction and reducing the burden on the park staff.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[0931] Step 2:

[0932] The user inputs information about the theme park visit, specifically, the arrival time, the duration of stay, the desired attractions, and the desired events, and clicks the "Submit" button.

[0933] Step 3:

[0934] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[0935] Step 4:

[0936] The server retrieves past visitor data from the database, querying information such as past crowd levels and attraction wait times.

[0937] Step 5:

[0938] The server collects data in real time. Information such as current congestion status and waiting times is obtained from sensors and devices within the theme park via an API, and is cached in memory or stored in a database.

[0939] Step 6:

[0940] The server combines the collected historical and real-time data to create aggregated data, which is then prepared as a data set for statistical analysis.

[0941] Step 7:

[0942] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[0943] Step 8:

[0944] The server generates an optimal schedule based on the user's input and analysis results. For example, a specific schedule such as "Arrival time: 10:00, roller coaster at 10:15, parade at 11:00, horror house at 12:30" is generated.

[0945] Step 9:

[0946] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[0947] Step 10:

[0948] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[0949] Step 11:

[0950] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[0951] Example 1

[0952] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0953] Leisure facilities such as theme parks require methods for visitors to move around the facility efficiently and avoid crowds. In particular, a system that can predict congestion in real time and provide optimal schedules based on that prediction is needed. However, current systems do not fully utilize past or real-time data, making it difficult to provide schedules that allow visitors to enjoy the facility comfortably. This often results in long waiting times and discomfort due to crowds.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0955] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting congestion using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal, thereby enabling visitors to move around the theme park efficiently and comfortably.

[0956] "Arrival time" is the time when the user plans to arrive at a leisure facility such as a theme park.

[0957] "Staying time" is the period of time that a user plans to stay at a leisure facility such as a theme park.

[0958] "Desired Attractions" is a list of attractions that the user wishes to experience during their visit.

[0959] "Desired Events" is a list of events that the user wishes to participate in during their visit.

[0960] "Past visit data" refers to data related to the behavioral history, length of stay, and waiting times for attractions of visitors who have visited the theme park in the past.

[0961] "Real-time data" refers to data that shows real-time conditions within the theme park, such as current wait times for attractions and congestion levels.

[0962] "Aggregated Data" is data that combines historical visit data and real-time data and is used for predictions and analysis.

[0963] A "generative AI model" is an artificial intelligence algorithm that uses past visit data and real-time data as input to predict congestion and generate optimal schedules.

[0964] The "optimal schedule" is a visit plan created by the generative AI model to help users move around the theme park efficiently and comfortably.

[0965] A "display terminal" is a device for presenting an optimal schedule to a user, and includes a smartphone, tablet, etc.

[0966] The present invention is a system for enabling visitors to move efficiently and avoid crowds at leisure facilities such as theme parks. The system includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting crowds using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal.

[0967] The system's hardware configuration includes the device used by the user (such as a smartphone or tablet), a server that processes data, and multiple sensors for collecting real-time data, while the software configuration includes an application for users to input information, data aggregation and analysis algorithms that run on the server, and a generative AI model.

[0968] Specifically, the system operates as follows.

[0969] First, a user uses their device to enter information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. For example, a user might enter the following information into the application form:

[0970] Arrival time: 10:00

[0971] Duration: 6 hours

[0972] Desired attractions: Roller coaster, horror house

[0973] Preferred event: Parade

[0974] The device then sends this input data to the server via an HTTP request, packaging the data in JSON format, which the server receives and stores in a database.

[0975] The server then retrieves past visit data from a database and collects real-time data from multiple sensors installed throughout the theme park, including past crowd levels and current wait times.

[0976] The server aggregates this data and uses a generative AI model to predict crowding. It uses past and real-time data to predict future wait times for each attraction and event. For example, the generative AI model outputs the following predictions:

[0977] Roller Coaster: 15 minute wait

[0978] Horror House: 20 minute wait

[0979] Parade: Starts at 11:00

[0980] Based on this prediction data and user input, the server generates an optimal schedule. The generated schedule provides the user with the order and time of visit to efficiently enjoy the theme park. For example, a suggested schedule might be "arrive at 10:00, ride the roller coaster from 10:15, see the parade from 11:00, and enjoy the horror house at 12:00."

[0981] Finally, the server sends this optimal schedule to the terminal. The user can check the schedule on the terminal and avoid congestion by following the instructions.

[0982] Here are some examples of prompts for generative AI models:

[0983] "I'll arrive at the theme park at 10:00 and plan to stay for 6 hours. I'd like to experience the roller coaster, the horror house, and see the parade. What's the best schedule to efficiently enjoy my time there?"

[0984] The present invention thus provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, not only improving visitor satisfaction but also reducing the burden on the park staff.

[0985] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0986] Step 1:

[0987] The user uses a terminal to input information about the theme park, including arrival time, duration, desired attractions, and desired events. The user enters the following information into the application:

[0988] Arrival time: 10:00

[0989] Duration: 6 hours

[0990] Desired attractions: Roller coaster, horror house

[0991] Preferred event: Parade

[0992] Input: User's visit information (arrival time, stay time, desired attractions, desired events)

[0993] Output: Data entered into the application

[0994] Specific action: The user enters information using the input form and taps the submit button.

[0995] Step 2:

[0996] The device sends the input information to the server. The device app issues an HTTP request to the server and sends the input information in JSON format.

[0997] Input: Visit information entered by the user

[0998] Output: Visit information data sent to the server

[0999] Specific operation: Data is sent via an HTTP request, received by the server, and stored in the database.

[1000] Step 3:

[1001] The server retrieves past visit data from the database, and uses SQL queries to search and retrieve data on crowding and attraction wait times for the past year.

[1002] Input: SQL query to retrieve past visit data

[1003] Output: Past visit data retrieved from the database

[1004] Specific operation: The server executes the SQL query and retrieves the required data from the database.

[1005] Step 4:

[1006] The server collects real-time data from sensors in the theme park, such as waiting times for each attraction, crowding levels, and scheduled event start times.

[1007] Input: Data streams from sensors to collect real-time data

[1008] Output: Real-time data collected

[1009] Specific operation: The server receives the data stream sent from the sensor and stores it in temporary memory as real-time data.

[1010] Step 5:

[1011] The server integrates the collected data and uses a generative AI model to predict congestion. It uses past and real-time data as input to predict future congestion and waiting times.

[1012] Inputs: Historical and real-time data

[1013] Output: Predicted congestion and waiting time

[1014] Specific operation: The server uses the generated AI model to analyze the data and output the congestion prediction results.

[1015] Step 6:

[1016] The server generates an optimal schedule based on user visit information and congestion forecast data. For example, it creates a schedule that includes "Riding the roller coaster at 10:15, the parade at 11:00, and enjoying the horror house at 12:00."

[1017] Input: User visit information and congestion prediction data

[1018] Output: Optimal schedule

[1019] Specific operation: The generative AI model uses a schedule generation algorithm to create an optimal visiting plan.

[1020] Step 7:

[1021] The server sends the generated schedule to the terminal, which receives the schedule and displays it on the application to notify the user.

[1022] Input: Generated optimal schedule

[1023] Output: Schedule displayed on the user's terminal

[1024] Specific operation: The server sends an HTTP response, and the device displays the received data.

[1025] As described above, this system automatically generates and provides the optimal schedule for users to efficiently enjoy a theme park.

[1026] (Application example 1)

[1027] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1028] In theme parks and virtual stores, it is difficult for visitors to move around the facility efficiently while avoiding crowds. In particular, there is a need to provide an optimal schedule based on real-time congestion status and past data, but current systems have difficulty achieving this. Furthermore, there are issues that need to be resolved to improve the efficiency of visitor movement within virtual stores in order to improve visitor satisfaction.

[1029] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1030] In this invention, the server includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion and generating an optimal schedule based on the aggregated data, a means for presenting the generated schedule to the user, and a means for streamlining visitor movement within the virtual environment. This allows visitors to move efficiently within the facility based on their arrival time, length of stay, and desired attractions and events, avoiding congestion. Similarly, visitors can move efficiently within virtual stores and smoothly use their desired products and services.

[1031] "Arrival time" is the time a visitor plans to arrive at a facility or virtual environment.

[1032] "Dwell Time" means the expected or desired length of time a visitor will remain at a facility or in a virtual environment.

[1033] "Desired attractions" are attractions or spots that visitors would like to visit or experience.

[1034] A "desired event" is an event or activity that a visitor would like to participate in or observe.

