System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting an event holding condition, a means for analyzing human flow data on the basis of the input event holding condition, and for retrieving an optimal event holding place, a means for calculating the optimal arrangement of staff on the basis of the selected event holding place, and a means for displaying a calculation result.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] In conventional event management, selecting an appropriate location and efficiently allocating staff relies on subjective judgment and experience, resulting in problems of time and effort. Furthermore, the successful use of people flow data is essential for an event to be successful, but the specialized knowledge required to analyze and apply this data makes it difficult for average event managers. The purpose of this invention is to solve these problems and automate and optimize event location and staff allocation efficiently and effectively. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting event hosting conditions, a means for analyzing people flow data based on the input event hosting conditions and searching for the optimal event location, a means for calculating the optimal staff allocation based on the selected event location, and a means for displaying the calculation results. Specifically, the system includes a means for receiving information on the date, location, and expected number of attendees included in the event hosting conditions, and uses a generative AI model for analysis based on people flow data to quickly search for the optimal event location. Furthermore, the system optimizes staff allocation according to the expected number of attendees at the selected event location and displays the results on a terminal, thereby achieving efficient and effective event management.

[0006] "Event hosting conditions" refers to information necessary to host an event, and specifically includes the location, date, expected number of attendees, and the like.

[0007] "People flow data" is data that shows the patterns of people's movements and stays in specific areas or locations, and it allows us to understand the congestion situation by time of day and day of the week.

[0008] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and provides optimal judgment for new situations.

[0009] "Event Venue" means a location used to host a particular event, including indoor and outdoor stadiums, halls, parks, etc.

[0010] "Optimal staff allocation" refers to a plan to assign staff to appropriate locations and roles in order to maximize the efficiency of event management.

[0011] "Calculation results" refers to the results of analysis based on various conditions and data, such as the selection of event venues and staff deployment.

[0012] A "terminal" is an electronic device used by a user to enter information or view results, including a PC, tablet, or smartphone. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention is a system for improving the efficiency of event management. By having users input the event hosting conditions, the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff allocation. The elements of this system are configured as follows:

[0035] 1. Enter the event conditions

[0036] The user uses an input form on the terminal to input the conditions for hosting an ideal event, specifically the desired area for the event, the date, and the expected number of attendees.

[0037] 2. Receiving and validating input data

[0038] The terminal transmits the input data to the server.

[0039] The server checks the validity of the received data. For example, if the entered location is blank or the expected number of visitors is a negative value, it returns an error and prompts the user to re-enter the information.

[0040] 3. Identifying the perfect event location

[0041] The server identifies the optimal event location using reliable people flow data and a generative AI model. The people flow data indicates the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the location that best meets the user's requirements.

[0042] 4. Optimizing staff allocation

[0043] The server determines the staff allocation according to the expected number of visitors based on the selected event location. For example, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff. To do this, it uses a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0044] 5. Displaying the results

[0045] The server transmits the analysis results and staff allocation results to the terminal.

[0046] The terminal displays the results to the user, allowing the user to quickly identify the optimal event location and staffing.

[0047] Specific examples

[0048] As a concrete example, consider the following scenario: A user inputs, "I would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0049] The server uses an AI model to search for the optimal location based on people flow data around train stations in central Tokyo, and selects the "station square," for example. It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0050] As a result, the device displays "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff," allowing users to quickly plan their event.

[0051] This invention automates decisions about event locations and staff allocation, significantly improving operational efficiency. Accurate analysis based on people flow data is also expected to increase the success rate of events.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user inputs the ideal conditions for hosting an event into the input form on the device, specifically, the desired area of ​​the venue, the desired date, and the expected number of attendees into the respective fields.

[0055] Step 2:

[0056] The terminal sends the entered event hosting conditions to the server, after performing basic format checks to ensure that the entered data is complete and correct.

[0057] Step 3:

[0058] The server validates the received event conditions, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0059] Step 4:

[0060] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0061] Step 5:

[0062] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0063] Step 6:

[0064] The server uses a staffing model to calculate the optimal staffing based on the characteristics of the selected venue and the input expected number of attendees, for example determining the required number of guides, receptionists, and security staff.

[0065] Step 7:

[0066] The server then sends the results of the optimal event location and staff allocation to the terminal, allowing the calculation results to be delivered to the user quickly.

[0067] Step 8:

[0068] The terminal displays the results sent from the server to the user, specifically visualizing the optimal event location and staff allocation information on the screen.

[0069] Step 9:

[0070] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event, which will help them efficiently prepare for the event.

[0071] Through the above steps, the present invention is a system that significantly improves the efficiency and success rate of event management.

[0072] Example 1

[0073] 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."

[0074] Traditional event management involves manually selecting venues and allocating staff, which can be inefficient and make it difficult to make decisions based on accurate predictions, resulting in lower event success rates and operational efficiency.

[0075] 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.

[0076] In this invention, the server includes a means for a user to input event hosting conditions, a means for receiving the input event hosting conditions and verifying their validity, a means for analyzing people flow data based on the valid event hosting conditions and identifying the optimal event location, a means for optimizing staff allocation according to the expected number of attendees based on the optimal event location, and a means for displaying the analysis results and staff allocation results. This automates decisions regarding event locations and staff allocation, making it possible to significantly improve operational efficiency.

[0077] "Event hosting conditions" refers to information such as the date, location, expected number of attendees, etc. required to host a specific event.

[0078] "People flow data" refers to data that shows the movement and crowding of people in a specific area at specific times and days of the week.

[0079] A "generative AI model" is a model built using artificial intelligence technology and used to perform data analysis and predictions.

[0080] "Staff allocation" refers to determining the number and roles of staff required to run an event and optimizing their allocation.

[0081] "User" refers to an individual or organization that operates the system and inputs the event hosting conditions.

[0082] "Server" refers to a central control device that processes and analyzes data.

[0083] "Terminal" means a device used by a user to access the system and enter or receive information.

[0084] "Analysis results" refers to the results of analysis performed based on input data.

[0085] "Expected Attendance" refers to the number of people expected to attend a particular event.

[0086] "Optimization" refers to the process of finding the most suitable state or condition for a particular purpose.

[0087] "Validation" refers to the process of checking whether input data is accurate and valid.

[0088] This invention is a system for improving the efficiency of event management. Users input the event hosting conditions, and the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff placement.

[0089] Enter event conditions

[0090] The user enters the event hosting conditions using an input form on the terminal. Specifically, the user enters the following information:

[0091] Desired area for holding the event (e.g. "Central Tokyo")

[0092] Event dates (e.g., "2023-12-01 to 2023-12-03")

[0093] Expected number of visitors (e.g., 1,000 people)

[0094] Receiving and validating input data

[0095] The terminal sends the entered event hosting conditions to the server. Data is sent using an HTTP request, and the data sent is structured in JSON format. The server verifies the validity of the received data. For example, it checks whether the "area" is blank or whether the "expected number of visitors" is a negative value. If invalid data is detected, it generates an error message and returns it to the terminal.

[0096] Identifying the perfect event location

[0097] The server uses a people flow database and a generative AI model to identify the optimal location for an event. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch. The server extracts and analyzes people flow data for a specified area to predict the optimal location. For example, it may identify a "station square in the city center" as optimal.

[0098] Optimizing staff allocation

[0099] The server determines the optimal staff allocation based on the specified event location and expected number of visitors. It calculates the number of people required for each role, such as guide staff, reception staff, and security staff. This calculation uses a staff allocation model that has learned from past successful and unsuccessful events. For example, for 1,000 visitors, five guide staff, three reception staff, and two security staff are required.

[0100] Displaying the results

[0101] The server structures the analysis results and staff allocation results in JSON format and sends them to the terminal. The terminal then displays the received results on the user interface. Specifically, it displays information such as "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff" on a web page. This allows users to efficiently plan and manage events.

[0102] Specific examples

[0103] For example, consider the following condition entered by the user:

[0104] "We would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0105] The server uses a generative AI model to search for the most suitable venue based on pedestrian flow data around stations in the city center, and selects the "station square," for example.

[0106] The optimal staffing for 1,000 visitors is then calculated, such as five guide staff, three reception staff, and two security staff.

[0107] The device displays, "Best location for event: Station square. Staffing: 5 guide staff, 3 reception staff, 2 security staff."

[0108] In this way, the system of the present invention automates decisions regarding event location and staffing, significantly improving operational efficiency. It is also expected that accurate analysis based on people flow data will increase the success rate of events.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] The user enters the event conditions into an input form on the device. The information entered includes the desired area, event dates, and expected number of attendees. This clearly defines the basic requirements of the event. Specifically, the user enters a specific area (e.g., "Central Tokyo"), event dates (e.g., "2023-12-01 to 2023-12-03"), and expected number of attendees (e.g., "1,000 people"). The entered information is encoded in JSON format.

[0112] input:

[0113] Desired area: Central Tokyo

[0114] Dates: 2023-12-01 to 2023-12-03

[0115] Expected number of visitors: 1,000 people

[0116] output:

[0117] Input data in JSON format

[0118] Step 2:

[0119] The device sends the event hosting conditions entered by the user to the server. Data is sent using an HTTP request. This request includes the input data in JSON format, allowing the server to receive the necessary data.

[0120] input:

[0121] Input data in JSON format

[0122] output:

[0123] HTTP request to the server

[0124] Step 3:

[0125] The server analyzes the data received from the device and verifies its validity. First, it checks that the "area" is not blank, the "expected number of visitors" is not a negative value, and the "date of event" is properly formatted. If these conditions are not met, it generates an error message and prompts the user to re-enter the information.

[0126] input:

[0127] JSON format input data sent from the terminal

[0128] output:

[0129] Validated data or error message

[0130] Step 4:

[0131] Based on the validated data, the server uses a people flow database and a generative AI model to identify the optimal event location. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to analyze data and make predictions. This process outputs the most suitable event location for the specified area and date.

[0132] input:

[0133] Validated Data

[0134] output:

[0135] Ideal event location (e.g., "Station Square")

[0136] Step 5:

[0137] The server calculates the optimal staff allocation based on the specified event location and expected number of attendees. This is done using a staff allocation model that has learned from past successes and failures of events. Specifically, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff.

[0138] input:

[0139] The perfect venue for your event

[0140] Expected number of visitors

[0141] output:

[0142] Staff allocation plan (e.g., 5 guide staff, 3 reception staff, 2 security staff)

[0143] Step 6:

[0144] The server structures the calculated optimal staff allocation information in JSON format and sends it to the terminal. The terminal receives this information and displays it on the user interface, allowing the user to see the optimal event location and staff allocation at a glance.

[0145] input:

[0146] Staffing Plan

[0147] output:

[0148] Information displayed in the user interface (e.g., "Optimal event location: Station square. Staffing: 5 guides, 3 receptionists, 2 security staff")

[0149] This detailed process flow allows users to quickly and efficiently plan their events.

[0150] (Application example 1)

[0151] 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."

[0152] Traditional event management required a lot of time and effort to select the optimal location and efficiently allocate staff. Analyzing people flow data and optimizing staff allocation manually placed a significant burden on event organizers, creating a need for more efficient operations. Similarly, it was difficult to quickly and accurately determine the optimal location and staff allocation for in-store events.

[0153] 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.

[0154] In this invention, the server includes means for inputting event hosting conditions, means for analyzing people flow data based on the input event hosting conditions and searching for the optimal event location, means for calculating the optimal staff allocation based on the selected event location, means for displaying the calculation results, means for presenting the optimal in-store event location based on the event hosting conditions in in-store event management, and means for automatically determining the optimal in-store staff allocation based on the predicted number of attendees. This makes it possible to select the optimal location and efficiently allocate staff in events and in-store event management.

[0155] "Event hosting conditions" refers to information such as the desired date and time, location, expected number of attendees, etc., set by the user when hosting an event.

[0156] "People flow data" refers to data that indicates the number of customers in a specific area at a specific time of day or on a specific day of the week.

[0157] A "generative AI model" is an artificial intelligence model that calculates optimal event locations and staff placements based on received data and people flow data.

[0158] A "server" is a computer system that validates input data and performs analysis and calculations.

[0159] "Store Event" means an event held within a particular store, including promotions, sales, campaigns, etc.

[0160] "Optimal staff allocation" refers to efficiently allocating the roles and numbers of staff required for the success of an event based on the predicted number of visitors.

[0161] This invention is a system that streamlines event management and automatically determines the optimal venue and staff allocation. The main function of this system is for a user to input the event hosting conditions, and the server determines the optimal venue and staff allocation using people flow data and a generative AI model, and displays the results on a terminal.

[0162] Program processing explanation

[0163] 1. Enter the event conditions:

[0164] Users enter their event requirements using a smartphone, tablet, or other device. They are provided with an input form for details such as desired venue, dates, and expected number of attendees.

[0165] 2. Receiving and validating input data:

[0166] The terminal sends the data entered by the user to the server. The server checks the validity of the received data and performs error checking. For example, if the location is blank or the expected number of visitors is a negative value, an error message is returned and the user is prompted to re-enter the data.

[0167] 3. Identifying the perfect event location:

[0168] The server uses people flow data and a generative AI model to identify the optimal event location. This people flow data includes the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the most suitable location for the input conditions.

[0169] 4. Optimizing staffing:

[0170] The server calculates the optimal staff allocation based on the selected event location and the expected number of attendees. Specifically, it uses a staff allocation model to determine the required number of guide staff, reception staff, security staff, etc.

[0171] 5. Displaying the results:

[0172] The server sends the analysis and calculation results to the terminal, which displays them to the user, allowing the user to quickly and accurately determine the optimal event location and staff allocation.

[0173] Hardware and software used

[0174] Hardware:

[0175] Smartphone, tablet, or other device

[0176] Servers (including cloud services and on-premise servers)

[0177] software:

[0178] Python environment (including libraries such as Pandas and Requests)

[0179] Cloud services (e.g., Firebase, AWS)

[0180] Specific examples

[0181] For example, consider the case where a user inputs, "I would like to hold an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03." The server analyzes the optimal location based on people flow data around the station in the city center and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0182] Prompt Sentence Examples

[0183] Enter "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people." The AI ​​model searches for the optimal location based on people flow data around the station in the city center, and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff, and displays the results.

[0184] In this way, the system of the present invention can improve the efficiency of event management and quickly and accurately determine the optimal venue and staff allocation. Furthermore, by applying the system to in-store events, the efficiency of event management at brick-and-mortar stores can be improved.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The user uses a device such as a smartphone or tablet to input the event conditions (desired location, event date, expected number of attendees). They then fill in the event details in the input form and press the submit button. The input data is then sent to the device in a specific format (for example, "an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03").

[0188] Step 2:

[0189] The terminal sends the event hosting condition data entered by the user to the server. The server checks the validity of the received data. Specifically, it performs error checks to see if the location is blank or if the expected number of attendees is a negative value. If there is a problem with the data, it returns an error message to the terminal and prompts the user to re-enter the data. If the data is valid, it proceeds to the next step.

[0190] Step 3:

[0191] The server collects reliable people flow data and uses a generative AI model to identify the optimal event location based on the input event conditions. Here, the people flow data includes the number of attendees in a specific area by time of day and day of the week. The generative AI model analyzes this data and predicts the optimal event location. For example, the "station square" may be selected.

[0192] Step 4:

[0193] The server calculates the optimal staff allocation based on the predicted number of visitors, based on the selected event location. As a specific example, it calculates the required number of guide staff, reception staff, and security staff. The generative AI model optimizes staff allocation by referring to past event data and success stories. For example, it calculates that 5 guide staff, 3 reception staff, and 2 security staff are required for 1,000 visitors.

