system

Abstract

We provide the system. [Solution] A means for analyzing demographic data and attribute data acquired from an information processing device, Based on the analysis results obtained, a means for selecting the optimal location for commercial activities, A means of determining the implementation site, taking into account the availability and resources of the selected implementation site, A means of presenting a confirmed location for implementation and personnel allocation suitable for commercial activities, A means of providing an optimal sales plan based on user input information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Selecting an optimal venue and determining personnel arrangements for maximizing the effects of an event often rely on intuition and past experience in conventional methods, which is a problem in terms of inefficiency. In particular, it is difficult to quickly make strategic judgments considering floating population and attribute data, and as a result, the success rate of an event may become unpredictable. Therefore, a new method for solving these problems is required.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for analyzing population flow data and attribute data acquired from an information processing device, means for selecting the optimal venue for an event based on the analysis results, means for confirming the venue considering the availability and budget of the selected venue, and means for presenting personnel allocation suitable for the confirmed venue and event. This system enables users to plan optimal events based on data without relying on intuition.

[0006] An "information processing device" is a computer system for inputting, processing, managing, and outputting electronic data.

[0007] "Population mobility data" refers to data that shows information about the movement and gathering of people in a specific area or time period.

[0008] "Attribute data" refers to data that shows characteristic information about an individual or group, such as age, gender, and income.

[0009] "Means of analysis" refers to a function or method for taking in and analyzing data and extracting useful information.

[0010] The "optimal venue" refers to the geographical location that is most effective and suitable for holding an event.

[0011] "Availability" refers to information indicating whether a particular facility or location is available for use.

[0012] A "budget" refers to a financial framework or set of constraints planned for a specific purpose.

[0013] "Means of confirmation" refers to the method or process for finally determining the selected conditions or options.

[0014] "Personnel allocation" refers to the efficient method of allocating the necessary staff and personnel to run an event.

[0015] The "prompting means" is a method or function for presenting information and proposals to the user in an easy-to-understand form.

Brief Explanation of Drawings

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

Embodiment for Carrying Out the Invention

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

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

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

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

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

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0037] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event, and the server, terminal, and user elements work together to function.

[0038] First, the user uses their device to enter information about the upcoming event. This includes the event date, target audience, budget, and expected number of attendees.

[0039] Next, the server uses the information received from the user to collect population movement data and attribute data from external and internal databases. This provides detailed information about current population movement trends and the attributes of specific regions.

[0040] The server then provides this data to the AI ​​agent, which analyzes it using an optimization algorithm. Based on the analysis of the flow of people, the AI ​​agent evaluates whether a specific date, time, and location is suitable for holding an event. It also identifies areas where the event's target customer base is likely to gather from attribute data, narrowing down the candidate locations.

[0041] Furthermore, the server compares data related to costs, such as availability and usage fees, for the selected candidate locations to determine the optimal venue. The user's budget is also taken into consideration during this process.

[0042] For the confirmed venue, the server optimizes the necessary staff allocation according to the characteristics of the event. This involves suggesting the most efficient staffing arrangement based on factors such as the number of attendees and the venue layout.

[0043] Finally, the device presents the user with detailed suggestions regarding the optimal venue and staffing. The user can then make a final decision based on the information provided and proceed with planning the event.

[0044] As a concrete example, suppose a user is planning a launch event for a new product. In this case, the server refers to inventory data and marketing information, and the AI ​​agent suggests the most suitable commercial facility in an urban area. Since the suggested location is confirmed to be a popular spot among the target age group, the user can use it and proceed with the necessary procedures. In this way, the system of the present invention enables users to make rational decisions based on data.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users enter detailed information about the event using their device. This includes the date, target audience, budget, and expected number of participants. Once the user has finished entering the information, it is sent from the device to the server.

[0048] Step 2:

[0049] The server analyzes the information received from the user and collects necessary population and attribute data from external databases or crowdsourced sources. At this stage, the server uses APIs to retrieve this data and stores it in its own database.

[0050] Step 3:

[0051] The server passes the collected data to the AI ​​agent. The AI ​​agent analyzes the distribution of the moving population and time-series data, and begins analysis to narrow down the locations and time periods in which events can be effectively implemented.

[0052] Step 4:

[0053] The AI ​​agent selects the optimal venue based on the analysis results. This includes considering areas where the target audience is concentrated and locations with a high number of visitors based on past data.

[0054] Step 5:

[0055] The server checks the database for the availability and fees of the selected candidate venues on the event date, and verifies whether they fit the budget. If the conditions are met, it confirms them as the optimal venue.

[0056] Step 6:

[0057] The server proposes a staffing plan based on the characteristics of the event. This plan is automatically calculated based on the event's scale, the number of attendees, and the venue layout, and is designed to ensure efficient staff allocation.

[0058] Step 7:

[0059] The terminal presents the user with a confirmed venue and staffing plan. The user can make adjustments as needed, and if they are satisfied with the presented optimization plan, they can finalize it.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Traditionally, a major challenge in event planning has been the significant time and effort required from organizers to determine appropriate venues and staffing levels. In particular, rational decision-making based on population trends, regional characteristics, and budgets has been difficult, often resulting in inefficient planning.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for collecting human flow data and target area characteristic data based on basic event information received from the user; means for using a machine learning model with the collected data to evaluate the suitability of the event based on the target area and date and time; and means for making a final decision based on the evaluation, taking into account the optimal event venue, its usage fee, and availability. This enables the user to efficiently select a rational venue and plan personnel allocation that suits their purpose.

[0065] A "user" refers to a person who provides information about event hosting and is the entity that receives proposals for the most suitable plan.

[0066] "Event basic information" refers to detailed data about the planned event, including the date, target audience, budget, and estimated number of attendees.

[0067] "Human flow data" refers to information that shows trends in people's movement and gatherings in a specific area, and is used to evaluate the timing and potential locations for events.

[0068] "Target region characteristics data" refers to data that includes attribute information such as population distribution and economic conditions in a specific region.

[0069] A "machine learning model" is a computer program that includes algorithms used to evaluate the optimal location and date / time for an event based on data analysis.

[0070] The term "event venue" refers to a suitable physical location for holding an event, and its selection is a crucial factor directly impacting the event's success or failure.

[0071] "Usage fees" refer to the costs incurred when using the proposed event venue and are determined based on the user's budget.

[0072] "Availability" refers to the factor used to determine whether a particular event venue is available at the specified date and time.

[0073] "Worker allocation" refers to the optimal staffing plan required for holding an event, and is a crucial element for efficient event management.

[0074] "Optimization" refers to the process of generating the most efficient and effective event plan based on user-defined conditions.

[0075] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event. This system functions through the collaborative efforts of the server, terminal, and user elements.

[0076] First, the user uses their device to enter basic information about the planned event. Specifically, this includes the date, target audience, budget, and estimated number of attendees. This data forms the basis for processing on the server.

[0077] Next, the server collects human flow data and regional characteristics data based on the information entered by the user. This data is obtained using database APIs or database query languages ​​such as SQL. After data collection is complete, the server uses an AI agent to analyze the data. This analysis uses machine learning libraries such as Python's scikit-learn, and the suitability of events based on the target region and date and time is evaluated using optimization algorithms.

[0078] This allows users to check the usage fees and availability of the selected potential venues. Furthermore, the optimal venue is determined after considering the user's budget. Simultaneously, a proposal for the optimal staffing arrangement based on the confirmed venue is made. This is to ensure efficient personnel allocation based on the number of attendees and the venue layout.

[0079] Ultimately, the terminal presents the server-generated suggestions to the user. This allows the user to quickly make data-driven, rational decisions and materialize their event plan. Through this process, the user can improve the accuracy and efficiency of their planning.

[0080] For example, if a user enters a prompt into the system such as, "Please suggest the best venue and staffing for the event. Event information: New product launch, Date: October 10th, Target audience: Young people, Budget: 1 million yen, Estimated attendees: 500 people," the server will use this information to suggest the most suitable commercial facility in an urban area. This kind of system operation supports the success of the event.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users enter information about the event they wish to host into a terminal. This includes basic information such as the date, target audience, budget, and estimated number of attendees. The entered information is sent to the server as basic data for event proposals.

[0084] Step 2:

[0085] The server receives basic event information provided by the user and collects human flow data and regional characteristics data from external and internal databases. First, the server calls a database API to retrieve population trends and attributes for a specific region. Based on this input information, the server extracts the necessary data and prepares for the next analysis step.

[0086] Step 3:

[0087] The server inputs the collected data into an AI agent, which then performs analysis using a machine learning model. Here, the Python scikit-learn library is used to apply algorithms such as clustering and regression analysis. As a result of the analysis, an assessment of the suitability of the optimal date, time, and location for the event is output. This output is then used to evaluate potential locations for the next event.

[0088] Step 4:

[0089] Based on the analysis results, the server compares data on usage fees and availability of selected candidate sites to perform a cost evaluation. At this stage, fee information is retrieved from the database via SQL queries and compared with the budget set by the user. Based on the cost evaluation results, the optimal event venue is determined, and its information is compiled.

[0090] Step 5:

[0091] The server proposes the optimal staffing arrangement for the confirmed venue, tailored to the characteristics of the event. This process uses shift management software to simulate the arrangement based on the number of attendees and the venue layout. An optimized staffing plan is then generated and saved within the system.

[0092] Step 6:

[0093] The terminal displays suggested information sent from the server to the user. Through the terminal's user interface, it visually presents information regarding the optimal venue and staffing, supporting the user in making actual decisions. Based on this information, the user can proceed to the next step in event planning.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In commercial activities, selecting the optimal location and staffing is essential for improving customer attraction and efficient operation. However, effectively selecting these requires utilizing a large amount of demographic and attribute data, and traditional methods present the challenge of expending significant time and resources on location selection and staffing.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for analyzing demographic and attribute data, means for selecting the optimal location for commercial activities, and means for determining the location considering the availability and resources of the location. This makes it possible to efficiently and effectively determine the location and personnel allocation for commercial activities.

[0099] An "information processing device" is a computer system used to collect, store, and manage a wide variety of data, and to perform data analysis based on that data.

[0100] "Demographic data" refers to information about population trends and composition in a specific region, and is used to identify target groups and develop marketing plans in commercial activities.

[0101] "Attribute data" refers to information that indicates characteristics and features associated with specific places or people, and is useful for analyzing target markets and consumer segments.

[0102] "Commercial activities" refer to events and promotional activities aimed at promoting the sale of products and services, with the goal of attracting customers and increasing sales.

[0103] "Personnel allocation" refers to appropriately assigning the necessary personnel to achieve a specific objective, enabling them to perform tasks effectively.

[0104] A "server" is a computer system that provides data over a network and allows multiple clients to access it.

[0105] In the system of this invention, the user first inputs detailed information related to the event into a terminal. This includes details of the planned commercial activity, target customer base, budget, etc. The terminal then transmits this information to a server.

[0106] Based on the information received, the server collects demographic and attribute data from external APIs and stores them in a database. MongoDB is used for the database, allowing for flexible data management. The AI ​​agent then analyzes the collected data using TENSORFLOW®. During the analysis, it predicts the optimal location and personnel allocation for commercial activities and makes a decision using an optimization algorithm proposed based on the above information.

[0107] For example, if a user is planning a promotional event for a new product in a specific region, the server can identify the commercial area with the highest foot traffic and recommend a suitable location for the target new market based on attribute data. Furthermore, it can collect feedback after the event and process it to improve the suggestion algorithm for future events.

[0108] An example of a prompt to the AI ​​model in such a system would be, "We are planning a launch event for a new product. Please tell us potential shopping mall locations that are likely to attract our target customer base, women in their 20s." By clearly indicating the information the user is seeking, more accurate suggestions can be made.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user enters event information into the terminal. This information includes the event name, date, target audience, and budget. The terminal organizes this information and prepares it for transmission to the server. The input is the information specified by the user, and the output is the event information converted into a transmittable data format.