[1035] "Historical Visitor Data" means data about visitors who have previously visited a facility or virtual environment, including information about crowd levels and wait times for attractions.

[1036] "Real-time data" refers to ongoing information such as current crowd levels and waiting times for attractions.

[1037] "Congestion forecasting" is the prediction of future congestion conditions based on past and real-time data.

[1038] An "optimal schedule" is one that allows visitors to move efficiently through the facility and experience the attractions and events they desire.

[1039] "Means for presenting to users" refers to the means by which visitors can view the generated schedule. This includes devices such as smartphones, tablets, and head-mounted displays.

[1040] A "virtual environment" is a digital space that mimics a real-world environment and that visitors can access using digital devices.

[1041] This invention is a system that allows visitors to move around a facility efficiently while avoiding crowds. This system can be applied to theme parks and virtual stores, and is realized by generating and presenting an optimal schedule based on the visitor's arrival time, stay time, desired attractions, desired events, etc.

[1042] First, the user uses a device such as a smartphone or head-mounted display to input information about their visit, such as their estimated time of arrival, length of stay, and desired attractions and events. This information is then sent from the device to the server.

[1043] The server retrieves historical visitor data from a database and collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including crowd levels, wait times, and current visitor locations.

[1044] The collected data is analyzed using Python and AI algorithms (e.g., scikit-learn and TensorFlow). The server combines historical and real-time data to predict congestion. Based on this prediction, an optimal schedule is generated to allow users to move around the facility efficiently.

[1045] The generated schedule is then sent back to the user's device, where the user can check and follow the schedule to avoid crowds.In particular, in virtual stores, users can efficiently use the products and services they desire.

[1046] As a specific example, if a user arrives at a theme park at 10:00, plans to stay for six hours, and wants to ride the roller coaster or the horror house, the server can use AI to generate an optimal schedule based on the past year's congestion data and real-time data: "Arrive at 10:00, ride the roller coaster from 10:15, parade from 11:00, horror house at 12:00." This schedule is immediately displayed on the user's device.

[1047] Example prompt sentence:

[1048] A user visits a virtual store, arrives at 10:00, stays for 4 hours, wants to use the shopping area and try on clothes, and also wants to watch a virtual talk show. Generate the optimal schedule.

[1049] In this way, this invention is expected to improve the visitor experience and also improve management efficiency on the part of operators.

[1050] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1051] Step 1:

[1052] Users use their smartphones or head-mounted displays to input information about their visit, including arrival time, duration, and desired attractions and events, which is then sent from the device to the server.

[1053] Input: Arrival time, stay time, desired attractions, desired events

[1054] Output: Sending visit information to the server

[1055] Step 2:

[1056] The server accesses a database that stores past visitor data, including the congestion status and waiting times for each attraction, and retrieves the visitor data.

[1057] Input: Visit information

[1058] Output: Past visitor data

[1059] Step 3:

[1060] The server collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including current crowd levels and wait times for attractions.

[1061] Input: Visit information

[1062] Output: Real-time data

[1063] Step 4:

[1064] The server combines collected past visitor data with real-time data and uses AI algorithms to predict congestion, which then predicts future congestion and generates an optimal schedule.

[1065] Input: Past visitor data, real-time data

[1066] Output: Congestion forecast data, optimal schedule

[1067] Step 5:

[1068] The server then sends the generated optimal schedule to the user's device, allowing the user to check the schedule and move around the facility efficiently based on it.

[1069] Input: Optimal schedule

[1070] Output: Schedule display on user's device

[1071] Step 6:

[1072] Users move around the theme park and virtual stores based on the schedule provided, allowing them to avoid crowds and efficiently experience the attractions and events they want.

[1073] Input: Proposed schedule

[1074] Output: Efficient travel and experiences

[1075] The above are the specific processing steps for carrying out the present invention.

[1076] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1077] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds, and further combines an emotion engine that recognizes the user's emotions and adjusts the schedule. This system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[1078] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[1079] Program processing

[1080] User Input

[1081] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. Users are also asked to input their emotional state.

[1082] Sending data

[1083] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[1084] View past data

[1085] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[1086] Real-time data collection

[1087] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[1088] Collecting Emotional Data

[1089] The server receives the user's emotional state data sent from the device, and the emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology.

[1090] Data aggregation and analysis

[1091] The server combines collected historical data, real-time data, and user sentiment data to create aggregated data. The aggregated data is then prepared as a data set for statistical analysis. AI algorithms are used to analyze the data and predict crowds at each attraction and event.

[1092] Generating an optimal schedule

[1093] The server generates an optimal schedule based on the user's input information, emotional data, and crowd prediction data to allow the user to move around the theme park efficiently and comfortably. The server also takes into account the user's emotional state, adjusting the schedule according to their emotions, for example, choosing a nearby attraction if they are tired.

[1094] Schedule suggestions

[1095] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[1096] Specific examples

[1097] 1. User input

[1098] Arrival time: 10:00

[1099] Duration: 6 hours

[1100] Preferred Attractions: Roller coaster, horror house

[1101] Preferred event: Parade

[1102] Emotional state: Slightly tired

[1103] The user enters and submits this information.

[1104] 2. Data collection and analysis

[1105] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[1106] The server collects waiting times and congestion status for each attraction in real time.

[1107] The emotion engine analyzes the user's emotional state and determines that they are "slightly tired."

[1108] 3. Generating the optimal schedule

[1109] Based on the user's wishes and emotions, an AI algorithm generates a schedule that might include "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying a horror house."

[1110] 4. Schedule proposal

[1111] The server transmits the generated schedule to the user's terminal.

[1112] Users can check the schedule on their terminal and move around the theme park efficiently.

[1113] In this way, the present invention allows visitors to avoid crowds and maximize their enjoyment of the theme park, and by taking emotional data into consideration, it provides a more comfortable and satisfying experience, thereby improving visitor satisfaction and reducing the burden on the park staff.

[1114] The processing flow will be explained below.

[1115] Step 1:

[1116] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[1117] Step 2:

[1118] The user inputs information about their visit to the theme park, such as arrival time, length of stay, desired attractions, and desired events, and then clicks the "Submit" button. The user also inputs their emotional state. For example, emotional state options such as "Tired," "Excited," and "Relaxed" are displayed.

[1119] Step 3:

[1120] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[1121] Step 4:

[1122] The server retrieves past visitor data from a database, querying the database for information such as past crowd levels and attraction wait times.

[1123] Step 5:

[1124] The server collects data in real time. Data such as current congestion status and waiting times is obtained via API from sensors and devices installed within the theme park, and is cached in memory or stored in a database.

[1125] Step 6:

[1126] The device sends the user's emotional state data to the server, and the emotion engine analyzes the emotional state using the user's text input, voice input, and facial recognition technology as needed, and generates specific emotional data.

[1127] Step 7:

[1128] The server combines the collected historical data, real-time data, and user sentiment data to create aggregated data, which is then prepared as a dataset for statistical analysis.

[1129] Step 8:

[1130] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[1131] Step 9:

[1132] The server generates an optimal schedule based on user input, emotional data, and congestion prediction data. If the server determines that the user is tired, it prioritizes nearby rest spots in the schedule. The generated schedule takes into account the user's preferences and current emotional state, ensuring efficient and comfortable travel.

[1133] Step 10:

[1134] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[1135] Step 11:

[1136] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[1137] Step 12:

[1138] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[1139] Example 2

[1140] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1141] Leisure facilities such as theme parks face the challenge of preventing visitors from crowding and moving around the facility efficiently. Furthermore, planning that doesn't take into account the emotional state of visitors can prevent them from experiencing a comfortable and satisfying experience. This can lead to lower visitor satisfaction and increased workloads for operational staff.

[1142] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1143] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, desired events, and emotional state, a means for aggregating past visitor data and real-time data, and a means for predicting congestion based on the aggregated data and emotional data to generate an optimal schedule, thereby enabling visitors to travel efficiently while avoiding congestion and adjusting schedules that take emotional state into consideration.

[1144] "Arrival time" is the time that a visitor plans to arrive at a leisure facility such as a theme park.

[1145] "Duration" is the length of time a visitor plans to stay at a theme park or other leisure facility.

[1146] A "desired attraction" is an attraction that a visitor wishes to visit at a leisure facility such as a theme park.

[1147] A "desired event" is an event that a visitor wishes to participate in at a leisure facility such as a theme park.

[1148] "Emotional state" refers to the visitor's current emotional state, which may be a psychological or physiological state such as fatigue, excitement, or stress.