[0194] Step 5:

[0195] The server sends the analysis results of the optimal event location and staff allocation to the terminal, which then displays them to the user. Specifically, the results are displayed in the format "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff."

[0196] Step 6:

[0197] Users can check the results displayed on their devices and make adjustments to the details of the event and arrange for staff as necessary. This system allows users to plan their events quickly and efficiently.

[0198] 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.

[0199] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. It then automatically determines the optimal staff allocation, and by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration. The elements of this system are configured as follows:

[0200] 1. Enter the event conditions

[0201] The user uses the device's input form to input the conditions for hosting an ideal event. Specifically, they input the desired area for the event, the date, and the expected number of attendees. The user's emotional state based on the input conditions is then analyzed by an emotion engine using a camera and microphone.

[0202] 2. Receiving and validating input data

[0203] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0204] The server validates the received data, for example, ensuring that the location is not blank, that the dates are entered in the correct format, and that the expected number of attendees is a positive integer.

[0205] 3. Identifying the perfect event location

[0206] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0207] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0208] The user's emotional data is also taken into consideration. For example, if the user is prone to stress, measures such as preferentially selecting less crowded locations are taken.

[0209] 4. Optimizing staff allocation

[0210] The server calculates the optimal staff allocation based on the characteristics of the selected event venue and the input expected number of attendees using a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0211] Furthermore, the system takes into consideration the user's emotional data, and if the user feels anxious, for example, it makes adjustments such as increasing the number of consultation staff and guidance staff.

[0212] 5. Displaying the results

[0213] The server sends the analysis results and staff allocation results to the terminal, and each suggestion clearly states that it reflects the user's emotional state.

[0214] The terminal displays the results to the user. Specifically, it visualizes the optimal event location and staff allocation information that takes emotions into account on the screen. It also provides feedback on the user's emotional state as recognized by the emotion engine.

[0215] Specific examples

[0216] For example, if a user inputs "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera recognizes the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion.If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two consultation staff.

[0217] The device displays messages such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotions: Allocation to address anxiety." This allows users to quickly create a specific event management plan while receiving suggestions that are sensitive to their emotions, giving them a sense of security.

[0218] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking into account user emotions.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] Users input the conditions for hosting their ideal event into the input form on the device. Specifically, they enter the desired location area, desired date, and expected number of attendees into the appropriate fields. The emotion engine also analyzes the user's emotional data via the camera and microphone.

[0222] Step 2:

[0223] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0224] Step 3:

[0225] The server checks the validity of the received event conditions and emotion data, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer. It also checks whether the emotion data has been correctly analyzed.

[0226] Step 4:

[0227] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0228] Step 5:

[0229] The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts congestion levels based on people flow data for the specified dates and identifies the location with the highest customer attraction potential within the desired area. Furthermore, it takes into account emotional data and prioritizes locations with lower congestion levels if the user is prone to stress.

[0230] Step 6:

[0231] The server uses a staff allocation model to calculate the optimal staff allocation based on the characteristics of the selected venue and the input expected number of visitors. For example, it determines the required number of guide staff, reception staff, and security staff. Taking into account emotional data, it makes adjustments such as adding counseling staff if the user feels anxious.

[0232] Step 7:

[0233] The server sends the results of optimal event locations and staff allocation to the device, with each suggestion clearly indicating that it reflects the user's emotional state.

[0234] Step 8:

[0235] The device displays the results sent from the server to the user. Specifically, the device visualizes the optimal event location, staff allocation information, and suggestions that take the user's emotions into consideration. The device also displays the user's emotional state as feedback, as recognized by the emotion engine.

[0236] Step 9:

[0237] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event based on this information. This allows users to prepare for the event quickly and efficiently, and the emotionally sensitive suggestions give them peace of mind.

[0238] Example 2

[0239] 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."

[0240] Conventional event management systems were inefficient in selecting event locations and allocating staff, and it was difficult to make proposals that took user emotions into account. As a result, issues remained regarding the effectiveness of attracting visitors to events and the user experience. There was also room for improvement in validating user input data and optimizing staff allocation.

[0241] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for a user to input event hosting conditions, means for receiving the input event hosting conditions and user emotion data and verifying their validity, means for analyzing people flow data based on the received event hosting conditions and emotion data and identifying an optimal event location using a generative AI model, means for calculating optimal staff allocation based on the identified event location and the expected number of attendees, and means for displaying the calculation results. This makes it possible to identify an optimal event location and optimize staff allocation, and to make suggestions that take user emotions into consideration.

[0242] "User" refers to an individual or organization that uses the system to input event hosting conditions.

[0243] "Event hosting conditions" refers to information necessary to host an event, including the desired area for the event, the date, the expected number of attendees, and so on.

[0244] "Emotion data" is data obtained by analyzing the user's emotional state, and includes information such as anxiety or excitement obtained using a camera or microphone.

[0245] "Server" refers to a device that receives, analyzes, and processes data input by a user.

[0246] A "terminal" is a device for user input, transmission of data and display of results.

[0247] A "generative AI model" is a pre-trained algorithm that is used to analyze people flow data to identify optimal event locations.

[0248] "People flow data" refers to data that records people's movement patterns in specific areas and at specific times and dates.

[0249] The "staff allocation model" is a model that learns from successful and unsuccessful examples of previous events and calculates the optimal staff allocation for an event.

[0250] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. In addition to automatically determining the optimal staff allocation, by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration.

[0251] The system is configured as follows:

[0252] Enter event conditions

[0253] The user uses the device's input form to input the conditions for hosting an ideal event, such as the desired area for the event, the date, and the expected number of attendees.The emotion engine then uses the camera and microphone to analyze the user's emotional state and generate emotion data.

[0254] Receiving and validating input data

[0255] The device sends the event conditions entered by the user and the analyzed emotion data to the server. At this time, basic format checks are performed to ensure that the entered data is complete and correct. The server then verifies the validity of the received data, for example, whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0256] Identifying the perfect event location

[0257] The server loads people flow data and a generative AI model. This model is pre-trained and is used to analyze people's movement patterns in a specific area and at a specific time and date. The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts the level of congestion based on people flow data for a specified date and identifies the location within the desired area that will be most effective in attracting customers. It also takes into account the user's emotional data; for example, if the user is prone to stress, it will prioritize locations with low congestion levels.

[0258] Optimizing staff allocation

[0259] The server uses a staff allocation model to calculate optimal staff allocation based on the characteristics of the selected event location and the input expected number of visitors. This model uses data learned from past successful and unsuccessful events. Furthermore, it takes into account the user's emotional data, and makes adjustments such as increasing the number of consultation and guidance staff if the user feels anxious.

[0260] Displaying the results

[0261] The server sends the analysis results and staff allocation results to the device. Each suggestion clearly states that the user's emotional state is reflected in the suggestion. The device then displays the results to the user. Specifically, the optimal event location and staff allocation information that takes emotions into account are visualized on the screen. The device also provides feedback on the user's emotional state as recognized by the emotion engine. This allows users to quickly create specific event management plans and feel reassured by receiving suggestions that are in line with their own emotions.

[0262] Specific examples

[0263] For example, if a user inputs, "I want to hold an event near a downtown station between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera detects the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion. If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two counseling staff. The device will display a message such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, three reception staff, two security staff, two counseling staff. User emotion: Allocation to address anxiety." This allows users to quickly create a specific event management plan and feel reassured by receiving suggestions that are tailored to their emotions.

[0264] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking user emotions into consideration.

[0265] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0266] Step 1: User inputs event conditions

[0267] The user uses the input form on the device to input the ideal conditions for hosting an event. For example, they input the desired area for the event, the date, and the expected number of visitors. Once the input is complete, the device's emotion engine uses the camera and microphone to analyze the user's emotional state and generate emotion data. The input in this step is specific information about the event hosting conditions and the user's emotion data. The output data is the analyzed emotion data and data on the event hosting conditions.

[0268] Step 2: Terminal receives and validates input data

[0269] The device sends the event hosting conditions entered by the user and the analyzed emotion data to the server. At this time, the device performs basic format checks to ensure that the input data is complete and complete. For example, it verifies that the venue is specified, the dates are entered in the correct format, and the expected number of attendees is a positive integer. The input is the event hosting conditions and emotion data entered by the user, and the output is the format-verified data.

[0270] Step 3: The server identifies the best location for the event

[0271] After receiving the formally verified data, the server loads the people flow data and the generative AI model. This generative AI model is pre-trained and is used to analyze people flow patterns. The server analyzes the people flow data based on the event hosting conditions and uses the generative AI model to identify the optimal event location. Specifically, the server predicts the level of congestion and selects the location with the highest customer attraction effect within the desired area. The input for this step is the event hosting conditions and emotion data, and the output is information on the optimal event location.

[0272] Step 4: Optimize staffing with servers

[0273] The server applies a staff allocation model based on the identified event location and expected number of attendees to calculate the optimal staff allocation. The staff allocation model learns from past successful and unsuccessful cases. It also takes into account the user's emotional data, and adjusts the number of consultation staff and guidance staff, for example, if the user feels anxious. The inputs for this step are the event location, expected number of attendees, and emotional data, and the output is the optimal staff allocation plan.

[0274] Step 5: Server generates results and sends them to the device

[0275] The server combines the analysis results and staff allocation results. It specifies that each suggestion reflects the user's emotional state. The generated result data is sent to the terminal. The input of this step is the optimal event location and staff allocation plan, and the output is the result data to display to the user.

[0276] Step 6: Viewing the results in the terminal

[0277] The terminal receives the result data sent from the server. The terminal visualizes and displays the optimal event location and staff allocation information on the screen. It also provides the user's emotional state recognized by the emotion engine as feedback. For example, it may display "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotion: Allocation to address anxiety." The input for this step is the result data sent from the server, and the output is a specific operation plan that is displayed to the user.

[0278] (Application example 2)

[0279] 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."

[0280] When managing events in virtual reality environments, it is important to quickly and accurately select an appropriate virtual space and optimally allocate staff. However, conventional systems have difficulty identifying the optimal event location within a virtual space or planning event management while taking into account user emotional data. Furthermore, to improve the user experience, it is necessary to make emotionally-based suggestions and adjustments, but conventional systems have not been able to adequately address these issues. Therefore, there is a need for a system that can improve the efficiency of event management and the participant experience in virtual spaces.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0282] In this invention, the server includes a means for inputting event conditions, a means for analyzing people flow data based on the input event conditions and searching for the optimal event location, a means for calculating the optimal staff allocation based on the selected event location, a means for displaying the calculation results, a means for using a generative AI model to identify the optimal event location in a virtual space, and a means for analyzing user emotion data and reflecting the analysis in event management, thereby enabling the identification of the optimal event location in a virtual space and the allocation of staff based on the user's emotions.

[0283] - "Event conditions" refers to the specific requirements of the user, such as the date, time, location, expected number of attendees, etc. of the event.

[0284] "People flow data" refers to information about people's movement patterns and stay status in specific areas and at specific times.

[0285] The "optimal event location" refers to the location where the event can be held most effectively and efficiently based on the input conditions and people flow data.

[0286] "Optimal staff allocation" refers to a plan to allocate the necessary staff in the appropriate numbers and locations to ensure the success of an event.

[0287] "Calculation results" refers to the data on optimal event locations and staff allocations analyzed by the system.

[0288] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform a specific task (in this case, identifying event locations).

[0289] "User emotional data" refers to information about the user's emotional state that is analyzed using the user's camera or microphone.

[0290] "Virtual space" refers to a computer-generated 3D environment or simulation space that can be experienced using VR devices.

[0291] This invention is a system that streamlines event management in virtual space and improves the user experience. The system is composed of the following elements:

[0292] 1. Enter the event conditions:

[0293] Users use a smartphone or head-mounted display (HMD) to input event conditions (e.g., date, time, location, expected number of attendees), and the emotion engine analyzes the user's emotional state using a camera and microphone.

[0294] 2. Receiving and validating input data:

[0295] The device sends the entered event conditions and analyzed emotion data to the server, which then checks the validity of the received data, such as whether the date and time are in the correct format and whether the expected number of attendees is a positive integer.

[0296] 3. Identifying the perfect event location:

[0297] The server loads the people flow data and the generative AI model to identify the optimal location for the event in the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and searches for the virtual event location that best suits the input conditions.

[0298] 4. Optimizing staffing:

[0299] The server calculates the optimal staff allocation using a staff allocation model based on the characteristics of the selected virtual event venue and the input expected number of visitors. Furthermore, it takes into account the user's emotional data and makes adjustments such as increasing the number of guide staff if the user feels anxious.

[0300] 5. Displaying the results:

[0301] The server sends the analysis results and staff allocation results to the device, which displays them to the user. The user can check the optimal event location in the virtual space and staff allocation information that takes emotions into account. For example, if the user is prone to stress, the system will suggest a less crowded virtual space and appropriately allocate staff accordingly.

[0302] Specific examples

[0303] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff. This allows the user to receive specific recommendations regarding the location and staff allocation of the event within the virtual space.

[0304] Example prompt for a generative AI model:

[0305] location: Virtual city center square

[0306] date: 2023-12-01

[0307] expected_attendees: 1000

[0308] user_emotion: excitement

[0309] This prompt sentence is used to request the generative AI model to identify the optimal virtual space.

[0310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0311] Step 1:

[0312] Users use a smartphone or head-mounted display (HMD) to input the event conditions, including the location, date and time, and expected number of attendees. In addition, a camera and microphone are used to obtain the user's emotional data from the emotion engine. The input data generated are the location, date and time, expected number of attendees, and emotional data.

[0313] Step 2:

[0314] The device sends the entered event conditions and analyzed emotion data to the server. The server then checks the validity of the received data. Specifically, it checks whether the date and time format is correct, whether the expected number of attendees is a positive integer, and whether the event location is blank. This ensures that the input data is valid.

[0315] Step 3:

[0316] The server loads people flow data based on valid input data and uses a generative AI model to search for the optimal event location within the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and identifies the optimal event location based on the input conditions. The input is people flow data and conditions entered by the user, and the output is the optimal event location.

[0317] Step 4:

[0318] The server uses a staffing model to calculate optimal staff allocation based on the characteristics of the identified optimal event venue and the expected number of attendees. It also takes into account emotional data and adjusts the number of guides and counselors if the user is anxious or excited. The inputs are venue characteristics, expected number of attendees, and emotional data, and the output is an optimal staffing plan.

[0319] Step 5:

[0320] The server sends the analysis results and staff allocation results to the terminal, which receives them and displays them to the user. The user can then check the optimal event location in the virtual space and staff allocation information that takes emotions into account. As a specific output, the system presents the optimal event location and staff allocation plan.

[0321] Specific examples

[0322] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff.

[0323] Example prompt for a generative AI model:

[0324] location: Virtual city center square

[0325] date: 2023-12-01

[0326] expected_attendees: 1000

[0327] user_emotion: excitement

[0328] 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.

[0329] 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.

[0330] 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.

[0331] [Second embodiment]

[0332] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0333] 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.

[0334] 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).

[0335] 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.

[0336] 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.

[0337] 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).

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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."

[0344] This invention is a system for improving the efficiency of event management. By having users input the event hosting conditions, the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff allocation. The elements of this system are configured as follows:

[0345] 1. Enter the event conditions

[0346] The user uses an input form on the terminal to input the conditions for hosting an ideal event, specifically the desired area for the event, the date, and the expected number of attendees.

[0347] 2. Receiving and validating input data

[0348] The terminal transmits the input data to the server.