[0112] Step 2:

[0113] The terminal sends the input event information to the server. The server receives this information and stores it in a database. Here, the input is the event information sent from the terminal, and the output is the information stored in the database.

[0114] Step 3:

[0115] The server uses external APIs to collect demographic and attribute data. Inputs are location and timing data related to the user's commercial activities, while outputs are the collected large amounts of demographic and attribute data. This allows for the acquisition of fundamental data on pedestrian flow and demographics in specific areas.

[0116] Step 4:

[0117] The server uses TensorFlow to analyze collected demographic and attribute data and uses an optimization algorithm to determine the optimal location and staffing. The input is the data collected in the previous step, and the output is a proposal for the optimal location and staffing. Specifically, predictive analysis is performed using an AI model.

[0118] Step 5:

[0119] Based on the analysis results, the server sends a suggestion to the user's terminal regarding the optimal implementation location and personnel allocation. The terminal then displays this information visually to the user. The input is the analysis results of the AI ​​model, and the output is the suggested information that can be presented to the user.

[0120] Step 6:

[0121] The user reviews the proposals displayed on the device and enters feedback to finalize the event plan. User input consists of reactions to the proposals and additional requests, while output is data in the form of feedback. The device then prepares to send this feedback to the server.

[0122] Step 7:

[0123] The server receives feedback from users and uses this information to improve the proposed algorithm. The input is the feedback information, and the output is the improved accuracy of the new proposals using the improved algorithm. At this stage, the system is retrained and parameters are tuned.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention combines a system that proposes the optimal venue and staffing for an event with an emotion engine that recognizes user emotions. This allows for the optimization of proposals and an improvement in the user experience.

[0126] First, users register detailed information about the event through their device. This information includes the date and time of the event, the target customer base, and the budget. Once the input is complete, this information is sent from the device to the server.

[0127] The server collects population movement data and attribute data based on the information it receives. This data is obtained from external databases and integrated within the server.

[0128] Next, the server passes the data to the AI ​​agent, which then analyzes the optimal event location and staffing. The AI ​​agent uses population trends and attribute information to select the best candidate locations.

[0129] A distinctive feature here is the use of an emotion engine. The server uses this engine to recognize the user's emotions when they input information or confirm suggestions, and fine-tunes parameters that affect the analysis results. For example, if a user shows a negative reaction to a suggested venue, it is possible to change the priority of alternative options.

[0130] The terminal not only presents the user with the optimal venue and staffing plan, but also collects user sentiment data regarding the proposal in real time and feeds it back to the server. This further improves the accuracy of the proposal.

[0131] As a concrete example, consider a scenario where a user is planning a sales promotion event for a large product. In this case, the server uses an AI agent to select event spaces within popular commercial facilities as potential venues. However, if the emotion engine detects dissatisfaction in the user's response, it will suggest a stadium with good transportation access as an alternative. This process can increase user satisfaction.

[0132] This invention makes it possible to achieve flexible and highly accurate event planning that takes into account the emotional responses of users.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] Users use their devices to enter event details, including the date and time of the event, target audience, budget constraints, and expected number of attendees. The entered information is sent to the server in real time.

[0136] Step 2:

[0137] The server receives event information sent by the user and initiates the process of collecting population movement data and attribute data from external databases. It uses APIs to obtain the necessary data and performs data formatting to prepare it for analysis.

[0138] Step 3:

[0139] The server starts analysis using an AI agent based on the collected data. The AI ​​agent analyzes the trends and attribute information of the moving population to identify optimal locations for holding events. At this time, the results of the data analysis are scored and ranked.

[0140] Step 4:

[0141] The emotion engine begins collecting user responses. As the user views the suggested results on their device, it analyzes their emotional state (e.g., joy, dissatisfaction, indifference, etc.) in real time via the camera and microphone, and feeds the results back to the server.

[0142] Step 5:

[0143] The server receives feedback from the emotion engine and adjusts the suggestions made by the AI ​​agent. For example, if a user expresses dissatisfaction with a suggested venue, the ranking will be changed and a more suitable candidate will be presented. It is also possible to take even more new factors into consideration.

[0144] Step 6:

[0145] The device presents a list of options optimized for the user. The user reviews the displayed choices, and if satisfied, makes a final selection and confirms their decision.

[0146] Step 7:

[0147] After the user confirms the suggestions, the sentiment engine continues to collect user feedback, which is stored on the server. The server uses this data to optimize future algorithms and improve the accuracy of suggestions.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Conventional meeting planning systems have difficulty taking into account the emotional aspects of users, making it challenging to suggest optimal meeting locations and staffing arrangements. Furthermore, there is a lack of methods to appropriately incorporate user feedback and improve the overall accuracy of the system's suggestions.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for analyzing statistical data and identification data acquired from an information processing device, means for selecting the optimal location for the meeting based on the obtained analysis results, and means for recognizing and adjusting the user's emotions using an emotion analysis device. This makes it possible to present an optimal meeting plan that also takes the user's emotions into consideration.

[0153] "Information processing equipment" is a general term for devices and systems used to collect, process, and analyze data.

[0154] "Statistical data" refers to data that shows trends in population movement and distribution within a specific region or time period.

[0155] "Identification data" refers to data that indicates the characteristics and attributes of individual users or subjects.

[0156] A "gathering" refers to an event or meeting in which a large number of people come together.

[0157] "Implementation site" is a term that refers to the location where a meeting or event takes place.

[0158] "Employee allocation" refers to a plan to assign employees to perform necessary duties at specific times and for specific tasks.

[0159] An "emotion analysis device" refers to a combination of hardware and software used to recognize and analyze a user's emotions.

[0160] A "user" is someone who uses the system to receive meeting plans and suggestions.

[0161] "Opinions" refers to feedback provided by users after the meeting.

[0162] A description of an embodiment for carrying out this invention will be provided. This system provides the information necessary for users to propose the optimal location and staffing arrangement for a meeting.

[0163] Users enter basic information about the meeting via their device. This information includes the date and time of the meeting, the target audience, and the budget. Once the information is entered, the device sends it to the server.

[0164] Based on the information received, the server uses an information processing device to collect statistical and identification data from an external database. The server integrates this data and uses a generative AI model to analyze it and select the optimal location for the meeting. In addition to conventional algorithms, an emotion analysis device is used to recognize the emotions of the users and reflect this in the analysis results.

[0165] For example, the AI ​​model can take into account the emotional response users give to suggestions and dynamically modify the list of implementation locations. If a user gives a negative response to a suggested implementation location, the AI ​​model will suggest alternative locations with good transportation access or popular facilities. This feature can provide users with a higher level of satisfaction.

[0166] A concrete example of a prompt message would be, "Please suggest the most suitable location for a meeting where young people are expected to participate."

[0167] This system allows users to create optimal meeting plans that take emotions into consideration, thereby maximizing the effectiveness of the event.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user enters basic information about the meeting into the terminal. Specifically, the user enters the date and time, target audience, and budget, and then presses the submit button. This action prepares the entered information as a dataset on the terminal.

[0171] Step 2:

[0172] The terminal sends information entered by the user to the server. The entered data is first verified on the terminal and then securely transferred to the server using encrypted communication. The server then receives the user's input data.

[0173] Step 3:

[0174] The server collects statistical and identification data from an external database based on the user information it receives. It uses an API to access the statistical database and retrieve necessary population data and customer attribute data. This allows the server to aggregate the external data necessary for analysis.

[0175] Step 4:

[0176] The server integrates the collected data and uses a generative AI model to analyze the optimal implementation location. Specific machine learning algorithms process the data to calculate candidate locations that consider convenience and profitability. This allows the server to generate a list of potential implementation locations.

[0177] Step 5:

[0178] The server uses an emotion analysis device to recognize the user's emotions. The emotion analysis device analyzes the user's reaction when reviewing the suggested content, and the results are reflected in the final suggestion generated by the AI ​​model.

[0179] Step 6:

[0180] The terminal displays optimized implementation locations and employee deployments sent from the server to the user. The user reviews the proposal on the terminal and makes selections and modifications as needed. This interaction results in the generated plan being presented to the user.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] When planning an event, efficiently selecting the optimal location and staffing is challenging. Furthermore, it's necessary to optimize proposals while considering the psychological state of the participants. This is essential to maximize the event's effectiveness and improve participant satisfaction.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for analyzing human movement data and characteristic data acquired from an information technology device, means for selecting the optimal location for an event based on the obtained analysis results, and means for determining the user's psychological state at the time of input and proposal confirmation using emotion recognition means, and adjusting indicators that affect the analysis results. This makes it possible to plan events flexibly and accurately while taking into account the user's psychological state.

[0186] An "information technology device" is a computer system used to record, process, and transmit data collected from humans or machines.

[0187] "Human movement data" refers to data that shows location information and movement routes related to people's movements.

[0188] "Characteristic data" refers to data that shows the attributes and characteristics of the individual or group being studied.

[0189] "Means of analysis" refer to devices and software that perform the process of collecting and analyzing data and extracting useful information.

[0190] "Methods for selecting the optimal location" refers to methods for choosing the most suitable location for an event or activity based on analyzed data.

[0191] "Emotion recognition means" refers to technology that determines a user's psychological state and emotions from their facial expressions, voice, and other factors.

[0192] "Indicators that influence analysis results" are factors or criteria that cause fluctuations in the results obtained through data analysis.

[0193] The server analyzes human movement data and feature data acquired from information technology devices to optimize the event's execution. The server integrates this data and selects the optimal location for the event based on the analysis results. In the selection process, TensorFlow and PyTorch are used as machine learning libraries to analyze movement patterns and feature data and calculate the location that best suits user requirements.

[0194] Furthermore, the server utilizes emotion recognition to determine the user's emotions when receiving information input or confirming suggested content. This involves using Google Cloud's emotion analysis AI to determine the user's psychological state from their facial expressions and voice. This allows for dynamic adjustment of metrics that influence the analysis results, optimizing the final suggested content according to the user's emotions.

[0195] The device is either a smartphone or smart glasses, designed to allow users to intuitively input event information and easily review suggestions from the server. The device also features real-time feedback collection and transmission to the server, contributing to improved suggestion accuracy in the future.

[0196] As a concrete example, when a shop owner arranges new seasonal products, the application suggests the optimal display arrangement based on local purchasing trends and population flow data. If the user does not accept the suggestion, the emotion engine detects feelings of anxiety or dissatisfaction and proposes an alternative arrangement. In this way, the optimal display is achieved in line with the shop owner's intentions.

[0197] An example of a prompt message would be, "What is the ideal layout to improve customer satisfaction through product display? Please suggest a method that is appropriate for the store's characteristics, taking into account recent purchasing trends." The ideas and suggestions generated by the AI ​​through this prompt message will be important support for store owners in making crucial decisions.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] Users input basic information about an event via their device. This information includes the event date, target audience, and budget, and is sent from the device to the server. The server receives the input data as structured data and stores it in a database.

[0201] Step 2:

[0202] The server collects human movement data and feature data from information technology devices. This data is retrieved from external databases using APIs and integrated within the server. A Python®-based library (e.g., Pandas) is used for data integration, and the integrated dataset is prepared for analysis.

[0203] Step 3:

[0204] The server uses integrated data to perform analysis to select the optimal location for the event. Here, machine learning models using TensorFlow and PyTorch run to calculate the best candidate locations from movement patterns and feature data. The analysis results are stored on the server as optimal location candidates.

[0205] Step 4:

[0206] The server uses emotion recognition to analyze the user's psychological state during input and suggestion confirmation. To do this, it sends the user's facial expressions and voice data to Google Cloud's emotion analysis AI to obtain emotional feedback. This feedback is then returned to the server to adjust the variables used to optimize the suggestions.

[0207] Step 5:

[0208] The server sends the adjusted proposal to the user's device and presents it for review. The user then chooses whether to accept or reject the proposal based on the presentation on their device. The user's decision is fed back to the server in real time.

