system

Abstract

To provide a system.SOLUTION: The system includes means for: inputting scale details of an event; confirming the stock situation of an event item on the basis of the scale details; proposing an item that fits the scale on the basis of the stock situation; answering the arrival date of the item on the basis of the proposal; confirming the content of the answer, modifying an additional item and a deletion item, and issuing an automatic order when confirmation is made; proposing the optimum combination of items by using the generative Al model; confirming the arrival date of the proposed items and notifying the user of the arrival date; and automatically ordering the item list modified by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditionally, selecting event items such as fixtures to suit the scale of an event, checking inventory, and ordering have required a great deal of experience and knowledge, and have been a time-consuming and labor-intensive process. In particular, proposing appropriate items based on the details of the event scale and providing information such as the arrival date of items are too complex for humans to perform. [Means for solving the problem]

[0005] The present invention provides a means for inputting details of the scale of an event, a means for checking the stock status of event items such as fixtures based on the details of the scale, a means for proposing items that suit the scale based on the stock status, a means for replying with information such as the arrival date of the items based on the proposal, a means for confirming the reply, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed. This allows anyone to easily and efficiently prepare for an event. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Also, the processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit),

[0010] Examples include a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0011] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0012] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g.,

[0013] hard disk), or magnetic tape.

[0014] In the following embodiments, the coded communication I / F (Interface) refers to a communication processor and

[0015] The communication I / F is an interface that includes a network interface (NIC) and an antenna. The communication I / F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0016] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0017] [First embodiment]

[0018] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0019] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0020] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0021] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50.

[0022] 0 are connected to the bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0023] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0024] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or CC

[0025] A compact digital camera equipped with an imaging element such as a D (Charge Coupled Device) image sensor.

[0026] It's a camera.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0033] "Example 1"

[0034] The system of the present invention allows the user to input details of the scale of the event through a user interface.

[0035] Possible scale details include the date and time, location, number of people working, event space, sales targets, number of customers, etc. For example, a user may input that they are holding an event for 500 people in Tokyo on December 25, 2022, and that they need event items such as fixtures for the event.

[0036] "Example 2"

[0037] The system then provides a means to check the availability of event items, such as fixtures, based on the input size details. The availability can be obtained from a database, cloud storage, etc. For example, the system can search the database and confirm that event items, such as fixtures for 500 people, are available in stock.

[0038] "Example 3"

[0039] Furthermore, the system of the present invention provides a means to suggest items appropriate for the scale based on inventory availability. These suggestions are generated automatically using algorithms and machine learning models. For example, the system could suggest the optimal combination of event items, such as furniture for a 500-person event.

[0040] "Example 4"

[0041] The system of the present invention also provides a means for responding with information such as the item arrival date based on the proposal. For example, the system may confirm that the proposed item will arrive at the date, time, and location specified by the user, and provide that information to the user.

[0042] "Example 5"

[0043] Finally, the system of the present invention provides a means for users to review their responses, fine-tune the items they add or remove as needed, and automatically place an order upon confirmation. For example, a user can review the list of suggested items and add or remove items as needed. After that, the user can press the confirm button, and the system will automatically place an order for the items.

[0044] The processing flow of each embodiment will be described below.

[0045] "Example 1"

[0046] Step 1: The user inputs details of the scale of the event through the user interface of the system of the present invention. Specifically, the user inputs that an event for 500 people will be held in Tokyo on December 25, 2022, and that event items such as fixtures are required.

[0047] "Example 2"

[0048] Step 1: The system checks the availability of event items such as fixtures based on the inputted size details. Specifically, the system searches the database and confirms that event items such as fixtures for 500 people are available in stock.

[0049] "Example 3"

[0050] Step 1: The system of the present invention proposes items appropriate for the scale based on inventory status. Specifically, the system proposes the optimal combination from event items such as fixtures for 500 people that are automatically generated using algorithms and machine learning models.

[0051] "Example 4"

[0052] Step 1: The system of the present invention responds with the item arrival date etc. based on the proposal. Specifically, the system confirms that the proposed item will arrive at the date, time and location specified by the user and provides that information to the user.

[0053] "Example 5"

[0054] Step 1: The user reviews the list of suggested items and adds or removes items as needed.

[0055] Step 2: When the user presses the confirm button, the system of the present invention automatically places an order for the item.

[0056] Example 1

[0057] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0058] In conventional event management systems, entering details about the scale of an event was time-consuming, and the process of checking the stock status of necessary fixtures and items and proposing the most suitable items was cumbersome. Furthermore, the instructions and suggestions generated based on the information entered by the user were insufficient, making it difficult to efficiently reflect user feedback. This resulted in the problem of event preparation and management taking a great deal of time and effort.

[0059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0060] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate for the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, means for creating prompt text using a generative AI model, and means for presenting the prompt text to the user and receiving feedback. This allows the user to efficiently input details of the event, receive suggestions for necessary items, and confirm and modify the generated prompt text, thereby enabling quick and accurate event preparation and management.

[0061] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, location, number of people working, event space, sales targets, and number of customers.

[0062] "Event fixtures and other items" refers to the goods and equipment necessary for holding an event, such as chairs, tables, exhibition booths, and audio equipment.

[0063] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0064] The "suggestion means" refers to a function that selects the most suitable items that match the details of the scale of the event based on the stock situation and suggests them to the user.

[0065] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[0066] "Automatic ordering means" refers to the function of automatically ordering necessary event items such as fixtures after the user confirms and confirms the proposal content.

[0067] "Generative AI model" refers to a model that uses artificial intelligence technology to generate prompt sentences based on event details entered by a user.

[0068] "Prompt sentence" refers to a sentence created by a generative AI model that contains specific instructions or suggestions regarding event details and required items.

[0069] "Feedback" refers to corrections or additional input the user makes to the generated prompt sentence.

[0070] This invention is a system that inputs details of the scale of an event, checks the stock status of necessary fixtures and other event items, suggests optimal items, responds with information such as the item's arrival date, makes minor adjustments to add or remove items as necessary, and automatically places orders once confirmed. It also includes a function to create prompts using a generative AI model, present them to the user, and receive feedback.

[0071] Hardware and software used

[0072] Hardware

[0073] Server: Receives, stores, processes data, and runs generative AI models.

[0074] Terminal: Used by the user to enter details of the event and to check and modify the generated prompt text.

[0075] software

[0076] Database: A database such as MySQL® is used to store details of the event size and inventory status.

[0077] Generative AI models: Use generative AI models such as OpenAI's GPT-3 to create prompts.

[0078] Web application: Provides an interface for users to enter details of the event size and review / modify the generated prompt text.

[0079] Data processing and calculation

[0080] Enter details about the scale of your event

[0081] The user enters details of the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.) through the web application. The entered data is sent to the server and stored in a database.

[0082] Check availability and suggest items

[0083] The server checks the inventory status of necessary event items such as fixtures based on the details of the scale of the event stored in the database, and selects the most suitable items based on the inventory status and suggests them to the user.

[0084] Item arrival date response and automatic ordering

[0085] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures.

[0086] Prompt creation using a generative AI model

[0087] The server uses a generative AI model to create a prompt based on the details of the event entered by the user, which is then presented to the user.

[0088] Receiving feedback

[0089] The user can check the generated prompt sentence and make corrections or add additional inputs as necessary. The server can receive the user's feedback and generate the prompt sentence again.

[0090] Specific examples

[0091] Example input

[0092] The user enters details about the event, such as:

[0093] Date: December 25, 2022

[0094] Location: Tokyo

[0095] Working staff: 50

[0096] Event space: 100 square meters

[0097] Sales target: 1 million yen

[0098] Number of customers: 500

[0099] Prompt Sentence Examples

[0100] An example of a prompt created by the server using a generative AI model:

[0101] Event details are as follows:

[0102] Date: December 25, 2022

[0103] Location: Tokyo

[0104] Working staff: 50

[0105] Event space: 100 square meters

[0106] Sales target: 1 million yen

[0107] Number of customers: 500

[0108] List the fixtures and items you need for this event, and also suggest a strategy for achieving your sales goals.

[0109] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] The user accesses the system's web application using a terminal and enters details of the scale of the event, such as the date and time, location, number of people working, event space, sales target, number of customers, etc., into a form, and clicks the "Submit" button.

[0113] Input: Details of the event scale (date, time, location, number of people working, event space, sales target, number of customers)

[0114] Output: The input data is sent to the server

[0115] Step 2:

[0116] The server receives the details of the event size sent by the user and stores them in a database. The server receives the HTTP request, extracts the event details from the request body, establishes a database connection, executes an SQL query, and stores the data.

[0117] Input: Details of the event size sent by the user

[0118] Output: Details of the event size stored in the database

[0119] Step 3:

[0120] The server checks the stock status of necessary event items such as fixtures based on the details of the event scale stored in the database. The server accesses the stock database and executes an SQL query to obtain the stock status.

[0121] Input: Details of the event size stored in the database

[0122] Output: Stock status data

[0123] Step 4:

[0124] The server generates a list of items based on inventory availability that fit the size of the event, and uses a Python script to calculate the required number of fixtures and staff and select the most appropriate items.

[0125] Input: Inventory status data, details of event size

[0126] Output: A list of suggested items

[0127] Step 5:

[0128] The server uses the generative AI model to create a prompt based on the event details entered by the user. The server sends a request to the generative AI model's API to generate a prompt that includes the event details. The generated prompt is returned to the server.

[0129] Input: Details of the event size, list of proposed items

[0130] Output: Generated prompt statement

[0131] Step 6:

[0132] The server presents the generated prompt text to the user, who can then use the terminal to check the prompt text and make corrections or add additional input as necessary. When the user clicks the "Confirm" button, the server generates the prompt text again.

[0133] Input: Generated prompt text

[0134] Output: User feedback

[0135] Step 7:

[0136] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures. The server then accesses the ordering system and places an order for the necessary items.

[0137] Input: User confirmed suggestion

[0138] Output: Item arrival date, order confirmation

[0139] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Conventional event management systems provided a way to input details about the scale of an event and a way to check the inventory status of event items such as fixtures, but lacked a way to track the preparation status of the event and progress on the day in real time, or to generate reports after the event based on data such as the degree of sales target achievement and the number of customers served. This made overall event management cumbersome and made efficient operation difficult.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for tracking the preparation status of the event and progress on the day in real time, and means for generating a report after the event based on data such as the degree of sales target achievement and the number of customers served. This allows for efficient overall event management and can increase the success rate of the event.

[0145] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0146] "Event fixtures and other items" refers to items such as tables, chairs, display shelves, posters, flyers, banners, etc. that are necessary for the event.

[0147] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0148] "Suggestion means" refers to a function that automatically selects the most suitable item based on stock availability and suggests it to the user.

[0149] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified location.

[0150] "Automatic ordering means" refers to a function that allows the user to check the proposed content, add or delete items as necessary, and then automatically order the confirmed items.

[0151] "Preparation status" refers to information indicating how far preparations for an event have progressed.

[0152] "Progress" refers to real-time tracking of how activities and plans are progressing on the day of the event.

[0153] "Sales target achievement rate" refers to an indicator that shows the degree to which actual sales were achieved relative to the sales target set after the event ended.

[0154] "Number of customers served" refers to the total number of customers served during the event.

[0155] "Report generation means" refers to a function that automatically generates a report after the event based on data such as the degree of sales target achievement and the number of customers served.

[0156] A system for implementing the present invention includes an event management application installed on a terminal such as a smartphone, a tablet, etc. A specific embodiment of this system will be described below.