[1149] "Past visitor data" is data about visitors who have visited a theme park or other leisure facility in the past, and includes information such as congestion levels and waiting times.

[1150] "Real-time data" refers to current data such as the current congestion situation at leisure facilities such as theme parks and waiting times for attractions.

[1151] "Aggregated Data" means data sets created by combining collected historical visitor data and real-time data.

[1152] "Emotional Data" is data regarding a visitor's emotional state analyzed based on their input.

[1153] "Crowd prediction" is the prediction of future congestion conditions based on past visitor data and real-time data.

[1154] An "optimal schedule" is a schedule designed to allow visitors to move through a theme park or other leisure facility efficiently and comfortably.

[1155] The "means for presenting to the user" is a method for transmitting the generated schedule to the user's terminal and displaying it.

[1156] The present invention is a system for allowing visitors to move around leisure facilities such as theme parks efficiently while avoiding crowds. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the schedule accordingly. The system includes the following main means:

[1157] First, users enter their visit information using a device such as a smartphone or tablet. Specifically, they enter their arrival time, duration of stay, desired attractions, desired events, and emotional state. This data is then sent to the server via the application. The accuracy of the data is ensured by using an API to send the input data to the server as an HTTP POST request.

[1158] The server collects past visitor data and real-time data, and uses this data to predict congestion. Past visitor data is retrieved from a database using SQL queries, and real-time data is collected from various sensors installed within the theme park (e.g., cameras, people counters, Wi-Fi terminals). The received data is temporarily stored in storage and used for subsequent processing.

[1159] The system also uses an emotion engine to analyze the user's emotional state. Based on the emotional data sent from the device, the system uses text input, voice input, and facial recognition technology to determine the user's emotional state. This emotional data is also integrated on the server and prepared as a dataset for statistical analysis along with the congestion forecast data.

[1160] Using AI algorithms, the server analyzes the collected data and predicts crowding at each attraction and event. Based on the user's input, emotional data, and crowding prediction data, the system generates an optimal schedule to allow users to move around the theme park efficiently and comfortably. Since the system also takes into account the user's emotional state, it adjusts the schedule according to the user's condition, for example, choosing a nearby attraction if the user is tired.

[1161] The generated schedule is sent from the server to the device and suggested to the user. By checking the schedule on the device and following it, the user can avoid congestion and have a more comfortable experience.

[1162] For example, consider the case where a user enters the following information:

[1163] Arrival time: 10:00

[1164] Duration: 6 hours

[1165] Preferred Attractions: Roller coaster, horror house

[1166] Preferred event: Parade

[1167] Emotional state: Slightly tired

[1168] Based on this information, the server analyzes a combination of past and real-time data and suggests a schedule to the user, such as "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying the horror house."

[1169] Examples of prompts:

[1170] "You are the developer of a system that efficiently plans theme park visitor schedules. The user has entered the following information:

[1171] Arrival time: 10:00

[1172] Duration: 6 hours

[1173] Preferred Attractions: Roller coaster, horror house

[1174] Preferred event: Parade

[1175] Emotional state: Slightly tired

[1176] Generate optimal schedules by taking into account historical data, real-time congestion information, and the user's emotional state."

[1177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] The user uses a terminal to input visit information, specifically, arrival time, duration of stay, desired attractions, desired events, and emotional state, into the application form. The input data is checked to prevent omissions and formatting errors.

[1180] Input: Arrival time, Stay time, Preferred attractions, Preferred events, Emotional state

[1181] Output: User data entered in the correct format

[1182] Step 2:

[1183] The device sends the input data to the server. The input data is sent to the server via the API as an HTTP POST request. Error handling is performed and the success / failure status is displayed.

[1184] Input: User data

[1185] Output: User data sent to the server

[1186] Step 3:

[1187] The server retrieves past visitor data from the database, issues SQL queries to retrieve information such as past crowd levels and attraction wait times, and caches the data in memory for subsequent processing.

[1188] Input: User data

[1189] Output: Historical visitor data

[1190] Step 4:

[1191] The server collects real-time data from sensors and devices within the theme park. It receives real-time data from various sensors (cameras, people counters, Wi-Fi devices, etc.) and temporarily stores it in storage.

[1192] Input: Real-time data request

[1193] Output: Real-time congestion data

[1194] Step 5:

[1195] The server receives the emotion data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and facial recognition technology. The analysis results are stored in a database and used to generate subsequent schedules.

[1196] Input: Emotional state data

[1197] Output: Parsed emotion data

[1198] Step 6:

[1199] The server integrates and analyzes the past data, real-time data, and sentiment data collected. It uses AI algorithms to predict crowds at each attraction and event. The results are then prepared as a dataset for statistical analysis.

[1200] Inputs: Historical visitor data, real-time data, sentiment data

[1201] Output: Congestion forecast data

[1202] Step 7:

[1203] The server generates an optimal schedule based on the user's input, emotional data, and crowd prediction data, allowing the user to move around the theme park efficiently and comfortably. It also takes into account the user's emotional state, adjusting the schedule to select nearby attractions if the user is tired, for example.

[1204] Input: User data, emotion data, congestion prediction data

[1205] Output: Optimal schedule data

[1206] Step 8:

[1207] The server sends the generated schedule to the terminal and proposes it to the user. The optimal schedule data is sent via HTTP response, allowing the user to check and use it on their terminal. It also provides a function to update the schedule according to real-time conditions.

[1208] Input: Optimal schedule data

[1209] Output: The schedule presented to the user

[1210] (Application example 2)

[1211] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1212] Conventional store navigation systems lacked methods for users to move around a store efficiently while avoiding crowds, and methods for suggesting appropriate routes based on the user's emotional state. As a result, users often felt stressed while shopping, and the decrease in satisfaction due to crowding in the store became a problem. The present invention aims to provide a system that suggests a comfortable shopping route while avoiding crowds, taking into account the user's emotional state.

[1213] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting arrival time, stay time, product information, and event information, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal route, and a means for presenting the generated route to the user. This enables the user to avoid congestion and move around the store efficiently and comfortably, improving the shopping experience.

[1214] "Arrival time" is the time when the user plans to arrive at the store or facility.

[1215] "Duration" is the length of time a user plans to stay in a store or facility.

[1216] "Product information" is information about the product that the user wishes to purchase, including the product name, category, etc.

[1217] "Event information" is information about an event that a user wishes to participate in or watch, and includes the event name, start time, and the like.

[1218] "Past visitor data" refers to data such as the behavior history and congestion status of users who have visited the facility in the past, and is stored in a database.

[1219] "Real-time data" refers to data collected in real time, such as current congestion levels and waiting times at each store.

[1220] "Emotion data" is data that indicates the user's current emotional state, and is obtained using text input, voice input, facial recognition technology, or the like.

[1221] "Crowd prediction" refers to predicting future congestion conditions within a facility or store based on past and real-time data.

[1222] An "optimal route" is a travel route proposed for a user to travel efficiently and comfortably within a facility or store.

[1223] The present invention is a shopping navigation system for commercial facilities that allows users to navigate the facility efficiently and comfortably. The system provides optimal routes that avoid crowds, taking into account the user's emotional state.

[1224] System configuration

[1225] User Input

[1226] Users use their smartphones to input arrival time, stay time, product information, and event information into the application. Users are also prompted to input their current emotional state, which can be selected from options such as "I want to relax," "I'm in a hurry," or "I'm tired."

[1227] Sending data

[1228] The device sends the entered information to the server. The API is used to send the input data to the server via an HTTP request. The server receives and stores this data.

[1229] Historical and real-time data collection

[1230] The server accesses a database that stores past visitor data to obtain information such as past congestion levels and waiting times at each store. It also collects real-time data on current congestion levels and waiting times from sensors and terminals installed in the store.

[1231] Collecting Emotional Data

[1232] The server receives the user's emotional state data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology using a camera. An example of a library that can be used is "emotion_engine."

[1233] Data aggregation and analysis

[1234] The server combines the collected historical data, real-time data, and emotion data to create aggregated data. It uses AI algorithms to analyze the data and predict crowding at each store or event. An example of a library used is "route_optimizer."

[1235] Generate optimal routes

[1236] The server generates an optimal route based on the user's input, emotion data, and congestion prediction data to enable the user to move around the facility efficiently and comfortably. For example, if the user inputs that they are "tired," the optimal route will include a stop at a nearby rest spot.