[0209] Step 6:

[0210] After the event concludes, the server collects feedback from participants regarding the event's success. This feedback is analyzed by an evaluation module within the server and used to improve the proposed algorithm. The proposed algorithm is then updated to achieve more accurate optimization in future event planning.

[0211] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0212] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0227] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event, and the server, terminal, and user elements work together to function.

[0228] First, the user uses their device to enter information about the upcoming event. This includes the event date, target audience, budget, and expected number of attendees.

[0229] Next, the server uses the information received from the user to collect population movement data and attribute data from external and internal databases. This provides detailed information about current population movement trends and the attributes of specific regions.

[0230] The server then provides this data to the AI ​​agent, which analyzes it using an optimization algorithm. Based on the analysis of the flow of people, the AI ​​agent evaluates whether a specific date, time, and location is suitable for holding an event. It also identifies areas where the event's target customer base is likely to gather from attribute data, narrowing down the candidate locations.

[0231] Furthermore, the server compares data related to costs, such as availability and usage fees, for the selected candidate locations to determine the optimal venue. The user's budget is also taken into consideration during this process.

[0232] For the confirmed venue, the server optimizes the necessary staff allocation according to the characteristics of the event. This involves suggesting the most efficient staffing arrangement based on factors such as the number of attendees and the venue layout.

[0233] Finally, the device presents the user with detailed suggestions regarding the optimal venue and staffing. The user can then make a final decision based on the information provided and proceed with planning the event.

[0234] As a concrete example, suppose a user is planning a launch event for a new product. In this case, the server refers to inventory data and marketing information, and the AI ​​agent suggests the most suitable commercial facility in an urban area. Since the suggested location is confirmed to be a popular spot among the target age group, the user can use it and proceed with the necessary procedures. In this way, the system of the present invention enables users to make rational decisions based on data.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users enter detailed information about the event using their device. This includes the date, target audience, budget, and expected number of participants. Once the user has finished entering the information, it is sent from the device to the server.

[0238] Step 2:

[0239] The server analyzes the information received from the user and collects necessary population and attribute data from external databases or crowdsourced sources. At this stage, the server uses APIs to retrieve this data and stores it in its own database.

[0240] Step 3:

[0241] The server passes the collected data to the AI ​​agent. The AI ​​agent analyzes the distribution of the moving population and time-series data, and begins analysis to narrow down the locations and time periods in which events can be effectively implemented.

[0242] Step 4:

[0243] The AI ​​agent selects the optimal venue based on the analysis results. This includes considering areas where the target audience is concentrated and locations with a high number of visitors based on past data.

[0244] Step 5:

[0245] The server checks the database for the availability and fees of the selected candidate venues on the event date, and verifies whether they fit the budget. If the conditions are met, it confirms them as the optimal venue.

[0246] Step 6:

[0247] The server proposes a staffing plan based on the characteristics of the event. This plan is automatically calculated based on the event's scale, the number of attendees, and the venue layout, and is designed to ensure efficient staff allocation.

[0248] Step 7:

[0249] The terminal presents the user with a confirmed venue and staffing plan. The user can make adjustments as needed, and if they are satisfied with the presented optimization plan, they can finalize it.

[0250] (Example 1)

[0251] Next, we will describe Example 1. 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."

[0252] Traditionally, a major challenge in event planning has been the significant time and effort required from organizers to determine appropriate venues and staffing levels. In particular, rational decision-making based on population trends, regional characteristics, and budgets has been difficult, often resulting in inefficient planning.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for collecting human flow data and target area characteristic data based on basic event information received from the user; means for using a machine learning model with the collected data to evaluate the suitability of the event based on the target area and date and time; and means for making a final decision based on the evaluation, taking into account the optimal event venue, its usage fee, and availability. This enables the user to efficiently select a rational venue and plan personnel allocation that suits their purpose.

[0255] A "user" refers to a person who provides information about event hosting and is the entity that receives proposals for the most suitable plan.

[0256] "Event basic information" refers to detailed data about the planned event, including the date, target audience, budget, and estimated number of attendees.

[0257] "Human flow data" refers to information that shows trends in people's movement and gatherings in a specific area, and is used to evaluate the timing and potential locations for events.

[0258] "Target region characteristics data" refers to data that includes attribute information such as population distribution and economic conditions in a specific region.

[0259] A "machine learning model" is a computer program that includes algorithms used to evaluate the optimal location and date / time for an event based on data analysis.

[0260] The term "event venue" refers to a suitable physical location for holding an event, and its selection is a crucial factor directly impacting the event's success or failure.

[0261] "Usage fees" refer to the costs incurred when using the proposed event venue and are determined based on the user's budget.

[0262] "Availability" refers to the factor used to determine whether a particular event venue is available at the specified date and time.

[0263] "Worker allocation" refers to the optimal staffing plan required for holding an event, and is a crucial element for efficient event management.

[0264] "Optimization" refers to the process of generating the most efficient and effective event plan based on user-defined conditions.

[0265] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event. This system functions through the collaborative efforts of the server, terminal, and user elements.

[0266] First, the user uses their device to enter basic information about the planned event. Specifically, this includes the date, target audience, budget, and estimated number of attendees. This data forms the basis for processing on the server.

[0267] Next, the server collects human flow data and regional characteristics data based on the information entered by the user. This data is obtained using database APIs or database query languages ​​such as SQL. After data collection is complete, the server uses an AI agent to analyze the data. This analysis uses machine learning libraries such as Python's scikit-learn, and the suitability of events based on the target region and date and time is evaluated using optimization algorithms.

[0268] This allows users to check the usage fees and availability of the selected potential venues. Furthermore, the optimal venue is determined after considering the user's budget. Simultaneously, a proposal for the optimal staffing arrangement based on the confirmed venue is made. This is to ensure efficient personnel allocation based on the number of attendees and the venue layout.

[0269] Ultimately, the terminal presents the server-generated suggestions to the user. This allows the user to quickly make data-driven, rational decisions and materialize their event plan. Through this process, the user can improve the accuracy and efficiency of their planning.

[0270] For example, if a user enters a prompt into the system such as, "Please suggest the best venue and staffing for the event. Event information: New product launch, Date: October 10th, Target audience: Young people, Budget: 1 million yen, Estimated attendees: 500 people," the server will use this information to suggest the most suitable commercial facility in an urban area. This kind of system operation supports the success of the event.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] Users enter information about the event they wish to host into a terminal. This includes basic information such as the date, target audience, budget, and estimated number of attendees. The entered information is sent to the server as basic data for event proposals.

[0274] Step 2:

[0275] The server receives basic event information provided by the user and collects human flow data and regional characteristics data from external and internal databases. First, the server calls a database API to retrieve population trends and attributes for a specific region. Based on this input information, the server extracts the necessary data and prepares for the next analysis step.

[0276] Step 3:

[0277] The server inputs the collected data into an AI agent, which then performs analysis using a machine learning model. Here, the Python scikit-learn library is used to apply algorithms such as clustering and regression analysis. As a result of the analysis, an assessment of the suitability of the optimal date, time, and location for the event is output. This output is then used to evaluate potential locations for the next event.

[0278] Step 4:

[0279] Based on the analysis results, the server collates data on the usage fees and availability of the selected candidate locations and conducts a cost evaluation. At this stage, the server retrieves fee information from the database through an SQL query and compares it with the budget set by the user. Based on the results of the cost evaluation, the optimal event location is determined and the information is refined.

[0280] Step 5:

[0281] For the determined event location, the server proposes an optimal staffing arrangement according to the characteristics of the event. In this process, a simulation is performed using shift management software based on the number of attendees and the venue layout. An optimized operator placement plan is output and saved within the system.

[0282] Step 6:

[0283] The terminal displays the proposed information sent from the server to the user. Through the user interface of the terminal, information regarding the optimal event location and staffing arrangement is visually presented to support the user in making an actual decision. Based on this information, the user can proceed to the next step of the event plan.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In commercial activities, selecting the optimal implementation location and staffing is essential for improving customer attraction and efficient operation. However, in order to effectively select these, it is necessary to utilize a large amount of demographic data and attribute data, and there is an issue that a lot of time and resources are consumed in location selection and staffing with conventional methods.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for analyzing demographic and attribute data, means for selecting the optimal location for commercial activities, and means for determining the location considering the availability and resources of the location. This makes it possible to efficiently and effectively determine the location and personnel allocation for commercial activities.

[0289] An "information processing device" is a computer system used to collect, store, and manage a wide variety of data, and to perform data analysis based on that data.

[0290] "Demographic data" refers to information about population trends and composition in a specific region, and is used to identify target groups and develop marketing plans in commercial activities.

[0291] "Attribute data" refers to information that indicates characteristics and features associated with specific places or people, and is useful for analyzing target markets and consumer segments.

[0292] "Commercial activities" refer to events and promotional activities aimed at promoting the sale of products and services, with the goal of attracting customers and increasing sales.

[0293] "Personnel allocation" refers to appropriately assigning the necessary personnel to achieve a specific objective, enabling them to perform tasks effectively.

[0294] A "server" is a computer system that provides data over a network and allows multiple clients to access it.

[0295] In the system of this invention, the user first inputs detailed information related to the event into a terminal. This includes details of the planned commercial activity, target customer base, budget, etc. The terminal then transmits this information to a server.

[0296] Based on the information received, the server collects demographic and attribute data from external APIs and stores them in a database. MongoDB is used for the database, allowing for flexible data management. The AI ​​agent then analyzes the collected data using TensorFlow. During the analysis, it predicts the optimal location and personnel allocation for commercial activities and makes a decision using an optimization algorithm proposed based on the above information.

[0297] For example, if a user is planning a promotional event for a new product in a specific region, the server can identify the commercial area with the highest foot traffic and recommend a suitable location for the target new market based on attribute data. Furthermore, it can collect feedback after the event and process it to improve the suggestion algorithm for future events.

[0298] An example of a prompt to the AI ​​model in such a system would be, "We are planning a launch event for a new product. Please tell us potential shopping mall locations that are likely to attract our target customer base, women in their 20s." By clearly indicating the information the user is seeking, more accurate suggestions can be made.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user enters event information into the terminal. This information includes the event name, date, target audience, and budget. The terminal organizes this information and prepares it for transmission to the server. The input is the information specified by the user, and the output is the event information converted into a transmittable data format.

[0302] Step 2:

[0303] The terminal sends the input event information to the server. The server receives this and performs the process of storing the information in the database. Here, the input is the event information sent from the terminal, and the output is the information stored in the database.

[0304] Step 3:

[0305] The server uses an external API to collect demographic data and attribute data. The input is data related to the location information and time period of the user's business activities, and the output is a large amount of demographic data and attribute data collected. Thus, basic data regarding the flow and attributes of people in a specific area can be obtained.

[0306] Step 4:

[0307] The server uses TensorFlow to analyze the collected demographic data and attribute data, and considers the implementation location and personnel allocation by means of an optimization algorithm. The input is the data collected in the previous step, and the output is a proposal regarding the optimal implementation location and personnel allocation. As a specific operation, predictive analysis by an AI model is performed.

[0308] Step 5:

[0309] Based on the analysis results, the server sends a proposal regarding the optimal implementation location and personnel allocation to the terminal for the user. The terminal visually displays this information to the user. The input is the analysis result of the AI model, and the output is proposal information that can be presented to the user.

[0310] Step 6:

[0311] The user examines the proposal displayed on the terminal and inputs feedback for finalizing the event plan. The user input is the reaction to the proposal and additional requests, and the output is data as feedback. The terminal prepares to send this feedback to the server.

[0312] Step 7:

[0313] The server receives feedback from users and uses this information to improve the proposed algorithm. The input is the feedback information, and the output is the improved accuracy of the new proposals using the improved algorithm. At this stage, the system is retrained and parameters are tuned.

[0314] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0315] This invention combines a system that proposes the optimal venue and staffing for an event with an emotion engine that recognizes user emotions. This allows for the optimization of proposals and an improvement in the user experience.