[0157] Hardware and software used

[0158] Hardware: Smartphones, tablets

[0159] Software: Event management applications, inventory management systems, report generation tools

[0160] Database: MySQL

[0161] Generative AI model: GPT-4 (registered trademark)

[0162] Data processing and calculation

[0163] Enter event details

[0164] Users enter details about their event into a form in the event management application, which then stores the information in a MySQL database, including the date, time, location, staffing, event space, sales targets, and number of attendees.

[0165] Resource Suggestions

[0166] The server sends prompts to a generative AI model (GPT-4) based on the input event details, which then suggests the necessary fixtures, promotional items, and staffing. The suggested resources are then displayed in the application's UI.

[0167] Example prompt sentence:

[0168] Event Details:

[0169] Date: December 25, 2022

[0170] Location: Tokyo

[0171] Operating staff: 10

[0172] Event space: 50 square meters

[0173] Sales target: 1 million yen

[0174] Number of customers: 500

[0175] Please suggest the fixtures, promotional items, and staffing required for this event.

[0176] Inventory management

[0177] The server retrieves the stock status of the suggested items from the inventory management system and, if there is a shortage, gives the user the option to reorder.

[0178] Progress management

[0179] The server tracks the preparation status and progress of the event in real time and displays it on the application's dashboard, allowing users to see the progress of the event at a glance.

[0180] Report Generation

[0181] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[0182] Specific examples

[0183] For example, if a user is planning an event for 500 people in Tokyo on December 25, 2022, they would use the system as follows:

[0184] 1. The user enters the details of the event into the application.

[0185] 2. The server sends prompts to the generative AI model to suggest the necessary fixtures, promotional items, and staff placement.

[0186] 3. Check the availability of the suggested items and place an additional order if there is a shortage.

[0187] 4. Track event preparation and on-the-day progress in real time.

[0188] 5. After the event, generate a report based on data such as sales target achievement and number of customers served.

[0189] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] The user enters details of the event into a form in the event management application. The input items include the date and time, location, number of people available, event space, sales target, number of customers, etc. The input data is saved in a MySQL database. This registers the basic information of the event in the system.

[0193] Input: Date and time, location, number of employees, event space, sales target, number of customers

[0194] Output: Event details stored in a MySQL database

[0195] Step 2:

[0196] The server retrieves event details from a MySQL database and sends prompts to a generative AI model (GPT-4), which includes detailed information about the event. The model then suggests the necessary fixtures, promotional items, and staffing.

[0197] Input: Event details retrieved from a MySQL database

[0198] Output: Proposals from the generative AI model (furniture, promotional items, staff placement)

[0199] Step 3:

[0200] The server receives suggestions from the generative AI model and displays them in the application's UI. The user reviews the suggested items and adds or removes them as needed. Once the user confirms the suggestions, the server orders the items using an automated ordering mechanism.

[0201] Input: Suggestions from the generative AI model, user confirmation and corrections

[0202] Output: Confirmed item list, automatic ordering

[0203] Step 4:

[0204] The server retrieves the stock status of the suggested item from the inventory management system. If the item is out of stock, the server gives the user the option to reorder. When the user places an order, the server again places an order with the inventory management system.

[0205] Input: Confirmed item list, stock status from inventory management system

[0206] Output: Stock check results, additional order options

[0207] Step 5:

[0208] The server tracks the preparation and progress of the event in real time, including the arrival status of each item, staffing status, etc. The server displays this information on the application's dashboard so that users can see the progress.

[0209] Input: Arrival status of each item, staff allocation status

[0210] Output: Real-time progress information, dashboard display

[0211] Step 6:

[0212] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[0213] Input: Sales target achievement rate, number of customers

[0214] Output: Report in PDF format

[0215] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[0216] Example 2

[0217] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0218] There is a need for a system that can quickly and accurately check the inventory status of fixtures and other event items according to the scale of the event, and can suggest and automatically order the most suitable items. However, conventional systems require users to manually check inventory status and select the necessary items, which is time-consuming and labor-intensive. Furthermore, if the inventory status confirmation and item suggestions are inaccurate, it can hinder event preparations.

[0219] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0220] In this invention, the server includes means for transmitting the details of the scale entered by the user to the server, means for the server to receive the details of the scale and access the database to obtain the stock status, and means for the server to analyze the obtained stock status and transmit the analysis results to the terminal. This allows the user to simply enter the details of the scale of the event, and the server will automatically check the stock status, suggest the most suitable items, and automatically place an order.

[0221] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers.

[0222] "Inventory status" refers to information stored in a database or cloud storage that indicates the current stock and availability of fixtures and other event items.

[0223] The "suggestion means" is a function for automatically selecting the most suitable items for the scale of the event based on the stock situation and proposing them to the user.

[0224] The "automatic ordering means" is a function that allows the user to check the proposed items, add or delete items as necessary, and then automatically order the confirmed items.

[0225] A "server" is a computer system that receives input from a user, accesses a database to obtain inventory status, and transmits the analysis results to a terminal.

[0226] A "terminal" is a device through which a user inputs details of the scale of an event and receives and displays the analysis results from the server.

[0227] The "database" is a system for storing the inventory status of fixtures and other event items and providing information in response to queries from the server.

[0228] The "analysis result" is information indicating whether the item required for the user's request is available in stock, based on the inventory status acquired by the server.

[0229] This invention is a system that checks the inventory status of event items such as fixtures based on the details of the scale of the event, and then proposes and automatically orders the most suitable items. This system is composed of multiple components, including users, terminals, and a server.

[0230] Hardware and software used

[0231] Hardware: Servers, database servers, cloud storage, user devices (PCs, smartphones, tablets, etc.)

[0232] Software: Database management systems (e.g., MySQL, PostgreSQL), cloud storage services (e.g., Amazon S3, Google® Cloud Storage), web browsers or dedicated applications

[0233] System Operation Overview

[0234] User operations

[0235] The user enters details of the scale of the event (e.g., furniture for 500 people is required) into an input form on the terminal. The entered information is sent from the terminal to the server.

[0236] Server Processing

[0237] The server receives the size details sent from the terminal and connects to the database management system to execute a query to check the stock status. The server analyzes the stock status retrieved from the database and checks whether the required items for the user's request are available in stock. The analysis result is sent from the server to the terminal.

[0238] Terminal display

[0239] The terminal displays the analysis results received from the server to the user. The user can check the proposed items and add or delete them as necessary. The finalized items are then ordered by the automatic ordering means.

[0240] Specific examples

[0241] For example, if a user inputs "We need furniture for 500 people," the process will proceed as follows:

[0242] 1. The user enters "I need furniture for 500 people" into the input form on the terminal.

[0243] 2. The device sends this information to the server.

[0244] 3. The server receives this information and connects to the MySQL database to query the inventory.

[0245] 4. The server retrieves from the database the information that "furniture for 500 people is available in stock."

[0246] 5. The server analyzes this information and verifies that there is sufficient inventory for the user's request.

[0247] 6. The server sends the analysis results to the device.

[0248] 7. The terminal displays the analysis results to the user, informing them that "furniture for 500 people is available in stock."

[0249] Prompt Sentence Examples

[0250] An example of a prompt sentence to input to the generative AI model is as follows:

[0251] "We need furniture for 500 people. Please check availability."

[0252] By inputting this prompt into the generative AI model, the system performs the above process and checks the inventory status.

[0253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0254] Step 1:

[0255] The user inputs the size details.

[0256] Input: Details of the event size (e.g., furniture for 500 people required)

[0257] Specific behavior: The user uses a web browser or a dedicated application to enter the quantity of the item they want into an input form.

[0258] Output: The scale details are entered into the terminal.

[0259] Step 2:

[0260] The terminal sends the entered size details to the server.

[0261] Input: User-entered size details

[0262] Specific operation: The terminal generates an HTTP request and sends a POST request to the server.

[0263] Output: The scale details are sent to the server.

[0264] Step 3:

[0265] The server receives the size details and accesses the database.

[0266] Input: Size details sent from the terminal

[0267] What happens: The server receives the HTTP request, connects to a database management system (e.g., MySQL), and generates an SQL query to check inventory status.

[0268] Output: The SQL query is sent to the database.

[0269] Step 4:

[0270] The server retrieves the inventory status from the database.

[0271] Input: SQL query

[0272] What happens: The database returns the query results, and the server receives them.

[0273] Output: Stock availability data is retrieved to the server.

[0274] Step 5:

[0275] The server analyzes the stock status obtained.

[0276] Input: Inventory status data

[0277] What happens: The server compares the quantity in stock with the quantity requested by the user to determine if the required item is available in stock.

[0278] Output: Analysis results are generated.

[0279] Step 6:

[0280] The server sends the analysis results to the device.

[0281] Input: Analysis results

[0282] Specific operation: The server generates an HTTP response and sends it to the device.

[0283] Output: The analysis results are sent to the terminal.

[0284] Step 7:

[0285] The terminal displays the analysis results to the user.

[0286] Input: Analysis results sent from the server

[0287] Specific operation: The device displays the analysis results on the screen so that the user can check them.

[0288] Output: The analysis results are displayed to the user.

[0289] (Application example 2)

[0290] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0291] In conventional logistics centers, it was difficult to check the inventory status of fixtures and event items according to the scale of the event in real time, which led to problems with shortages and excess inventory. In addition, the process of proposing the most suitable items based on the inventory status and quickly ordering them was complicated, making efficient operation difficult.

[0292] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting details of the scale of the event; means for checking the inventory status of event items such as fixtures based on the details of the scale; means for proposing items appropriate to the scale based on the inventory status; means for responding with information such as the arrival date of the items based on the proposal; means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing an order once confirmed; and means installed on a smartphone that allows a manager or staff member at a logistics center to check the inventory status in real time. This makes it possible to check the inventory status of fixtures and event items appropriate to the scale of the event in real time at the logistics center and quickly propose and order the most appropriate items.

[0293] "Event scale details" is information indicating the specific scale and conditions of the event, such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0294] "Event fixtures and other items" refers to items such as furniture, equipment, and decorations necessary for holding an event.

[0295] "Stock status" is information indicating how many event items such as fixtures are currently in stock and how many are available for use.

[0296] "Suggestion method" refers to the function that automatically selects the most suitable items based on stock availability and suggests items that suit the scale of the event.

[0297] "Item Arrival Date" means the date the proposed item is expected to ship from the distribution center and arrive at the specified location.

[0298] "Automatic ordering means" refers to a function that checks the contents of the proposed items, adds or deletes as necessary, and then automatically carries out the ordering procedure once the contents are confirmed.

[0299] "Means installed on smartphones" refers to applications that allow logistics center managers and staff to check inventory status in real time using their smartphones.

[0300] The system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures based on the details of the scale, a means for proposing items that suit the scale based on the stock status, a means for responding with the arrival date of the items etc. based on the proposal, a means for confirming the content of the response, making minor adjustments to added items or deleted items as necessary, and automatically placing an order once confirmed, and a means that is installed on a smartphone and allows managers and staff at the logistics center to check the stock status in real time.

[0301] The server provides an interface for users to input details about the scale of the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers. The server receives this information and stores it in a database.

[0302] The server then searches its database to see if there is enough fixtures and other event items available based on the size details entered. For example, it checks whether there is enough fixtures for 500 people available. This availability can come from cloud storage or a local database.