[1237] Route suggestions

[1238] The server then sends the generated optimal route to the device and presents it to the user. The user can then check the proposed route through the app and follow it to avoid congestion and enjoy a comfortable shopping experience.

[1239] Specific examples

[1240] 1. User input

[1241] Arrival time: 13:00

[1242] Visit duration: 3 hours

[1243] Planned purchases: Luxury brand bags and accessories

[1244] Preferred event: Sales event

[1245] Emotional state: I want to relax

[1246] The user enters and submits this information.

[1247] 2. Data collection and analysis

[1248] The server analyzes visitor data from the past year and identifies congestion patterns at the same time of day and on the same day of the week.

[1249] The server collects information on the congestion status and waiting times of each store in real time.

[1250] The emotion engine analyzes the user's emotional state and determines that they "want to relax."

[1251] 3. Generating the optimal route

[1252] Based on the user's wishes and emotions, an AI algorithm generates a route such as "first take a break in the refreshment space, then buy a luxury brand bag, and then stop by an accessory shop."

[1253] 4. Route suggestions

[1254] The server sends the generated route to the user's device.

[1255] The user can check the route on the terminal and move around the shopping facility efficiently.

[1256] Prompt Sentence Examples

[1257] Analyze the user's emotional state.

[1258] Emotional state: "I want to relax"

[1259] User input data:

[1260] Arrival time: 13:00

[1261] Visit duration: 3 hours

[1262] Planned purchases: Luxury brand bags and accessories

[1263] Preferred event: Sales event

[1264] Based on this data, suggest the best shopping route.

[1265] In this way, the present invention is a system that allows users to avoid crowds and enjoy shopping comfortably within the facility.By taking into account the user's emotional state, the system provides a more satisfying shopping experience.

[1266] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1267] Step 1:

[1268] The user launches the application using a smartphone and inputs the arrival time, stay time, planned purchases, and desired event information. The user also selects their current emotional state. The input contents are as follows:

[1269] Input: Arrival time (e.g., 13:00), duration (e.g., 3 hours), planned purchase (e.g., luxury brand bags, accessories), desired event (e.g., sale event), emotional state (e.g., wanting to relax)

[1270] Output: User input data

[1271] Step 2:

[1272] The device sends the input information to the server via an HTTP request. Specifically, it uses an API to send the input data to the server in JSON format.

[1273] Input: User-entered data

[1274] Output: Sending to server completed

[1275] Step 3:

[1276] The server stores the received user-entered data, records it in a database, and checks each data for consistency and accuracy.

[1277] Input: User-entered data

[1278] Output: Saved user-entered data

[1279] Step 4:

[1280] The server retrieves past visitor data from the database, extracting information such as congestion status, waiting times, and store popularity for each data set.

[1281] Input: A request from the database

[1282] Output: Historical data

[1283] Step 5:

[1284] The server collects information on current congestion and waiting times from sensors and real-time data collection terminals installed in the store, and temporarily stores the real-time data in memory for analysis.

[1285] Input: Data from real-time data collection terminal

[1286] Output: Real-time data

[1287] Step 6:

[1288] The server launches an emotion engine to analyze the user's emotional state data, and outputs an evaluation result through text input, voice input, and facial recognition technology.

[1289] Input: User's emotional state data

[1290] Output: Parsed emotion data

[1291] Step 7:

[1292] The server combines historical, real-time, and sentiment data to create an aggregated dataset, which serves as input for statistical analysis and predictive models.

[1293] Input: Historical data, real-time data, analyzed sentiment data

[1294] Output: Aggregated dataset

[1295] Step 8:

[1296] The server uses AI algorithms to analyze the aggregated data set and make crowd predictions, which then predicts future crowding conditions at each store or event.

[1297] Input: Aggregated dataset

[1298] Output: Congestion forecast data

[1299] Step 9:

[1300] The server generates the optimal shopping route based on the user's input information and emotional data. The AI ​​algorithm considers the user's wishes and emotions and suggests an efficient and comfortable route.

[1301] Input: User input data, analyzed emotion data, congestion prediction data

[1302] Output: Optimal route

[1303] Step 10:

[1304] The server sends the generated optimal route to the terminal and presents it to the user, who can then check the proposed route through the application to move efficiently through the shopping facility.

[1305] Input: Optimal Route

[1306] Output: Route suggestions to the user

[1307] The above are the specific processing steps for implementing this invention. The techniques and procedures used in each step are effectively realized by making full use of modern data analysis and AI technology.

[1308] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1309] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1310] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1311] [Fourth embodiment]

[1312] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1313] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1314] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1315] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1316] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1317] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1318] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1319] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1320] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1321] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1322] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1323] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1324] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1325] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds. The system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting crowds based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[1326] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[1327] Program processing

[1328] User Input

[1329] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration of stay, desired attractions, and desired events.

[1330] Sending data

[1331] The device sends the entered information to the server, which receives and stores this data.

[1332] View past data

[1333] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[1334] Real-time data collection

[1335] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[1336] Data aggregation and analysis

[1337] The server combines the collected historical data with real-time data and analyzes it using AI algorithms, which then predicts crowds at each attraction and event.

[1338] Generating an optimal schedule

[1339] Based on the user's input information and congestion forecast data, the server generates an optimal schedule that allows the user to move around the theme park efficiently and comfortably.

[1340] Schedule suggestions

[1341] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[1342] Specific examples

[1343] 1. User input

[1344] Arrival time: 10:00

[1345] Duration: 6 hours

[1346] Preferred Attractions: Roller coaster, horror house

[1347] Preferred event: Parade

[1348] The user enters and submits this information.

[1349] 2. Data collection and analysis

[1350] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[1351] The server collects waiting times and congestion status for each attraction in real time.

[1352] 3. Generating the optimal schedule

[1353] Based on the user's preferences, the AI ​​algorithm generates the optimal schedule: "Arrive at 10:00, ride the roller coaster from 10:15, enjoy the parade from 11:00, and enjoy the horror house at 12:00."

[1354] 4. Schedule proposal

[1355] The server transmits the generated schedule to the user's terminal.

[1356] Users can check the schedule on their terminal and move around the theme park efficiently.

[1357] In this way, the present invention provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, thereby improving visitor satisfaction and reducing the burden on the park staff.

[1358] The processing flow will be explained below.

[1359] Step 1:

[1360] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[1361] Step 2:

[1362] The user inputs information about the theme park visit, specifically, the arrival time, the duration of stay, the desired attractions, and the desired events, and clicks the "Submit" button.

[1363] Step 3:

[1364] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[1365] Step 4:

[1366] The server retrieves past visitor data from the database, querying information such as past crowd levels and attraction wait times.

[1367] Step 5:

[1368] The server collects data in real time. Information such as current congestion status and waiting times is obtained from sensors and devices within the theme park via an API, and is cached in memory or stored in a database.

[1369] Step 6:

[1370] The server combines the collected historical and real-time data to create aggregated data, which is then prepared as a data set for statistical analysis.

[1371] Step 7:

[1372] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[1373] Step 8:

[1374] The server generates an optimal schedule based on the user's input and analysis results. For example, a specific schedule such as "Arrival time: 10:00, roller coaster at 10:15, parade at 11:00, horror house at 12:30" is generated.

[1375] Step 9:

[1376] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[1377] Step 10:

[1378] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[1379] Step 11:

[1380] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[1381] Example 1

[1382] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1383] Leisure facilities such as theme parks require methods for visitors to move around the facility efficiently and avoid crowds. In particular, a system that can predict congestion in real time and provide optimal schedules based on that prediction is needed. However, current systems do not fully utilize past or real-time data, making it difficult to provide schedules that allow visitors to enjoy the facility comfortably. This often results in long waiting times and discomfort due to crowds.

[1384] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1385] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting congestion using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal, thereby enabling visitors to move around the theme park efficiently and comfortably.

[1386] "Arrival time" is the time when the user plans to arrive at a leisure facility such as a theme park.

[1387] "Staying time" is the period of time that a user plans to stay at a leisure facility such as a theme park.

[1388] "Desired Attractions" is a list of attractions that the user wishes to experience during their visit.

[1389] "Desired Events" is a list of events that the user wishes to participate in during their visit.

[1390] "Past visit data" refers to data related to the behavioral history, length of stay, and waiting times for attractions of visitors who have visited the theme park in the past.

[1391] "Real-time data" refers to data that shows real-time conditions within the theme park, such as current wait times for attractions and congestion levels.

[1392] "Aggregated Data" is data that combines historical visit data and real-time data and is used for predictions and analysis.