[0316] First, users register detailed information about the event through their device. This information includes the date and time of the event, the target customer base, and the budget. Once the input is complete, this information is sent from the device to the server.

[0317] The server collects population movement data and attribute data based on the information it receives. This data is obtained from external databases and integrated within the server.

[0318] Next, the server passes the data to the AI ​​agent, which then analyzes the optimal event location and staffing. The AI ​​agent uses population trends and attribute information to select the best candidate locations.

[0319] A distinctive feature here is the use of an emotion engine. The server uses this engine to recognize the user's emotions when they input information or confirm suggestions, and fine-tunes parameters that affect the analysis results. For example, if a user shows a negative reaction to a suggested venue, it is possible to change the priority of alternative options.

[0320] The terminal not only presents the user with the optimal venue and staffing plan, but also collects user sentiment data regarding the proposal in real time and feeds it back to the server. This further improves the accuracy of the proposal.

[0321] As a concrete example, consider a scenario where a user is planning a sales promotion event for a large product. In this case, the server uses an AI agent to select event spaces within popular commercial facilities as potential venues. However, if the emotion engine detects dissatisfaction in the user's response, it will suggest a stadium with good transportation access as an alternative. This process can increase user satisfaction.

[0322] This invention makes it possible to achieve flexible and highly accurate event planning that takes into account the emotional responses of users.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] Users use their devices to enter event details, including the date and time of the event, target audience, budget constraints, and expected number of attendees. The entered information is sent to the server in real time.

[0326] Step 2:

[0327] The server receives event information sent by the user and initiates the process of collecting population movement data and attribute data from external databases. It uses APIs to obtain the necessary data and performs data formatting to prepare it for analysis.

[0328] Step 3:

[0329] The server starts analysis using an AI agent based on the collected data. The AI ​​agent analyzes the trends and attribute information of the moving population to identify optimal locations for holding events. At this time, the results of the data analysis are scored and ranked.

[0330] Step 4:

[0331] The emotion engine begins collecting user responses. As the user views the suggested results on their device, it analyzes their emotional state (e.g., joy, dissatisfaction, indifference, etc.) in real time via the camera and microphone, and feeds the results back to the server.

[0332] Step 5:

[0333] The server receives feedback from the emotion engine and adjusts the suggestions made by the AI ​​agent. For example, if a user expresses dissatisfaction with a suggested venue, the ranking will be changed and a more suitable candidate will be presented. It is also possible to take even more new factors into consideration.

[0334] Step 6:

[0335] The device presents a list of options optimized for the user. The user reviews the displayed choices, and if satisfied, makes a final selection and confirms their decision.

[0336] Step 7:

[0337] After the user confirms the suggestions, the sentiment engine continues to collect user feedback, which is stored on the server. The server uses this data to optimize future algorithms and improve the accuracy of suggestions.

[0338] (Example 2)

[0339] Next, we will describe Example 2. 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".

[0340] Conventional meeting planning systems have difficulty taking into account the emotional aspects of users, making it challenging to suggest optimal meeting locations and staffing arrangements. Furthermore, there is a lack of methods to appropriately incorporate user feedback and improve the overall accuracy of the system's suggestions.

[0341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0342] In this invention, the server includes means for analyzing statistical data and identification data acquired from an information processing device, means for selecting the optimal location for the meeting based on the obtained analysis results, and means for recognizing and adjusting the user's emotions using an emotion analysis device. This makes it possible to present an optimal meeting plan that also takes the user's emotions into consideration.

[0343] "Information processing equipment" is a general term for devices and systems used to collect, process, and analyze data.

[0344] "Statistical data" refers to data that shows trends in population movement and distribution within a specific region or time period.

[0345] "Identification data" refers to data that indicates the characteristics and attributes of individual users or subjects.

[0346] A "gathering" refers to an event or meeting in which a large number of people come together.

[0347] "Implementation site" is a term that refers to the location where a meeting or event takes place.

[0348] "Employee allocation" refers to a plan to assign employees to perform necessary duties at specific times and for specific tasks.

[0349] An "emotion analysis device" refers to a combination of hardware and software used to recognize and analyze a user's emotions.

[0350] A "user" is someone who uses the system to receive meeting plans and suggestions.

[0351] "Opinions" refers to feedback provided by users after the meeting.

[0352] A description of an embodiment for carrying out this invention will be provided. This system provides the information necessary for users to propose the optimal location and staffing arrangement for a meeting.

[0353] Users enter basic information about the meeting via their device. This information includes the date and time of the meeting, the target audience, and the budget. Once the information is entered, the device sends it to the server.

[0354] Based on the information received, the server uses an information processing device to collect statistical and identification data from an external database. The server integrates this data and uses a generative AI model to analyze it and select the optimal location for the meeting. In addition to conventional algorithms, an emotion analysis device is used to recognize the emotions of the users and reflect this in the analysis results.

[0355] For example, the AI ​​model can take into account the emotional response users give to suggestions and dynamically modify the list of implementation locations. If a user gives a negative response to a suggested implementation location, the AI ​​model will suggest alternative locations with good transportation access or popular facilities. This feature can provide users with a higher level of satisfaction.

[0356] A concrete example of a prompt message would be, "Please suggest the most suitable location for a meeting where young people are expected to participate."

[0357] This system allows users to create optimal meeting plans that take emotions into consideration, thereby maximizing the effectiveness of the event.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user enters basic information about the meeting into the terminal. Specifically, the user enters the date and time, target audience, and budget, and then presses the submit button. This action prepares the entered information as a dataset on the terminal.

[0361] Step 2:

[0362] The terminal sends information entered by the user to the server. The entered data is first verified on the terminal and then securely transferred to the server using encrypted communication. The server then receives the user's input data.

[0363] Step 3:

[0364] The server collects statistical and identification data from an external database based on the user information it receives. It uses an API to access the statistical database and retrieve necessary population data and customer attribute data. This allows the server to aggregate the external data necessary for analysis.

[0365] Step 4:

[0366] The server integrates the collected data and uses a generative AI model to analyze the optimal implementation location. Specific machine learning algorithms process the data to calculate candidate locations that consider convenience and profitability. This allows the server to generate a list of potential implementation locations.

[0367] Step 5:

[0368] The server uses an emotion analysis device to recognize the user's emotions. The emotion analysis device analyzes the user's reaction when reviewing the suggested content, and the results are reflected in the final suggestion generated by the AI ​​model.

[0369] Step 6:

[0370] The terminal displays optimized implementation locations and employee deployments sent from the server to the user. The user reviews the proposal on the terminal and makes selections and modifications as needed. This interaction results in the generated plan being presented to the user.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] When planning an event, efficiently selecting the optimal location and staffing is challenging. Furthermore, it's necessary to optimize proposals while considering the psychological state of the participants. This is essential to maximize the event's effectiveness and improve participant satisfaction.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for analyzing human movement data and characteristic data acquired from an information technology device, means for selecting the optimal location for an event based on the obtained analysis results, and means for determining the user's psychological state at the time of input and proposal confirmation using emotion recognition means, and adjusting indicators that affect the analysis results. This makes it possible to plan events flexibly and accurately while taking into account the user's psychological state.

[0376] An "information technology device" is a computer system used to record, process, and transmit data collected from humans or machines.

[0377] "Human movement data" refers to data that shows location information and movement routes related to people's movements.

[0378] "Characteristic data" refers to data that shows the attributes and characteristics of the individual or group being studied.

[0379] "Means of analysis" refer to devices and software that perform the process of collecting and analyzing data and extracting useful information.

[0380] "Methods for selecting the optimal location" refers to methods for choosing the most suitable location for an event or activity based on analyzed data.

[0381] "Emotion recognition means" refers to technology that determines a user's psychological state and emotions from their facial expressions, voice, and other factors.

[0382] "Indicators that influence analysis results" are factors or criteria that cause fluctuations in the results obtained through data analysis.

[0383] The server analyzes human movement data and feature data acquired from information technology devices to optimize the event's execution. The server integrates this data and selects the optimal location for the event based on the analysis results. In the selection process, TensorFlow and PyTorch are used as machine learning libraries to analyze movement patterns and feature data and calculate the location that best suits user requirements.

[0384] Furthermore, the server utilizes emotion recognition to determine the user's emotions when receiving information input or confirming suggested content. This involves using Google Cloud's emotion analysis AI to determine the user's psychological state from their facial expressions and voice. This allows for dynamic adjustment of metrics that influence the analysis results, optimizing the final suggested content according to the user's emotions.

[0385] The device is either a smartphone or smart glasses, designed to allow users to intuitively input event information and easily review suggestions from the server. The device also features real-time feedback collection and transmission to the server, contributing to improved suggestion accuracy in the future.

[0386] As a concrete example, when a shop owner arranges new seasonal products, the application suggests the optimal display arrangement based on local purchasing trends and population flow data. If the user does not accept the suggestion, the emotion engine detects feelings of anxiety or dissatisfaction and proposes an alternative arrangement. In this way, the optimal display is achieved in line with the shop owner's intentions.

[0387] An example of a prompt message would be, "What is the ideal layout to improve customer satisfaction through product display? Please suggest a method that is appropriate for the store's characteristics, taking into account recent purchasing trends." The ideas and suggestions generated by the AI ​​through this prompt message will be important support for store owners in making crucial decisions.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] Users input basic information about an event via their device. This information includes the event date, target audience, and budget, and is sent from the device to the server. The server receives the input data as structured data and stores it in a database.

[0391] Step 2:

[0392] The server collects human movement data and feature data from information technology devices. This data is retrieved from external databases using APIs and integrated within the server. A Python-based library (e.g., Pandas) is used for data integration, and the integrated dataset is prepared for analysis.

[0393] Step 3:

[0394] The server uses integrated data to perform analysis to select the optimal location for the event. Here, machine learning models using TensorFlow and PyTorch run to calculate the best candidate locations from movement patterns and feature data. The analysis results are stored on the server as optimal location candidates.

[0395] Step 4:

[0396] The server uses emotion recognition to analyze the user's psychological state during input and suggestion confirmation. To do this, it sends the user's facial expressions and voice data to Google Cloud's emotion analysis AI to obtain emotional feedback. This feedback is then returned to the server to adjust the variables used to optimize the suggestions.

[0397] Step 5:

[0398] The server sends the adjusted proposal to the user's device and presents it for review. The user then chooses whether to accept or reject the proposal based on the presentation on their device. The user's decision is fed back to the server in real time.

[0399] Step 6:

[0400] After the event concludes, the server collects feedback from participants regarding the event's success. This feedback is analyzed by an evaluation module within the server and used to improve the proposed algorithm. The proposed algorithm is then updated to achieve more accurate optimization in future event planning.

[0401] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event, and the server, terminal, and user elements work together to function.

[0418] First, the user uses their device to enter information about the upcoming event. This includes the event date, target audience, budget, and expected number of attendees.

[0419] Next, the server uses the information received from the user to collect population movement data and attribute data from external and internal databases. This provides detailed information about current population movement trends and the attributes of specific regions.

[0420] The server then provides this data to the AI ​​agent, which analyzes it using an optimization algorithm. Based on the analysis of the flow of people, the AI ​​agent evaluates whether a specific date, time, and location is suitable for holding an event. It also identifies areas where the event's target customer base is likely to gather from attribute data, narrowing down the candidate locations.

[0421] Furthermore, the server compares data related to costs, such as availability and usage fees, for the selected candidate locations to determine the optimal venue. The user's budget is also taken into consideration during this process.

[0422] For the confirmed venue, the server optimizes the necessary staff allocation according to the characteristics of the event. This involves suggesting the most efficient staffing arrangement based on factors such as the number of attendees and the venue layout.

[0423] Finally, the device presents the user with detailed suggestions regarding the optimal venue and staffing. The user can then make a final decision based on the information provided and proceed with planning the event.