[0303] After checking the inventory status, the server uses a generative AI model to automatically select the most suitable items and suggest them to the user, including details about the items and their expected arrival dates. The user can review the suggestions and add or remove items as needed.

[0304] Once the proposal is confirmed, the server initiates an automated ordering process, which ensures the required items are shipped from the distribution center and delivered to the specified location.

[0305] The application installed on a smartphone allows managers and staff at the distribution center to check inventory status in real time. The application uses Python and the requests library to retrieve inventory information from the server and display it to the user.

[0306] For example, if a logistics center manager inputs the size of an event for 500 people, the system will search the database to see if there is enough fixtures available in stock for 500 people. It will then use a generative AI model to suggest the most suitable items, and once the user has confirmed and revised the suggestions, it will automatically process the order.

[0307] Example prompts to input to a generative AI model:

[0308] "If your event size is 500 people, check how many fixtures you have available in stock."

[0309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0310] Step 1:

[0311] The user inputs details about the scale of the event. Using a smartphone application, the user inputs details such as the date and time of the event, location, number of people working, event space, sales target, and number of customers. The input data is sent to the server.

[0312] Input: Event date and time, location, number of people in operation, event space, sales target, number of customers

[0313] Output: Detailed data on the scale of events sent to the server

[0314] Step 2:

[0315] The server saves the detailed data of the scale of the received event in a database. The server converts the input data into an appropriate format and stores it in the database.

[0316] Input: Detailed data on the scale of the event

[0317] Output: Detailed event size data stored in the database

[0318] Step 3:

[0319] The server searches the database to check the stock status of event items such as fixtures. The server calculates the number of items required based on the details of the scale of the event and retrieves stock information from the database or cloud storage.

[0320] Input: Detailed data on the scale of the event

[0321] Output: Stock status data

[0322] Step 4:

[0323] The server uses a generative AI model to automatically select the most suitable items and suggest them to the user. The server inputs inventory status data into the generative AI model and generates a list of the most suitable items. The generated list is then suggested to the user.

[0324] Input: Inventory status data

[0325] Output: A list of the best items

[0326] Step 5:

[0327] The user checks the list of suggested items and adds or removes them as necessary. The user then uses a smartphone application to check and modify the suggestions. The modified data is then sent to the server.

[0328] Input: List of best items

[0329] Output: A list of modified items

[0330] Step 6:

[0331] The server receives the revised list of items and initiates an automated ordering process. The server then performs the process to order the required items based on the revised list. The ordering information is then sent to the logistics center.

[0332] Input: List of modified items

[0333] Output: Order information

[0334] Step 7:

[0335] An application installed on a smartphone displays real-time inventory status to the distribution center manager and staff. The application retrieves inventory information from the server and displays it to the user.

[0336] Input: Inventory information

[0337] Output: Stock status displayed on smartphone

[0338] Example 3

[0339] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] Conventional event item suggestion systems often required manual processes such as checking inventory, suggesting items, confirming arrival dates, and placing an order, resulting in inefficiencies. Furthermore, they lacked functionality to automatically suggest the optimal combination of items needed by the user, placing a significant burden on the user. Furthermore, there was an insufficient means for users to confirm the arrival dates of the suggested items, potentially resulting in delays in event preparations. To solve these issues, an efficient and automated system was needed.

[0341] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0342] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items, means for proposing items appropriate to the scale, means for responding with information such as item arrival dates, means for confirming the responses, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the responses are confirmed, means for acquiring inventory data from a database, means for preprocessing the acquired inventory data, means for generating item suggestions using a machine learning model, means for confirming item arrival dates in cooperation with a delivery system, and means for providing information through a user interface. This makes it possible to propose optimal items based on inventory status, confirm arrival dates, and automate ordering.

[0343] "Event scale details" refers to information such as the date and time of the event, location, number of people working, event space, sales targets, and number of customers.

[0344] "Inventory status" refers to the current inventory quantity and status of an item as recorded in the database.

[0345] "Item suggestion" refers to automatically selecting and suggesting the optimal combination of items based on the details of the event's scale and inventory status.

[0346] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified date, time, and location.

[0347] "Automatic ordering" refers to the process where the system automatically orders the item once the user confirms the proposal and presses the confirm button.

[0348] "Database" means an information management system for storing inventory data and other related information.

[0349] "Preprocessing" refers to processes such as filling in missing values ​​and normalizing data before applying the acquired data to analysis or machine learning models.

[0350] A "machine learning model" refers to an algorithm that learns patterns based on data and makes predictions and classifications.

[0351] "Delivery System" refers to an external system or service that allows you to check the delivery status and arrival date of an item.

[0352] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[0353] MODE FOR CARRYING OUT THE INVENTION

[0354] This invention is a system that proposes optimal items based on the details of the scale of the event, confirms the arrival date of the items, and finally places an automatic order. A specific embodiment of this system will be described below.

[0355] System configuration

[0356] The server includes a means for inputting details of the scale of the event, a means for checking the stock status of event items, a means for proposing items that suit the scale, a means for responding with information such as the arrival date of items, a means for confirming the content of the responses, making minor adjustments to added items or deleted items as necessary, and automatically placing orders once the responses are confirmed, a means for acquiring inventory data from a database, a means for preprocessing the acquired inventory data, a means for generating item suggestions using a machine learning model, a means for confirming item arrival dates in cooperation with a delivery system, and a means for providing information through a user interface.

[0357] Hardware and software used

[0358] Database: Information management system for storing inventory data (e.g. MySQL)

[0359] Preprocessing software: A program for preprocessing data (e.g., the Pandas library in Python)

[0360] Machine learning models: Algorithms that learn patterns from data and make predictions or classifications (e.g., Tensorflow, Scikit-learn)

[0361] Delivery system: An external system to check the item's delivery status and arrival date (e.g. FedEx API)

[0362] User interface: The screens and controls that allow users to interact with the system (e.g., the Django framework)

[0363] System Operation

[0364] The server first retrieves inventory data from the database. The retrieved data is preprocessed using Python, specifically by filling in missing values ​​and normalizing the data. Next, a machine learning model is used to suggest the optimal combination of items based on the scale of the event. For example, it suggests the optimal combination of event items, such as fixtures for 500 people.

[0365] The server verifies that the proposed items will arrive at the user's specified date, time, and location. To do this, it communicates with the delivery system to confirm the arrival date. The confirmed arrival date and information about the proposed items are provided to the user through a user interface.

[0366] The user checks the list of suggested items and adds or removes items as needed. When the user presses the confirm button, the server automatically places an order for the items. This ordering process is performed in conjunction with the ERP system.

[0367] Specific examples

[0368] As a concrete example, the following prompt sentence is input into the generative AI model:

[0369] Example prompt sentence:

[0370] "Please suggest the fixtures needed for an event for 500 people. The event will be held on December 25, 2023, in Shibuya Ward, Tokyo."

[0371] Based on this prompt, the server retrieves inventory data, performs preprocessing, and generates optimal item recommendations using a machine learning model. It then connects with the delivery system to confirm the item arrival date and provides the information to the user. Once the user confirms the recommendations and presses the confirm button, the server automatically places an order for the item.

[0372] In this way, the system of the present invention realizes the automation of proposing the most suitable item based on the inventory status, confirming the arrival date, and placing an order. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0373] Program processing flow

[0374] Step 1: Enter details about the size of your event

[0375] The user inputs details of the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.) through the terminal. The input data is sent to the server.

[0376] Input: Details of the event scale (date, time, location, number of people working, event space, sales target, number of customers, etc.)

[0377] Output: Detailed data on the scale of the event sent to the server

[0378] Step 2: Retrieving inventory data

[0379] The server retrieves inventory data from the database by executing SQL queries to extract the required inventory information.

[0380] Input: Event size details

[0381] Output: Inventory data retrieved from the database

[0382] Step 3: Preprocessing the data

[0383] The server preprocesses the acquired inventory data, specifically by using the Python Pandas library to impute missing values ​​and normalize the data.

[0384] Input: Inventory data retrieved from the database

[0385] Output: Preprocessed inventory data

[0386] Step 4: Generate item suggestions

[0387] The server uses machine learning models to suggest the optimal combination of items depending on the scale of the event, for example, from among event items such as fixtures for 500 people.

[0388] Input: Pre-processed inventory data, event size details

[0389] Output: A list of suggested items

[0390] Step 5: Confirm the item arrival date

[0391] The server verifies that the proposed item will arrive at the user's specified date and time, by interfacing with the delivery system to verify the arrival date.

[0392] Input: List of proposed items, details of the event size

[0393] Output: Confirmed item arrival date

[0394] Step 6: Inform users

[0395] The server provides the user with information on confirmed arrival dates and suggested items, using a web application framework to build a user interface.

[0396] Input: Confirmed item arrival date, list of proposed items

[0397] Output: Information provided to the user (item list, arrival date)

[0398] Step 7: Fine-tune and finalize the item

[0399] The user reviews the list of suggested items and adds or removes items as needed. Once the user presses the confirm button, the server automatically places an order for the items.

[0400] Input: User-generated item addition / deletion information, confirmation button press

[0401] Output: Confirmed item list

[0402] Step 8: Automated ordering

[0403] The server works in conjunction with the ERP system to automatically place an order for the confirmed items.

[0404] Input: Confirmed item list

[0405] Output: Sending order information, notification of order completion

[0406] In this way, the server, terminal, and user work together to carry out a series of processes from proposing items based on stock status to placing an order.

[0407] (Application example 3)

[0408] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0409] Conventional inventory management systems have problems with suggesting optimal items based on the scale and details of an event, and there is a lack of a way to check the arrival date of suggested items, which means users have to manually check and correct them. Furthermore, there is a lack of functionality to automatically order item lists revised by users, making efficient inventory management difficult.

[0410] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0411] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for proposing an optimal combination of items using a generative AI model, means for confirming the arrival dates of the proposed items and notifying the user, and means for automatically ordering the item list modified by the user. This makes it possible to propose optimal items according to the scale and details of the event, confirm arrival dates, and automatically place orders after user adjustments.

[0412] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0413] "Event fixtures and other items" refers to items such as furniture and equipment used at events.

[0414] "Inventory status" refers to the current number and condition of event items such as fixtures in logistics centers and warehouses.

[0415] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to suggest optimal item combinations from data.

[0416] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified date, time, and location.

[0417] "Means for notifying the user" refers to a method for informing the user of the proposed item's arrival date and other important information.

[0418] "Means for automatic ordering" refers to the function in which the system automatically carries out the ordering procedure based on the item list confirmed and modified by the user.

[0419] A system for implementing this invention includes means for inputting details of the scale of the event, means for checking inventory status, means for proposing optimal items, means for responding with the arrival date of the items, means for confirming the response, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for proposing the optimal combination of items using a generative AI model, means for confirming the arrival dates of the proposed items and notifying the user, and means for automatically ordering the item list modified by the user.

[0420] Hardware and software used

[0421] Hardware: Smartphone

[0422] Software: Python, Requests library, Scikit-learn library

[0423] Data processing and calculation

[0424] The server first receives information from the user about the event's scale, such as the date and time, location, number of people working, event space, sales targets, and number of customers, through a means for inputting details about the scale of the event. Next, the server uses a means for checking inventory status to obtain the current number and status of event items, such as fixtures, in the distribution center or warehouse.

[0425] Using a generative AI model, the server proposes an optimal combination of items based on inventory data and details of the event scale. The server confirms the arrival date of the proposed items and notifies the user through a notification mechanism. After the user confirms the proposed item list and adds or removes items as necessary, the server automatically processes the order using an automatic ordering mechanism once the order is confirmed.