[0349] The server checks the validity of the received data. For example, if the entered location is blank or the expected number of visitors is a negative value, it returns an error and prompts the user to re-enter the information.

[0350] 3. Identifying the perfect event location

[0351] The server identifies the optimal event location using reliable people flow data and a generative AI model. The people flow data indicates the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the location that best meets the user's requirements.

[0352] 4. Optimizing staff allocation

[0353] The server determines the staff allocation according to the expected number of visitors based on the selected event location. For example, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff. To do this, it uses a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0354] 5. Displaying the results

[0355] The server transmits the analysis results and staff allocation results to the terminal.

[0356] The terminal displays the results to the user, allowing the user to quickly identify the optimal event location and staffing.

[0357] Specific examples

[0358] As a concrete example, consider the following scenario: A user inputs, "I would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0359] The server uses an AI model to search for the optimal location based on people flow data around train stations in central Tokyo, and selects the "station square," for example. It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0360] As a result, the device displays "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff," allowing users to quickly plan their event.

[0361] This invention automates decisions about event locations and staff allocation, significantly improving operational efficiency. Accurate analysis based on people flow data is also expected to increase the success rate of events.

[0362] The processing flow will be explained below.

[0363] Step 1:

[0364] The user inputs the ideal conditions for hosting an event into the input form on the device, specifically, the desired area of ​​the venue, the desired date, and the expected number of attendees into the respective fields.

[0365] Step 2:

[0366] The terminal sends the entered event hosting conditions to the server, after performing basic format checks to ensure that the entered data is complete and correct.

[0367] Step 3:

[0368] The server validates the received event conditions, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0369] Step 4:

[0370] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0371] Step 5:

[0372] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0373] Step 6:

[0374] The server uses a staffing model to calculate the optimal staffing based on the characteristics of the selected venue and the input expected number of attendees, for example determining the required number of guides, receptionists, and security staff.

[0375] Step 7:

[0376] The server then sends the results of the optimal event location and staff allocation to the terminal, allowing the calculation results to be delivered to the user quickly.

[0377] Step 8:

[0378] The terminal displays the results sent from the server to the user, specifically visualizing the optimal event location and staff allocation information on the screen.

[0379] Step 9:

[0380] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event, which will help them efficiently prepare for the event.

[0381] Through the above steps, the present invention is a system that significantly improves the efficiency and success rate of event management.

[0382] Example 1

[0383] 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."

[0384] Traditional event management involves manually selecting venues and allocating staff, which can be inefficient and make it difficult to make decisions based on accurate predictions, resulting in lower event success rates and operational efficiency.

[0385] 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.

[0386] In this invention, the server includes a means for a user to input event hosting conditions, a means for receiving the input event hosting conditions and verifying their validity, a means for analyzing people flow data based on the valid event hosting conditions and identifying the optimal event location, a means for optimizing staff allocation according to the expected number of attendees based on the optimal event location, and a means for displaying the analysis results and staff allocation results. This automates decisions regarding event locations and staff allocation, making it possible to significantly improve operational efficiency.

[0387] "Event hosting conditions" refers to information such as the date, location, expected number of attendees, etc. required to host a specific event.

[0388] "People flow data" refers to data that shows the movement and crowding of people in a specific area at specific times and days of the week.

[0389] A "generative AI model" is a model built using artificial intelligence technology and used to perform data analysis and predictions.

[0390] "Staff allocation" refers to determining the number and roles of staff required to run an event and optimizing their allocation.

[0391] "User" refers to an individual or organization that operates the system and inputs the event hosting conditions.

[0392] "Server" refers to a central control device that processes and analyzes data.

[0393] "Terminal" means a device used by a user to access the system and enter or receive information.

[0394] "Analysis results" refers to the results of analysis performed based on input data.

[0395] "Expected Attendance" refers to the number of people expected to attend a particular event.

[0396] "Optimization" refers to the process of finding the most suitable state or condition for a particular purpose.

[0397] "Validation" refers to the process of checking whether input data is accurate and valid.

[0398] This invention is a system for improving the efficiency of event management. Users input the event hosting conditions, and the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff placement.

[0399] Enter event conditions

[0400] The user enters the event hosting conditions using an input form on the terminal. Specifically, the user enters the following information:

[0401] Desired area for holding the event (e.g. "Central Tokyo")

[0402] Event dates (e.g., "2023-12-01 to 2023-12-03")

[0403] Expected number of visitors (e.g., 1,000 people)

[0404] Receiving and validating input data

[0405] The terminal sends the entered event hosting conditions to the server. Data is sent using an HTTP request, and the data sent is structured in JSON format. The server verifies the validity of the received data. For example, it checks whether the "area" is blank or whether the "expected number of visitors" is a negative value. If invalid data is detected, it generates an error message and returns it to the terminal.

[0406] Identifying the perfect event location

[0407] The server uses a people flow database and a generative AI model to identify the optimal location for an event. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch. The server extracts and analyzes people flow data for a specified area to predict the optimal location. For example, it may identify a "station square in the city center" as optimal.

[0408] Optimizing staff allocation

[0409] The server determines the optimal staff allocation based on the specified event location and expected number of visitors. It calculates the number of people required for each role, such as guide staff, reception staff, and security staff. This calculation uses a staff allocation model that has learned from past successful and unsuccessful events. For example, for 1,000 visitors, five guide staff, three reception staff, and two security staff are required.

[0410] Displaying the results

[0411] The server structures the analysis results and staff allocation results in JSON format and sends them to the terminal. The terminal then displays the received results on the user interface. Specifically, it displays information such as "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff" on a web page. This allows users to efficiently plan and manage events.

[0412] Specific examples

[0413] For example, consider the following condition entered by the user:

[0414] "We would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0415] The server uses a generative AI model to search for the most suitable venue based on pedestrian flow data around stations in the city center, and selects the "station square," for example.

[0416] The optimal staffing for 1,000 visitors is then calculated, such as five guide staff, three reception staff, and two security staff.

[0417] The device displays, "Best location for event: Station square. Staffing: 5 guide staff, 3 reception staff, 2 security staff."

[0418] In this way, the system of the present invention automates decisions regarding event location and staffing, significantly improving operational efficiency. It is also expected that accurate analysis based on people flow data will increase the success rate of events.

[0419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] The user enters the event conditions into an input form on the device. The information entered includes the desired area, event dates, and expected number of attendees. This clearly defines the basic requirements of the event. Specifically, the user enters a specific area (e.g., "Central Tokyo"), event dates (e.g., "2023-12-01 to 2023-12-03"), and expected number of attendees (e.g., "1,000 people"). The entered information is encoded in JSON format.

[0422] input:

[0423] Desired area: Central Tokyo

[0424] Dates: 2023-12-01 to 2023-12-03

[0425] Expected number of visitors: 1,000 people

[0426] output:

[0427] Input data in JSON format

[0428] Step 2:

[0429] The device sends the event hosting conditions entered by the user to the server. Data is sent using an HTTP request. This request includes the input data in JSON format, allowing the server to receive the necessary data.

[0430] input:

[0431] Input data in JSON format

[0432] output:

[0433] HTTP request to the server

[0434] Step 3:

[0435] The server analyzes the data received from the device and verifies its validity. First, it checks that the "area" is not blank, the "expected number of visitors" is not a negative value, and the "date of event" is properly formatted. If these conditions are not met, it generates an error message and prompts the user to re-enter the information.

[0436] input:

[0437] JSON format input data sent from the terminal

[0438] output:

[0439] Validated data or error message

[0440] Step 4:

[0441] Based on the validated data, the server uses a people flow database and a generative AI model to identify the optimal event location. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to analyze data and make predictions. This process outputs the most suitable event location for the specified area and date.

[0442] input:

[0443] Validated Data

[0444] output:

[0445] Ideal event location (e.g., "Station Square")

[0446] Step 5:

[0447] The server calculates the optimal staff allocation based on the specified event location and expected number of attendees. This is done using a staff allocation model that has learned from past successes and failures of events. Specifically, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff.

[0448] input:

[0449] The perfect venue for your event

[0450] Expected number of visitors

[0451] output:

[0452] Staff allocation plan (e.g., 5 guide staff, 3 reception staff, 2 security staff)

[0453] Step 6:

[0454] The server structures the calculated optimal staff allocation information in JSON format and sends it to the terminal. The terminal receives this information and displays it on the user interface, allowing the user to see the optimal event location and staff allocation at a glance.

[0455] input:

[0456] Staffing Plan

[0457] output:

[0458] Information displayed in the user interface (e.g., "Optimal event location: Station square. Staffing: 5 guides, 3 receptionists, 2 security staff")

[0459] This detailed process flow allows users to quickly and efficiently plan their events.

[0460] (Application example 1)

[0461] 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."

[0462] Traditional event management required a lot of time and effort to select the optimal location and efficiently allocate staff. Analyzing people flow data and optimizing staff allocation manually placed a significant burden on event organizers, creating a need for more efficient operations. Similarly, it was difficult to quickly and accurately determine the optimal location and staff allocation for in-store events.

[0463] 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.

[0464] In this invention, the server includes means for inputting event hosting conditions, means for analyzing people flow data based on the input event hosting conditions and searching for the optimal event location, means for calculating the optimal staff allocation based on the selected event location, means for displaying the calculation results, means for presenting the optimal in-store event location based on the event hosting conditions in in-store event management, and means for automatically determining the optimal in-store staff allocation based on the predicted number of attendees. This makes it possible to select the optimal location and efficiently allocate staff in events and in-store event management.

[0465] "Event hosting conditions" refers to information such as the desired date and time, location, expected number of attendees, etc., set by the user when hosting an event.

[0466] "People flow data" refers to data that indicates the number of customers in a specific area at a specific time of day or on a specific day of the week.

[0467] A "generative AI model" is an artificial intelligence model that calculates optimal event locations and staff placements based on received data and people flow data.

[0468] A "server" is a computer system that validates input data and performs analysis and calculations.

[0469] "Store Event" means an event held within a particular store, including promotions, sales, campaigns, etc.

[0470] "Optimal staff allocation" refers to efficiently allocating the roles and numbers of staff required for the success of an event based on the predicted number of visitors.

[0471] This invention is a system that streamlines event management and automatically determines the optimal venue and staff allocation. The main function of this system is for a user to input the event hosting conditions, and the server determines the optimal venue and staff allocation using people flow data and a generative AI model, and displays the results on a terminal.

[0472] Program processing explanation

[0473] 1. Enter the event conditions:

[0474] Users enter their event requirements using a smartphone, tablet, or other device. They are provided with an input form for details such as desired venue, dates, and expected number of attendees.

[0475] 2. Receiving and validating input data:

[0476] The terminal sends the data entered by the user to the server. The server checks the validity of the received data and performs error checking. For example, if the location is blank or the expected number of visitors is a negative value, an error message is returned and the user is prompted to re-enter the data.

[0477] 3. Identifying the perfect event location:

[0478] The server uses people flow data and a generative AI model to identify the optimal event location. This people flow data includes the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the most suitable location for the input conditions.

[0479] 4. Optimizing staffing:

[0480] The server calculates the optimal staff allocation based on the selected event location and the expected number of attendees. Specifically, it uses a staff allocation model to determine the required number of guide staff, reception staff, security staff, etc.

[0481] 5. Displaying the results:

[0482] The server sends the analysis and calculation results to the terminal, which displays them to the user, allowing the user to quickly and accurately determine the optimal event location and staff allocation.

[0483] Hardware and software used

[0484] Hardware:

[0485] Smartphone, tablet, or other device

[0486] Servers (including cloud services and on-premise servers)

[0487] software:

[0488] Python environment (including libraries such as Pandas and Requests)

[0489] Cloud services (e.g., Firebase, AWS)

[0490] Specific examples

[0491] For example, consider the case where a user inputs, "I would like to hold an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03." The server analyzes the optimal location based on people flow data around the station in the city center and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0492] Prompt Sentence Examples

[0493] Enter "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people." The AI ​​model searches for the optimal location based on people flow data around the station in the city center, and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff, and displays the results.

[0494] In this way, the system of the present invention can improve the efficiency of event management and quickly and accurately determine the optimal venue and staff allocation. Furthermore, by applying the system to in-store events, the efficiency of event management at brick-and-mortar stores can be improved.

[0495] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0496] Step 1:

[0497] The user uses a device such as a smartphone or tablet to input the event conditions (desired location, event date, expected number of attendees). They then fill in the event details in the input form and press the submit button. The input data is then sent to the device in a specific format (for example, "an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03").

[0498] Step 2:

[0499] The terminal sends the event hosting condition data entered by the user to the server. The server checks the validity of the received data. Specifically, it performs error checks to see if the location is blank or if the expected number of attendees is a negative value. If there is a problem with the data, it returns an error message to the terminal and prompts the user to re-enter the data. If the data is valid, it proceeds to the next step.

[0500] Step 3:

[0501] The server collects reliable people flow data and uses a generative AI model to identify the optimal event location based on the input event conditions. Here, the people flow data includes the number of attendees in a specific area by time of day and day of the week. The generative AI model analyzes this data and predicts the optimal event location. For example, the "station square" may be selected.

[0502] Step 4:

[0503] The server calculates the optimal staff allocation based on the predicted number of visitors, based on the selected event location. As a specific example, it calculates the required number of guide staff, reception staff, and security staff. The generative AI model optimizes staff allocation by referring to past event data and success stories. For example, it calculates that 5 guide staff, 3 reception staff, and 2 security staff are required for 1,000 visitors.

[0504] Step 5:

[0505] The server sends the analysis results of the optimal event location and staff allocation to the terminal, which then displays them to the user. Specifically, the results are displayed in the format "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff."

[0506] Step 6:

[0507] Users can check the results displayed on their devices and make adjustments to the details of the event and arrange for staff as necessary. This system allows users to plan their events quickly and efficiently.

[0508] 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.

[0509] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. It then automatically determines the optimal staff allocation, and by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration. The elements of this system are configured as follows:

[0510] 1. Enter the event conditions

[0511] The user uses the device's input form to input the conditions for hosting an ideal event. Specifically, they input the desired area for the event, the date, and the expected number of attendees. The user's emotional state based on the input conditions is then analyzed by an emotion engine using a camera and microphone.

[0512] 2. Receiving and validating input data

[0513] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0514] The server validates the received data, for example, ensuring that the location is not blank, that the dates are entered in the correct format, and that the expected number of attendees is a positive integer.

[0515] 3. Identifying the perfect event location

[0516] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0517] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0518] The user's emotional data is also taken into consideration. For example, if the user is prone to stress, measures such as preferentially selecting less crowded locations are taken.

[0519] 4. Optimizing staff allocation

[0520] The server calculates the optimal staff allocation based on the characteristics of the selected event venue and the input expected number of attendees using a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0521] Furthermore, the system takes into consideration the user's emotional data, and if the user feels anxious, for example, it makes adjustments such as increasing the number of consultation staff and guidance staff.

[0522] 5. Displaying the results

[0523] The server sends the analysis results and staff allocation results to the terminal, and each suggestion clearly states that it reflects the user's emotional state.

[0524] The terminal displays the results to the user. Specifically, it visualizes the optimal event location and staff allocation information that takes emotions into account on the screen. It also provides feedback on the user's emotional state as recognized by the emotion engine.

[0525] Specific examples

[0526] For example, if a user inputs "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera recognizes the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion.If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two consultation staff.

[0527] The device displays messages such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotions: Allocation to address anxiety." This allows users to quickly create a specific event management plan while receiving suggestions that are sensitive to their emotions, giving them a sense of security.