[0424] As a concrete example, suppose a user is planning a launch event for a new product. In this case, the server refers to inventory data and marketing information, and the AI ​​agent suggests the most suitable commercial facility in an urban area. Since the suggested location is confirmed to be a popular spot among the target age group, the user can use it and proceed with the necessary procedures. In this way, the system of the present invention enables users to make rational decisions based on data.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] Users enter detailed information about the event using their device. This includes the date, target audience, budget, and expected number of participants. Once the user has finished entering the information, it is sent from the device to the server.

[0428] Step 2:

[0429] The server analyzes the information received from the user and collects necessary population and attribute data from external databases or crowdsourced sources. At this stage, the server uses APIs to retrieve this data and stores it in its own database.

[0430] Step 3:

[0431] The server passes the collected data to the AI ​​agent. The AI ​​agent analyzes the distribution of the moving population and time-series data, and begins analysis to narrow down the locations and time periods in which events can be effectively implemented.

[0432] Step 4:

[0433] The AI ​​agent selects the optimal venue based on the analysis results. This includes considering areas where the target audience is concentrated and locations with a high number of visitors based on past data.

[0434] Step 5:

[0435] The server checks the database for the availability and fees of the selected candidate venues on the event date, and verifies whether they fit the budget. If the conditions are met, it confirms them as the optimal venue.

[0436] Step 6:

[0437] The server proposes a staffing plan based on the characteristics of the event. This plan is automatically calculated based on the event's scale, the number of attendees, and the venue layout, and is designed to ensure efficient staff allocation.

[0438] Step 7:

[0439] The terminal presents the user with a confirmed venue and staffing plan. The user can make adjustments as needed, and if they are satisfied with the presented optimization plan, they can finalize it.

[0440] (Example 1)

[0441] Next, we will describe Example 1. 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."

[0442] Traditionally, a major challenge in event planning has been the significant time and effort required from organizers to determine appropriate venues and staffing levels. In particular, rational decision-making based on population trends, regional characteristics, and budgets has been difficult, often resulting in inefficient planning.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes means for collecting human flow data and target area characteristic data based on basic event information received from the user; means for using a machine learning model with the collected data to evaluate the suitability of the event based on the target area and date and time; and means for making a final decision based on the evaluation, taking into account the optimal event venue, its usage fee, and availability. This enables the user to efficiently select a rational venue and plan personnel allocation that suits their purpose.

[0445] A "user" refers to a person who provides information about event hosting and is the entity that receives proposals for the most suitable plan.

[0446] "Event basic information" refers to detailed data about the planned event, including the date, target audience, budget, and estimated number of attendees.

[0447] "Human flow data" refers to information that shows trends in people's movement and gatherings in a specific area, and is used to evaluate the timing and potential locations for events.

[0448] "Target region characteristics data" refers to data that includes attribute information such as population distribution and economic conditions in a specific region.

[0449] A "machine learning model" is a computer program that includes algorithms used to evaluate the optimal location and date / time for an event based on data analysis.

[0450] The term "event venue" refers to a suitable physical location for holding an event, and its selection is a crucial factor directly impacting the event's success or failure.

[0451] "Usage fees" refer to the costs incurred when using the proposed event venue and are determined based on the user's budget.

[0452] "Availability" refers to the factor used to determine whether a particular event venue is available at the specified date and time.

[0453] "Worker allocation" refers to the optimal staffing plan required for holding an event, and is a crucial element for efficient event management.

[0454] "Optimization" refers to the process of generating the most efficient and effective event plan based on user-defined conditions.

[0455] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event. This system functions through the collaborative efforts of the server, terminal, and user elements.

[0456] First, the user uses their device to enter basic information about the planned event. Specifically, this includes the date, target audience, budget, and estimated number of attendees. This data forms the basis for processing on the server.

[0457] Next, the server collects human flow data and regional characteristics data based on the information entered by the user. This data is obtained using database APIs or database query languages ​​such as SQL. After data collection is complete, the server uses an AI agent to analyze the data. This analysis uses machine learning libraries such as Python's scikit-learn, and the suitability of events based on the target region and date and time is evaluated using optimization algorithms.

[0458] This allows users to check the usage fees and availability of the selected potential venues. Furthermore, the optimal venue is determined after considering the user's budget. Simultaneously, a proposal for the optimal staffing arrangement based on the confirmed venue is made. This is to ensure efficient personnel allocation based on the number of attendees and the venue layout.

[0459] Ultimately, the terminal presents the server-generated suggestions to the user. This allows the user to quickly make data-driven, rational decisions and materialize their event plan. Through this process, the user can improve the accuracy and efficiency of their planning.

[0460] For example, if a user enters a prompt into the system such as, "Please suggest the best venue and staffing for the event. Event information: New product launch, Date: October 10th, Target audience: Young people, Budget: 1 million yen, Estimated attendees: 500 people," the server will use this information to suggest the most suitable commercial facility in an urban area. This kind of system operation supports the success of the event.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] Users enter information about the event they wish to host into a terminal. This includes basic information such as the date, target audience, budget, and estimated number of attendees. The entered information is sent to the server as basic data for event proposals.

[0464] Step 2:

[0465] The server receives basic event information provided by the user and collects human flow data and regional characteristics data from external and internal databases. First, the server calls a database API to retrieve population trends and attributes for a specific region. Based on this input information, the server extracts the necessary data and prepares for the next analysis step.

[0466] Step 3:

[0467] The server inputs the collected data into an AI agent, which then performs analysis using a machine learning model. Here, the Python scikit-learn library is used to apply algorithms such as clustering and regression analysis. As a result of the analysis, an assessment of the suitability of the optimal date, time, and location for the event is output. This output is then used to evaluate potential locations for the next event.

[0468] Step 4:

[0469] Based on the analysis results, the server compares data on usage fees and availability of selected candidate sites to perform a cost evaluation. At this stage, fee information is retrieved from the database via SQL queries and compared with the budget set by the user. Based on the cost evaluation results, the optimal event venue is determined, and its information is compiled.

[0470] Step 5:

[0471] The server proposes the optimal staffing arrangement for the confirmed venue, tailored to the characteristics of the event. This process uses shift management software to simulate the arrangement based on the number of attendees and the venue layout. An optimized staffing plan is then generated and saved within the system.

[0472] Step 6:

[0473] The terminal displays suggested information sent from the server to the user. Through the terminal's user interface, it visually presents information regarding the optimal venue and staffing, supporting the user in making actual decisions. Based on this information, the user can proceed to the next step in event planning.

[0474] (Application Example 1)

[0475] Next, we will explain Application Example 1. In the following explanation, 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."

[0476] In commercial activities, selecting the optimal location and staffing is essential for improving customer attraction and efficient operation. However, effectively selecting these requires utilizing a large amount of demographic and attribute data, and traditional methods present the challenge of expending significant time and resources on location selection and staffing.

[0477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0478] In this invention, the server includes means for analyzing demographic and attribute data, means for selecting the optimal location for commercial activities, and means for determining the location considering the availability and resources of the location. This makes it possible to efficiently and effectively determine the location and personnel allocation for commercial activities.

[0479] An "information processing device" is a computer system used to collect, store, and manage a wide variety of data, and to perform data analysis based on that data.

[0480] "Demographic data" refers to information about population trends and composition in a specific region, and is used to identify target groups and develop marketing plans in commercial activities.

[0481] "Attribute data" refers to information that indicates characteristics and features associated with specific places or people, and is useful for analyzing target markets and consumer segments.

[0482] "Commercial activities" refer to events and promotional activities aimed at promoting the sale of products and services, with the goal of attracting customers and increasing sales.

[0483] "Personnel allocation" refers to appropriately assigning the necessary personnel to achieve a specific objective, enabling them to perform tasks effectively.

[0484] A "server" is a computer system that provides data over a network and allows multiple clients to access it.

[0485] In the system of this invention, the user first inputs detailed information related to the event into a terminal. This includes details of the planned commercial activity, target customer base, budget, etc. The terminal then transmits this information to a server.

[0486] Based on the information received, the server collects demographic and attribute data from external APIs and stores them in a database. MongoDB is used for the database, allowing for flexible data management. The AI ​​agent then analyzes the collected data using TensorFlow. During the analysis, it predicts the optimal location and personnel allocation for commercial activities and makes a decision using an optimization algorithm proposed based on the above information.

[0487] For example, if a user is planning a promotional event for a new product in a specific region, the server can identify the commercial area with the highest foot traffic and recommend a suitable location for the target new market based on attribute data. Furthermore, it can collect feedback after the event and process it to improve the suggestion algorithm for future events.

[0488] An example of a prompt to the AI ​​model in such a system would be, "We are planning a launch event for a new product. Please tell us potential shopping mall locations that are likely to attract our target customer base, women in their 20s." By clearly indicating the information the user is seeking, more accurate suggestions can be made.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user enters event information into the terminal. This information includes the event name, date, target audience, and budget. The terminal organizes this information and prepares it for transmission to the server. The input is the information specified by the user, and the output is the event information converted into a transmittable data format.

[0492] Step 2:

[0493] The terminal sends the input event information to the server. The server receives this information and stores it in a database. Here, the input is the event information sent from the terminal, and the output is the information stored in the database.

[0494] Step 3:

[0495] The server uses external APIs to collect demographic and attribute data. Inputs are location and timing data related to the user's commercial activities, while outputs are the collected large amounts of demographic and attribute data. This allows for the acquisition of fundamental data on pedestrian flow and demographics in specific areas.

[0496] Step 4:

[0497] The server uses TensorFlow to analyze collected demographic and attribute data and uses an optimization algorithm to determine the optimal location and staffing. The input is the data collected in the previous step, and the output is a proposal for the optimal location and staffing. Specifically, predictive analysis is performed using an AI model.

[0498] Step 5:

[0499] Based on the analysis results, the server sends a suggestion to the user's terminal regarding the optimal implementation location and personnel allocation. The terminal then displays this information visually to the user. The input is the analysis results of the AI ​​model, and the output is the suggested information that can be presented to the user.

[0500] Step 6:

[0501] The user reviews the proposals displayed on the device and enters feedback to finalize the event plan. User input consists of reactions to the proposals and additional requests, while output is data in the form of feedback. The device then prepares to send this feedback to the server.

[0502] Step 7:

[0503] The server receives feedback from users and uses this information to improve the proposed algorithm. The input is the feedback information, and the output is the improved accuracy of the new proposals using the improved algorithm. At this stage, the system is retrained and parameters are tuned.

[0504] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0505] This invention combines a system that proposes the optimal venue and staffing for an event with an emotion engine that recognizes user emotions. This allows for the optimization of proposals and an improvement in the user experience.

[0506] First, users register detailed information about the event through their device. This information includes the date and time of the event, the target customer base, and the budget. Once the input is complete, this information is sent from the device to the server.

[0507] The server collects population movement data and attribute data based on the information it receives. This data is obtained from external databases and integrated within the server.

[0508] Next, the server passes the data to the AI ​​agent, which then analyzes the optimal event location and staffing. The AI ​​agent uses population trends and attribute information to select the best candidate locations.

[0509] A distinctive feature here is the use of an emotion engine. The server uses this engine to recognize the user's emotions when they input information or confirm suggestions, and fine-tunes parameters that affect the analysis results. For example, if a user shows a negative reaction to a suggested venue, it is possible to change the priority of alternative options.

[0510] The terminal not only presents the user with the optimal venue and staffing plan, but also collects user sentiment data regarding the proposal in real time and feeds it back to the server. This further improves the accuracy of the proposal.

[0511] As a concrete example, consider a scenario where a user is planning a sales promotion event for a large product. In this case, the server uses an AI agent to select event spaces within popular commercial facilities as potential venues. However, if the emotion engine detects dissatisfaction in the user's response, it will suggest a stadium with good transportation access as an alternative. This process can increase user satisfaction.

[0512] This invention makes it possible to achieve flexible and highly accurate event planning that takes into account the emotional responses of users.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] Users use their devices to enter event details, including the date and time of the event, target audience, budget constraints, and expected number of attendees. The entered information is sent to the server in real time.