[1393] A "generative AI model" is an artificial intelligence algorithm that uses past visit data and real-time data as input to predict congestion and generate optimal schedules.

[1394] The "optimal schedule" is a visit plan created by the generative AI model to help users move around the theme park efficiently and comfortably.

[1395] A "display terminal" is a device for presenting an optimal schedule to a user, and includes a smartphone, tablet, etc.

[1396] The present invention is a system for enabling visitors to move efficiently and avoid crowds at leisure facilities such as theme parks. The system includes a means for inputting arrival time, stay time, desired attractions, and desired events, a means for aggregating past visit data and real-time data, a means for predicting crowds using a generative AI model based on the aggregated data and generating an optimal schedule, and a means for presenting the generated schedule on a display terminal.

[1397] The system's hardware configuration includes the device used by the user (such as a smartphone or tablet), a server that processes data, and multiple sensors for collecting real-time data, while the software configuration includes an application for users to input information, data aggregation and analysis algorithms that run on the server, and a generative AI model.

[1398] Specifically, the system operates as follows.

[1399] First, a user uses their device to enter information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. For example, a user might enter the following information into the application form:

[1400] Arrival time: 10:00

[1401] Duration: 6 hours

[1402] Desired attractions: Roller coaster, horror house

[1403] Preferred event: Parade

[1404] The device then sends this input data to the server via an HTTP request, packaging the data in JSON format, which the server receives and stores in a database.

[1405] The server then retrieves past visit data from a database and collects real-time data from multiple sensors installed throughout the theme park, including past crowd levels and current wait times.

[1406] The server aggregates this data and uses a generative AI model to predict crowding. It uses past and real-time data to predict future wait times for each attraction and event. For example, the generative AI model outputs the following predictions:

[1407] Roller Coaster: 15 minute wait

[1408] Horror House: 20 minute wait

[1409] Parade: Starts at 11:00

[1410] Based on this prediction data and user input, the server generates an optimal schedule. The generated schedule provides the user with the order and time of visits that will allow them to enjoy the theme park efficiently. For example, a suggested schedule might be, "Arrive at 10:00, ride the roller coaster from 10:15, see the parade from 11:00, and enjoy the horror house at 12:00."

[1411] Finally, the server sends this optimal schedule to the terminal, and the user can check the schedule on the terminal and avoid congestion by following the instructions.

[1412] Here are some examples of prompts for generative AI models:

[1413] "I'll arrive at the theme park at 10:00 and plan to stay for 6 hours. I'd like to experience the roller coaster, the horror house, and see the parade. What's the best schedule to efficiently enjoy my time there?"

[1414] The present invention thus provides a system that allows visitors to avoid crowds and maximize their enjoyment of the theme park, not only improving visitor satisfaction but also reducing the burden on the park staff.

[1415] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1416] Step 1:

[1417] The user uses a terminal to input information about the theme park, including arrival time, duration, desired attractions, and desired events. The user enters the following information into the application:

[1418] Arrival time: 10:00

[1419] Duration: 6 hours

[1420] Desired attractions: Roller coaster, horror house

[1421] Preferred event: Parade

[1422] Input: User's visit information (arrival time, stay time, desired attractions, desired events)

[1423] Output: Data entered into the application

[1424] Specific action: The user enters information using the input form and taps the submit button.

[1425] Step 2:

[1426] The device sends the input information to the server. The device app issues an HTTP request to the server and sends the input information in JSON format.

[1427] Input: Visit information entered by the user

[1428] Output: Visit information data sent to the server

[1429] Specific operation: Data is sent via an HTTP request, received by the server, and stored in the database.

[1430] Step 3:

[1431] The server retrieves past visit data from the database, and uses SQL queries to search and retrieve data on crowding and attraction wait times for the past year.

[1432] Input: SQL query to retrieve past visit data

[1433] Output: Past visit data retrieved from the database

[1434] Specific operation: The server executes the SQL query and retrieves the required data from the database.

[1435] Step 4:

[1436] The server collects real-time data from sensors in the theme park, such as waiting times for each attraction, crowding levels, and scheduled event start times.

[1437] Input: Data streams from sensors to collect real-time data

[1438] Output: Real-time data collected

[1439] Specific operation: The server receives the data stream sent from the sensor and stores it in temporary memory as real-time data.

[1440] Step 5:

[1441] The server integrates the collected data and uses a generative AI model to predict congestion. It uses past and real-time data as input to predict future congestion and waiting times.

[1442] Inputs: Historical and real-time data

[1443] Output: Predicted congestion and waiting time

[1444] Specific operation: The server uses the generated AI model to analyze the data and output the congestion prediction results.

[1445] Step 6:

[1446] The server generates an optimal schedule based on user visit information and congestion forecast data. For example, it creates a schedule that includes "Riding the roller coaster at 10:15, the parade at 11:00, and enjoying the horror house at 12:00."

[1447] Input: User visit information and congestion prediction data

[1448] Output: Optimal schedule

[1449] Specific operation: The generative AI model uses a schedule generation algorithm to create an optimal visiting plan.

[1450] Step 7:

[1451] The server sends the generated schedule to the terminal, which receives the schedule and displays it on the application to notify the user.

[1452] Input: Generated optimal schedule

[1453] Output: Schedule displayed on the user's terminal

[1454] Specific operation: The server sends an HTTP response, and the device displays the received data.

[1455] As described above, this system automatically generates and provides the optimal schedule for users to efficiently enjoy a theme park.

[1456] (Application example 1)

[1457] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1458] In theme parks and virtual stores, it is difficult for visitors to move around the facility efficiently while avoiding crowds. In particular, there is a need to provide an optimal schedule based on real-time congestion status and past data, but current systems have difficulty achieving this. Furthermore, there are issues that need to be resolved to improve the efficiency of visitor movement within virtual stores in order to improve visitor satisfaction.

[1459] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1460] In this invention, the server includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and generating an optimal schedule, a means for presenting the generated schedule to the user, and a means for streamlining visitor movement within the virtual environment. This allows visitors to move efficiently within the facility based on their arrival time, length of stay, and desired attractions and events, thereby avoiding congestion. Similarly, visitors can move efficiently within virtual stores and smoothly use their desired products and services.

[1461] "Arrival time" is the time a visitor plans to arrive at a facility or virtual environment.

[1462] "Dwell Time" means the expected or desired length of time a visitor will remain at a facility or in a virtual environment.

[1463] "Desired attractions" are attractions or spots that visitors would like to visit or experience.

[1464] A "desired event" is an event or activity that a visitor would like to participate in or observe.

[1465] "Historical Visitor Data" means data about visitors who have previously visited a facility or virtual environment, including information about crowd levels and wait times for attractions.

[1466] "Real-time data" refers to ongoing information, such as current crowd levels and waiting times for attractions.

[1467] "Congestion forecasting" is the prediction of future congestion conditions based on past and real-time data.

[1468] An "optimal schedule" is one that is planned to allow visitors to move efficiently through the facility and experience the attractions and events they desire.

[1469] "Means for presenting to users" refers to the means by which visitors can view the generated schedule. This includes devices such as smartphones, tablets, and head-mounted displays.

[1470] A "virtual environment" is a digital space that mimics a real-world environment and that visitors can access using digital devices.

[1471] This invention is a system that allows visitors to move around a facility efficiently while avoiding crowds. This system can be applied to theme parks and virtual stores, and is realized by generating and presenting an optimal schedule based on the visitor's arrival time, stay time, desired attractions, desired events, etc.

[1472] First, the user uses a device such as a smartphone or head-mounted display to input information about their visit, such as their estimated time of arrival, length of stay, and desired attractions and events. This information is then sent from the device to the server.

[1473] The server retrieves historical visitor data from a database and collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including crowd levels, wait times, and current visitor locations.

[1474] The collected data is analyzed using Python and AI algorithms (e.g., scikit-learn and TensorFlow). The server combines historical and real-time data to predict congestion. Based on this prediction, an optimal schedule is generated to allow users to move around the facility efficiently.

[1475] The generated schedule is then sent back to the user's device, where the user can check and follow the schedule to avoid crowds.In particular, in virtual stores, users can efficiently use the products and services they desire.

[1476] As a specific example, if a user arrives at a theme park at 10:00, plans to stay for six hours, and wants to ride the roller coaster or the horror house, the server can use AI to generate an optimal schedule based on the past year's congestion data and real-time data: "Arrive at 10:00, ride the roller coaster from 10:15, parade from 11:00, horror house at 12:00." This schedule is immediately displayed on the user's device.