[0528] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking into account user emotions.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] Users input the conditions for hosting their ideal event into the input form on the device. Specifically, they enter the desired location area, desired date, and expected number of attendees into the appropriate fields. The emotion engine also analyzes the user's emotional data via the camera and microphone.

[0532] Step 2:

[0533] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0534] Step 3:

[0535] The server checks the validity of the received event conditions and emotion data, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer. It also checks whether the emotion data has been correctly analyzed.

[0536] Step 4:

[0537] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0538] Step 5:

[0539] The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts congestion levels based on people flow data for the specified dates and identifies the location with the highest customer attraction potential within the desired area. Furthermore, it takes into account emotional data and prioritizes locations with lower congestion levels if the user is prone to stress.

[0540] Step 6:

[0541] The server uses a staff allocation model to calculate the optimal staff allocation based on the characteristics of the selected venue and the input expected number of visitors. For example, it determines the required number of guide staff, reception staff, and security staff. Taking into account emotional data, it makes adjustments such as adding counseling staff if the user feels anxious.

[0542] Step 7:

[0543] The server sends the results of optimal event locations and staff allocation to the device, with each suggestion clearly indicating that it reflects the user's emotional state.

[0544] Step 8:

[0545] The device displays the results sent from the server to the user. Specifically, the device visualizes the optimal event location, staff allocation information, and suggestions that take the user's emotions into consideration. The device also displays the user's emotional state as feedback, as recognized by the emotion engine.

[0546] Step 9:

[0547] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event based on this information. This allows users to prepare for the event quickly and efficiently, and the emotionally sensitive suggestions give them peace of mind.

[0548] Example 2

[0549] 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."

[0550] Conventional event management systems were inefficient in selecting event locations and allocating staff, and it was difficult to make proposals that took user emotions into account. As a result, issues remained regarding the effectiveness of attracting visitors to events and the user experience. There was also room for improvement in validating user input data and optimizing staff allocation.

[0551] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for a user to input event hosting conditions, means for receiving the input event hosting conditions and user emotion data and verifying their validity, means for analyzing people flow data based on the received event hosting conditions and emotion data and identifying an optimal event location using a generative AI model, means for calculating optimal staff allocation based on the identified event location and the expected number of attendees, and means for displaying the calculation results. This makes it possible to identify an optimal event location and optimize staff allocation, and to make suggestions that take user emotions into consideration.

[0552] "User" refers to an individual or organization that uses the system to input event hosting conditions.

[0553] "Event hosting conditions" refers to information necessary to host an event, including the desired area for the event, the date, the expected number of attendees, and so on.

[0554] "Emotion data" is data obtained by analyzing the user's emotional state, and includes information such as anxiety or excitement obtained using a camera or microphone.

[0555] "Server" refers to a device that receives, analyzes, and processes data input by a user.

[0556] A "terminal" is a device for user input, transmission of data and display of results.

[0557] A "generative AI model" is a pre-trained algorithm that is used to analyze people flow data to identify optimal event locations.

[0558] "People flow data" refers to data that records people's movement patterns in specific areas and at specific times and dates.

[0559] The "staff allocation model" is a model that learns from successful and unsuccessful examples of previous events and calculates the optimal staff allocation for an event.

[0560] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. In addition to automatically determining the optimal staff allocation, by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration.

[0561] The system is configured as follows:

[0562] Enter event conditions

[0563] The user uses the device's input form to input the conditions for hosting an ideal event, such as the desired area for the event, the date, and the expected number of attendees.The emotion engine then uses the camera and microphone to analyze the user's emotional state and generate emotion data.

[0564] Receiving and validating input data

[0565] The device sends the event conditions entered by the user and the analyzed emotion data to the server. At this time, basic format checks are performed to ensure that the entered data is complete and correct. The server then verifies the validity of the received data, for example, whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0566] Identifying the perfect event location

[0567] The server loads people flow data and a generative AI model. This model is pre-trained and is used to analyze people's movement patterns in a specific area and at a specific time and date. The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts the level of congestion based on people flow data for a specified date and identifies the location within the desired area that will be most effective in attracting customers. It also takes into account the user's emotional data; for example, if the user is prone to stress, it will prioritize locations with low congestion levels.

[0568] Optimizing staff allocation

[0569] The server uses a staff allocation model to calculate optimal staff allocation based on the characteristics of the selected event location and the input expected number of visitors. This model uses data learned from past successful and unsuccessful events. Furthermore, it takes into account the user's emotional data, and makes adjustments such as increasing the number of consultation and guidance staff if the user feels anxious.

[0570] Displaying the results

[0571] The server sends the analysis results and staff allocation results to the device. Each suggestion clearly states that the user's emotional state is reflected in the suggestion. The device then displays the results to the user. Specifically, the optimal event location and staff allocation information that takes emotions into account are visualized on the screen. The device also provides feedback on the user's emotional state as recognized by the emotion engine. This allows users to quickly create specific event management plans and feel reassured by receiving suggestions that are in line with their own emotions.

[0572] Specific examples

[0573] For example, if a user inputs, "I want to hold an event near a downtown station between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera detects the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion. If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two counseling staff. The device will display a message such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, three reception staff, two security staff, two counseling staff. User emotion: Allocation to address anxiety." This allows users to quickly create a specific event management plan and feel reassured by receiving suggestions that are tailored to their emotions.

[0574] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking user emotions into consideration.

[0575] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0576] Step 1: User inputs event conditions

[0577] The user uses the input form on the device to input the ideal conditions for hosting an event. For example, they input the desired area for the event, the date, and the expected number of visitors. Once the input is complete, the device's emotion engine uses the camera and microphone to analyze the user's emotional state and generate emotion data. The input in this step is specific information about the event hosting conditions and the user's emotion data. The output data is the analyzed emotion data and data on the event hosting conditions.

[0578] Step 2: Terminal receives and validates input data

[0579] The device sends the event hosting conditions entered by the user and the analyzed emotion data to the server. At this time, the device performs basic format checks to ensure that the input data is complete and complete. For example, it verifies that the venue is specified, the dates are entered in the correct format, and the expected number of attendees is a positive integer. The input is the event hosting conditions and emotion data entered by the user, and the output is the format-verified data.

[0580] Step 3: The server identifies the best location for the event

[0581] After receiving the formally verified data, the server loads the people flow data and the generative AI model. This generative AI model is pre-trained and is used to analyze people flow patterns. The server analyzes the people flow data based on the event hosting conditions and uses the generative AI model to identify the optimal event location. Specifically, the server predicts the level of congestion and selects the location with the highest customer attraction effect within the desired area. The input for this step is the event hosting conditions and emotion data, and the output is information on the optimal event location.

[0582] Step 4: Optimize staffing with servers

[0583] The server applies a staff allocation model based on the identified event location and expected number of attendees to calculate the optimal staff allocation. The staff allocation model learns from past successful and unsuccessful cases. It also takes into account the user's emotional data, and adjusts the number of consultation staff and guidance staff, for example, if the user feels anxious. The inputs for this step are the event location, expected number of attendees, and emotional data, and the output is the optimal staff allocation plan.

[0584] Step 5: Server generates results and sends them to the device

[0585] The server combines the analysis results and staff allocation results. It specifies that each suggestion reflects the user's emotional state. The generated result data is sent to the terminal. The input of this step is the optimal event location and staff allocation plan, and the output is the result data to display to the user.

[0586] Step 6: Viewing the results in the terminal

[0587] The terminal receives the result data sent from the server. The terminal visualizes and displays the optimal event location and staff allocation information on the screen. It also provides the user's emotional state recognized by the emotion engine as feedback. For example, it may display "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotion: Allocation to address anxiety." The input for this step is the result data sent from the server, and the output is a specific operation plan that is displayed to the user.

[0588] (Application example 2)

[0589] 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."

[0590] When managing events in virtual reality environments, it is important to quickly and accurately select an appropriate virtual space and optimally allocate staff. However, conventional systems have difficulty identifying the optimal event location within a virtual space or planning event management while taking into account user emotional data. Furthermore, to improve the user experience, it is necessary to make emotionally-based suggestions and adjustments, but conventional systems have not been able to adequately address these issues. Therefore, there is a need for a system that can improve the efficiency of event management and the participant experience in virtual spaces.

[0591] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0592] In this invention, the server includes a means for inputting event conditions, a means for analyzing people flow data based on the input event conditions and searching for the optimal event location, a means for calculating the optimal staff allocation based on the selected event location, a means for displaying the calculation results, a means for using a generative AI model to identify the optimal event location in a virtual space, and a means for analyzing user emotion data and reflecting the analysis in event management, thereby enabling the identification of the optimal event location in a virtual space and the allocation of staff based on the user's emotions.

[0593] - "Event conditions" refers to the specific requirements of the user, such as the date, time, location, expected number of attendees, etc. of the event.

[0594] "People flow data" refers to information about people's movement patterns and stay status in specific areas and at specific times.

[0595] The "optimal event location" refers to the location where the event can be held most effectively and efficiently based on the input conditions and people flow data.

[0596] "Optimal staff allocation" refers to a plan to allocate the necessary staff in the appropriate numbers and locations to ensure the success of an event.

[0597] "Calculation results" refers to the data on optimal event locations and staff allocations analyzed by the system.

[0598] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform a specific task (in this case, identifying event locations).

[0599] "User emotional data" refers to information about the user's emotional state that is analyzed using the user's camera or microphone.

[0600] "Virtual space" refers to a computer-generated 3D environment or simulation space that can be experienced using VR devices.

[0601] This invention is a system that streamlines event management in virtual space and improves the user experience. The system is composed of the following elements:

[0602] 1. Enter the event conditions:

[0603] Users use a smartphone or head-mounted display (HMD) to input event conditions (e.g., date, time, location, expected number of attendees), and the emotion engine analyzes the user's emotional state using a camera and microphone.

[0604] 2. Receiving and validating input data:

[0605] The device sends the entered event conditions and analyzed emotion data to the server, which then checks the validity of the received data, such as whether the date and time are in the correct format and whether the expected number of attendees is a positive integer.

[0606] 3. Identifying the perfect event location:

[0607] The server loads the people flow data and the generative AI model to identify the optimal location for the event in the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and searches for the virtual event location that best suits the input conditions.

[0608] 4. Optimizing staffing:

[0609] The server calculates the optimal staff allocation using a staff allocation model based on the characteristics of the selected virtual event venue and the input expected number of visitors. Furthermore, it takes into account the user's emotional data and makes adjustments such as increasing the number of guide staff if the user feels anxious.

[0610] 5. Displaying the results:

[0611] The server sends the analysis results and staff allocation results to the device, which displays them to the user. The user can check the optimal event location in the virtual space and staff allocation information that takes emotions into account. For example, if the user is prone to stress, the system will suggest a less crowded virtual space and appropriately allocate staff accordingly.

[0612] Specific examples

[0613] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff. This allows the user to receive specific recommendations regarding the location and staff allocation of the event within the virtual space.

[0614] Example prompt for a generative AI model:

[0615] location: Virtual city center square

[0616] date: 2023-12-01

[0617] expected_attendees: 1000

[0618] user_emotion: excitement

[0619] This prompt sentence is used to request the generative AI model to identify the optimal virtual space.

[0620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0621] Step 1:

[0622] Users use a smartphone or head-mounted display (HMD) to input the event conditions, including the location, date and time, and expected number of attendees. In addition, a camera and microphone are used to obtain the user's emotional data from the emotion engine. The input data generated are the location, date and time, expected number of attendees, and emotional data.

[0623] Step 2:

[0624] The device sends the entered event conditions and analyzed emotion data to the server. The server then checks the validity of the received data. Specifically, it checks whether the date and time format is correct, whether the expected number of attendees is a positive integer, and whether the event location is blank. This ensures that the input data is valid.

[0625] Step 3:

[0626] The server loads people flow data based on valid input data and uses a generative AI model to search for the optimal event location within the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and identifies the optimal event location based on the input conditions. The input is people flow data and conditions entered by the user, and the output is the optimal event location.

[0627] Step 4:

[0628] The server uses a staffing model to calculate optimal staff allocation based on the characteristics of the identified optimal event venue and the expected number of attendees. It also takes into account emotional data and adjusts the number of guides and counselors if the user is anxious or excited. The inputs are venue characteristics, expected number of attendees, and emotional data, and the output is an optimal staffing plan.

[0629] Step 5:

[0630] The server sends the analysis results and staff allocation results to the terminal, which receives them and displays them to the user. The user can then check the optimal event location in the virtual space and staff allocation information that takes emotions into account. As a specific output, the system presents the optimal event location and staff allocation plan.

[0631] Specific examples

[0632] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff.

[0633] Example prompt for a generative AI model:

[0634] location: Virtual city center square

[0635] date: 2023-12-01

[0636] expected_attendees: 1000

[0637] user_emotion: excitement

[0638] 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.

[0639] 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.

[0640] 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.

[0641] [Third embodiment]

[0642] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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."

[0654] This invention is a system for improving the efficiency of event management. By having users input the event hosting conditions, the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff allocation. The elements of this system are configured as follows:

[0655] 1. Enter the event conditions

[0656] The user uses an input form on the terminal to input the conditions for hosting an ideal event, specifically the desired area for the event, the date, and the expected number of attendees.

[0657] 2. Receiving and validating input data

[0658] The terminal transmits the input data to the server.

[0659] The server checks the validity of the received data. For example, if the entered location is blank or the expected number of visitors is a negative value, it returns an error and prompts the user to re-enter the information.

[0660] 3. Identifying the perfect event location

[0661] The server identifies the optimal event location using reliable people flow data and a generative AI model. The people flow data indicates the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the location that best meets the user's requirements.

[0662] 4. Optimizing staff allocation

[0663] The server determines the staff allocation according to the expected number of visitors based on the selected event location. For example, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff. To do this, it uses a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0664] 5. Displaying the results

[0665] The server transmits the analysis results and staff allocation results to the terminal.

[0666] The terminal displays the results to the user, allowing the user to quickly identify the optimal event location and staffing.

[0667] Specific examples

[0668] As a concrete example, consider the following scenario: A user inputs, "I would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0669] The server uses an AI model to search for the optimal location based on people flow data around train stations in central Tokyo, and selects the "station square," for example. It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0670] As a result, the device displays "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff," allowing users to quickly plan their event.

[0671] This invention automates decisions about event locations and staff allocation, significantly improving operational efficiency. Accurate analysis based on people flow data is also expected to increase the success rate of events.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] The user inputs the ideal conditions for hosting an event into the input form on the device, specifically, the desired area of ​​the venue, the desired date, and the expected number of attendees into the respective fields.

[0675] Step 2:

[0676] The terminal sends the entered event hosting conditions to the server, after performing basic format checks to ensure that the entered data is complete and correct.

[0677] Step 3:

[0678] The server validates the received event conditions, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0679] Step 4:

[0680] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0681] Step 5:

[0682] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0683] Step 6:

[0684] The server uses a staffing model to calculate the optimal staffing based on the characteristics of the selected venue and the input expected number of attendees, for example determining the required number of guides, receptionists, and security staff.

[0685] Step 7:

[0686] The server then sends the results of the optimal event location and staff allocation to the terminal, allowing the calculation results to be delivered to the user quickly.

[0687] Step 8:

[0688] The terminal displays the results sent from the server to the user, specifically visualizing the optimal event location and staff allocation information on the screen.

[0689] Step 9:

[0690] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event, which will help them efficiently prepare for the event.

[0691] Through the above steps, the present invention is a system that significantly improves the efficiency and success rate of event management.

[0692] Example 1

[0693] 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."