[0516] Step 2:

[0517] The server receives event information sent by the user and initiates the process of collecting population movement data and attribute data from external databases. It uses APIs to obtain the necessary data and performs data formatting to prepare it for analysis.

[0518] Step 3:

[0519] The server starts analysis using an AI agent based on the collected data. The AI ​​agent analyzes the trends and attribute information of the moving population to identify optimal locations for holding events. At this time, the results of the data analysis are scored and ranked.

[0520] Step 4:

[0521] The emotion engine begins collecting user responses. As the user views the suggested results on their device, it analyzes their emotional state (e.g., joy, dissatisfaction, indifference, etc.) in real time via the camera and microphone, and feeds the results back to the server.

[0522] Step 5:

[0523] The server receives feedback from the emotion engine and adjusts the suggestions made by the AI ​​agent. For example, if a user expresses dissatisfaction with a suggested venue, the ranking will be changed and a more suitable candidate will be presented. It is also possible to take even more new factors into consideration.

[0524] Step 6:

[0525] The device presents a list of options optimized for the user. The user reviews the displayed choices, and if satisfied, makes a final selection and confirms their decision.

[0526] Step 7:

[0527] After the user confirms the suggestions, the sentiment engine continues to collect user feedback, which is stored on the server. The server uses this data to optimize future algorithms and improve the accuracy of suggestions.

[0528] (Example 2)

[0529] Next, we will describe Example 2. 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."

[0530] Conventional meeting planning systems have difficulty taking into account the emotional aspects of users, making it challenging to suggest optimal meeting locations and staffing arrangements. Furthermore, there is a lack of methods to appropriately incorporate user feedback and improve the overall accuracy of the system's suggestions.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes means for analyzing statistical data and identification data acquired from an information processing device, means for selecting the optimal location for the meeting based on the obtained analysis results, and means for recognizing and adjusting the user's emotions using an emotion analysis device. This makes it possible to present an optimal meeting plan that also takes the user's emotions into consideration.

[0533] "Information processing equipment" is a general term for devices and systems used to collect, process, and analyze data.

[0534] "Statistical data" refers to data that shows trends in population movement and distribution within a specific region or time period.

[0535] "Identification data" refers to data that indicates the characteristics and attributes of individual users or subjects.

[0536] A "gathering" refers to an event or meeting in which a large number of people come together.

[0537] "Implementation site" is a term that refers to the location where a meeting or event takes place.

[0538] "Employee allocation" refers to a plan to assign employees to perform necessary duties at specific times and for specific tasks.

[0539] An "emotion analysis device" refers to a combination of hardware and software used to recognize and analyze a user's emotions.

[0540] A "user" is someone who uses the system to receive meeting plans and suggestions.

[0541] "Opinions" refers to feedback provided by users after the meeting.

[0542] A description of an embodiment for carrying out this invention will be provided. This system provides the information necessary for users to propose the optimal location and staffing arrangement for a meeting.

[0543] Users enter basic information about the meeting via their device. This information includes the date and time of the meeting, the target audience, and the budget. Once the information is entered, the device sends it to the server.

[0544] Based on the information received, the server uses an information processing device to collect statistical and identification data from an external database. The server integrates this data and uses a generative AI model to analyze it and select the optimal location for the meeting. In addition to conventional algorithms, an emotion analysis device is used to recognize the emotions of the users and reflect this in the analysis results.

[0545] For example, the AI ​​model can take into account the emotional response users give to suggestions and dynamically modify the list of implementation locations. If a user gives a negative response to a suggested implementation location, the AI ​​model will suggest alternative locations with good transportation access or popular facilities. This feature can provide users with a higher level of satisfaction.

[0546] A concrete example of a prompt message would be, "Please suggest the most suitable location for a meeting where young people are expected to participate."

[0547] This system allows users to create optimal meeting plans that take emotions into consideration, thereby maximizing the effectiveness of the event.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The user enters basic information about the meeting into the terminal. Specifically, the user enters the date and time, target audience, and budget, and then presses the submit button. This action prepares the entered information as a dataset on the terminal.

[0551] Step 2:

[0552] The terminal sends information entered by the user to the server. The entered data is first verified on the terminal and then securely transferred to the server using encrypted communication. The server then receives the user's input data.

[0553] Step 3:

[0554] The server collects statistical and identification data from an external database based on the user information it receives. It uses an API to access the statistical database and retrieve necessary population data and customer attribute data. This allows the server to aggregate the external data necessary for analysis.

[0555] Step 4:

[0556] The server integrates the collected data and uses a generative AI model to analyze the optimal implementation location. Specific machine learning algorithms process the data to calculate candidate locations that consider convenience and profitability. This allows the server to generate a list of potential implementation locations.

[0557] Step 5:

[0558] The server uses an emotion analysis device to recognize the user's emotions. The emotion analysis device analyzes the user's reaction when reviewing the suggested content, and the results are reflected in the final suggestion generated by the AI ​​model.

[0559] Step 6:

[0560] The terminal displays optimized implementation locations and employee deployments sent from the server to the user. The user reviews the proposal on the terminal and makes selections and modifications as needed. This interaction results in the generated plan being presented to the user.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, 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."

[0563] When planning an event, efficiently selecting the optimal location and staffing is challenging. Furthermore, it's necessary to optimize proposals while considering the psychological state of the participants. This is essential to maximize the event's effectiveness and improve participant satisfaction.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for analyzing human movement data and characteristic data acquired from an information technology device, means for selecting the optimal location for an event based on the obtained analysis results, and means for determining the user's psychological state at the time of input and proposal confirmation using emotion recognition means, and adjusting indicators that affect the analysis results. This makes it possible to plan events flexibly and accurately while taking into account the user's psychological state.

[0566] An "information technology device" is a computer system used to record, process, and transmit data collected from humans or machines.

[0567] "Human movement data" refers to data that shows location information and movement routes related to people's movements.

[0568] "Characteristic data" refers to data that shows the attributes and characteristics of the individual or group being studied.

[0569] "Means of analysis" refer to devices and software that perform the process of collecting and analyzing data and extracting useful information.

[0570] "Methods for selecting the optimal location" refers to methods for choosing the most suitable location for an event or activity based on analyzed data.

[0571] "Emotion recognition means" refers to technology that determines a user's psychological state and emotions from their facial expressions, voice, and other factors.

[0572] "Indicators that influence analysis results" are factors or criteria that cause fluctuations in the results obtained through data analysis.

[0573] The server analyzes human movement data and feature data acquired from information technology devices to optimize the event's execution. The server integrates this data and selects the optimal location for the event based on the analysis results. In the selection process, TensorFlow and PyTorch are used as machine learning libraries to analyze movement patterns and feature data and calculate the location that best suits user requirements.

[0574] Furthermore, the server utilizes emotion recognition to determine the user's emotions when receiving information input or confirming suggested content. This involves using Google Cloud's emotion analysis AI to determine the user's psychological state from their facial expressions and voice. This allows for dynamic adjustment of metrics that influence the analysis results, optimizing the final suggested content according to the user's emotions.

[0575] The device is either a smartphone or smart glasses, designed to allow users to intuitively input event information and easily review suggestions from the server. The device also features real-time feedback collection and transmission to the server, contributing to improved suggestion accuracy in the future.

[0576] As a concrete example, when a shop owner arranges new seasonal products, the application suggests the optimal display arrangement based on local purchasing trends and population flow data. If the user does not accept the suggestion, the emotion engine detects feelings of anxiety or dissatisfaction and proposes an alternative arrangement. In this way, the optimal display is achieved in line with the shop owner's intentions.

[0577] An example of a prompt message would be, "What is the ideal layout to improve customer satisfaction through product display? Please suggest a method that is appropriate for the store's characteristics, taking into account recent purchasing trends." The ideas and suggestions generated by the AI ​​through this prompt message will be important support for store owners in making crucial decisions.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] Users input basic information about an event via their device. This information includes the event date, target audience, and budget, and is sent from the device to the server. The server receives the input data as structured data and stores it in a database.

[0581] Step 2:

[0582] The server collects human movement data and feature data from information technology devices. This data is retrieved from external databases using APIs and integrated within the server. A Python-based library (e.g., Pandas) is used for data integration, and the integrated dataset is prepared for analysis.

[0583] Step 3:

[0584] The server uses integrated data to perform analysis to select the optimal location for the event. Here, machine learning models using TensorFlow and PyTorch run to calculate the best candidate locations from movement patterns and feature data. The analysis results are stored on the server as optimal location candidates.

[0585] Step 4:

[0586] The server uses emotion recognition to analyze the user's psychological state during input and suggestion confirmation. To do this, it sends the user's facial expressions and voice data to Google Cloud's emotion analysis AI to obtain emotional feedback. This feedback is then returned to the server to adjust the variables used to optimize the suggestions.

[0587] Step 5:

[0588] The server sends the adjusted proposal to the user's device and presents it for review. The user then chooses whether to accept or reject the proposal based on the presentation on their device. The user's decision is fed back to the server in real time.

[0589] Step 6:

[0590] After the event concludes, the server collects feedback from participants regarding the event's success. This feedback is analyzed by an evaluation module within the server and used to improve the proposed algorithm. The proposed algorithm is then updated to achieve more accurate optimization in future event planning.

[0591] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0592] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0593] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] As shown in Figure 7, the 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.

[0597] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0598] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0599] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0600] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0601] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0602] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0603] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0604] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0605] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0606] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0607] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event, and the server, terminal, and user elements work together to function.

[0609] First, the user uses their device to enter information about the upcoming event. This includes the event date, target audience, budget, and expected number of attendees.

[0610] Next, the server uses the information received from the user to collect population movement data and attribute data from external and internal databases. This provides detailed information about current population movement trends and the attributes of specific regions.

[0611] The server then provides this data to the AI ​​agent, which analyzes it using an optimization algorithm. Based on the analysis of the flow of people, the AI ​​agent evaluates whether a specific date, time, and location is suitable for holding an event. It also identifies areas where the event's target customer base is likely to gather from attribute data, narrowing down the candidate locations.

[0612] Furthermore, the server compares data related to costs, such as availability and usage fees, for the selected candidate locations to determine the optimal venue. The user's budget is also taken into consideration during this process.

[0613] For the confirmed venue, the server optimizes the necessary staff allocation according to the characteristics of the event. This involves suggesting the most efficient staffing arrangement based on factors such as the number of attendees and the venue layout.

[0614] Finally, the device presents the user with detailed suggestions regarding the optimal venue and staffing. The user can then make a final decision based on the information provided and proceed with planning the event.

[0615] As a concrete example, suppose a user is planning a launch event for a new product. In this case, the server refers to inventory data and marketing information, and the AI ​​agent suggests the most suitable commercial facility in an urban area. Since the suggested location is confirmed to be a popular spot among the target age group, the user can use it and proceed with the necessary procedures. In this way, the system of the present invention enables users to make rational decisions based on data.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] Users enter detailed information about the event using their device. This includes the date, target audience, budget, and expected number of participants. Once the user has finished entering the information, it is sent from the device to the server.

[0619] Step 2:

[0620] The server analyzes the information received from the user and collects necessary population and attribute data from external databases or crowdsourced sources. At this stage, the server uses APIs to retrieve this data and stores it in its own database.

[0621] Step 3:

[0622] The server passes the collected data to the AI ​​agent. The AI ​​agent analyzes the distribution of the moving population and time-series data, and begins analysis to narrow down the locations and time periods in which events can be effectively implemented.

[0623] Step 4:

[0624] The AI ​​agent selects the optimal venue based on the analysis results. This includes considering areas where the target audience is concentrated and locations with a high number of visitors based on past data.

[0625] Step 5:

[0626] The server checks the database for the availability and fees of the selected candidate venues on the event date, and verifies whether they fit the budget. If the conditions are met, it confirms them as the optimal venue.