[1477] Example prompt sentence:

[1478] A user visits a virtual store, arrives at 10:00, stays for 4 hours, wants to use the shopping area and try on clothes, and also wants to watch a virtual talk show. Generate the optimal schedule.

[1479] In this way, this invention is expected to improve the visitor experience and also improve management efficiency for operators.

[1480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1481] Step 1:

[1482] Users use their smartphones or head-mounted displays to input information about their visit, including arrival time, duration, and desired attractions and events, which is then sent from the device to the server.

[1483] Input: Arrival time, stay time, desired attractions, desired events

[1484] Output: Sending visit information to the server

[1485] Step 2:

[1486] The server accesses a database that stores past visitor data, including the congestion status and waiting times for each attraction, and retrieves the visitor data.

[1487] Input: Visit information

[1488] Output: Past visitor data

[1489] Step 3:

[1490] The server collects real-time data from sensors and tracking systems installed within the theme park and virtual stores, including current crowd levels and wait times for attractions.

[1491] Input: Visit information

[1492] Output: Real-time data

[1493] Step 4:

[1494] The server combines collected past visitor data with real-time data and uses AI algorithms to predict congestion, which then predicts future congestion and generates an optimal schedule.

[1495] Input: Past visitor data, real-time data

[1496] Output: Congestion forecast data, optimal schedule

[1497] Step 5:

[1498] The server then sends the generated optimal schedule to the user's device, allowing the user to check the schedule and move around the facility efficiently based on it.

[1499] Input: Optimal schedule

[1500] Output: Schedule display on user's device

[1501] Step 6:

[1502] Users move around the theme park and virtual stores based on the schedule provided, allowing them to avoid crowds and efficiently experience the attractions and events they want.

[1503] Input: Proposed schedule

[1504] Output: Efficient travel and experiences

[1505] The above are the specific processing steps for carrying out the present invention.

[1506] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1507] The present invention is a system for allowing visitors to efficiently move around a leisure facility such as a theme park while avoiding crowds, and further combines an emotion engine that recognizes the user's emotions and adjusts the schedule. This system includes a means for inputting arrival time, length of stay, desired attractions, and desired events, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal schedule, and a means for presenting the generated schedule to the user.

[1508] The following is a natural language explanation of the program processing of this system, along with a concrete example.

[1509] Program processing

[1510] User Input

[1511] Users use a device (such as a smartphone or tablet) to input information about their visit to the theme park into the application, such as arrival time, duration, desired attractions, desired events, etc. Users are also asked to input their emotional state.

[1512] Sending data

[1513] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[1514] View past data

[1515] The server accesses a database that holds past visitor data and obtains information such as past crowd levels and waiting times for attractions.

[1516] Real-time data collection

[1517] The server collects real-time data (such as current crowding levels and waiting times) from various sensors and terminals installed in the theme park, including the current status of each attraction and event.

[1518] Collecting Emotional Data

[1519] The server receives the user's emotional state data sent from the device, and the emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology.

[1520] Data aggregation and analysis

[1521] The server combines collected historical data, real-time data, and user sentiment data to create aggregated data. The aggregated data is then prepared as a data set for statistical analysis. AI algorithms are used to analyze the data and predict crowds at each attraction and event.

[1522] Generating an optimal schedule

[1523] The server generates an optimal schedule based on the user's input information, emotional data, and crowd prediction data to allow the user to move around the theme park efficiently and comfortably. The server also takes into account the user's emotional state, adjusting the schedule according to their emotions, for example, choosing a nearby attraction if they are tired.

[1524] Schedule suggestions

[1525] The server sends the generated schedule to the terminal and proposes it to the user. The user can check the schedule through the terminal and avoid congestion by following it.

[1526] Specific examples

[1527] 1. User input

[1528] Arrival time: 10:00

[1529] Duration: 6 hours

[1530] Preferred Attractions: Roller coaster, horror house

[1531] Preferred event: Parade

[1532] Emotional state: Slightly tired

[1533] The user enters and submits this information.

[1534] 2. Data collection and analysis

[1535] The server analyzes visitor data from the past year to identify congestion patterns at the same time of day and on the same day of the week.

[1536] The server collects waiting times and congestion status for each attraction in real time.

[1537] The emotion engine analyzes the user's emotional state and determines that they are "slightly tired."

[1538] 3. Generating the optimal schedule

[1539] Based on the user's wishes and emotions, an AI algorithm generates a schedule that might include "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying a horror house."

[1540] 4. Schedule proposal

[1541] The server transmits the generated schedule to the user's terminal.

[1542] Users can check the schedule on their terminal and move around the theme park efficiently.

[1543] In this way, the present invention allows visitors to avoid crowds and maximize their enjoyment of the theme park, and by taking emotional data into consideration, it provides a more comfortable and satisfying experience, thereby improving visitor satisfaction and reducing the burden on the park staff.

[1544] The processing flow will be explained below.

[1545] Step 1:

[1546] The user launches the application on the device. The application displays the welcome screen and the user logs in.

[1547] Step 2:

[1548] The user inputs information about their visit to the theme park, such as arrival time, length of stay, desired attractions, and desired events, and then clicks the "Submit" button. The user also inputs their emotional state. For example, emotional state options such as "Tired," "Excited," and "Relaxed" are displayed.

[1549] Step 3:

[1550] The device sends the entered information to the server. Using the API, the input data is sent to the server via an HTTP request. The server receives and stores this data.

[1551] Step 4:

[1552] The server retrieves past visitor data from a database, querying the database for information such as past crowd levels and attraction wait times.

[1553] Step 5:

[1554] The server collects data in real time. Data such as current congestion status and waiting times is obtained via API from sensors and devices installed within the theme park, and is cached in memory or stored in a database.

[1555] Step 6:

[1556] The device sends the user's emotional state data to the server, and the emotion engine analyzes the emotional state using the user's text input, voice input, and facial recognition technology as needed, and generates specific emotional data.

[1557] Step 7:

[1558] The server combines the collected historical data, real-time data, and user sentiment data to create aggregated data, which is then prepared as a dataset for statistical analysis.

[1559] Step 8:

[1560] The server uses AI algorithms to predict crowds, and machine learning models to generate crowd forecasts for each attraction and event, for example, predicting whether a roller coaster will be crowded at a certain time.

[1561] Step 9:

[1562] The server generates an optimal schedule based on user input, emotional data, and congestion prediction data. If the server determines that the user is tired, it prioritizes nearby rest spots in the schedule. The generated schedule takes into account the user's preferences and current emotional state, ensuring efficient and comfortable travel.

[1563] Step 10:

[1564] The server sends the generated schedule to the device. It calls an API to return the completed schedule to the device. The device receives the notification and displays it to the user.

[1565] Step 11:

[1566] The user reviews the proposed schedule. The app displays the best possible schedule and the user confirms it, providing options to fine-tune the schedule if necessary.

[1567] Step 12:

[1568] (Optional) The user provides feedback on their actual behavior. The app sends a survey to the user requesting feedback after the schedule is executed. The user provides feedback, which is used for the next data analysis.

[1569] Example 2

[1570] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1571] Leisure facilities such as theme parks face the challenge of preventing visitors from crowding and moving around the facility efficiently. Furthermore, planning that doesn't take into account the emotional state of visitors can prevent them from experiencing a comfortable and satisfying experience. This can lead to lower visitor satisfaction and increased workloads for operational staff.

[1572] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1573] In this invention, the server includes a means for inputting arrival time, stay time, desired attractions, desired events, and emotional state, a means for aggregating past visitor data and real-time data, and a means for predicting congestion based on the aggregated data and emotional data to generate an optimal schedule, thereby enabling visitors to travel efficiently while avoiding congestion and adjusting schedules that take emotional state into consideration.

[1574] "Arrival time" is the time that a visitor plans to arrive at a leisure facility such as a theme park.

[1575] "Duration" is the length of time a visitor plans to stay at a theme park or other leisure facility.

[1576] A "desired attraction" is an attraction that a visitor wishes to visit at a leisure facility such as a theme park.

[1577] A "desired event" is an event that a visitor wishes to participate in at a leisure facility such as a theme park.

[1578] "Emotional state" refers to the visitor's current emotional state, which may be a psychological or physiological state such as fatigue, excitement, or stress.

[1579] "Past visitor data" is data about visitors who have visited a theme park or other leisure facility in the past, and includes information such as congestion levels and waiting times.

[1580] "Real-time data" refers to current data such as the current congestion situation at leisure facilities such as theme parks and waiting times for attractions.