[0694] Traditional event management involves manually selecting venues and allocating staff, which can be inefficient and make it difficult to make decisions based on accurate predictions, resulting in lower event success rates and operational efficiency.

[0695] 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.

[0696] In this invention, the server includes a means for a user to input event hosting conditions, a means for receiving the input event hosting conditions and verifying their validity, a means for analyzing people flow data based on the valid event hosting conditions and identifying the optimal event location, a means for optimizing staff allocation according to the expected number of attendees based on the optimal event location, and a means for displaying the analysis results and staff allocation results. This automates decisions regarding event locations and staff allocation, making it possible to significantly improve operational efficiency.

[0697] "Event hosting conditions" refers to information such as the date, location, expected number of attendees, etc. required to host a specific event.

[0698] "People flow data" refers to data that shows the movement and crowding of people in a specific area at specific times and days of the week.

[0699] A "generative AI model" is a model built using artificial intelligence technology and used to perform data analysis and predictions.

[0700] "Staff allocation" refers to determining the number and roles of staff required to run an event and optimizing their allocation.

[0701] "User" refers to an individual or organization that operates the system and inputs the event hosting conditions.

[0702] "Server" refers to a central control device that processes and analyzes data.

[0703] "Terminal" means a device used by a user to access the system and enter or receive information.

[0704] "Analysis results" refers to the results of analysis performed based on input data.

[0705] "Expected Attendance" refers to the number of people expected to attend a particular event.

[0706] "Optimization" refers to the process of finding the most suitable state or condition for a particular purpose.

[0707] "Validation" refers to the process of checking whether input data is accurate and valid.

[0708] This invention is a system for improving the efficiency of event management. Users input the event hosting conditions, and the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff placement.

[0709] Enter event conditions

[0710] The user enters the event hosting conditions using an input form on the terminal. Specifically, the user enters the following information:

[0711] Desired area for holding the event (e.g. "Central Tokyo")

[0712] Event dates (e.g., "2023-12-01 to 2023-12-03")

[0713] Expected number of visitors (e.g., 1,000 people)

[0714] Receiving and validating input data

[0715] The terminal sends the entered event hosting conditions to the server. Data is sent using an HTTP request, and the data sent is structured in JSON format. The server verifies the validity of the received data. For example, it checks whether the "area" is blank or whether the "expected number of visitors" is a negative value. If invalid data is detected, it generates an error message and returns it to the terminal.

[0716] Identifying the perfect event location

[0717] The server uses a people flow database and a generative AI model to identify the optimal location for an event. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch. The server extracts and analyzes people flow data for a specified area to predict the optimal location. For example, it may identify a "station square in the city center" as optimal.

[0718] Optimizing staff allocation

[0719] The server determines the optimal staff allocation based on the specified event location and expected number of visitors. It calculates the number of people required for each role, such as guide staff, reception staff, and security staff. This calculation uses a staff allocation model that has learned from past successful and unsuccessful events. For example, for 1,000 visitors, five guide staff, three reception staff, and two security staff are required.

[0720] Displaying the results

[0721] The server structures the analysis results and staff allocation results in JSON format and sends them to the terminal. The terminal then displays the received results on the user interface. Specifically, it displays information such as "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff" on a web page. This allows users to efficiently plan and manage events.

[0722] Specific examples

[0723] For example, consider the following condition entered by the user:

[0724] "We would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0725] The server uses a generative AI model to search for the most suitable venue based on pedestrian flow data around stations in the city center, and selects the "station square," for example.

[0726] The optimal staffing for 1,000 visitors is then calculated, such as five guide staff, three reception staff, and two security staff.

[0727] The device displays, "Best location for event: Station square. Staffing: 5 guide staff, 3 reception staff, 2 security staff."

[0728] In this way, the system of the present invention automates decisions regarding event location and staffing, significantly improving operational efficiency. It is also expected that accurate analysis based on people flow data will increase the success rate of events.

[0729] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0730] Step 1:

[0731] The user enters the event conditions into an input form on the device. The information entered includes the desired area, event dates, and expected number of attendees. This clearly defines the basic requirements of the event. Specifically, the user enters a specific area (e.g., "Central Tokyo"), event dates (e.g., "2023-12-01 to 2023-12-03"), and expected number of attendees (e.g., "1,000 people"). The entered information is encoded in JSON format.

[0732] input:

[0733] Desired area: Central Tokyo

[0734] Dates: 2023-12-01 to 2023-12-03

[0735] Expected number of visitors: 1,000 people

[0736] output:

[0737] Input data in JSON format

[0738] Step 2:

[0739] The device sends the event hosting conditions entered by the user to the server. Data is sent using an HTTP request. This request includes the input data in JSON format, allowing the server to receive the necessary data.

[0740] input:

[0741] Input data in JSON format

[0742] output:

[0743] HTTP request to the server

[0744] Step 3:

[0745] The server analyzes the data received from the device and verifies its validity. First, it checks that the "area" is not blank, the "expected number of visitors" is not a negative value, and the "date of event" is properly formatted. If these conditions are not met, it generates an error message and prompts the user to re-enter the information.

[0746] input:

[0747] JSON format input data sent from the terminal

[0748] output:

[0749] Validated data or error message

[0750] Step 4:

[0751] Based on the validated data, the server uses a people flow database and a generative AI model to identify the optimal event location. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to analyze data and make predictions. This process outputs the most suitable event location for the specified area and date.

[0752] input:

[0753] Validated Data

[0754] output:

[0755] Ideal event location (e.g., "Station Square")

[0756] Step 5:

[0757] The server calculates the optimal staff allocation based on the specified event location and expected number of attendees. This is done using a staff allocation model that has learned from past successes and failures of events. Specifically, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff.

[0758] input:

[0759] The perfect venue for your event

[0760] Expected number of visitors

[0761] output:

[0762] Staff allocation plan (e.g., 5 guide staff, 3 reception staff, 2 security staff)

[0763] Step 6:

[0764] The server structures the calculated optimal staff allocation information in JSON format and sends it to the terminal. The terminal receives this information and displays it on the user interface, allowing the user to see the optimal event location and staff allocation at a glance.

[0765] input:

[0766] Staffing Plan

[0767] output:

[0768] Information displayed in the user interface (e.g., "Optimal event location: Station square. Staffing: 5 guides, 3 receptionists, 2 security staff")

[0769] This detailed process flow allows users to quickly and efficiently plan their events.

[0770] (Application example 1)

[0771] 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."

[0772] Traditional event management required a lot of time and effort to select the optimal location and efficiently allocate staff. Analyzing people flow data and optimizing staff allocation manually placed a significant burden on event organizers, creating a need for more efficient operations. Similarly, it was difficult to quickly and accurately determine the optimal location and staff allocation for in-store events.

[0773] 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.

[0774] In this invention, the server includes means for inputting event hosting conditions, means for analyzing people flow data based on the input event hosting conditions and searching for the optimal event location, means for calculating the optimal staff allocation based on the selected event location, means for displaying the calculation results, means for presenting the optimal in-store event location based on the event hosting conditions in in-store event management, and means for automatically determining the optimal in-store staff allocation based on the predicted number of attendees. This makes it possible to select the optimal location and efficiently allocate staff in events and in-store event management.

[0775] "Event hosting conditions" refers to information such as the desired date and time, location, expected number of attendees, etc., set by the user when hosting an event.

[0776] "People flow data" refers to data that indicates the number of customers in a specific area at a specific time of day or on a specific day of the week.

[0777] A "generative AI model" is an artificial intelligence model that calculates optimal event locations and staff placements based on received data and people flow data.

[0778] A "server" is a computer system that validates input data and performs analysis and calculations.

[0779] "Store Event" means an event held within a particular store, including promotions, sales, campaigns, etc.

[0780] "Optimal staff allocation" refers to efficiently allocating the roles and numbers of staff required for the success of an event based on the predicted number of visitors.

[0781] This invention is a system that streamlines event management and automatically determines the optimal venue and staff allocation. The main function of this system is for a user to input the event hosting conditions, and the server determines the optimal venue and staff allocation using people flow data and a generative AI model, and displays the results on a terminal.

[0782] Program processing explanation

[0783] 1. Enter the event conditions:

[0784] Users enter their event requirements using a smartphone, tablet, or other device. They are provided with an input form for details such as desired venue, dates, and expected number of attendees.

[0785] 2. Receiving and validating input data:

[0786] The terminal sends the data entered by the user to the server. The server checks the validity of the received data and performs error checking. For example, if the location is blank or the expected number of visitors is a negative value, an error message is returned and the user is prompted to re-enter the data.

[0787] 3. Identifying the perfect event location:

[0788] The server uses people flow data and a generative AI model to identify the optimal event location. This people flow data includes the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the most suitable location for the input conditions.

[0789] 4. Optimizing staffing:

[0790] The server calculates the optimal staff allocation based on the selected event location and the expected number of attendees. Specifically, it uses a staff allocation model to determine the required number of guide staff, reception staff, security staff, etc.

[0791] 5. Displaying the results:

[0792] The server sends the analysis and calculation results to the terminal, which displays them to the user, allowing the user to quickly and accurately determine the optimal event location and staff allocation.

[0793] Hardware and software used

[0794] Hardware:

[0795] Smartphone, tablet, or other device

[0796] Servers (including cloud services and on-premise servers)

[0797] software:

[0798] Python environment (including libraries such as Pandas and Requests)

[0799] Cloud services (e.g., Firebase, AWS)

[0800] Specific examples

[0801] For example, consider the case where a user inputs, "I would like to hold an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03." The server analyzes the optimal location based on people flow data around the station in the city center and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0802] Prompt Sentence Examples

[0803] Enter "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people." The AI ​​model searches for the optimal location based on people flow data around the station in the city center, and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff, and displays the results.

[0804] In this way, the system of the present invention can improve the efficiency of event management and quickly and accurately determine the optimal venue and staff allocation. Furthermore, by applying the system to in-store events, the efficiency of event management at brick-and-mortar stores can be improved.

[0805] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0806] Step 1:

[0807] The user uses a device such as a smartphone or tablet to input the event conditions (desired location, event date, expected number of attendees). They then fill in the event details in the input form and press the submit button. The input data is then sent to the device in a specific format (for example, "an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03").

[0808] Step 2:

[0809] The terminal sends the event hosting condition data entered by the user to the server. The server checks the validity of the received data. Specifically, it performs error checks to see if the location is blank or if the expected number of attendees is a negative value. If there is a problem with the data, it returns an error message to the terminal and prompts the user to re-enter the data. If the data is valid, it proceeds to the next step.

[0810] Step 3:

[0811] The server collects reliable people flow data and uses a generative AI model to identify the optimal event location based on the input event conditions. Here, the people flow data includes the number of attendees in a specific area by time of day and day of the week. The generative AI model analyzes this data and predicts the optimal event location. For example, the "station square" may be selected.

[0812] Step 4:

[0813] The server calculates the optimal staff allocation based on the predicted number of visitors, based on the selected event location. As a specific example, it calculates the required number of guide staff, reception staff, and security staff. The generative AI model optimizes staff allocation by referring to past event data and success stories. For example, it calculates that 5 guide staff, 3 reception staff, and 2 security staff are required for 1,000 visitors.

[0814] Step 5:

[0815] The server sends the analysis results of the optimal event location and staff allocation to the terminal, which then displays them to the user. Specifically, the results are displayed in the format "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff."

[0816] Step 6:

[0817] Users can check the results displayed on their devices and make adjustments to the details of the event and arrange for staff as necessary. This system allows users to plan their events quickly and efficiently.

[0818] 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.

[0819] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. It then automatically determines the optimal staff allocation, and by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration. The elements of this system are configured as follows:

[0820] 1. Enter the event conditions

[0821] The user uses the device's input form to input the conditions for hosting an ideal event. Specifically, they input the desired area for the event, the date, and the expected number of attendees. The user's emotional state based on the input conditions is then analyzed by an emotion engine using a camera and microphone.

[0822] 2. Receiving and validating input data

[0823] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0824] The server validates the received data, for example, ensuring that the location is not blank, that the dates are entered in the correct format, and that the expected number of attendees is a positive integer.

[0825] 3. Identifying the perfect event location

[0826] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0827] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0828] The user's emotional data is also taken into consideration. For example, if the user is prone to stress, measures such as preferentially selecting less crowded locations are taken.

[0829] 4. Optimizing staff allocation

[0830] The server calculates the optimal staff allocation based on the characteristics of the selected event venue and the input expected number of attendees using a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0831] Furthermore, the system takes into consideration the user's emotional data, and if the user feels anxious, for example, it makes adjustments such as increasing the number of consultation staff and guidance staff.

[0832] 5. Displaying the results

[0833] The server sends the analysis results and staff allocation results to the terminal, and each suggestion clearly states that it reflects the user's emotional state.

[0834] The terminal displays the results to the user. Specifically, it visualizes the optimal event location and staff allocation information that takes emotions into account on the screen. It also provides feedback on the user's emotional state as recognized by the emotion engine.

[0835] Specific examples

[0836] For example, if a user inputs "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera recognizes the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion.If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two consultation staff.

[0837] The device displays messages such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotions: Allocation to address anxiety." This allows users to quickly create a specific event management plan while receiving suggestions that are sensitive to their emotions, giving them a sense of security.

[0838] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking into account user emotions.

[0839] The processing flow will be explained below.

[0840] Step 1:

[0841] Users input the conditions for hosting their ideal event into the input form on the device. Specifically, they enter the desired location area, desired date, and expected number of attendees into the appropriate fields. The emotion engine also analyzes the user's emotional data via the camera and microphone.

[0842] Step 2:

[0843] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[0844] Step 3:

[0845] The server checks the validity of the received event conditions and emotion data, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer. It also checks whether the emotion data has been correctly analyzed.

[0846] Step 4:

[0847] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0848] Step 5:

[0849] The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts congestion levels based on people flow data for the specified dates and identifies the location with the highest customer attraction potential within the desired area. Furthermore, it takes into account emotional data and prioritizes locations with lower congestion levels if the user is prone to stress.

[0850] Step 6:

[0851] The server uses a staff allocation model to calculate the optimal staff allocation based on the characteristics of the selected venue and the input expected number of visitors. For example, it determines the required number of guide staff, reception staff, and security staff. Taking into account emotional data, it makes adjustments such as adding counseling staff if the user feels anxious.

[0852] Step 7:

[0853] The server sends the results of optimal event locations and staff allocation to the device, with each suggestion clearly indicating that it reflects the user's emotional state.

[0854] Step 8:

[0855] The device displays the results sent from the server to the user. Specifically, the device visualizes the optimal event location, staff allocation information, and suggestions that take the user's emotions into consideration. The device also displays the user's emotional state as feedback, as recognized by the emotion engine.

[0856] Step 9:

[0857] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event based on this information. This allows users to prepare for the event quickly and efficiently, and the emotionally sensitive suggestions give them peace of mind.

[0858] Example 2

[0859] 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."

[0860] Conventional event management systems were inefficient in selecting event locations and allocating staff, and it was difficult to make proposals that took user emotions into account. As a result, issues remained regarding the effectiveness of attracting visitors to events and the user experience. There was also room for improvement in validating user input data and optimizing staff allocation.

[0861] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for a user to input event hosting conditions, means for receiving the input event hosting conditions and user emotion data and verifying their validity, means for analyzing people flow data based on the received event hosting conditions and emotion data and identifying an optimal event location using a generative AI model, means for calculating optimal staff allocation based on the identified event location and the expected number of attendees, and means for displaying the calculation results. This makes it possible to identify an optimal event location and optimize staff allocation, and to make suggestions that take user emotions into consideration.