[0426] Specific examples

[0427] For example, if you want to suggest the fixtures needed for an event with 500 people, you can input the following prompt into the generative AI model:

[0428] Prompt Sentence Examples

[0429] Event size: 500 people

[0430] Required items: Furniture

[0431] Inventory Data: {Inventory Data JSON}

[0432] User Modification: {User Modification JSON}

[0433] Using this prompt, the generative AI model proposes the most suitable items, confirms the arrival date, and automatically orders the list as modified by the user. This makes it possible to propose the most suitable items based on the scale and details of the event, confirm the arrival date, and automatically order after the user has made modifications.

[0434] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0435] Step 1:

[0436] The user inputs details about the scale of the event. Specifically, the user inputs information such as the date and time of the event, location, number of people working, event space, sales target, and number of customers into the smartphone application. This sends the details of the scale of the event as input data to the server.

[0437] Step 2:

[0438] The server checks the inventory status of the logistics center and warehouse based on the details of the scale of the event that have been entered. Specifically, it calls an API to obtain inventory data and obtains the current number and status of event items such as fixtures. This inputs the inventory data into the server.

[0439] Step 3:

[0440] The server uses a generative AI model to suggest the optimal combination of items based on inventory data and details of the event scale. Specifically, it uses a machine learning algorithm (RandomForestClassifier) ​​to select items that best fit the event requirements. This generates a list of optimal item suggestions.

[0441] Step 4:

[0442] The server checks the arrival date of the suggested items. Specifically, it retrieves the arrival date of each item based on the suggestion list through the API and notifies the user. This provides the user with information on the item arrival date.

[0443] Step 5:

[0444] The user can check the proposed item list and add or delete items as necessary. Specifically, the user displays the item list through a smartphone application and makes modifications. The modified item list is then sent to the server.

[0445] Step 6:

[0446] The server automatically processes the order based on the item list modified by the user. Specifically, it sends the modified item list to the ordering system via API and completes the ordering process. This places an order for the final item list.

[0447] Furthermore, an emotion engine that estimates the user's emotion may be combined.

[0448] The logic unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0449] "Example 1"

[0450] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts item suggestions and automatic ordering based on those emotions. Specifically, when a user inputs details about the scale of an event, the system analyzes that information and the user's emotions. For example, if the user expresses joy, the system suggests more luxurious items. On the other hand, if the user expresses anxiety, the system suggests less expensive items. In this way, the system suggests optimal items according to the user's emotions, improving user satisfaction.

[0451] "Example 2"

[0452] Another embodiment of the present invention provides a system in which an emotion engine adjusts automatic ordering of items. Specifically, when a user confirms an item, the system analyzes the information and the user's emotion. For example, if the user expresses joy, the system immediately orders the item. On the other hand, if the user expresses anxiety, the system delays the order by, for example, asking the user for reconfirmation. In this way, the system adjusts the timing of ordering according to the user's emotion, improving user satisfaction.

[0453] "Example 3"

[0454] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts item suggestions and automatic ordering based on those emotions. Specifically, when a user inputs details about the scale of an event, the system analyzes that information and the user's emotions. For example, if the user expresses joy, the system suggests more luxurious items. On the other hand, if the user expresses anxiety, the system suggests less expensive items. In this way, the system suggests optimal items according to the user's emotions, improving user satisfaction.

[0455] The processing flow of each embodiment will be described below.

[0456] "Example 1"

[0457] Step 1: The user enters details of the scale of the event into the system.

[0458] Step 2: The system recognizes the user's emotion using the user's emotion engine.

[0459] Step 3: The system analyzes the user's emotions and the details of the event scale, and suggests the most suitable items. For users who show emotions of joy, it suggests luxury items, and for users who show emotions of anxiety, it suggests inexpensive items.

[0460] "Example 2"

[0461] Step 1: The user commits the item to the system.

[0462] Step 2: The system recognizes the user's emotion using the user's emotion engine.

[0463] Step 3: The system analyzes the user's emotions and item confirmation information and adjusts the timing of ordering. For users who express joy, the system orders the item immediately, and for users who express anxiety, the system delays the order.

[0464] Example 1

[0465] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0466] Conventional event management systems suggest items based on the details of the event scale entered by the user, but do not consider the user's emotions. This makes it difficult to suggest optimal items to improve user satisfaction. Another issue is that suggestions based solely on inventory status do not allow for flexible responses based on the user's emotions.

[0467] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0468] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, means for recognizing the user's emotions, means for adjusting the item proposals based on the emotions, means for generating optimal item proposals using a generative AI model, and means for displaying the generated item proposals to the user. This enables optimal item proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[0469] "Event scale details" refers to the specific conditions and requirements of the event, such as the date, time, location, number of people working, event space, sales targets, and number of customers.

[0470] "Event fixtures and other items" refers to the equipment, decorations, and facilities necessary for holding an event.

[0471] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0472] "Item suggestion" refers to selecting the most suitable event items, such as fixtures, and presenting them to the user based on the details of the event scale and stock status entered by the user.

[0473] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[0474] "Automatic ordering" refers to the system automatically placing an order for a suggested item after the user confirms it.

[0475] "Means for recognizing emotions" refers to technology that analyzes emotions from a user's facial expressions and input content and detects a specific emotional state.

[0476] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate optimal results for a specific task.

[0477] "Means for adjusting item suggestions" refers to technology that changes the content and type of suggested event items, such as fixtures, based on the user's emotions.

[0478] The "means for displaying to the user" refers to an interface that displays the generated item suggestions so that the user can visually confirm them.

[0479] The present invention is a system that inputs details of the scale of an event and proposes optimal items taking into consideration the emotions of the user. A specific embodiment of this system will be described below.

[0480] System configuration

[0481] User Interface

[0482] The user enters details of the event using the system's user interface, which can be implemented as a web or mobile application. The user enters information such as the date, time, location, number of people available, event space, sales target, and number of customers.

[0483] Data transmission

[0484] The device sends the details of the event scale entered by the user to the server, where the input data is converted to JSON format and sent using a secure communication protocol (e.g., HTTPS).

[0485] emotion recognition

[0486] The server starts an emotion engine to recognize the user's emotion along with the details of the scale of the received event. The emotion engine analyzes the emotion from the user's facial expression and input content. For example, if the user is smiling through the camera, it recognizes the emotion of joy.

[0487] Data Integration

[0488] The server integrates the emotion data obtained from the emotion engine with the magnitude details of the event entered by the user, and this integrated data is used in the next step.

[0489] Generate item suggestions

[0490] The server uses a generative AI model based on the integrated data to generate optimal item suggestions for the user. The generative AI model selects optimal items taking into account the user's emotions and the details of the scale of the event.

[0491] Submit your proposal

[0492] The server sends the generated item suggestions to the terminal, and the suggestions are displayed in a format that is easy for the user to understand.

[0493] Displaying suggestions to the user

[0494] The terminal displays the item suggestions received from the server to the user, who can then check the suggestions and make selections or modifications as necessary.

[0495] Specific examples

[0496] For example, if a user enters, "I'm holding an event for 500 people in Tokyo on December 25, 2022, and I need event items such as fixtures," and expresses joy, the system will suggest luxurious decorations and high-quality fixtures.

[0497] Prompt Sentence Examples

[0498] Examples of prompts to be input to a generative AI model include:

[0499] "A user has entered that they are planning an event for 500 people in Tokyo on December 25, 2022, and need event items such as fixtures. The user's emotion is joy. What items would you suggest?"

[0500] By inputting this prompt into a generative AI model, the system can suggest optimal items based on the user's emotions.

[0501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] The user uses the system's user interface to input details about the scale of the event. Specifically, the user enters information such as the date and time, location, number of people working, event space, sales target, and number of customers. For example, the user might enter, "I'm holding an event for 500 people in Tokyo on December 25, 2022, and I need event items such as fixtures." The input data is divided into fields such as date and time, location, and number of people working.

[0504] Step 2:

[0505] The device sends the details of the scale of the event entered by the user to the server. At this time, the input data is converted to JSON format and sent using a secure communication protocol (e.g., HTTPS). For example, the following JSON data is sent: json{ "date": "2022-12-25", "location": "Tokyo", "staff": 500, "items_needed": true}

[0506] The input data is sent to the server, which receives it.

[0507] Step 3:

[0508] The server launches an emotion engine to recognize the user's emotion along with the details of the scale of the received event. The emotion engine analyzes the emotion from the user's facial expression and input content. For example, if the user is smiling through the camera, it recognizes the emotion of joy. The input data is the user's facial expression data and text data, and the output is the recognized emotion data.

[0509] Step 4:

[0510] The server combines the emotion data obtained from the emotion engine with the details of the scale of the event entered by the user. This combined data will be used in the next step. For example, the following combined data will be generated: json{ "date": "2022-12-25", "location": "Tokyo", "staff": 500, "items_needed": true, "emotion": "joy"}

[0511] The input data is emotion data and event detail data, and the output is the integrated data.

[0512] Step 5:

[0513] The server uses a generative AI model based on the integrated data to generate optimal item suggestions for the user. The generative AI model selects the optimal item taking into account the user's emotions and the details of the scale of the event. For example, the following prompt sentence is input to the generative AI model:

[0514] "A user has entered that they are planning an event for 500 people in Tokyo on December 25, 2022, and need event items such as fixtures. The user's emotion is joy. What items would you suggest?"

[0515] The input data are the synthesis data and the prompt statements, and the output is the generated item suggestions.

[0516] Step 6:

[0517] The server sends the generated item suggestions to the device. The suggestions are displayed in a format that is easy for the user to understand. For example, the following suggestions are sent: json{ "suggestions": [ "Luxurious decorations", "High-quality fixtures" ]}

[0518] The input data is the generated item suggestions, and the output is the submitted suggestions.

[0519] Step 7:

[0520] The terminal displays the item suggestions received from the server to the user. The user can review the suggestions and make selections or modifications as needed. For example, if the user selects "luxurious decorations," the system records the selection and proceeds to the next step. The input data is the received suggestions, and the output is the user's selection.

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0523] In conventional event planning systems, users can input details about the scale of the event, but items are not suggested based on the user's emotions, which means that user satisfaction cannot be fully enhanced. Also, while items are suggested based on stock availability, they are not adjusted according to the user's emotions, so the system may not be able to suggest the most appropriate items.

[0524] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for recognizing the user's emotions, and means for adjusting the proposed items based on the emotions. This makes it possible to propose optimal items taking the user's emotions into consideration, thereby improving user satisfaction.

[0525] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0526] "Event fixtures and other items" refers to items such as furniture, decorations, and equipment used at events.

[0527] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0528] "Item suggestion" refers to the act of selecting the most suitable event items, such as fixtures, based on the details of the event's scale and stock availability, and presenting them to the user.

[0529] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[0530] "Automatic ordering" refers to the process where the system automatically orders a suggested item after the user confirms and confirms the item.

[0531] "User emotions" refers to the psychological states such as joy, anxiety, and excitement that users feel when planning and preparing for an event.

[0532] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions, tone of voice, etc. to identify their emotions.

[0533] "Means for adjusting item suggestions based on emotions" refers to technology that changes the type and quality of suggested event items, such as fixtures, depending on the recognized emotions of the user.

[0534] A system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures, a means for proposing items that suit the scale, a means for responding with information such as the date of arrival of items, a means for confirming the response and making minor adjustments to add or delete items as necessary, and automatically placing an order once the response is confirmed, a means for recognizing the user's emotions, and a means for adjusting the item suggestions based on the emotions.