[0627] Step 6:

[0628] The server proposes a staffing plan based on the characteristics of the event. This plan is automatically calculated based on the event's scale, the number of attendees, and the venue layout, and is designed to ensure efficient staff allocation.

[0629] Step 7:

[0630] The terminal presents the user with a confirmed venue and staffing plan. The user can make adjustments as needed, and if they are satisfied with the presented optimization plan, they can finalize it.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Traditionally, a major challenge in event planning has been the significant time and effort required from organizers to determine appropriate venues and staffing levels. In particular, rational decision-making based on population trends, regional characteristics, and budgets has been difficult, often resulting in inefficient planning.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for collecting human flow data and target area characteristic data based on basic event information received from the user; means for using a machine learning model with the collected data to evaluate the suitability of the event based on the target area and date and time; and means for making a final decision based on the evaluation, taking into account the optimal event venue, its usage fee, and availability. This enables the user to efficiently select a rational venue and plan personnel allocation that suits their purpose.

[0636] A "user" refers to a person who provides information about event hosting and is the entity that receives proposals for the most suitable plan.

[0637] "Event basic information" refers to detailed data about the planned event, including the date, target audience, budget, and estimated number of attendees.

[0638] "Human flow data" refers to information that shows trends in people's movement and gatherings in a specific area, and is used to evaluate the timing and potential locations for events.

[0639] "Target region characteristics data" refers to data that includes attribute information such as population distribution and economic conditions in a specific region.

[0640] A "machine learning model" is a computer program that includes algorithms used to evaluate the optimal location and date / time for an event based on data analysis.

[0641] The term "event venue" refers to a suitable physical location for holding an event, and its selection is a crucial factor directly impacting the event's success or failure.

[0642] "Usage fees" refer to the costs incurred when using the proposed event venue and are determined based on the user's budget.

[0643] "Availability" refers to the factor used to determine whether a particular event venue is available at the specified date and time.

[0644] "Worker allocation" refers to the optimal staffing plan required for holding an event, and is a crucial element for efficient event management.

[0645] "Optimization" refers to the process of generating the most efficient and effective event plan based on user-defined conditions.

[0646] The system of this invention proposes the optimal venue and personnel allocation to users who wish to hold an event. This system functions through the collaborative efforts of the server, terminal, and user elements.

[0647] First, the user uses their device to enter basic information about the planned event. Specifically, this includes the date, target audience, budget, and estimated number of attendees. This data forms the basis for processing on the server.

[0648] Next, the server collects human flow data and regional characteristics data based on the information entered by the user. This data is obtained using database APIs or database query languages ​​such as SQL. After data collection is complete, the server uses an AI agent to analyze the data. This analysis uses machine learning libraries such as Python's scikit-learn, and the suitability of events based on the target region and date and time is evaluated using optimization algorithms.

[0649] This allows users to check the usage fees and availability of the selected potential venues. Furthermore, the optimal venue is determined after considering the user's budget. Simultaneously, a proposal for the optimal staffing arrangement based on the confirmed venue is made. This is to ensure efficient personnel allocation based on the number of attendees and the venue layout.

[0650] Ultimately, the terminal presents the server-generated suggestions to the user. This allows the user to quickly make data-driven, rational decisions and materialize their event plan. Through this process, the user can improve the accuracy and efficiency of their planning.

[0651] For example, if a user enters a prompt into the system such as, "Please suggest the best venue and staffing for the event. Event information: New product launch, Date: October 10th, Target audience: Young people, Budget: 1 million yen, Estimated attendees: 500 people," the server will use this information to suggest the most suitable commercial facility in an urban area. This kind of system operation supports the success of the event.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] Users enter information about the event they wish to host into a terminal. This includes basic information such as the date, target audience, budget, and estimated number of attendees. The entered information is sent to the server as basic data for event proposals.

[0655] Step 2:

[0656] The server receives basic event information provided by the user and collects human flow data and regional characteristics data from external and internal databases. First, the server calls a database API to retrieve population trends and attributes for a specific region. Based on this input information, the server extracts the necessary data and prepares for the next analysis step.

[0657] Step 3:

[0658] The server inputs the collected data into an AI agent, which then performs analysis using a machine learning model. Here, the Python scikit-learn library is used to apply algorithms such as clustering and regression analysis. As a result of the analysis, an assessment of the suitability of the optimal date, time, and location for the event is output. This output is then used to evaluate potential locations for the next event.

[0659] Step 4:

[0660] Based on the analysis results, the server compares data on usage fees and availability of selected candidate sites to perform a cost evaluation. At this stage, fee information is retrieved from the database via SQL queries and compared with the budget set by the user. Based on the cost evaluation results, the optimal event venue is determined, and its information is compiled.

[0661] Step 5:

[0662] The server proposes the optimal staffing arrangement for the confirmed venue, tailored to the characteristics of the event. This process uses shift management software to simulate the arrangement based on the number of attendees and the venue layout. An optimized staffing plan is then generated and saved within the system.

[0663] Step 6:

[0664] The terminal displays suggested information sent from the server to the user. Through the terminal's user interface, it visually presents information regarding the optimal venue and staffing, supporting the user in making actual decisions. Based on this information, the user can proceed to the next step in event planning.

[0665] (Application Example 1)

[0666] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In commercial activities, selecting the optimal location and staffing is essential for improving customer attraction and efficient operation. However, effectively selecting these requires utilizing a large amount of demographic and attribute data, and traditional methods present the challenge of expending significant time and resources on location selection and staffing.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0669] In this invention, the server includes means for analyzing demographic and attribute data, means for selecting the optimal location for commercial activities, and means for determining the location considering the availability and resources of the location. This makes it possible to efficiently and effectively determine the location and personnel allocation for commercial activities.

[0670] An "information processing device" is a computer system used to collect, store, and manage a wide variety of data, and to perform data analysis based on that data.

[0671] "Demographic data" refers to information about population trends and composition in a specific region, and is used to identify target groups and develop marketing plans in commercial activities.

[0672] "Attribute data" refers to information that indicates characteristics and features associated with specific places or people, and is useful for analyzing target markets and consumer segments.

[0673] "Commercial activities" refer to events and promotional activities aimed at promoting the sale of products and services, with the goal of attracting customers and increasing sales.

[0674] "Personnel allocation" refers to appropriately assigning the necessary personnel to achieve a specific objective, enabling them to perform tasks effectively.

[0675] A "server" is a computer system that provides data over a network and allows multiple clients to access it.

[0676] In the system of this invention, the user first inputs detailed information related to the event into a terminal. This includes details of the planned commercial activity, target customer base, budget, etc. The terminal then transmits this information to a server.

[0677] Based on the information received, the server collects demographic and attribute data from external APIs and stores them in a database. MongoDB is used for the database, allowing for flexible data management. The AI ​​agent then analyzes the collected data using TensorFlow. During the analysis, it predicts the optimal location and personnel allocation for commercial activities and makes a decision using an optimization algorithm proposed based on the above information.

[0678] For example, if a user is planning a promotional event for a new product in a specific region, the server can identify the commercial area with the highest foot traffic and recommend a suitable location for the target new market based on attribute data. Furthermore, it can collect feedback after the event and process it to improve the suggestion algorithm for future events.

[0679] An example of a prompt to the AI ​​model in such a system would be, "We are planning a launch event for a new product. Please tell us potential shopping mall locations that are likely to attract our target customer base, women in their 20s." By clearly indicating the information the user is seeking, more accurate suggestions can be made.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The user enters event information into the terminal. This information includes the event name, date, target audience, and budget. The terminal organizes this information and prepares it for transmission to the server. The input is the information specified by the user, and the output is the event information converted into a transmittable data format.

[0683] Step 2:

[0684] The terminal sends the input event information to the server. The server receives this information and stores it in a database. Here, the input is the event information sent from the terminal, and the output is the information stored in the database.

[0685] Step 3:

[0686] The server uses external APIs to collect demographic and attribute data. Inputs are location and timing data related to the user's commercial activities, while outputs are the collected large amounts of demographic and attribute data. This allows for the acquisition of fundamental data on pedestrian flow and demographics in specific areas.

[0687] Step 4:

[0688] The server uses TensorFlow to analyze collected demographic and attribute data and uses an optimization algorithm to determine the optimal location and staffing. The input is the data collected in the previous step, and the output is a proposal for the optimal location and staffing. Specifically, predictive analysis is performed using an AI model.

[0689] Step 5:

[0690] Based on the analysis results, the server sends a suggestion to the user's terminal regarding the optimal implementation location and personnel allocation. The terminal then displays this information visually to the user. The input is the analysis results of the AI ​​model, and the output is the suggested information that can be presented to the user.

[0691] Step 6:

[0692] The user reviews the proposals displayed on the device and enters feedback to finalize the event plan. User input consists of reactions to the proposals and additional requests, while output is data in the form of feedback. The device then prepares to send this feedback to the server.

[0693] Step 7:

[0694] The server receives feedback from users and uses this information to improve the proposed algorithm. The input is the feedback information, and the output is the improved accuracy of the new proposals using the improved algorithm. At this stage, the system is retrained and parameters are tuned.

[0695] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0696] This invention combines a system that proposes the optimal venue and staffing for an event with an emotion engine that recognizes user emotions. This allows for the optimization of proposals and an improvement in the user experience.

[0697] First, users register detailed information about the event through their device. This information includes the date and time of the event, the target customer base, and the budget. Once the input is complete, this information is sent from the device to the server.

[0698] The server collects population movement data and attribute data based on the information it receives. This data is obtained from external databases and integrated within the server.

[0699] Next, the server passes the data to the AI ​​agent, which then analyzes the optimal event location and staffing. The AI ​​agent uses population trends and attribute information to select the best candidate locations.

[0700] A distinctive feature here is the use of an emotion engine. The server uses this engine to recognize the user's emotions when they input information or confirm suggestions, and fine-tunes parameters that affect the analysis results. For example, if a user shows a negative reaction to a suggested venue, it is possible to change the priority of alternative options.

[0701] The terminal not only presents the user with the optimal venue and staffing plan, but also collects user sentiment data regarding the proposal in real time and feeds it back to the server. This further improves the accuracy of the proposal.

[0702] As a concrete example, consider a scenario where a user is planning a sales promotion event for a large product. In this case, the server uses an AI agent to select event spaces within popular commercial facilities as potential venues. However, if the emotion engine detects dissatisfaction in the user's response, it will suggest a stadium with good transportation access as an alternative. This process can increase user satisfaction.

[0703] This invention makes it possible to achieve flexible and highly accurate event planning that takes into account the emotional responses of users.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] Users use their devices to enter event details, including the date and time of the event, target audience, budget constraints, and expected number of attendees. The entered information is sent to the server in real time.

[0707] Step 2:

[0708] The server receives event information sent by the user and initiates the process of collecting population movement data and attribute data from external databases. It uses APIs to obtain the necessary data and performs data formatting to prepare it for analysis.

[0709] Step 3:

[0710] The server starts analysis using an AI agent based on the collected data. The AI ​​agent analyzes the trends and attribute information of the moving population to identify optimal locations for holding events. At this time, the results of the data analysis are scored and ranked.

[0711] Step 4:

[0712] The emotion engine begins collecting user responses. As the user views the suggested results on their device, it analyzes their emotional state (e.g., joy, dissatisfaction, indifference, etc.) in real time via the camera and microphone, and feeds the results back to the server.

[0713] Step 5:

[0714] The server receives feedback from the emotion engine and adjusts the suggestions made by the AI ​​agent. For example, if a user expresses dissatisfaction with a suggested venue, the ranking will be changed and a more suitable candidate will be presented. It is also possible to take even more new factors into consideration.

[0715] Step 6:

[0716] The device presents a list of options optimized for the user. The user reviews the displayed choices, and if satisfied, makes a final selection and confirms their decision.

[0717] Step 7:

[0718] After the user confirms the suggestions, the sentiment engine continues to collect user feedback, which is stored on the server. The server uses this data to optimize future algorithms and improve the accuracy of suggestions.