[1581] "Aggregated Data" means data sets created by combining collected historical visitor data and real-time data.

[1582] "Emotional Data" is data regarding a visitor's emotional state analyzed based on their input.

[1583] "Crowd prediction" is the prediction of future congestion conditions based on past visitor data and real-time data.

[1584] An "optimal schedule" is a schedule designed to allow visitors to move through a theme park or other leisure facility efficiently and comfortably.

[1585] The "means for presenting to the user" is a method for transmitting the generated schedule to the user's terminal and displaying it.

[1586] The present invention is a system for allowing visitors to move around leisure facilities such as theme parks efficiently while avoiding crowds. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the schedule accordingly. The system includes the following main means:

[1587] First, users enter their visit information using a device such as a smartphone or tablet. Specifically, they enter their arrival time, duration of stay, desired attractions, desired events, and emotional state. This data is then sent to the server via the application. The accuracy of the data is ensured by using an API to send the input data to the server as an HTTP POST request.

[1588] The server collects past visitor data and real-time data, and uses this data to predict congestion. Past visitor data is retrieved from a database using SQL queries, and real-time data is collected from various sensors installed within the theme park (e.g., cameras, people counters, Wi-Fi terminals). The received data is temporarily stored in storage and used for subsequent processing.

[1589] The system also uses an emotion engine to analyze the user's emotional state. Based on the emotional data sent from the device, the system uses text input, voice input, and facial recognition technology to determine the user's emotional state. This emotional data is also integrated on the server and prepared as a dataset for statistical analysis along with the congestion forecast data.

[1590] Using AI algorithms, the server analyzes the collected data and predicts crowding at each attraction and event. Based on the user's input, emotional data, and crowding prediction data, the system generates an optimal schedule to allow users to move around the theme park efficiently and comfortably. Since the system also takes into account the user's emotional state, it adjusts the schedule according to the user's condition, for example, choosing a nearby attraction if the user is tired.

[1591] The generated schedule is sent from the server to the device and suggested to the user. By checking the schedule on the device and following it, the user can avoid congestion and have a more comfortable experience.

[1592] For example, consider the case where a user enters the following information:

[1593] Arrival time: 10:00

[1594] Duration: 6 hours

[1595] Preferred Attractions: Roller coaster, horror house

[1596] Preferred event: Parade

[1597] Emotional state: Slightly tired

[1598] Based on this information, the server analyzes a combination of past and real-time data and suggests a schedule to the user, such as "guiding them to a rest spot immediately after arrival, then riding a roller coaster after the rest, followed by a parade, and then enjoying the horror house."

[1599] Examples of prompts:

[1600] "You are the developer of a system that efficiently plans theme park visitor schedules. The user has entered the following information:

[1601] Arrival time: 10:00

[1602] Duration: 6 hours

[1603] Preferred Attractions: Roller coaster, horror house

[1604] Preferred event: Parade

[1605] Emotional state: Slightly tired

[1606] Generate optimal schedules by taking into account historical data, real-time congestion information, and the user's emotional state."

[1607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1608] Step 1:

[1609] The user uses a terminal to enter visit information, specifically the arrival time, duration of stay, desired attractions, desired events, and emotional state, into the application form. The input data is checked to prevent omissions and formatting errors.

[1610] Input: Arrival time, Stay time, Preferred attractions, Preferred events, Emotional state

[1611] Output: User data entered in the correct format

[1612] Step 2:

[1613] The device sends the input data to the server. The input data is sent to the server via the API as an HTTP POST request. Error handling is performed and the success / failure status is displayed.

[1614] Input: User data

[1615] Output: User data sent to the server

[1616] Step 3:

[1617] The server retrieves past visitor data from the database, issues SQL queries to retrieve information such as past crowd levels and attraction wait times, and caches the data in memory for subsequent processing.

[1618] Input: User data

[1619] Output: Historical visitor data

[1620] Step 4:

[1621] The server collects real-time data from sensors and devices within the theme park. It receives real-time data from various sensors (cameras, people counters, Wi-Fi devices, etc.) and temporarily stores it in storage.

[1622] Input: Real-time data request

[1623] Output: Real-time congestion data

[1624] Step 5:

[1625] The server receives the emotion data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and facial recognition technology. The analysis results are stored in a database and used to generate subsequent schedules.

[1626] Input: Emotional state data

[1627] Output: Parsed emotion data

[1628] Step 6:

[1629] The server integrates and analyzes the past data, real-time data, and sentiment data collected. It uses AI algorithms to predict crowds at each attraction and event. The results are then prepared as a dataset for statistical analysis.

[1630] Inputs: Historical visitor data, real-time data, sentiment data

[1631] Output: Congestion forecast data

[1632] Step 7:

[1633] The server generates an optimal schedule based on the user's input, emotional data, and crowd prediction data, allowing the user to move around the theme park efficiently and comfortably. It also takes into account the user's emotional state, adjusting the schedule to select nearby attractions if the user is tired, for example.

[1634] Input: User data, emotion data, congestion prediction data

[1635] Output: Optimal schedule data

[1636] Step 8:

[1637] The server sends the generated schedule to the terminal and proposes it to the user. The optimal schedule data is sent via HTTP response, allowing the user to check and use it on their terminal. It also provides a function to update the schedule according to real-time conditions.

[1638] Input: Optimal schedule data

[1639] Output: The schedule presented to the user

[1640] (Application example 2)

[1641] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1642] Conventional store navigation systems lacked methods for users to move around a store efficiently while avoiding crowds, and methods for suggesting appropriate routes based on the user's emotional state. As a result, users often felt stressed while shopping, and the decrease in satisfaction due to crowding in the store became a problem. The present invention aims to provide a system that suggests a comfortable shopping route while avoiding crowds, taking into account the user's emotional state.

[1643] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting arrival time, stay time, product information, and event information, a means for aggregating past visitor data and real-time data, a means for predicting congestion based on the aggregated data and user emotion data and generating an optimal route, and a means for presenting the generated route to the user. This enables the user to avoid congestion and move around the store efficiently and comfortably, improving the shopping experience.

[1644] "Arrival time" is the time when the user plans to arrive at the store or facility.

[1645] "Staying time" is the length of time a user plans to stay in a store or facility.

[1646] "Product information" is information about the product that the user wishes to purchase, including the product name, category, etc.

[1647] "Event information" is information about an event that a user wishes to participate in or watch, and includes the event name, start time, and the like.

[1648] "Past visitor data" refers to data such as the behavior history and congestion status of users who have visited the facility in the past, and is stored in a database.

[1649] "Real-time data" refers to data collected in real time, such as current congestion levels and waiting times at each store.

[1650] "Emotion data" is data that indicates the user's current emotional state, and is obtained using text input, voice input, facial recognition technology, or the like.

[1651] "Crowd prediction" refers to predicting future congestion conditions within a facility or store based on past and real-time data.

[1652] An "optimal route" is a travel route proposed for a user to travel efficiently and comfortably within a facility or store.

[1653] The present invention is a shopping navigation system for commercial facilities that allows users to navigate the facility efficiently and comfortably. The system provides optimal routes that avoid crowds, taking into account the user's emotional state.

[1654] System configuration

[1655] User Input

[1656] Users use their smartphones to input arrival time, stay time, product information, and event information into the application. Users are also prompted to input their current emotional state, which can be selected from options such as "I want to relax," "I'm in a hurry," or "I'm tired."

[1657] Sending data

[1658] The device sends the entered information to the server. The API is used to send the input data to the server via an HTTP request. The server receives and stores this data.

[1659] Historical and real-time data collection

[1660] The server accesses a database that stores past visitor data to obtain information such as past congestion levels and waiting times at each store. It also collects real-time data on current congestion levels and waiting times from sensors and terminals installed in the store.

[1661] Collecting Emotional Data

[1662] The server receives the user's emotional state data sent from the device. The emotion engine analyzes the user's emotional state using text input, voice input, and in some cases, facial recognition technology using a camera. An example of a library that can be used is "emotion_engine."

[1663] Data aggregation and analysis

[1664] The server combines the collected historical data, real-time data, and emotion data to create aggregated data. It uses AI algorithms to analyze the data and predict crowding at each store or event. An example of a library used is "route_optimizer."

[1665] Generate optimal routes

[1666] The server generates an optimal route based on the user's input, emotion data, and congestion prediction data to enable the user to move around the facility efficiently and comfortably. For example, if the user inputs that they are "tired," the optimal route will include a stop at a nearby rest spot.