[0862] "User" refers to an individual or organization that uses the system to input event hosting conditions.

[0863] "Event hosting conditions" refers to information necessary to host an event, including the desired area for the event, the date, the expected number of attendees, and so on.

[0864] "Emotion data" is data obtained by analyzing the user's emotional state, and includes information such as anxiety or excitement obtained using a camera or microphone.

[0865] "Server" refers to a device that receives, analyzes, and processes data input by a user.

[0866] A "terminal" is a device for user input, transmission of data and display of results.

[0867] A "generative AI model" is a pre-trained algorithm that is used to analyze people flow data to identify optimal event locations.

[0868] "People flow data" refers to data that records people's movement patterns in specific areas and at specific times and dates.

[0869] The "staff allocation model" is a model that learns from successful and unsuccessful examples of previous events and calculates the optimal staff allocation for an event.

[0870] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. In addition to automatically determining the optimal staff allocation, by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration.

[0871] The system is configured as follows:

[0872] Enter event conditions

[0873] The user uses the device's input form to input the conditions for hosting an ideal event, such as the desired area for the event, the date, and the expected number of attendees.The emotion engine then uses the camera and microphone to analyze the user's emotional state and generate emotion data.

[0874] Receiving and validating input data

[0875] The device sends the event conditions entered by the user and the analyzed emotion data to the server. At this time, basic format checks are performed to ensure that the entered data is complete and correct. The server then verifies the validity of the received data, for example, whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0876] Identifying the perfect event location

[0877] The server loads people flow data and a generative AI model. This model is pre-trained and is used to analyze people's movement patterns in a specific area and at a specific time and date. The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts the level of congestion based on people flow data for a specified date and identifies the location within the desired area that will be most effective in attracting customers. It also takes into account the user's emotional data; for example, if the user is prone to stress, it will prioritize locations with low congestion levels.

[0878] Optimizing staff allocation

[0879] The server uses a staff allocation model to calculate optimal staff allocation based on the characteristics of the selected event location and the input expected number of visitors. This model uses data learned from past successful and unsuccessful events. Furthermore, it takes into account the user's emotional data, and makes adjustments such as increasing the number of consultation and guidance staff if the user feels anxious.

[0880] Displaying the results

[0881] The server sends the analysis results and staff allocation results to the device. Each suggestion clearly states that the user's emotional state is reflected in the suggestion. The device then displays the results to the user. Specifically, the optimal event location and staff allocation information that takes emotions into account are visualized on the screen. The device also provides feedback on the user's emotional state as recognized by the emotion engine. This allows users to quickly create specific event management plans and feel reassured by receiving suggestions that are in line with their own emotions.

[0882] Specific examples

[0883] For example, if a user inputs, "I want to hold an event near a downtown station between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera detects the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion. If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two counseling staff. The device will display a message such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, three reception staff, two security staff, two counseling staff. User emotion: Allocation to address anxiety." This allows users to quickly create a specific event management plan and feel reassured by receiving suggestions that are tailored to their emotions.

[0884] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking user emotions into consideration.

[0885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0886] Step 1: User inputs event conditions

[0887] The user uses the input form on the device to input the ideal conditions for hosting an event. For example, they input the desired area for the event, the date, and the expected number of visitors. Once the input is complete, the device's emotion engine uses the camera and microphone to analyze the user's emotional state and generate emotion data. The input in this step is specific information about the event hosting conditions and the user's emotion data. The output data is the analyzed emotion data and data on the event hosting conditions.

[0888] Step 2: Terminal receives and validates input data

[0889] The device sends the event hosting conditions entered by the user and the analyzed emotion data to the server. At this time, the device performs basic format checks to ensure that the input data is complete and complete. For example, it verifies that the venue is specified, the dates are entered in the correct format, and the expected number of attendees is a positive integer. The input is the event hosting conditions and emotion data entered by the user, and the output is the format-verified data.

[0890] Step 3: The server identifies the best location for the event

[0891] After receiving the formally verified data, the server loads the people flow data and the generative AI model. This generative AI model is pre-trained and is used to analyze people flow patterns. The server analyzes the people flow data based on the event hosting conditions and uses the generative AI model to identify the optimal event location. Specifically, the server predicts the level of congestion and selects the location with the highest customer attraction effect within the desired area. The input for this step is the event hosting conditions and emotion data, and the output is information on the optimal event location.

[0892] Step 4: Optimize staffing with servers

[0893] The server applies a staff allocation model based on the identified event location and expected number of attendees to calculate the optimal staff allocation. The staff allocation model learns from past successful and unsuccessful cases. It also takes into account the user's emotional data, and adjusts the number of consultation staff and guidance staff, for example, if the user feels anxious. The inputs for this step are the event location, expected number of attendees, and emotional data, and the output is the optimal staff allocation plan.

[0894] Step 5: Server generates results and sends them to the device

[0895] The server combines the analysis results and staff allocation results. It specifies that each suggestion reflects the user's emotional state. The generated result data is sent to the terminal. The input of this step is the optimal event location and staff allocation plan, and the output is the result data to display to the user.

[0896] Step 6: Viewing the results in the terminal

[0897] The terminal receives the result data sent from the server. The terminal visualizes and displays the optimal event location and staff allocation information on the screen. It also provides the user's emotional state recognized by the emotion engine as feedback. For example, it may display "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotion: Allocation to address anxiety." The input for this step is the result data sent from the server, and the output is a specific operation plan that is displayed to the user.

[0898] (Application example 2)

[0899] 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."

[0900] When managing events in virtual reality environments, it is important to quickly and accurately select an appropriate virtual space and optimally allocate staff. However, conventional systems have difficulty identifying the optimal event location within a virtual space or planning event management while taking into account user emotional data. Furthermore, to improve the user experience, it is necessary to make emotionally-based suggestions and adjustments, but conventional systems have not been able to adequately address these issues. Therefore, there is a need for a system that can improve the efficiency of event management and the participant experience in virtual spaces.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0902] In this invention, the server includes a means for inputting event conditions, a means for analyzing people flow data based on the input event conditions and searching for the optimal event location, a means for calculating the optimal staff allocation based on the selected event location, a means for displaying the calculation results, a means for using a generative AI model to identify the optimal event location in a virtual space, and a means for analyzing user emotion data and reflecting the analysis in event management, thereby enabling the identification of the optimal event location in a virtual space and the allocation of staff based on the user's emotions.

[0903] - "Event conditions" refers to the specific requirements of the user, such as the date, time, location, expected number of attendees, etc. of the event.

[0904] "People flow data" refers to information about people's movement patterns and stay status in specific areas and at specific times.

[0905] The "optimal event location" refers to the location where the event can be held most effectively and efficiently based on the input conditions and people flow data.

[0906] "Optimal staff allocation" refers to a plan to allocate the necessary staff in the appropriate numbers and locations to ensure the success of an event.

[0907] "Calculation results" refers to the data on optimal event locations and staff allocations analyzed by the system.

[0908] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform a specific task (in this case, identifying event locations).

[0909] "User emotional data" refers to information about the user's emotional state that is analyzed using the user's camera or microphone.

[0910] "Virtual space" refers to a computer-generated 3D environment or simulation space that can be experienced using VR devices.

[0911] This invention is a system that streamlines event management in virtual space and improves the user experience. The system is composed of the following elements:

[0912] 1. Enter the event conditions:

[0913] Users use a smartphone or head-mounted display (HMD) to input event conditions (e.g., date, time, location, expected number of attendees), and the emotion engine analyzes the user's emotional state using a camera and microphone.

[0914] 2. Receiving and validating input data:

[0915] The device sends the entered event conditions and analyzed emotion data to the server, which then checks the validity of the received data, such as whether the date and time are in the correct format and whether the expected number of attendees is a positive integer.

[0916] 3. Identifying the perfect event location:

[0917] The server loads the people flow data and the generative AI model to identify the optimal location for the event in the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and searches for the virtual event location that best suits the input conditions.

[0918] 4. Optimizing staffing:

[0919] The server calculates the optimal staff allocation using a staff allocation model based on the characteristics of the selected virtual event venue and the input expected number of visitors. Furthermore, it takes into account the user's emotional data and makes adjustments such as increasing the number of guide staff if the user feels anxious.

[0920] 5. Displaying the results:

[0921] The server sends the analysis results and staff allocation results to the device, which displays them to the user. The user can check the optimal event location in the virtual space and staff allocation information that takes emotions into account. For example, if the user is prone to stress, the system will suggest a less crowded virtual space and appropriately allocate staff accordingly.

[0922] Specific examples

[0923] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff. This allows the user to receive specific recommendations regarding the location and staff allocation of the event within the virtual space.

[0924] Example prompt for a generative AI model:

[0925] location: Virtual city center square

[0926] date: 2023-12-01

[0927] expected_attendees: 1000

[0928] user_emotion: excitement

[0929] This prompt sentence is used to request the generative AI model to identify the optimal virtual space.

[0930] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0931] Step 1:

[0932] Users use a smartphone or head-mounted display (HMD) to input the event conditions, including the location, date and time, and expected number of attendees. In addition, a camera and microphone are used to obtain the user's emotional data from the emotion engine. The input data generated are the location, date and time, expected number of attendees, and emotional data.

[0933] Step 2:

[0934] The device sends the entered event conditions and analyzed emotion data to the server. The server then checks the validity of the received data. Specifically, it checks whether the date and time format is correct, whether the expected number of attendees is a positive integer, and whether the event location is blank. This ensures that the input data is valid.

[0935] Step 3:

[0936] The server loads people flow data based on valid input data and uses a generative AI model to search for the optimal event location within the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and identifies the optimal event location based on the input conditions. The input is people flow data and conditions entered by the user, and the output is the optimal event location.

[0937] Step 4:

[0938] The server uses a staffing model to calculate optimal staff allocation based on the characteristics of the identified optimal event venue and the expected number of attendees. It also takes into account emotional data and adjusts the number of guides and counselors if the user is anxious or excited. The inputs are venue characteristics, expected number of attendees, and emotional data, and the output is an optimal staffing plan.

[0939] Step 5:

[0940] The server sends the analysis results and staff allocation results to the terminal, which receives them and displays them to the user. The user can then check the optimal event location in the virtual space and staff allocation information that takes emotions into account. As a specific output, the system presents the optimal event location and staff allocation plan.

[0941] Specific examples

[0942] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff.

[0943] Example prompt for a generative AI model:

[0944] location: Virtual city center square

[0945] date: 2023-12-01

[0946] expected_attendees: 1000

[0947] user_emotion: excitement

[0948] 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.

[0949] 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.

[0950] 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.

[0951] [Fourth embodiment]

[0952] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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).

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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."

[0965] This invention is a system for improving the efficiency of event management. By having users input the event hosting conditions, the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff allocation. The elements of this system are configured as follows:

[0966] 1. Enter the event conditions

[0967] The user uses an input form on the terminal to input the conditions for hosting an ideal event, specifically the desired area for the event, the date, and the expected number of attendees.

[0968] 2. Receiving and validating input data

[0969] The terminal transmits the input data to the server.

[0970] The server checks the validity of the received data. For example, if the entered location is blank or the expected number of visitors is a negative value, it returns an error and prompts the user to re-enter the information.

[0971] 3. Identifying the perfect event location

[0972] The server identifies the optimal event location using reliable people flow data and a generative AI model. The people flow data indicates the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the location that best meets the user's requirements.

[0973] 4. Optimizing staff allocation

[0974] The server determines the staff allocation according to the expected number of visitors based on the selected event location. For example, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff. To do this, it uses a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[0975] 5. Displaying the results

[0976] The server transmits the analysis results and staff allocation results to the terminal.

[0977] The terminal displays the results to the user, allowing the user to quickly identify the optimal event location and staffing.

[0978] Specific examples

[0979] As a concrete example, consider the following scenario: A user inputs, "I would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[0980] The server uses an AI model to search for the optimal location based on people flow data around train stations in central Tokyo, and selects the "station square," for example. It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[0981] As a result, the device displays "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff," allowing users to quickly plan their event.

[0982] This invention automates decisions about event locations and staff allocation, significantly improving operational efficiency. Accurate analysis based on people flow data is also expected to increase the success rate of events.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The user inputs the ideal conditions for hosting an event into the input form on the device, specifically, the desired area of ​​the venue, the desired date, and the expected number of attendees into the respective fields.

[0986] Step 2:

[0987] The terminal sends the entered event hosting conditions to the server, after performing basic format checks to ensure that the entered data is complete and correct.

[0988] Step 3:

[0989] The server validates the received event conditions, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[0990] Step 4:

[0991] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[0992] Step 5:

[0993] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[0994] Step 6:

[0995] The server uses a staffing model to calculate the optimal staffing based on the characteristics of the selected venue and the input expected number of attendees, for example determining the required number of guides, receptionists, and security staff.

[0996] Step 7:

[0997] The server then sends the results of the optimal event location and staff allocation to the terminal, allowing the calculation results to be delivered to the user quickly.

[0998] Step 8:

[0999] The terminal displays the results sent from the server to the user, specifically visualizing the optimal event location and staff allocation information on the screen.

[1000] Step 9:

[1001] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event, which will help them efficiently prepare for the event.

[1002] Through the above steps, the present invention is a system that significantly improves the efficiency and success rate of event management.

[1003] Example 1

[1004] 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."

[1005] Traditional event management involves manually selecting venues and allocating staff, which can be inefficient and make it difficult to make decisions based on accurate predictions, resulting in lower event success rates and operational efficiency.

[1006] 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.

[1007] In this invention, the server includes a means for a user to input event hosting conditions, a means for receiving the input event hosting conditions and verifying their validity, a means for analyzing people flow data based on the valid event hosting conditions and identifying the optimal event location, a means for optimizing staff allocation according to the expected number of attendees based on the optimal event location, and a means for displaying the analysis results and staff allocation results. This automates decisions regarding event locations and staff allocation, making it possible to significantly improve operational efficiency.

[1008] "Event hosting conditions" refers to information such as the date, location, expected number of attendees, etc. required to host a specific event.

[1009] "People flow data" refers to data that shows the movement and crowding of people in a specific area at specific times and days of the week.

[1010] A "generative AI model" is a model built using artificial intelligence technology and used to perform data analysis and predictions.

[1011] "Staff allocation" refers to determining the number and roles of staff required to run an event and optimizing their allocation.

[1012] "User" refers to an individual or organization that operates the system and inputs the event hosting conditions.

[1013] "Server" refers to a central control device that processes and analyzes data.

[1014] "Terminal" means a device used by a user to access the system and enter or receive information.

[1015] "Analysis results" refers to the results of analysis performed based on input data.

[1016] "Expected Attendance" refers to the number of people expected to attend a particular event.

[1017] "Optimization" refers to the process of finding the most suitable state or condition for a particular purpose.

[1018] "Validation" refers to the process of checking whether input data is accurate and valid.

[1019] This invention is a system for improving the efficiency of event management. Users input the event hosting conditions, and the system analyzes people flow data to identify the optimal event location and automatically determines the optimal staff placement.

[1020] Enter event conditions

[1021] The user enters the event hosting conditions using an input form on the terminal. Specifically, the user enters the following information:

[1022] Desired area for holding the event (e.g. "Central Tokyo")

[1023] Event dates (e.g., "2023-12-01 to 2023-12-03")

[1024] Expected number of visitors (e.g., 1,000 people)

[1025] Receiving and validating input data

[1026] The terminal sends the entered event hosting conditions to the server. Data is sent using an HTTP request, and the data sent is structured in JSON format. The server verifies the validity of the received data. For example, it checks whether the "area" is blank or whether the "expected number of visitors" is a negative value. If invalid data is detected, it generates an error message and returns it to the terminal.