[0535] Program processing explanation

[0536] Hardware and Software Configuration

[0537] Server: Enter details of the event size, check stock availability, suggest items, provide item arrival dates, and automatically place orders.

[0538] User device: A device such as a smartphone or tablet where the user enters event details and recognizes emotions.

[0539] Emotion recognition software: Software used to analyze a user's facial expressions and tone of voice (e.g., EmotionRecognizer).

[0540] Item suggestion algorithms: Algorithms for selecting the best items based on stock availability and user sentiment (e.g., ItemSuggester).

[0541] Data processing and calculation

[0542] 1. Entering details of the scale of the event: Detailed information such as the date and time of the event, location, number of people working, event space, sales target, and number of customers is sent from the user terminal to the server.

[0543] 2. Check inventory status: The server retrieves the current inventory status of event items such as fixtures from the database.

[0544] 3. Emotion recognition: Using the camera and microphone on the user's device, emotion recognition software analyzes the user's facial expressions and tone of voice to identify the user's emotions.

[0545] 4. Item suggestion: Based on the acquired inventory status and the recognized user sentiment, the server uses an item suggestion algorithm to select the most suitable event items, such as fixtures, and suggest them to the user.

[0546] 5. Item Arrival Date Answer: Calculate the arrival date of the proposed item and notify the user.

[0547] 6. Automatic ordering: After the user confirms and confirms the proposed item, the server will automatically order the item.

[0548] Specific examples

[0549] For example, a user uses a smartphone to enter details of an event such as:

[0550] Example prompt sentence:

[0551] Enter your event details:

[0552] Date: 2022-12-25

[0553] Location: Tokyo

[0554] Number of participants: 500 people

[0555] Working staff: 50

[0556] Event space: 100 square meters

[0557] Sales target: 1 million yen

[0558] Number of customers served: 200 people

[0559] Recognize user emotions:

[0560] Emotion: Joy

[0561] Suggested items:

[0562] Luxurious decorations

[0563] Luxury catering services

[0564] Premium Gift

[0565] In this way, a system can be realized that makes optimal suggestions based on the user's emotions and the details of the event.

[0566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0567] Step 1:

[0568] The user uses a device such as a smartphone or tablet to enter details about the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.). The entered data is sent from the device to the server. The input data includes the event date, location, number of participants, number of people working, size of the event space, sales target amount, and number of customers.

[0569] Step 2:

[0570] The server checks the stock status of event items such as fixtures from the database based on the details of the scale of the received event. The server accesses the inventory database to obtain the current number of required items and their availability status. As an output, a list of stock status is generated.

[0571] Step 3:

[0572] Using the camera and microphone of the user's device, emotion recognition software (e.g., EmotionRecognizer) analyzes the user's facial expressions and tone of voice to identify the user's emotions. The input data is the user's facial expressions and tone of voice, and the output is the user's emotion (joy, anxiety, excitement, etc.).

[0573] Step 4:

[0574] The server uses an item suggestion algorithm (e.g., ItemSuggester) to select the most suitable event items, such as fixtures, based on the acquired inventory status and the recognized user sentiment. The input data are inventory status and user sentiment, and the output is a list of suggested items.

[0575] Step 5:

[0576] The server calculates the arrival date of the proposed items and notifies the user. The input data is the list of proposed items and delivery information, and the output is the item arrival date.

[0577] Step 6:

[0578] The user confirms the proposed items and makes minor modifications to add or remove items as necessary. After the user confirms, the server automatically orders the items. The input data are the user's confirmations and modifications, and the output is order information.

[0579] In this way, a system can be realized that makes optimal suggestions based on the user's emotions and the details of the event.

[0580] Example 2

[0581] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0582] Conventional event management systems lacked sufficient means to check the inventory status of fixtures and other items according to the scale of the event, which resulted in problems with shortages and excess inventory. Furthermore, automatic ordering was performed without considering the user's feelings, which could lead to a decrease in user satisfaction. There is a need to solve these problems and realize efficient event management that provides high user satisfaction.

[0583] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0584] In this invention, the server includes means for inputting details of the scale of the event, means for checking the stock status of event items such as fixtures based on the details of the scale, means for proposing items that suit the scale based on the stock status, means for replying with the arrival date of the items based on the proposal, means for confirming the content of the reply, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, and means for analyzing the user's emotions and adjusting the timing of ordering based on the emotions, thereby making it possible to accurately check the stock status and adjust the timing of ordering according to the user's emotions.

[0585] "Details of the scale of the event" is specific information about the scale of the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers.

[0586] "Event fixtures and other items" refers to items such as chairs, tables, and display stands used at events.

[0587] "Stock status" is information indicating how many event items such as fixtures are currently in stock or available.

[0588] The "suggestion means" is a function that automatically selects the most suitable items for the scale of the event based on the inventory status and suggests them to the user.

[0589] "Item arrival date" is the date on which the event items, such as fixtures, ordered by the user are scheduled to arrive at the specified location.

[0590] "Automatic ordering means" is a function in which the system automatically orders an item after the user has confirmed the item.

[0591] "Means for analyzing emotions" is a function that analyzes the user's emotions and adjusts the system's operation based on the results.

[0592] The "means for adjusting the timing of ordering" is a function for adjusting the timing of ordering, such as whether to order an item immediately or to request reconfirmation, based on the user's feelings.

[0593] This invention is a system that checks the inventory status of event items such as fixtures based on the details of the scale of the event and adjusts the timing of ordering according to the user's emotions. A specific embodiment of this system will be described below.

[0594] Hardware and software used

[0595] The server manages inventory data using a database or cloud storage (e.g., Amazon RDS or Google Cloud Storage), and for sentiment analysis, it uses the Microsoft® Azure® sentiment analysis API and the Google Cloud Natural Language API.

[0596] The terminal provides an interface for users to input details of the event scale, check stock status and suggested items, and also acquires the user's emotions and sends them to the server.

[0597] The user inputs details of the event scale into the terminal, checks the system's suggestions and stock status, and proceeds with the ordering process by confirming the items and expressing their emotions.

[0598] System processing flow

[0599] 1. Enter the details of the scale

[0600] The user inputs details of the scale of the event (for example, "chairs and tables for 500 people") into the terminal.

[0601] 2. Obtaining inventory data

[0602] The terminal sends the entered size details to the server, which retrieves inventory data from a database or cloud storage.

[0603] 3. Check stock availability

[0604] The server checks the inventory data to see if the required items are in stock, for example, whether there are enough chairs and tables for 500 people.

[0605] 4. Returning the results

[0606] The server returns the results of the stock status check to the terminal, which then displays the results to the user.

[0607] 5. Confirmation of items

[0608] The user confirms the items they need on the terminal, for example, "100 chairs."

[0609] 6. Acquiring Emotion Data

[0610] The terminal uses an emotion engine to analyze the user's emotions.

[0611] 7. Sentiment Analysis

[0612] The device analyzes the user's emotions based on the acquired emotion data, for example, determining whether the user is expressing joy or anxiety.

[0613] 8. Order Adjustments

[0614] The server adjusts the timing of ordering based on the user's emotions: if the user expresses joy, the server orders the item immediately; if the user expresses anxiety, the server prompts the user for reconfirmation.

[0615] Specific examples

[0616] If a user inputs "500 people" as the size of the event, the device will send that information to the server. The server will search the database to see if there are enough chairs and tables in stock for 500 people. If the result is "in stock," the server will send that back to the device, which will then display "in stock" to the user.

[0617] When the user confirms "100 chairs," the device uses an emotion engine to analyze the user's emotions. If the user expresses joy, the server immediately orders 100 chairs. If the user expresses anxiety, the server asks, "Are you sure you want to order?" for confirmation.

[0618] Prompt Sentence Examples

[0619] "Please check the availability of event items for 500 people."

[0620] "Order 100 chairs. Adjust the order timing based on user sentiment."

[0621] In this way, the system can accurately check inventory status and adjust order timing according to the user's emotions, improving user satisfaction.

[0622] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0623] Step 1:

[0624] The user inputs details of the scale of the event into the terminal, for example, "chairs and tables for 500 people."

[0625] Input: Details of the event size (e.g., "500 chairs and tables")

[0626] Output: Detailed scale data is saved to the terminal.

[0627] Step 2:

[0628] The terminal transmits the entered size details to the server.

[0629] Input: Detailed scale data

[0630] Output: Scale details data is sent to the server.

[0631] Step 3:

[0632] The server retrieves inventory data from a database or cloud storage, for example, Amazon RDS or Google Cloud Storage.

[0633] Input: Detailed scale data

[0634] Output: Inventory data is retrieved to the server.

[0635] Step 4:

[0636] The server checks the inventory data to see if the required items are in stock, for example, whether there are enough chairs and tables for 500 people.

[0637] Input: Inventory data, detailed size data

[0638] Output: Stock check result (e.g. "In stock")

[0639] Step 5:

[0640] The server returns the results of the stock status check to the terminal.

[0641] Input: Inventory check result

[0642] Output: The inventory check results are sent to the terminal.

[0643] Step 6:

[0644] The terminal displays the inventory check results to the user.

[0645] Input: Inventory check result

[0646] Output: The result of the stock check is displayed to the user (e.g. "In stock").

[0647] Step 7:

[0648] The user confirms the items they need on the terminal, for example, "100 chairs."

[0649] Input: Item confirmation information (e.g. "100 chairs")

[0650] Output: Item confirmation information is saved on the device.

[0651] Step 8:

[0652] The device uses an emotion engine to analyze the user's emotions, for example, using the Microsoft Azure Sentiment Analysis API or the Google Cloud Natural Language API.

[0653] Input: User emotion data (e.g., user facial expressions and text)

[0654] Output: Sentiment analysis result (e.g. "joy")

[0655] Step 9:

[0656] The device analyzes the user's emotions based on the acquired emotion data, for example, determining whether the user is expressing joy or anxiety.

[0657] Input: Emotion data

[0658] Output: Sentiment analysis result (e.g. "joy")

[0659] Step 10:

[0660] The server adjusts the timing of ordering based on the user's emotions: if the user expresses joy, the server orders the item immediately; if the user expresses anxiety, the server prompts the user for reconfirmation.

[0661] Input: Sentiment analysis results, item confirmation information

[0662] Output: Order instruction (e.g. "Order now" or "Reconfirm")

[0663] Specific examples of operation

[0664] When a user enters "chairs and tables for 500 people," the device sends that information to the server. The server searches the database to see if 500 chairs and tables are in stock. If the result is "in stock," the server sends the result back to the device, which displays "in stock" to the user.

[0665] When the user confirms "100 chairs," the device uses an emotion engine to analyze the user's emotions. If the user expresses joy, the server immediately orders 100 chairs. If the user expresses anxiety, the server asks, "Are you sure you want to order?" for confirmation.

[0666] (Application example 2)

[0667] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0668] In conventional inventory management systems, item suggestions and ordering based on the scale of the event are often done manually, resulting in inefficiencies. Furthermore, orders are placed without considering the user's feelings, which can lead to lower user satisfaction. This can affect the success of the event. Furthermore, there are issues with the system, such as difficulty in checking inventory status and appropriately adjusting the timing of orders.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0670] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items suitable for the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, means for analyzing user emotions, and means for adjusting the timing of ordering based on the emotion analysis. This enables more efficient inventory management and ordering, and allows the timing of ordering to be adjusted according to the user's emotions, thereby improving user satisfaction.