[1667] Route suggestions

[1668] The server then sends the generated optimal route to the device and presents it to the user. The user can then check the proposed route through the app and follow it to avoid congestion and enjoy a comfortable shopping experience.

[1669] Specific examples

[1670] 1. User input

[1671] Arrival time: 13:00

[1672] Visit duration: 3 hours

[1673] Planned purchases: Luxury brand bags and accessories

[1674] Preferred event: Sales event

[1675] Emotional state: I want to relax

[1676] The user enters and submits this information.

[1677] 2. Data collection and analysis

[1678] The server analyzes visitor data from the past year and identifies congestion patterns at the same time of day and on the same day of the week.

[1679] The server collects information on the congestion status and waiting times of each store in real time.

[1680] The emotion engine analyzes the user's emotional state and determines that they "want to relax."

[1681] 3. Generating the optimal route

[1682] Based on the user's wishes and emotions, an AI algorithm generates a route such as "first take a break in the refreshment space, then buy a luxury brand bag, and then stop by an accessory shop."

[1683] 4. Route suggestions

[1684] The server sends the generated route to the user's device.

[1685] The user can check the route on the terminal and move around the shopping facility efficiently.

[1686] Prompt Sentence Examples

[1687] Analyze the user's emotional state.

[1688] Emotional state: "I want to relax"

[1689] User input data:

[1690] Arrival time: 13:00

[1691] Visit duration: 3 hours

[1692] Planned purchases: Luxury brand bags and accessories

[1693] Preferred event: Sales event

[1694] Based on this data, suggest the best shopping route.

[1695] In this way, the present invention is a system that allows users to avoid crowds and enjoy shopping comfortably within the facility.By taking into account the user's emotional state, the system provides a more satisfying shopping experience.

[1696] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1697] Step 1:

[1698] The user launches the application using a smartphone and inputs the arrival time, stay time, planned purchases, and desired event information. The user also selects their current emotional state. The input contents are as follows:

[1699] Input: Arrival time (e.g., 13:00), duration (e.g., 3 hours), planned purchase (e.g., luxury brand bags, accessories), desired event (e.g., sale event), emotional state (e.g., wanting to relax)

[1700] Output: User input data

[1701] Step 2:

[1702] The device sends the input information to the server via an HTTP request. Specifically, it uses an API to send the input data to the server in JSON format.

[1703] Input: User-entered data

[1704] Output: Sending to server completed

[1705] Step 3:

[1706] The server stores the received user-entered data, records it in a database, and checks each data for consistency and accuracy.

[1707] Input: User-entered data

[1708] Output: Saved user-entered data

[1709] Step 4:

[1710] The server retrieves past visitor data from the database, extracting information such as congestion status, waiting times, and store popularity for each data set.

[1711] Input: A request from the database

[1712] Output: Historical data

[1713] Step 5:

[1714] The server collects information on current congestion and waiting times from sensors and real-time data collection terminals installed in the store, and temporarily stores the real-time data in memory for analysis.

[1715] Input: Data from real-time data collection terminal

[1716] Output: Real-time data

[1717] Step 6:

[1718] The server launches an emotion engine to analyze the user's emotional state data, and outputs an evaluation result through text input, voice input, and facial recognition technology.

[1719] Input: User's emotional state data

[1720] Output: Parsed emotion data

[1721] Step 7:

[1722] The server combines historical, real-time, and sentiment data to create an aggregated dataset, which serves as input for statistical analysis and predictive models.

[1723] Input: Historical data, real-time data, analyzed sentiment data

[1724] Output: Aggregated dataset

[1725] Step 8:

[1726] The server uses AI algorithms to analyze the aggregated data set and make crowd predictions, which then predicts future crowding conditions at each store or event.

[1727] Input: Aggregated dataset

[1728] Output: Congestion forecast data

[1729] Step 9:

[1730] The server generates the optimal shopping route based on the user's input information and emotional data. The AI ​​algorithm considers the user's wishes and emotions and suggests an efficient and comfortable route.

[1731] Input: User input data, analyzed emotion data, congestion prediction data

[1732] Output: Optimal route

[1733] Step 10:

[1734] The server sends the generated optimal route to the terminal and presents it to the user, who can then check the proposed route through the application to move efficiently through the shopping facility.

[1735] Input: Optimal Route

[1736] Output: Route suggestions to the user

[1737] The above are the specific processing steps for implementing this invention. The techniques and procedures used in each step are effectively realized by making full use of modern data analysis and AI technology.

[1738] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1739] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1741] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1742] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1743] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1744] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1745] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1746] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1747] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1748] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1749] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1750] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1751] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1752] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1753] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1754] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1755] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1756] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1757] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1758] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1759] The following is further disclosed regarding the above embodiment.

[1760] (Claim 1)

[1761] A means for inputting arrival time, stay time, desired attractions, and desired events;

[1762] a means of aggregating historical visitor data and real-time data;

[1763] A means for predicting congestion based on the aggregated data and generating an optimal schedule;

[1764] means for presenting the generated schedule to a user;

[1765] A system including:

[1766] (Claim 2)

[1767] 10. The system of claim 1, wherein the historical visitor data is obtained from a database and the real-time data is collected from a sensor.

[1768] (Claim 3)

[1769] The system of claim 1 uses an AI algorithm to predict congestion and generate an optimal schedule.

[1770] "Example 1"

[1771] (Claim 1)

[1772] A means for inputting arrival time, stay time, desired attractions, and desired events;

[1773] a means of aggregating historical visit data and real-time data;

[1774] A means to predict congestion and generate an optimal schedule using a generative AI model based on aggregated data;

[1775] means for presenting the generated schedule on a display terminal;

[1776] A system including:

[1777] (Claim 2)

[1778] 10. The system of claim 1, wherein the historical visit data is obtained from a database and the real-time data is collected from a plurality of sensors.

[1779] (Claim 3)

[1780] The system of claim 1 uses a generative AI model to predict congestion and generate an optimal schedule.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] A means for inputting arrival time, stay time, desired attractions, and desired events;

[1784] a means of aggregating historical visitor data and real-time data;

[1785] A means for predicting congestion based on the aggregated data and generating an optimal schedule;

[1786] means for presenting the generated schedule to a user;

[1787] A means of streamlining visitor movement within the virtual environment; and

[1788] A system including:

[1789] (Claim 2)

[1790] 10. The system of claim 1, wherein the historical visitor data is obtained from a database and the real-time data is collected from a sensor.

[1791] (Claim 3)

[1792] The system of claim 1 uses an AI algorithm to predict congestion and generate an optimal schedule.

[1793] (Claim 4)

[1794] 2. The system according to claim 1, which generates and presents an optimal travel schedule within the virtual environment based on input information from a user.

[1795] "Example 2: Combining Emotion Engines"

[1796] (Claim 1)

[1797] A means for inputting arrival time, stay time, desired attractions, desired events, and emotional state;

[1798] a means of aggregating historical visitor data and real-time data;

[1799] A means for predicting congestion based on the aggregated data and emotion data and generating an optimal schedule;

[1800] means for presenting the generated schedule to a user;

[1801] A system including:

[1802] (Claim 2)

[1803] 10. The system of claim 1, wherein the historical visitor data is obtained from a database and the real-time data is collected from a sensor.

[1804] (Claim 3)

[1805] The system according to claim 1 uses an AI algorithm to predict congestion and generates an optimal schedule taking into account the user's emotional state.

[1806] "Application example 2 when combining emotion engines"

[1807] (Claim 1)

[1808] A means for inputting arrival time, stay time, product information, and event information;

[1809] a means of aggregating historical and real-time visitor data;

[1810] A means for predicting congestion based on the aggregated data and user emotion data and generating an optimal route;

[1811] means for presenting the generated route to a user;

[1812] A system including:

[1813] (Claim 2)

[1814] 10. The system of claim 1, wherein historical visitor data is obtained from a database and real-time data is collected from a sensor.

[1815] (Claim 3)

[1816] The system of claim 1 uses an AI algorithm and a sentiment analysis engine to predict congestion and generate optimal routes. [Explanation of symbols]

[1817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting arrival time, stay time, desired attractions, and desired events; a means of aggregating historical visitor data and real-time data; A means for predicting congestion based on the aggregated data and generating an optimal schedule; means for presenting the generated schedule to a user; A system including:

2. 10. The system of claim 1, wherein historical visitor data is obtained from a database and real-time data is collected from a sensor.

3. The system according to claim 1, which uses an AI algorithm to predict congestion and generate an optimal schedule.

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