[1027] Identifying the perfect event location

[1028] The server uses a people flow database and a generative AI model to identify the optimal location for an event. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch. The server extracts and analyzes people flow data for a specified area to predict the optimal location. For example, it may identify a "station square in the city center" as optimal.

[1029] Optimizing staff allocation

[1030] The server determines the optimal staff allocation based on the specified event location and expected number of visitors. It calculates the number of people required for each role, such as guide staff, reception staff, and security staff. This calculation uses a staff allocation model that has learned from past successful and unsuccessful events. For example, for 1,000 visitors, five guide staff, three reception staff, and two security staff are required.

[1031] Displaying the results

[1032] The server structures the analysis results and staff allocation results in JSON format and sends them to the terminal. The terminal then displays the received results on the user interface. Specifically, it displays information such as "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff" on a web page. This allows users to efficiently plan and manage events.

[1033] Specific examples

[1034] For example, consider the following condition entered by the user:

[1035] "We would like to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people."

[1036] The server uses a generative AI model to search for the most suitable venue based on pedestrian flow data around stations in the city center, and selects the "station square," for example.

[1037] The optimal staffing for 1,000 visitors is then calculated, such as five guide staff, three reception staff, and two security staff.

[1038] The device displays, "Best location for event: Station square. Staffing: 5 guide staff, 3 reception staff, 2 security staff."

[1039] In this way, the system of the present invention automates decisions regarding event location and staffing, significantly improving operational efficiency. It is also expected that accurate analysis based on people flow data will increase the success rate of events.

[1040] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] The user enters the event conditions into an input form on the device. The information entered includes the desired area, event dates, and expected number of attendees. This clearly defines the basic requirements of the event. Specifically, the user enters a specific area (e.g., "Central Tokyo"), event dates (e.g., "2023-12-01 to 2023-12-03"), and expected number of attendees (e.g., "1,000 people"). The entered information is encoded in JSON format.

[1043] input:

[1044] Desired area: Central Tokyo

[1045] Dates: 2023-12-01 to 2023-12-03

[1046] Expected number of visitors: 1,000 people

[1047] output:

[1048] Input data in JSON format

[1049] Step 2:

[1050] The device sends the event hosting conditions entered by the user to the server. Data is sent using an HTTP request. This request includes the input data in JSON format, allowing the server to receive the necessary data.

[1051] input:

[1052] Input data in JSON format

[1053] output:

[1054] HTTP request to the server

[1055] Step 3:

[1056] The server analyzes the data received from the device and verifies its validity. First, it checks that the "area" is not blank, the "expected number of visitors" is not a negative value, and the "date of event" is properly formatted. If these conditions are not met, it generates an error message and prompts the user to re-enter the information.

[1057] input:

[1058] JSON format input data sent from the terminal

[1059] output:

[1060] Validated data or error message

[1061] Step 4:

[1062] Based on the validated data, the server uses a people flow database and a generative AI model to identify the optimal event location. The people flow database stores data on past events and daily attendance. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to analyze data and make predictions. This process outputs the most suitable event location for the specified area and date.

[1063] input:

[1064] Validated Data

[1065] output:

[1066] Ideal event location (e.g., "Station Square")

[1067] Step 5:

[1068] The server calculates the optimal staff allocation based on the specified event location and expected number of attendees. This is done using a staff allocation model that has learned from past successes and failures of events. Specifically, it calculates the number of people required for each role, such as guide staff, reception staff, and security staff.

[1069] input:

[1070] The perfect venue for your event

[1071] Expected number of visitors

[1072] output:

[1073] Staff allocation plan (e.g., 5 guide staff, 3 reception staff, 2 security staff)

[1074] Step 6:

[1075] The server structures the calculated optimal staff allocation information in JSON format and sends it to the terminal. The terminal receives this information and displays it on the user interface, allowing the user to see the optimal event location and staff allocation at a glance.

[1076] input:

[1077] Staffing Plan

[1078] output:

[1079] Information displayed in the user interface (e.g., "Optimal event location: Station square. Staffing: 5 guides, 3 receptionists, 2 security staff")

[1080] This detailed process flow allows users to quickly and efficiently plan their events.

[1081] (Application example 1)

[1082] 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."

[1083] Traditional event management required a lot of time and effort to select the optimal location and efficiently allocate staff. Analyzing people flow data and optimizing staff allocation manually placed a significant burden on event organizers, creating a need for more efficient operations. Similarly, it was difficult to quickly and accurately determine the optimal location and staff allocation for in-store events.

[1084] 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.

[1085] In this invention, the server includes means for inputting event hosting conditions, means for analyzing people flow data based on the input event hosting conditions and searching for the optimal event location, means for calculating the optimal staff allocation based on the selected event location, means for displaying the calculation results, means for presenting the optimal in-store event location based on the event hosting conditions in in-store event management, and means for automatically determining the optimal in-store staff allocation based on the predicted number of attendees. This makes it possible to select the optimal location and efficiently allocate staff in events and in-store event management.

[1086] "Event hosting conditions" refers to information such as the desired date and time, location, expected number of attendees, etc., set by the user when hosting an event.

[1087] "People flow data" refers to data that indicates the number of customers in a specific area at a specific time of day or on a specific day of the week.

[1088] A "generative AI model" is an artificial intelligence model that calculates optimal event locations and staff placements based on received data and people flow data.

[1089] A "server" is a computer system that validates input data and performs analysis and calculations.

[1090] "Store Event" means an event held within a particular store, including promotions, sales, campaigns, etc.

[1091] "Optimal staff allocation" refers to efficiently allocating the roles and numbers of staff required for the success of an event based on the predicted number of visitors.

[1092] This invention is a system that streamlines event management and automatically determines the optimal venue and staff allocation. The main function of this system is for a user to input the event hosting conditions, and the server determines the optimal venue and staff allocation using people flow data and a generative AI model, and displays the results on a terminal.

[1093] Program processing explanation

[1094] 1. Enter the event conditions:

[1095] Users enter their event requirements using a smartphone, tablet, or other device. They are provided with an input form for details such as desired venue, dates, and expected number of attendees.

[1096] 2. Receiving and validating input data:

[1097] The terminal sends the data entered by the user to the server. The server checks the validity of the received data and performs error checking. For example, if the location is blank or the expected number of visitors is a negative value, an error message is returned and the user is prompted to re-enter the data.

[1098] 3. Identifying the perfect event location:

[1099] The server uses people flow data and a generative AI model to identify the optimal event location. This people flow data includes the number of people in a specific area at specific times and days of the week. The generative AI model analyzes this data and predicts the most suitable location for the input conditions.

[1100] 4. Optimizing staffing:

[1101] The server calculates the optimal staff allocation based on the selected event location and the expected number of attendees. Specifically, it uses a staff allocation model to determine the required number of guide staff, reception staff, security staff, etc.

[1102] 5. Displaying the results:

[1103] The server sends the analysis and calculation results to the terminal, which displays them to the user, allowing the user to quickly and accurately determine the optimal event location and staff allocation.

[1104] Hardware and software used

[1105] Hardware:

[1106] Smartphone, tablet, or other device

[1107] Servers (including cloud services and on-premise servers)

[1108] software:

[1109] Python environment (including libraries such as Pandas and Requests)

[1110] Cloud services (e.g., Firebase, AWS)

[1111] Specific examples

[1112] For example, consider the case where a user inputs, "I would like to hold an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03." The server analyzes the optimal location based on people flow data around the station in the city center and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff.

[1113] Prompt Sentence Examples

[1114] Enter "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people." The AI ​​model searches for the optimal location based on people flow data around the station in the city center, and selects the "station square." It then calculates the optimal staff allocation for 1,000 visitors, such as five guide staff, three reception staff, and two security staff, and displays the results.

[1115] In this way, the system of the present invention can improve the efficiency of event management and quickly and accurately determine the optimal venue and staff allocation. Furthermore, by applying the system to in-store events, the efficiency of event management at brick-and-mortar stores can be improved.

[1116] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1117] Step 1:

[1118] The user uses a device such as a smartphone or tablet to input the event conditions (desired location, event date, expected number of attendees). They then fill in the event details in the input form and press the submit button. The input data is then sent to the device in a specific format (for example, "an event near a station in the city center, with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03").

[1119] Step 2:

[1120] The terminal sends the event hosting condition data entered by the user to the server. The server checks the validity of the received data. Specifically, it performs error checks to see if the location is blank or if the expected number of attendees is a negative value. If there is a problem with the data, it returns an error message to the terminal and prompts the user to re-enter the data. If the data is valid, it proceeds to the next step.

[1121] Step 3:

[1122] The server collects reliable people flow data and uses a generative AI model to identify the optimal event location based on the input event conditions. Here, the people flow data includes the number of attendees in a specific area by time of day and day of the week. The generative AI model analyzes this data and predicts the optimal event location. For example, the "station square" may be selected.

[1123] Step 4:

[1124] The server calculates the optimal staff allocation based on the predicted number of visitors, based on the selected event location. As a specific example, it calculates the required number of guide staff, reception staff, and security staff. The generative AI model optimizes staff allocation by referring to past event data and success stories. For example, it calculates that 5 guide staff, 3 reception staff, and 2 security staff are required for 1,000 visitors.

[1125] Step 5:

[1126] The server sends the analysis results of the optimal event location and staff allocation to the terminal, which then displays them to the user. Specifically, the results are displayed in the format "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff."

[1127] Step 6:

[1128] Users can check the results displayed on their devices and make adjustments to the details of the event and arrange for staff as necessary. This system allows users to plan their events quickly and efficiently.

[1129] 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.

[1130] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. It then automatically determines the optimal staff allocation, and by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration. The elements of this system are configured as follows:

[1131] 1. Enter the event conditions

[1132] The user uses the device's input form to input the conditions for hosting an ideal event. Specifically, they input the desired area for the event, the date, and the expected number of attendees. The user's emotional state based on the input conditions is then analyzed by an emotion engine using a camera and microphone.

[1133] 2. Receiving and validating input data

[1134] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[1135] The server validates the received data, for example, ensuring that the location is not blank, that the dates are entered in the correct format, and that the expected number of attendees is a positive integer.

[1136] 3. Identifying the perfect event location

[1137] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[1138] The server uses the generative AI model to search for the most suitable event location for the received event conditions. For example, it predicts the level of congestion based on people flow data for the specified date and identifies the location with the highest potential for attracting customers within the desired area.

[1139] The user's emotional data is also taken into consideration. For example, if the user is prone to stress, measures such as preferentially selecting less crowded locations are taken.

[1140] 4. Optimizing staff allocation

[1141] The server calculates the optimal staff allocation based on the characteristics of the selected event venue and the input expected number of attendees using a staff allocation model that has learned from successful and unsuccessful cases of previous events.

[1142] Furthermore, the system takes into consideration the user's emotional data, and if the user feels anxious, for example, it makes adjustments such as increasing the number of consultation staff and guidance staff.

[1143] 5. Displaying the results

[1144] The server sends the analysis results and staff allocation results to the terminal, and each suggestion clearly states that it reflects the user's emotional state.

[1145] The terminal displays the results to the user. Specifically, it visualizes the optimal event location and staff allocation information that takes emotions into account on the screen. It also provides feedback on the user's emotional state as recognized by the emotion engine.

[1146] Specific examples

[1147] For example, if a user inputs "I want to hold an event near a station in the city center between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera recognizes the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion.If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two consultation staff.

[1148] The device displays messages such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotions: Allocation to address anxiety." This allows users to quickly create a specific event management plan while receiving suggestions that are sensitive to their emotions, giving them a sense of security.

[1149] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking into account user emotions.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] Users input the conditions for hosting their ideal event into the input form on the device. Specifically, they enter the desired location area, desired date, and expected number of attendees into the appropriate fields. The emotion engine also analyzes the user's emotional data via the camera and microphone.

[1153] Step 2:

[1154] The device sends the entered event conditions and analyzed user emotion data to the server, after performing basic format checks to ensure that the entered data is complete and complete.

[1155] Step 3:

[1156] The server checks the validity of the received event conditions and emotion data, such as whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer. It also checks whether the emotion data has been correctly analyzed.

[1157] Step 4:

[1158] The server loads people flow data and a generative AI model that is pre-trained and used to analyze people's movement patterns in specific areas and times of day.

[1159] Step 5:

[1160] The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts congestion levels based on people flow data for the specified dates and identifies the location with the highest customer attraction potential within the desired area. Furthermore, it takes into account emotional data and prioritizes locations with lower congestion levels if the user is prone to stress.

[1161] Step 6:

[1162] The server uses a staff allocation model to calculate the optimal staff allocation based on the characteristics of the selected venue and the input expected number of visitors. For example, it determines the required number of guide staff, reception staff, and security staff. Taking into account emotional data, it makes adjustments such as adding counseling staff if the user feels anxious.

[1163] Step 7:

[1164] The server sends the results of optimal event locations and staff allocation to the device, with each suggestion clearly indicating that it reflects the user's emotional state.

[1165] Step 8:

[1166] The device displays the results sent from the server to the user. Specifically, the device visualizes the optimal event location, staff allocation information, and suggestions that take the user's emotions into consideration. The device also displays the user's emotional state as feedback, as recognized by the emotion engine.

[1167] Step 9:

[1168] Users can check the optimal event location and staff allocation displayed on their device and create specific plans for the event based on this information. This allows users to prepare for the event quickly and efficiently, and the emotionally sensitive suggestions give them peace of mind.

[1169] Example 2

[1170] 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."

[1171] Conventional event management systems were inefficient in selecting event locations and allocating staff, and it was difficult to make proposals that took user emotions into account. As a result, issues remained regarding the effectiveness of attracting visitors to events and the user experience. There was also room for improvement in validating user input data and optimizing staff allocation.

[1172] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for a user to input event hosting conditions, means for receiving the input event hosting conditions and user emotion data and verifying their validity, means for analyzing people flow data based on the received event hosting conditions and emotion data and identifying an optimal event location using a generative AI model, means for calculating optimal staff allocation based on the identified event location and the expected number of attendees, and means for displaying the calculation results. This makes it possible to identify an optimal event location and optimize staff allocation, and to make suggestions that take user emotions into consideration.

[1173] "User" refers to an individual or organization that uses the system to input event hosting conditions.

[1174] "Event hosting conditions" refers to information necessary to host an event, including the desired area for the event, the date, the expected number of attendees, and so on.

[1175] "Emotion data" is data obtained by analyzing the user's emotional state, and includes information such as anxiety or excitement obtained using a camera or microphone.

[1176] "Server" refers to a device that receives, analyzes, and processes data input by a user.

[1177] A "terminal" is a device for user input, transmission of data and display of results.

[1178] A "generative AI model" is a pre-trained algorithm that is used to analyze people flow data to identify optimal event locations.

[1179] "People flow data" refers to data that records people's movement patterns in specific areas and at specific times and dates.

[1180] The "staff allocation model" is a model that learns from successful and unsuccessful examples of previous events and calculates the optimal staff allocation for an event.

[1181] This invention is a system for improving event management efficiency and user experience. It analyzes people flow data based on the event hosting conditions entered by the user and identifies the optimal event location. In addition to automatically determining the optimal staff allocation, by combining it with an emotion engine, it is possible to make suggestions and adjustments that take user emotions into consideration.