[0671] "Event scale details" is information indicating the specific scale and conditions of the event, such as the date and time, location, number of people working, event space, sales target, number of customers, etc.

[0672] "Event fixtures and other items" refers to items such as furniture, equipment, and decorations used at events.

[0673] "Stock status" is information that indicates the current quantity of a particular item in stock.

[0674] The "suggestion means" is a means having a function of automatically selecting the most suitable item based on the stock status and suggesting it to the user.

[0675] "Item Arrival Date" is the date on which the ordered item is scheduled to arrive at the specified location.

[0676] The "automatic ordering means" is a means having a function of automatically ordering an item after the user has confirmed the item.

[0677] The "means for analyzing the user's emotions" is a means having a function for determining the emotions of the user from facial expressions, voice, etc.

[0678] The "means for adjusting the timing of an order" is a means having a function for appropriately changing the timing of an order based on an analysis of the user's emotions.

[0679] A system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures, a means for proposing items that suit the scale, a means for responding with information such as the date of arrival of items, a means for confirming the responses, making minor adjustments to added or deleted items as necessary, and automatically placing an order once the responses are confirmed, a means for analyzing user emotions, and a means for adjusting the timing of ordering based on the emotion analysis.

[0680] Hardware and software used

[0681] Hardware: Smartphones, smart glasses

[0682] Software: Python, EmotionRecognizer library, InventoryManager library

[0683] Data processing and calculation

[0684] Stock Check

[0685] The server uses the InventoryManager library to retrieve inventory information from the database. For example, to check the stock status of a specific product (e.g., item123), the server sends a query to the database to retrieve the current stock quantity.

[0686] sentiment analysis

[0687] The device uses the EmotionRecognizer library to analyze the user's facial expressions and voice to determine their emotions. For example, if the user is happy to see a product, the device will recognize that emotion as "joy."

[0688] Automatic ordering

[0689] The server adjusts the timing of ordering based on the user's emotions. For example, if the user expresses "joy," the server immediately orders the product. On the other hand, if the user expresses "anxiety," the server asks the user for reconfirmation.

[0690] Specific examples

[0691] Specific examples of inventory checks

[0692] A store clerk uses smart glasses to check the stock status of a specific product (e.g., item 123). The smart glasses display shows the current stock quantity in real time.

[0693] Sentiment analysis examples

[0694] If a customer is happy looking at a product, the device analyzes their facial expression and recognizes the emotion of "happiness." This allows the server to immediately order the product.

[0695] Prompt Sentence Examples

[0696] "Develop a system that analyzes customer sentiment and automatically orders products based on availability. If the customer is happy, place the order immediately; if they're anxious, ask for reassurance."

[0697] In this way, an inventory management and automatic ordering system can be realized in a physical store.

[0698] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0699] Step 1:

[0700] The user enters details about the scale of the event.

[0701] Input: Details of the event scale, such as date, time, location, number of people available, event space, sales target, number of customers.

[0702] Output: Detailed scale data entered.

[0703] Specific operation: The user inputs details of the scale of the event using a smartphone or smart glasses, and the device sends this information to the server.

[0704] Step 2:

[0705] The server checks the inventory status of fixtures and other event items.

[0706] Input: Scale details data.

[0707] Output: Inventory status data.

[0708] Specific operation: The server uses the InventoryManager library to retrieve the inventory status of the relevant event items from the database. For example, it checks the stock quantity of a specific product (e.g., item 123).

[0709] Step 3:

[0710] The server will suggest items that fit the scale.

[0711] Input: Inventory availability data.

[0712] Output: A list of suggested items.

[0713] Specific operation: Based on the inventory status data, the server automatically selects the most suitable items for the scale of the event and generates a list of suggested items.

[0714] Step 4:

[0715] The server will respond with the item arrival date, etc.

[0716] Input: Suggested items list.

[0717] Output: Response data such as item arrival date.

[0718] Specific operation: Based on the suggested item list, the server calculates the arrival date and other related information for each item and returns the answer to the user.

[0719] Step 5:

[0720] The user checks the answers and makes minor corrections to add or delete items as necessary.

[0721] Input: Response data such as item arrival date.

[0722] Output: The modified item list.

[0723] Specific operation: The user uses a smartphone or smart glasses to check the response from the server and modify the item list as necessary. The modified item list is then sent back to the server.

[0724] Step 6:

[0725] The server analyzes the user's emotions.

[0726] Input: Emotional data such as the user's facial expressions and voice.

[0727] Output: Sentiment analysis results.

[0728] Specific operation: The device uses the EmotionRecognizer library to analyze the user's facial expressions and voice to determine their emotions. The determined emotion data is sent to the server.

[0729] Step 7:

[0730] The server adjusts the timing of orders based on sentiment analysis.

[0731] Input: Sentiment analysis results, modified item list.

[0732] Output: Order instruction.

[0733] Specific operation: The server adjusts the timing of ordering based on the results of emotion analysis. For example, if the user expresses "joy," the server immediately orders the product. On the other hand, if the user expresses "anxiety," the server asks the user for reconfirmation.

[0734] Example 3

[0735] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0736] Conventional event management systems suggest and automatically order items based on inventory status, but they cannot make suggestions that take user emotions into account, which means they are unable to fully improve user satisfaction. Additionally, minor adjustments to the arrival date of suggested items and additions / deletions must be made manually, which is inefficient.

[0737] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0738] In this invention, the server includes means for inputting details of the scale of the event, means for checking the stock status of event items based on the details of the scale, means for proposing items appropriate to the scale based on the stock status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, and means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions. This enables optimal item suggestions and efficient automatic ordering that take the user's emotions into consideration.

[0739] "Event scale details" refers to the specific conditions and requirements of the event, such as the date and time of the event, location, number of people working, event space, sales targets, number of customers, etc.

[0740] "Stock status" refers to information indicating the current number of event items in stock and their availability.

[0741] "Item suggestion" refers to selecting the optimal combination of event items based on stock availability and details of the scale of the event, and presenting it to the user.

[0742] "Item Arrival Date" refers to the date that the proposed event item is scheduled to arrive at the user's specified date, time, and location.

[0743] "Automatic ordering" refers to the system automatically ordering event items based on the item list confirmed by the user.

[0744] "User emotion" refers to the emotional state, such as joy or anxiety, that the user exhibits when entering the event size details.

[0745] "Means for recognizing emotions" refers to technology that analyzes emotions from user input and behavior and identifies their emotional state.

[0746] "Means for adjusting item suggestions and automatic ordering based on emotions" refers to technology that changes the types of items suggested and the content of automatic ordering according to the recognized user emotions.

[0747] As an embodiment of the present invention, the following system is constructed.

[0748] The server provides a means for inputting details of the scale of the event. The user uses the terminal to input details of the scale of the event (e.g., number of participants, type of event, budget, etc.). The terminal transmits the input information to the server.

[0749] The server provides a means to check the stock status of event items based on the details of the scale of the received event. The server references the stock database to check the current stock status. For this process, general database software is used as the database management system (DBMS).

[0750] The server then provides a means to suggest items appropriate for the scale based on inventory status. Using software such as Python and TensorFlow, the server runs machine learning models to generate optimal combinations of items. For example, it selects the most suitable items from fixtures and decorations for 500 people.

[0751] Additionally, the server provides a means to respond with information such as the arrival date of the proposed item. The server checks the inventory status and delivery schedule of the proposed item and calculates the arrival date of the item. This information is provided to the user and can be viewed through the terminal.

[0752] The user can review the list of suggested items and make minor adjustments to add or remove items as needed. Once the user presses the confirm button, the server automatically places the order for the items. This process uses a common web framework (e.g., React) as the front end and a common server-side framework (e.g., Node.js) as the back end.

[0753] The system also provides a means to recognize the user's emotions and adjust item suggestions and automatic ordering based on those emotions. The server uses common emotion analysis software (e.g., IBM Watson (registered trademark)) as an emotion recognition engine to analyze emotions from the user's input and behavior. For example, if the user expresses joy, the server will suggest more luxurious items. On the other hand, if the user expresses anxiety, the server will suggest less expensive items.

[0754] As a specific example, a user enters "500-person event, budget 1 million yen, type of event wedding" into an input form on their terminal and presses the submit button. The server queries the inventory database and selects the optimal combination of fixtures and decorations for 500 people. For example, it proposes "50 tables, 500 chairs, and a complete set of decorations." The server checks the delivery schedule and notifies the user that "the proposed items can arrive on the specified date and time." The user checks the proposed list and enters "10 additional tables and 100 chairs" to update the list. When the user presses the confirm button, the server automatically places an order for "60 tables, 600 chairs, and a complete set of decorations" and sends the user a notification that the order has been completed.

[0755] An example prompt is "Please suggest furniture for an event for 500 people. The user expresses happiness."

[0756] In this way, the system can suggest optimal items based on the user's input and emotions, thereby improving user satisfaction. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0757] Step 1:

[0758] The user enters details about the scale of the event.

[0759] The user enters details of the scale of the event (for example, the number of participants, the type of event, the budget, etc.) into the input form on the terminal and presses the send button. The entered information is sent from the terminal to the server.

[0760] Input: Details of the event size (number of participants, type of event, budget, etc.)

[0761] Output: Details of the scale of the event sent to the server

[0762] Step 2:

[0763] The server checks the inventory status.

[0764] The server queries the inventory database based on the details of the size of the received event to check the current inventory status. The server retrieves inventory data using a database management system (DBMS).

[0765] Input: Details of the event size

[0766] Output: Stock status data

[0767] Step 3:

[0768] The server will suggest the best combination of items.

[0769] The server uses Python and TensorFlow to run machine learning models based on inventory data and details of the event's scale to generate the optimal combination of items, such as selecting the most suitable fixtures and decorations for 500 people.

[0770] Input: Inventory status data, details of event size

[0771] Output: Optimal item combination

[0772] Step 4:

[0773] The server will confirm the item arrival date and provide it to the user.

[0774] The server checks the inventory status and delivery schedule of the proposed item and calculates the arrival date of the item. The calculation result is notified to the user, who can check it through their terminal.

[0775] Inputs: Optimal item mix, availability data, delivery schedule

[0776] Output: Item arrival date information

[0777] Step 5:

[0778] The user reviews the proposed items and adds or removes them as necessary.

[0779] The user uses the terminal to review the list of suggested items and add or remove items as needed. For example, if the user needs additional tables or chairs, they add them to the list.

[0780] Input: A list of suggested items

[0781] Output: A list of modified items

[0782] Step 6:

[0783] When the user presses the confirm button, the server automatically places an order for the item.

[0784] When the user presses the confirm button, the device sends that information to the server. The server then automatically places an order for the item based on the received information. This process uses a common web framework (e.g., React) as the front end and a common server-side framework (e.g., Node.js) as the back end.

[0785] Input: List of modified items, confirmation button press information

[0786] Output: Order completion notification

[0787] Step 7:

[0788] The server recognizes the user's emotions and adjusts suggestions and automatic ordering.

[0789] The server uses common emotion analysis software (e.g., IBM Watson) as an emotion recognition engine to analyze emotions from the user's input and behavior. For example, if the user expresses joy, the server will suggest more luxurious items. On the other hand, if the user expresses anxiety, the server will suggest less expensive items.