[0719] (Example 2)

[0720] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] Conventional meeting planning systems have difficulty taking into account the emotional aspects of users, making it challenging to suggest optimal meeting locations and staffing arrangements. Furthermore, there is a lack of methods to appropriately incorporate user feedback and improve the overall accuracy of the system's suggestions.

[0722] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0723] In this invention, the server includes means for analyzing statistical data and identification data acquired from an information processing device, means for selecting the optimal location for the meeting based on the obtained analysis results, and means for recognizing and adjusting the user's emotions using an emotion analysis device. This makes it possible to present an optimal meeting plan that also takes the user's emotions into consideration.

[0724] "Information processing equipment" is a general term for devices and systems used to collect, process, and analyze data.

[0725] "Statistical data" refers to data that shows trends in population movement and distribution within a specific region or time period.

[0726] "Identification data" refers to data that indicates the characteristics and attributes of individual users or subjects.

[0727] A "gathering" refers to an event or meeting in which a large number of people come together.

[0728] "Implementation site" is a term that refers to the location where a meeting or event takes place.

[0729] "Employee allocation" refers to a plan to assign employees to perform necessary duties at specific times and for specific tasks.

[0730] An "emotion analysis device" refers to a combination of hardware and software used to recognize and analyze a user's emotions.

[0731] A "user" is someone who uses the system to receive meeting plans and suggestions.

[0732] "Opinions" refers to feedback provided by users after the meeting.

[0733] A description of an embodiment for carrying out this invention will be provided. This system provides the information necessary for users to propose the optimal location and staffing arrangement for a meeting.

[0734] Users enter basic information about the meeting via their device. This information includes the date and time of the meeting, the target audience, and the budget. Once the information is entered, the device sends it to the server.

[0735] Based on the information received, the server uses an information processing device to collect statistical and identification data from an external database. The server integrates this data and uses a generative AI model to analyze it and select the optimal location for the meeting. In addition to conventional algorithms, an emotion analysis device is used to recognize the emotions of the users and reflect this in the analysis results.

[0736] For example, the AI ​​model can take into account the emotional response users give to suggestions and dynamically modify the list of implementation locations. If a user gives a negative response to a suggested implementation location, the AI ​​model will suggest alternative locations with good transportation access or popular facilities. This feature can provide users with a higher level of satisfaction.

[0737] A concrete example of a prompt message would be, "Please suggest the most suitable location for a meeting where young people are expected to participate."

[0738] This system allows users to create optimal meeting plans that take emotions into consideration, thereby maximizing the effectiveness of the event.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The user enters basic information about the meeting into the terminal. Specifically, the user enters the date and time, target audience, and budget, and then presses the submit button. This action prepares the entered information as a dataset on the terminal.

[0742] Step 2:

[0743] The terminal sends information entered by the user to the server. The entered data is first verified on the terminal and then securely transferred to the server using encrypted communication. The server then receives the user's input data.

[0744] Step 3:

[0745] The server collects statistical and identification data from an external database based on the user information it receives. It uses an API to access the statistical database and retrieve necessary population data and customer attribute data. This allows the server to aggregate the external data necessary for analysis.

[0746] Step 4:

[0747] The server integrates the collected data and uses a generative AI model to analyze the optimal implementation location. Specific machine learning algorithms process the data to calculate candidate locations that consider convenience and profitability. This allows the server to generate a list of potential implementation locations.

[0748] Step 5:

[0749] The server uses an emotion analysis device to recognize the user's emotions. The emotion analysis device analyzes the user's reaction when reviewing the suggested content, and the results are reflected in the final suggestion generated by the AI ​​model.

[0750] Step 6:

[0751] The terminal displays optimized implementation locations and employee deployments sent from the server to the user. The user reviews the proposal on the terminal and makes selections and modifications as needed. This interaction results in the generated plan being presented to the user.

[0752] (Application Example 2)

[0753] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] When planning an event, efficiently selecting the optimal location and staffing is challenging. Furthermore, it's necessary to optimize proposals while considering the psychological state of the participants. This is essential to maximize the event's effectiveness and improve participant satisfaction.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0756] In this invention, the server includes means for analyzing human movement data and characteristic data acquired from an information technology device, means for selecting the optimal location for an event based on the obtained analysis results, and means for determining the user's psychological state at the time of input and proposal confirmation using emotion recognition means, and adjusting indicators that affect the analysis results. This makes it possible to plan events flexibly and accurately while taking into account the user's psychological state.

[0757] An "information technology device" is a computer system used to record, process, and transmit data collected from humans or machines.

[0758] "Human movement data" refers to data that shows location information and movement routes related to people's movements.

[0759] "Characteristic data" refers to data that shows the attributes and characteristics of the individual or group being studied.

[0760] "Means of analysis" refer to devices and software that perform the process of collecting and analyzing data and extracting useful information.

[0761] "Methods for selecting the optimal location" refers to methods for choosing the most suitable location for an event or activity based on analyzed data.

[0762] "Emotion recognition means" refers to technology that determines a user's psychological state and emotions from their facial expressions, voice, and other factors.

[0763] "Indicators that influence analysis results" are factors or criteria that cause fluctuations in the results obtained through data analysis.

[0764] The server analyzes human movement data and feature data acquired from information technology devices to optimize the event's execution. The server integrates this data and selects the optimal location for the event based on the analysis results. In the selection process, TensorFlow and PyTorch are used as machine learning libraries to analyze movement patterns and feature data and calculate the location that best suits user requirements.

[0765] Furthermore, the server utilizes emotion recognition to determine the user's emotions when receiving information input or confirming suggested content. This involves using Google Cloud's emotion analysis AI to determine the user's psychological state from their facial expressions and voice. This allows for dynamic adjustment of metrics that influence the analysis results, optimizing the final suggested content according to the user's emotions.

[0766] The device is either a smartphone or smart glasses, designed to allow users to intuitively input event information and easily review suggestions from the server. The device also features real-time feedback collection and transmission to the server, contributing to improved suggestion accuracy in the future.

[0767] As a concrete example, when a shop owner arranges new seasonal products, the application suggests the optimal display arrangement based on local purchasing trends and population flow data. If the user does not accept the suggestion, the emotion engine detects feelings of anxiety or dissatisfaction and proposes an alternative arrangement. In this way, the optimal display is achieved in line with the shop owner's intentions.

[0768] An example of a prompt message would be, "What is the ideal layout to improve customer satisfaction through product display? Please suggest a method that is appropriate for the store's characteristics, taking into account recent purchasing trends." The ideas and suggestions generated by the AI ​​through this prompt message will be important support for store owners in making crucial decisions.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] Users input basic information about an event via their device. This information includes the event date, target audience, and budget, and is sent from the device to the server. The server receives the input data as structured data and stores it in a database.

[0772] Step 2:

[0773] The server collects human movement data and feature data from information technology devices. This data is retrieved from external databases using APIs and integrated within the server. A Python-based library (e.g., Pandas) is used for data integration, and the integrated dataset is prepared for analysis.

[0774] Step 3:

[0775] The server uses integrated data to perform analysis to select the optimal location for the event. Here, machine learning models using TensorFlow and PyTorch run to calculate the best candidate locations from movement patterns and feature data. The analysis results are stored on the server as optimal location candidates.

[0776] Step 4:

[0777] The server uses emotion recognition to analyze the user's psychological state during input and suggestion confirmation. To do this, it sends the user's facial expressions and voice data to Google Cloud's emotion analysis AI to obtain emotional feedback. This feedback is then returned to the server to adjust the variables used to optimize the suggestions.

[0778] Step 5:

[0779] The server sends the adjusted proposal to the user's device and presents it for review. The user then chooses whether to accept or reject the proposal based on the presentation on their device. The user's decision is fed back to the server in real time.

[0780] Step 6:

[0781] After the event concludes, the server collects feedback from participants regarding the event's success. This feedback is analyzed by an evaluation module within the server and used to improve the proposed algorithm. The proposed algorithm is then updated to achieve more accurate optimization in future event planning.

[0782] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0783] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0784] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0785] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0786] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0787] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0788] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0789] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0790] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0791] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0792] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0793] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0794] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0795] 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.

[0796] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0797] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0798] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0799] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0800] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0801] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0802] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] A means for analyzing population flow data and attribute data acquired from an information processing device,

[0806] Based on the analysis results obtained, a means to select the optimal venue for the event,

[0807] A means of determining the venue, taking into consideration the availability of selected venues and the budget,

[0808] A means of presenting the confirmed venue and appropriate staffing arrangements for the event,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, further comprising means for presenting optimal suggestions based on basic event information received from the user.

[0812] (Claim 3)

[0813] The system according to claim 1, further comprising means for collecting user feedback after the event and improving the proposed algorithm based on the collected feedback.

[0814] "Example 1"

[0815] (Claim 1)

[0816] A means for collecting human flow data and target area characteristic data based on basic event information received from users,

[0817] A means of evaluating the suitability of events based on the target region and date and time using machine learning models with collected data,

[0818] Based on the evaluation, a final decision is made considering the most suitable event venue, its usage fee, and availability.

[0819] A means of optimizing and proposing worker allocation according to the determined event location and number of participants,

[0820] A means of displaying the proposed event location and worker assignments to the user terminal,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for presenting an efficient plan according to the budget and objectives set by the user.

[0824] (Claim 3)

[0825] The system according to claim 1, further characterized by having a means to collect user feedback after the event and to improve the suggestion algorithm based on the collected feedback.

[0826] "Application Example 1"

[0827] (Claim 1)

[0828] A means for analyzing demographic data and attribute data acquired from an information processing device,

[0829] Based on the analysis results obtained, a means for selecting the optimal location for commercial activities,

[0830] A means of determining the implementation site, taking into account the availability and resources of the selected implementation site,

[0831] A means of presenting a confirmed location for implementation and personnel allocation suitable for commercial activities,

[0832] A means of providing an optimal sales plan based on user input information,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for predicting and proposing a location suitable for a specific target group based on attribute data of the aforementioned location.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for collecting user evaluation information after the completion of commercial activities and updating the proposed algorithm based on the collected evaluation information.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] A means for analyzing statistical data and identification data acquired from an information processing device,

[0841] Based on the analysis results obtained, a means for selecting the optimal location for the meeting,

[0842] A means of determining the implementation site, taking into account the availability and budget of the selected implementation sites,

[0843] A means of presenting the confirmed implementation location and the appropriate staffing arrangement for the assembly,

[0844] A means of recognizing the user's emotions using an emotion analysis device and making adjustments according to the proposed content,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for presenting optimal suggestions based on basic meeting information obtained from users.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for collecting opinions from users after the meeting and improving the proposed method based on those opinions.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A means for analyzing human movement data and characteristic data acquired from information technology devices,

[0853] Based on the analysis results obtained, a means for selecting the optimal venue for the event,

[0854] A means of determining the implementation location, taking into account the availability of selected locations and costs,

[0855] A means of presenting the appropriate personnel allocation for the decided venue and event,

[0856] A means for determining the user's psychological state during input and proposal confirmation using emotion recognition means, and for adjusting indicators that influence the analysis results,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for presenting optimal suggestions based on basic event information received from users.

[0860] (Claim 3)

[0861] The system according to claim 1, further characterized by having means to collect feedback from users after the event has ended and to improve the proposal algorithm based on the collected feedback. [Explanation of symbols]

[0862] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing demographic data and attribute data acquired from an information processing device, Based on the analysis results obtained, a means for selecting the optimal location for commercial activities, A means of determining the implementation site, taking into account the availability and resources of the selected implementation site, A means of presenting a confirmed location for implementation and personnel allocation suitable for commercial activities, A means of providing an optimal sales plan based on user input information, A system that includes this.

2. The system according to claim 1, further comprising means for predicting and proposing a location suitable for a specific target group based on attribute data of the aforementioned implementation location.

3. The system according to claim 1, further comprising means for collecting user evaluation information after the completion of commercial activities and updating the proposed algorithm based on the collected evaluation information.

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