[1182] The system is configured as follows:

[1183] Enter event conditions

[1184] The user uses the device's input form to input the conditions for hosting an ideal event, such as the desired area for the event, the date, and the expected number of attendees.The emotion engine then uses the camera and microphone to analyze the user's emotional state and generate emotion data.

[1185] Receiving and validating input data

[1186] The device sends the event conditions entered by the user and the analyzed emotion data to the server. At this time, basic format checks are performed to ensure that the entered data is complete and correct. The server then verifies the validity of the received data, for example, whether the location is blank, whether the dates are entered in the correct format, and whether the expected number of attendees is a positive integer.

[1187] Identifying the perfect event location

[1188] The server loads people flow data and a generative AI model. This model is pre-trained and is used to analyze people's movement patterns in a specific area and at a specific time and date. The server uses the generative AI model to search for the event location that best suits the received event conditions. For example, it predicts the level of congestion based on people flow data for a specified date and identifies the location within the desired area that will be most effective in attracting customers. It also takes into account the user's emotional data; for example, if the user is prone to stress, it will prioritize locations with low congestion levels.

[1189] Optimizing staff allocation

[1190] The server uses a staff allocation model to calculate optimal staff allocation based on the characteristics of the selected event location and the input expected number of visitors. This model uses data learned from past successful and unsuccessful events. Furthermore, it takes into account the user's emotional data, and makes adjustments such as increasing the number of consultation and guidance staff if the user feels anxious.

[1191] Displaying the results

[1192] The server sends the analysis results and staff allocation results to the device. Each suggestion clearly states that the user's emotional state is reflected in the suggestion. The device then displays the results to the user. Specifically, the optimal event location and staff allocation information that takes emotions into account are visualized on the screen. The device also provides feedback on the user's emotional state as recognized by the emotion engine. This allows users to quickly create specific event management plans and feel reassured by receiving suggestions that are in line with their own emotions.

[1193] Specific examples

[1194] For example, if a user inputs, "I want to hold an event near a downtown station between 2023-12-01 and 2023-12-03, with an expected attendance of 1,000 people," and the camera detects the user's emotion as "anxiety," the system will prioritize selecting a venue with a lower level of congestion. If the selected venue is the "station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two counseling staff. The device will display a message such as, "Optimal event location: Station square. Staff allocation: 5 guide staff, three reception staff, two security staff, two counseling staff. User emotion: Allocation to address anxiety." This allows users to quickly create a specific event management plan and feel reassured by receiving suggestions that are tailored to their emotions.

[1195] The present invention not only significantly improves the efficiency and success rate of event management, but also provides a better user experience by taking user emotions into consideration.

[1196] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1197] Step 1: User inputs event conditions

[1198] The user uses the input form on the device to input the ideal conditions for hosting an event. For example, they input the desired area for the event, the date, and the expected number of visitors. Once the input is complete, the device's emotion engine uses the camera and microphone to analyze the user's emotional state and generate emotion data. The input in this step is specific information about the event hosting conditions and the user's emotion data. The output data is the analyzed emotion data and data on the event hosting conditions.

[1199] Step 2: Terminal receives and validates input data

[1200] The device sends the event hosting conditions entered by the user and the analyzed emotion data to the server. At this time, the device performs basic format checks to ensure that the input data is complete and complete. For example, it verifies that the venue is specified, the dates are entered in the correct format, and the expected number of attendees is a positive integer. The input is the event hosting conditions and emotion data entered by the user, and the output is the format-verified data.

[1201] Step 3: The server identifies the best location for the event

[1202] After receiving the formally verified data, the server loads the people flow data and the generative AI model. This generative AI model is pre-trained and is used to analyze people flow patterns. The server analyzes the people flow data based on the event hosting conditions and uses the generative AI model to identify the optimal event location. Specifically, the server predicts the level of congestion and selects the location with the highest customer attraction effect within the desired area. The input for this step is the event hosting conditions and emotion data, and the output is information on the optimal event location.

[1203] Step 4: Optimize staffing with servers

[1204] The server applies a staff allocation model based on the identified event location and expected number of attendees to calculate the optimal staff allocation. The staff allocation model learns from past successful and unsuccessful cases. It also takes into account the user's emotional data, and adjusts the number of consultation staff and guidance staff, for example, if the user feels anxious. The inputs for this step are the event location, expected number of attendees, and emotional data, and the output is the optimal staff allocation plan.

[1205] Step 5: Server generates results and sends them to the device

[1206] The server combines the analysis results and staff allocation results. It specifies that each suggestion reflects the user's emotional state. The generated result data is sent to the terminal. The input of this step is the optimal event location and staff allocation plan, and the output is the result data to display to the user.

[1207] Step 6: Viewing the results in the terminal

[1208] The terminal receives the result data sent from the server. The terminal visualizes and displays the optimal event location and staff allocation information on the screen. It also provides the user's emotional state recognized by the emotion engine as feedback. For example, it may display "Optimal event location: Station square. Staff allocation: 5 guide staff, 3 reception staff, 2 security staff, 2 consultation staff. User emotion: Allocation to address anxiety." The input for this step is the result data sent from the server, and the output is a specific operation plan that is displayed to the user.

[1209] (Application example 2)

[1210] 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."

[1211] When managing events in virtual reality environments, it is important to quickly and accurately select an appropriate virtual space and optimally allocate staff. However, conventional systems have difficulty identifying the optimal event location within a virtual space or planning event management while taking into account user emotional data. Furthermore, to improve the user experience, it is necessary to make emotionally-based suggestions and adjustments, but conventional systems have not been able to adequately address these issues. Therefore, there is a need for a system that can improve the efficiency of event management and the participant experience in virtual spaces.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1213] In this invention, the server includes a means for inputting event conditions, a means for analyzing people flow data based on the input event conditions and searching for the optimal event location, a means for calculating the optimal staff allocation based on the selected event location, a means for displaying the calculation results, a means for using a generative AI model to identify the optimal event location in a virtual space, and a means for analyzing user emotion data and reflecting the analysis in event management, thereby enabling the identification of the optimal event location in a virtual space and the allocation of staff based on the user's emotions.

[1214] - "Event conditions" refers to the specific requirements of the user, such as the date, time, location, expected number of attendees, etc. of the event.

[1215] "People flow data" refers to information about people's movement patterns and stay status in specific areas and at specific times.

[1216] The "optimal event location" refers to the location where the event can be held most effectively and efficiently based on the input conditions and people flow data.

[1217] "Optimal staff allocation" refers to a plan to allocate the necessary staff in the appropriate numbers and locations to ensure the success of an event.

[1218] "Calculation results" refers to the data on optimal event locations and staff allocations analyzed by the system.

[1219] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform a specific task (in this case, identifying event locations).

[1220] "User emotional data" refers to information about the user's emotional state that is analyzed using the user's camera or microphone.

[1221] "Virtual space" refers to a computer-generated 3D environment or simulation space that can be experienced using VR devices.

[1222] This invention is a system that streamlines event management in virtual space and improves the user experience. The system is composed of the following elements:

[1223] 1. Enter the event conditions:

[1224] Users use a smartphone or head-mounted display (HMD) to input event conditions (e.g., date, time, location, expected number of attendees), and the emotion engine analyzes the user's emotional state using a camera and microphone.

[1225] 2. Receiving and validating input data:

[1226] The device sends the entered event conditions and analyzed emotion data to the server, which then checks the validity of the received data, such as whether the date and time are in the correct format and whether the expected number of attendees is a positive integer.

[1227] 3. Identifying the perfect event location:

[1228] The server loads the people flow data and the generative AI model to identify the optimal location for the event in the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and searches for the virtual event location that best suits the input conditions.

[1229] 4. Optimizing staffing:

[1230] The server calculates the optimal staff allocation using a staff allocation model based on the characteristics of the selected virtual event venue and the input expected number of visitors. Furthermore, it takes into account the user's emotional data and makes adjustments such as increasing the number of guide staff if the user feels anxious.

[1231] 5. Displaying the results:

[1232] The server sends the analysis results and staff allocation results to the device, which displays them to the user. The user can check the optimal event location in the virtual space and staff allocation information that takes emotions into account. For example, if the user is prone to stress, the system will suggest a less crowded virtual space and appropriately allocate staff accordingly.

[1233] Specific examples

[1234] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff. This allows the user to receive specific recommendations regarding the location and staff allocation of the event within the virtual space.

[1235] Example prompt for a generative AI model:

[1236] location: Virtual city center square

[1237] date: 2023-12-01

[1238] expected_attendees: 1000

[1239] user_emotion: excitement

[1240] This prompt sentence is used to request the generative AI model to identify the optimal virtual space.

[1241] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1242] Step 1:

[1243] Users use a smartphone or head-mounted display (HMD) to input the event conditions, including the location, date and time, and expected number of attendees. In addition, a camera and microphone are used to obtain the user's emotional data from the emotion engine. The input data generated are the location, date and time, expected number of attendees, and emotional data.

[1244] Step 2:

[1245] The device sends the entered event conditions and analyzed emotion data to the server. The server then checks the validity of the received data. Specifically, it checks whether the date and time format is correct, whether the expected number of attendees is a positive integer, and whether the event location is blank. This ensures that the input data is valid.

[1246] Step 3:

[1247] The server loads people flow data based on valid input data and uses a generative AI model to search for the optimal event location within the virtual space. The generative AI model analyzes the conditions of the virtual space for a specific area and date and time, and identifies the optimal event location based on the input conditions. The input is people flow data and conditions entered by the user, and the output is the optimal event location.

[1248] Step 4:

[1249] The server uses a staffing model to calculate optimal staff allocation based on the characteristics of the identified optimal event venue and the expected number of attendees. It also takes into account emotional data and adjusts the number of guides and counselors if the user is anxious or excited. The inputs are venue characteristics, expected number of attendees, and emotional data, and the output is an optimal staffing plan.

[1250] Step 5:

[1251] The server sends the analysis results and staff allocation results to the terminal, which receives them and displays them to the user. The user can then check the optimal event location in the virtual space and staff allocation information that takes emotions into account. As a specific output, the system presents the optimal event location and staff allocation plan.

[1252] Specific examples

[1253] For example, if a user requests an event near a virtual city center square with an expected attendance of 1,000 people between 2023-12-01 and 2023-12-03, and the user's emotion is recognized as "excited" through a camera, the system will prioritize selecting a lively virtual space. If the selected virtual space is a "virtual station square," the staff allocation calculation will suggest assigning five guide staff, three reception staff, two security staff, and two event management staff.

[1254] Example prompt for a generative AI model:

[1255] location: Virtual city center square

[1256] date: 2023-12-01

[1257] expected_attendees: 1000

[1258] user_emotion: excitement

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] FIG. 9 illustrates 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 behaviors 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.

[1264] 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.

[1265] 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).

[1266] 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.

[1267] 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."

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] The following is further disclosed regarding the above embodiment.

[1281] (Claim 1)

[1282] A means for inputting event holding conditions;

[1283] A means for analyzing people flow data based on input event hosting conditions and searching for the most suitable event venue;

[1284] means for calculating optimal staff allocation based on the selected event location;

[1285] a means for displaying the calculation results;

[1286] A system including:

[1287] (Claim 2)

[1288] The system according to claim 1, further comprising means for receiving information on the date, location and expected number of attendees included in the event holding conditions.

[1289] (Claim 3)

[1290] 10. The system of claim 1, further comprising means for using a generative AI model for analysis based on people flow data.

[1291] (Claim 4)

[1292] 10. The system of claim 1, further comprising means for quickly searching for an optimal event location as a result of the analysis.

[1293] (Claim 5)

[1294] 10. The system of claim 1, further comprising means for optimizing staff deployment at a selected event location according to the expected number of attendees.

[1295] (Claim 6)

[1296] 2. The system of claim 1, further comprising means for displaying the analysis results and staff allocation results on a terminal.

[1297] "Example 1"

[1298] (Claim 1)

[1299] A means for a user to input event holding conditions;

[1300] A means for receiving the input event holding conditions and verifying their validity;

[1301] A means for analyzing people flow data based on reasonable event hosting conditions and identifying the optimal event hosting location;

[1302] A means of optimizing staff deployment according to expected attendance based on the optimal event location;

[1303] a means for displaying the analysis results and staffing results;

[1304] A system including:

[1305] (Claim 2)

[1306] The system according to claim 1, further comprising means for receiving information on the date, location and expected number of attendees included in the event holding conditions and verifying the validity of the information.

[1307] (Claim 3)

[1308] The system of claim 1, further comprising means for using a generative AI model for analysis based on people flow data to identify optimal event locations.

[1309] "Application Example 1"

[1310] (Claim 1)

[1311] A means for inputting event holding conditions;

[1312] A means for analyzing people flow data based on input event hosting conditions and searching for the most suitable event venue;

[1313] means for calculating optimal staff allocation based on the selected event location;

[1314] a means for displaying the calculation results;

[1315] In the operation of an in-store event, a means for presenting the optimal in-store event location based on the event holding conditions;

[1316] A system that includes a means for automatically determining the optimal allocation of store staff based on the predicted number of visitors.

[1317] (Claim 2)

[1318] The system according to claim 1, further comprising means for receiving information on the date, location and expected number of attendees included in the event holding conditions.

[1319] (Claim 3)

[1320] 10. The system of claim 1, further comprising means for using a generative AI model for analysis based on people flow data.

[1321] "Example 2: Combining Emotion Engines"

[1322] (Claim 1)

[1323] A means for a user to input event holding conditions;

[1324] a means for receiving the input event holding conditions and user emotion data and verifying their validity;

[1325] A means for analyzing people flow data based on the received event conditions and emotion data, and identifying the optimal event location using a generative AI model;

[1326] a means for calculating optimal staff allocation based on the identified event location and expected attendee numbers;

[1327] a means for displaying the calculation results;

[1328] A system including:

[1329] (Claim 2)

[1330] 2. The system according to claim 1, further comprising means for receiving information on the date, location, and expected number of attendees included in the event holding conditions, and emotion data of the user.

[1331] (Claim 3)

[1332] 10. The system of claim 1, further comprising means for using a generative AI model for analysis based on people flow data.

[1333] "Application example 2 when combining emotion engines"

[1334] (Claim 1)

[1335] A means for inputting event holding conditions;

[1336] A means for analyzing people flow data based on input event hosting conditions and searching for the most suitable event venue;

[1337] means for calculating optimal staff allocation based on the selected event location;

[1338] a means for displaying the calculation results;

[1339] using a generative AI model to identify optimal event locations within the virtual space;

[1340] A means of analyzing user emotional data and reflecting it in event management;

[1341] A system including:

[1342] (Claim 2)

[1343] The system according to claim 1, further comprising means for receiving information on the date, location and expected number of attendees included in the event holding conditions.

[1344] (Claim 3)

[1345] 10. The system of claim 1, further comprising means for using a generative AI model for analysis based on people flow data and for identifying optimal event locations within the virtual space. [Explanation of symbols]

[1346] 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 event holding conditions; A means for analyzing people flow data based on input event hosting conditions and searching for the most suitable event venue; means for calculating optimal staff allocation based on the selected event location; a means for displaying the calculation results; A system including:

2. The system according to claim 1, further comprising means for receiving information on the date, location and expected number of attendees included in the event holding conditions.

3. The system of claim 1 , further comprising means for using a generative AI model for analysis based on people flow data.

4. 2. The system of claim 1, further comprising means for quickly searching for an optimal event location as a result of the analysis.

5. 10. The system of claim 1, further comprising means for optimizing staff deployment at a selected event location according to the expected number of attendees.

6. 2. The system according to claim 1, further comprising means for displaying the analysis results and the staff allocation results on a terminal.

Images