[0790] Input: User input and behavioral data

[0791] Output: Sentiment-based item suggestions and automated ordering adjustments

[0792] (Application example 3)

[0793] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0794] While conventional event planning systems have the ability to suggest items based on inventory status, they have problems in that they are unable to take user sentiment into account when making suggestions or adjust automatic ordering. Furthermore, there are insufficient methods for users to easily plan events using their smartphones. This has led to issues such as lower user satisfaction and a decline in the efficiency of event planning.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0796] In this invention, the server includes a means for inputting details of the scale of the event, a means for checking the inventory status of event items such as fixtures based on the details of the scale, a means for proposing items appropriate to the scale based on the inventory status, a means for responding with information such as the arrival date of the items based on the proposal, a means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, a means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions, and an application installed on the smartphone. This enables optimal item suggestions and automatic ordering that take the user's emotions into consideration, improving the efficiency of event planning and user satisfaction.

[0797] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0798] "Event fixtures and other items" refers to items such as furniture and equipment used at events.

[0799] "Inventory status" refers to information indicating how many of a particular item are currently in stock.

[0800] "Item suggestions" refers to presenting the optimal combination of items based on stock availability and details of the event scale.

[0801] "Item Arrival Date" refers to the date the proposed item will arrive at the date, time and location specified by the user.

[0802] "Automatic ordering" refers to the process where the system automatically orders an item when the user presses the confirm button.

[0803] "Means for recognizing emotions" refers to technology for analyzing a user's emotions and adjusting the system's behavior based on those emotions.

[0804] "Application installed on a smartphone" refers to software that runs on a smartphone and allows users to plan events and receive item suggestions.

[0805] A system for carrying out this invention is configured as follows: The server includes means for inputting details of the scale of the event, means for checking the stock status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the stock status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions, and an application installed on a smartphone.

[0806] Hardware and Software Configuration

[0807] Hardware: Smartphone

[0808] Software: Python, EmotionRecognizer library, InventoryManager library, OrderProcessor library

[0809] Processing flow

[0810] 1. Obtaining user input: The user inputs the date and scale of the event using their smartphone, which then sends the details of the event scale to the server.

[0811] 2. Emotion Analysis: The server recognizes emotions based on the user's input using the EmotionRecognizer library.

[0812] 3. Item Suggestion: The server checks the inventory status using the InventoryManager library and suggests items that fit the scale. Based on the results of sentiment analysis, if the user expresses joy, it suggests luxury items, and if the user expresses anxiety, it suggests inexpensive items.

[0813] 4. Item arrival date response: The server verifies that the proposed item will arrive at the date, time and location specified by the user and provides this information to the user.

[0814] 5. Order confirmation and automatic ordering: The user reviews the proposal and adds or removes items as needed. Once the user presses the confirm button, the items are automatically ordered using the OrderProcessor library.

[0815] Specific examples

[0816] For example, suppose a user is planning an event for 100 people on 2023-12-25. When the user enters this information using their smartphone, the server receives the information and analyzes the user's emotions using the EmotionRecognizer library. If the user expresses joy, the server suggests luxurious items using the InventoryManager library. When the user confirms the suggestions and presses the confirm button, the items are automatically ordered using the OrderProcessor library.

[0817] Prompt Sentence Examples

[0818] The user enters the date and size of the event. Perform sentiment analysis and suggest the best items based on stock availability. If the user expresses joy, suggest luxury items; if the user expresses anxiety, suggest cheaper items.

[0819] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0820] Step 1:

[0821] The user uses a smartphone to input the date and scale of the event. The input information is sent to the server as details of the event scale. Specifically, if the user is planning an event for "100 people" on "2023-12-25," that information is sent to the server. The input data is the date and scale of the event, and the output data is the details of the event scale sent to the server.

[0822] Step 2:

[0823] The server uses the EmotionRecognizer library to recognize emotions based on user input. The input data is the user's event details, and the output data is the user's emotion analysis result. Specifically, it analyzes whether the user is expressing joy or anxiety.

[0824] Step 3:

[0825] The server checks the inventory status using the InventoryManager library and suggests items that fit the scale. The input data is the details of the scale of the event and the result of emotion analysis, and the output data is a list of suggested items. Specifically, if the emotion is joyful, luxurious items are suggested, and if the emotion is anxiety, inexpensive items are suggested.

[0826] Step 4:

[0827] The server confirms that the proposed items will arrive at the user's specified date, time, and location, and provides that information to the user. The input data is the list of proposed items and the user's specified date, time, and location, and the output data is the confirmation result of the item arrival date. Specifically, it confirms that the proposed items will arrive at the specified location on "2023-12-25."

[0828] Step 5:

[0829] The user reviews the suggestions and adds or removes items as needed. The input data is a list of suggested items, and the output data is a list of revised items. Specifically, the user reviews the suggested items and adds or removes them as needed.

[0830] Step 6:

[0831] When the user presses the Confirm button, the server automatically places an order for the items using the OrderProcessor library. The input data is the list of modified items, and the output data is the order confirmation result. Specifically, when the user presses the Confirm button, the system automatically places an order for the items and notifies the user that the order has been completed.

[0832] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0833] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[0834] An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0835] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0836] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0837] [Second embodiment]

[0838] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0839] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0840] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0841] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0842] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0843] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0844] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0845] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0846] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is performed by the processor 28 executing the specific processing program 56 on the RAM 30.

[0847] This is realized by operating as a specific processing unit 290 in accordance with the specific processing program 56 executed above.

[0848] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0849] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0850] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0851] "Example 1"

[0852] The system of the present invention provides a means for inputting details of the scale of an event through a user interface. Examples of details of the scale include the date, time, location, number of people working, event space, sales target, number of attendees, etc. For example, a user may input that they are planning to hold an event for 500 people in Tokyo on December 25, 2022, and that they need event items such as fixtures for the event.

[0853] "Example 2"

[0854] The system then provides a means to check the availability of event items, such as fixtures, based on the input size details. The availability can be obtained from a database, cloud storage, etc. For example, the system can search the database and confirm that event items, such as fixtures for 500 people, are available in stock.

[0855] "Example 3"

[0856] Furthermore, the system of the present invention provides a means to suggest items appropriate for the scale based on inventory availability. These suggestions are generated automatically using algorithms and machine learning models. For example, the system could suggest the optimal combination of event items, such as furniture for a 500-person event.

[0857] "Example 4"

[0858] The system of the present invention also provides a means for responding with information such as the item arrival date based on the proposal. For example, the system may confirm that the proposed item will arrive at the date, time, and location specified by the user, and provide that information to the user.

[0859] "Example 5"

[0860] Finally, the system of the present invention provides a means for users to review their responses, fine-tune the items they add or remove as needed, and automatically place an order upon confirmation. For example, a user can review the list of suggested items and add or remove items as needed. After that, the user can press the confirm button, and the system will automatically place an order for the items.

[0861] The processing flow of each embodiment will be described below.

[0862] "Example 1"

[0863] Step 1: The user inputs details of the scale of the event through the user interface of the system of the present invention. Specifically, the user inputs that an event for 500 people will be held in Tokyo on December 25, 2022, and that event items such as fixtures are required.

[0864] "Example 2"

[0865] Step 1: The system of the present invention creates an event item such as fixtures based on the input scale details.

[0866] Specifically, the system searches the database to confirm that event items, such as fixtures, for 500 people are available in stock.

[0867] "Example 3"

[0868] Step 1: The system of the present invention proposes items appropriate for the scale based on inventory status. Specifically, the system proposes the optimal combination from event items such as fixtures for 500 people that are automatically generated using algorithms and machine learning models.

[0869] "Example 4"

[0870] Step 1: The system of the present invention responds with the item arrival date etc. based on the proposal. Specifically, the system confirms that the proposed item will arrive at the date, time and location specified by the user and provides that information to the user.

[0871] "Example 5"

[0872] Step 1: The user reviews the list of suggested items and adds or removes items as needed.

[0873] Step 2: When the user presses the confirm button, the system of the present invention automatically places an order for the item.

[0874] Example 1

[0875] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] In conventional event management systems, entering details about the scale of an event was time-consuming, and the process of checking the stock status of necessary fixtures and items and proposing the most suitable items was cumbersome. Furthermore, the instructions and suggestions generated based on the information entered by the user were insufficient, making it difficult to efficiently reflect user feedback. This resulted in the problem of event preparation and management taking a great deal of time and effort.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0878] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate for the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, means for creating prompt text using a generative AI model, and means for presenting the prompt text to the user and receiving feedback. This allows the user to efficiently input details of the event, receive suggestions for necessary items, and confirm and modify the generated prompt text, thereby enabling quick and accurate event preparation and management.

[0879] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, location, number of people working, event space, sales targets, and number of customers.

[0880] "Event fixtures and other items" refers to the goods and equipment necessary for holding an event, such as chairs, tables, exhibition booths, and audio equipment.

[0881] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0882] The "suggestion means" refers to a function that selects the most suitable items that match the details of the scale of the event based on the stock situation and suggests them to the user.

[0883] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[0884] "Automatic ordering means" refers to the function of automatically ordering necessary event items such as fixtures after the user confirms and confirms the proposal content.

[0885] "Generative AI model" refers to a model that uses artificial intelligence technology to generate prompt sentences based on event details entered by a user.

[0886] "Prompt sentence" refers to a sentence created by a generative AI model that contains specific instructions or suggestions regarding event details and required items.

[0887] "Feedback" refers to corrections or additional input the user makes to the generated prompt sentence.

[0888] This invention is a system that inputs details of the scale of an event, checks the stock status of necessary fixtures and other event items, suggests optimal items, responds with information such as the item's arrival date, makes minor adjustments to add or remove items as necessary, and automatically places orders once confirmed. It also includes a function to create prompts using a generative AI model, present them to the user, and receive feedback.

[0889] Hardware and software used

[0890] Hardware

[0891] Server: Receives, stores, processes data, and runs generative AI models.

[0892] Terminal: Used by the user to enter details of the event and to check and modify the generated prompt text.

[0893] software

[0894] Database: Use a database such as MySQL to store details of the event size and inventory status.

[0895] Generative AI models: Use generative AI models, such as OpenAI's GPT-3, to create prompts.

[0896] Web application: Provides an interface for users to enter details of the event size and review / modify the generated prompt text.

[0897] Data processing and calculation

[0898] Enter details about the scale of your event

[0899] The user enters details of the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.) through the web application. The entered data is sent to the server and stored in a database.

[0900] Check availability and suggest items

[0901] The server checks the inventory status of necessary event items such as fixtures based on the details of the scale of the event stored in the database, and selects the most suitable items based on the inventory status and suggests them to the user.

[0902] Item arrival date response and automatic ordering

[0903] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures.

[0904] Prompt creation using a generative AI model

[0905] The server uses a generative AI model to create a prompt based on the details of the event entered by the user, which is then presented to the user.

[0906] Receiving feedback

[0907] The user can check the generated prompt sentence and make corrections or add additional inputs as necessary. The server can receive the user's feedback and generate the prompt sentence again.

[0908] Specific examples

[0909] Example input

[0910] The user enters details about the event, such as:

[0911] Date: December 25, 2022

[0912] Location: Tokyo

[0913] Working staff: 50

[0914] Event space: 100 square meters

[0915] Sales target: 1 million yen

[0916] Number of customers: 500

[0917] Prompt Sentence Examples

[0918] An example of a prompt created by the server using a generative AI model:

[0919] Event details are as follows:

[0920] Date: December 25, 2022

[0921] Location: Tokyo

[0922] Working staff: 50

[0923] Event space: 100 square meters

[0924] Sales target: 1 million yen

[0925] Number of customers: 500

[0926] List the fixtures and items you need for this event, and also suggest a strategy for achieving your sales goals.

[0927] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1:

[0930] The user accesses the system's web application using a terminal and enters details of the scale of the event, such as the date and time, location, number of people working, event space, sales target, number of customers, etc., into a form, and clicks the "Submit" button.

[0931] Input: Details of the event scale (date, time, location, number of people working, event space, sales target, number of customers)

[0932] Output: The input data is sent to the server

[0933] Step 2:

[0934] The server receives the details of the event size sent by the user and stores them in a database. The server receives the HTTP request, extracts the event details from the request body, establishes a database connection, executes an SQL query, and stores the data.

[0935] Input: Details of the event size sent by the user

[0936] Output: Details of the event size stored in the database

[0937] Step 3:

[0938] The server checks the stock status of necessary event items such as fixtures based on the details of the event scale stored in the database. The server accesses the stock database and executes an SQL query to obtain the stock status.

[0939] Input: Details of the event size stored in the database

[0940] Output: Stock status data

[0941] Step 4:

[0942] The server generates a list of items based on inventory availability that fit the size of the event, and uses a Python script to calculate the required number of fixtures and staff and select the most appropriate items.

[0943] Input: Inventory status data, details of event size

[0944] Output: A list of suggested items

[0945] Step 5:

[0946] The server uses the generative AI model to create a prompt based on the event details entered by the user. The server sends a request to the generative AI model's API to generate a prompt that includes the event details. The generated prompt is returned to the server.

[0947] Input: Details of the event size, list of proposed items

[0948] Output: Generated prompt statement

[0949] Step 6:

[0950] The server presents the generated prompt text to the user, who can then use the terminal to check the prompt text and make corrections or add additional input as necessary. When the user clicks the "Confirm" button, the server generates the prompt text again.

[0951] Input: Generated prompt text

[0952] Output: User feedback

[0953] Step 7:

[0954] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures. The server then accesses the ordering system and places an order for the necessary items.

[0955] Input: User confirmed suggestion

[0956] Output: Item arrival date, order confirmation

[0957] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[0958] (Application example 1)

[0959] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0960] Conventional event management systems provided a way to input details about the scale of an event and a way to check the inventory status of event items such as fixtures, but lacked a way to track the preparation status of the event and progress on the day in real time, or to generate reports after the event based on data such as the degree of sales target achievement and the number of customers served. This made overall event management cumbersome and made efficient operation difficult.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0962] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for tracking the preparation status of the event and progress on the day in real time, and means for generating a report after the event based on data such as the degree of sales target achievement and the number of customers served. This allows for efficient overall event management and can increase the success rate of the event.

[0963] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[0964] "Event fixtures and other items" refers to items such as tables, chairs, display shelves, posters, flyers, banners, etc. that are necessary for the event.

[0965] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[0966] "Suggestion means" refers to a function that automatically selects the most suitable item based on stock availability and suggests it to the user.

[0967] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified location.

[0968] "Automatic ordering means" refers to a function that allows the user to check the proposed content, add or delete items as necessary, and then automatically order the confirmed items.

[0969] "Preparation status" refers to information indicating how far preparations for an event have progressed.

[0970] "Progress" refers to real-time tracking of how activities and plans are progressing on the day of the event.

[0971] "Sales target achievement rate" refers to an indicator that shows the degree to which actual sales were achieved relative to the sales target set after the event ended.

[0972] "Number of customers served" refers to the total number of customers served during the event.

[0973] "Report generation means" refers to a function that automatically generates a report after the event based on data such as the degree of sales target achievement and the number of customers served.

[0974] A system for implementing the present invention includes an event management application installed on a terminal such as a smartphone, a tablet, etc. A specific embodiment of this system will be described below.

[0975] Hardware and software used

[0976] Hardware: Smartphones, tablets

[0977] Software: Event management applications, inventory management systems, report generation tools

[0978] Database: MySQL

[0979] Generative AI model: GPT-4

[0980] Data processing and calculation

[0981] Enter event details

[0982] Users enter details about their event into a form in the event management application, which then stores the information in a MySQL database, including the date, time, location, staffing, event space, sales targets, and number of attendees.

[0983] Resource Suggestions

[0984] The server sends prompts to a generative AI model (GPT-4) based on the input event details, which then suggests the necessary fixtures, promotional items, and staffing. The suggested resources are then displayed in the application's UI.

[0985] Example prompt sentence:

[0986] Event Details:

[0987] Date: December 25, 2022

[0988] Location: Tokyo

[0989] Operating staff: 10

[0990] Event space: 50 square meters

[0991] Sales target: 1 million yen

[0992] Number of customers: 500

[0993] Please suggest the fixtures, promotional items, and staffing required for this event.

[0994] Inventory management

[0995] The server retrieves the stock status of the suggested items from the inventory management system and, if there is a shortage, gives the user the option to reorder.

[0996] Progress management

[0997] The server tracks the preparation status and progress of the event in real time and displays it on the application's dashboard, allowing users to see the progress of the event at a glance.

[0998] Report Generation

[0999] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[1000] Specific examples

[1001] For example, if a user is planning an event for 500 people in Tokyo on December 25, 2022, they would use the system as follows:

[1002] 1. The user enters the details of the event into the application.

[1003] 2. The server sends prompts to the generative AI model to suggest the necessary fixtures, promotional items, and staff placement.

[1004] 3. Check the availability of the suggested items and place an additional order if there is a shortage.

[1005] 4. Track event preparation and on-the-day progress in real time.

[1006] 5. After the event, generate a report based on data such as sales target achievement and number of customers served.

[1007] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[1008] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1009] Step 1:

[1010] The user enters details of the event into a form in the event management application. The input items include the date and time, location, number of people available, event space, sales target, number of customers, etc. The input data is saved in a MySQL database. This registers the basic information of the event in the system.

[1011] Input: Date and time, location, number of employees, event space, sales target, number of customers

[1012] Output: Event details stored in a MySQL database

[1013] Step 2:

[1014] The server retrieves event details from a MySQL database and sends prompts to a generative AI model (GPT-4), which includes detailed information about the event. The model then suggests the necessary fixtures, promotional items, and staffing.

[1015] Input: Event details retrieved from a MySQL database

[1016] Output: Proposals from the generative AI model (furniture, promotional items, staff placement)

[1017] Step 3:

[1018] The server receives suggestions from the generative AI model and displays them in the application's UI. The user reviews the suggested items and adds or removes them as needed. Once the user confirms the suggestions, the server orders the items using an automated ordering mechanism.

[1019] Input: Suggestions from the generative AI model, user confirmation and corrections

[1020] Output: Confirmed item list, automatic ordering

[1021] Step 4:

[1022] The server retrieves the stock status of the suggested item from the inventory management system. If the item is out of stock, the server gives the user the option to reorder. When the user places an order, the server again places an order with the inventory management system.

[1023] Input: Confirmed item list, stock status from inventory management system

[1024] Output: Stock check results, additional order options

[1025] Step 5:

[1026] The server tracks the preparation and progress of the event in real time, including the arrival status of each item, staffing status, etc. The server displays this information on the application's dashboard so that users can see the progress.

[1027] Input: Arrival status of each item, staff allocation status

[1028] Output: Real-time progress information, dashboard display

[1029] Step 6:

[1030] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[1031] Input: Sales target achievement rate, number of customers

[1032] Output: Report in PDF format

[1033] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[1034] Example 2

[1035] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1036] There is a need for a system that can quickly and accurately check the inventory status of fixtures and other event items according to the scale of the event, and can suggest and automatically order the most suitable items. However, conventional systems require users to manually check inventory status and select the necessary items, which is time-consuming and labor-intensive. Furthermore, if the inventory status confirmation and item suggestions are inaccurate, it can hinder event preparations.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1038] In this invention, the server includes means for transmitting the details of the scale entered by the user to the server, means for the server to receive the details of the scale and access the database to obtain the stock status, and means for the server to analyze the obtained stock status and transmit the analysis results to the terminal. This allows the user to simply enter the details of the scale of the event, and the server will automatically check the stock status, suggest the most suitable items, and automatically place an order.

[1039] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers.

[1040] "Inventory status" refers to information stored in a database or cloud storage that indicates the current stock and availability of fixtures and other event items.

[1041] The "suggestion means" is a function for automatically selecting the most suitable items for the scale of the event based on the stock situation and proposing them to the user.

[1042] The "automatic ordering means" is a function that allows the user to check the proposed items, add or delete items as necessary, and then automatically order the confirmed items.

[1043] A "server" is a computer system that receives input from a user, accesses a database to obtain inventory status, and transmits the analysis results to a terminal.

[1044] A "terminal" is a device through which a user inputs details of the scale of an event and receives and displays the analysis results from the server.

[1045] The "database" is a system for storing the inventory status of fixtures and other event items and providing information in response to queries from the server.

[1046] The "analysis result" is information indicating whether the item required for the user's request is available in stock, based on the inventory status acquired by the server.

[1047] This invention is a system that checks the inventory status of event items such as fixtures based on the details of the scale of the event, and then proposes and automatically orders the most suitable items. This system is composed of multiple components, including users, terminals, and a server.

[1048] Hardware and software used

[1049] Hardware: Servers, database servers, cloud storage, user devices (PCs, smartphones, tablets, etc.)

[1050] Software: Database management system (e.g., MySQL, PostgreSQL), cloud storage service (e.g., Amazon S3, Google Cloud Storage), web browser or dedicated application

[1051] System Operation Overview

[1052] User operations

[1053] The user enters details of the scale of the event (e.g., furniture for 500 people is required) into an input form on the terminal. The entered information is sent from the terminal to the server.

[1054] Server Processing

[1055] The server receives the size details sent from the terminal and connects to the database management system to execute a query to check the stock status. The server analyzes the stock status retrieved from the database and checks whether the required items for the user's request are available in stock. The analysis result is sent from the server to the terminal.

[1056] Terminal display

[1057] The terminal displays the analysis results received from the server to the user. The user can check the proposed items and add or delete them as necessary. The finalized items are then ordered by the automatic ordering means.

[1058] Specific examples

[1059] For example, if a user inputs "We need furniture for 500 people," the process will proceed as follows:

[1060] 1. The user enters "I need furniture for 500 people" into the input form on the terminal.

[1061] 2. The device sends this information to the server.

[1062] 3. The server receives this information and connects to the MySQL database to query the inventory.

[1063] 4. The server retrieves from the database the information that "furniture for 500 people is available in stock."

[1064] 5. The server analyzes this information and verifies that there is sufficient inventory for the user's request.

[1065] 6. The server sends the analysis results to the device.

[1066] 7. The terminal displays the analysis results to the user, informing them that "furniture for 500 people is available in stock."

[1067] Prompt Sentence Examples

[1068] An example of a prompt sentence to input to the generative AI model is as follows:

[1069] "We need furniture for 500 people. Please check availability."

[1070] By inputting this prompt into the generative AI model, the system performs the above process and checks the inventory status.

[1071] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1072] Step 1:

[1073] The user inputs the size details.

[1074] Input: Details of the event size (e.g., furniture for 500 people required)

[1075] Specific behavior: The user uses a web browser or a dedicated application to enter the quantity of the item they want into an input form.

[1076] Output: The scale details are entered into the terminal.

[1077] Step 2:

[1078] The terminal sends the entered size details to the server.

[1079] Input: User-entered size details

[1080] Specific operation: The terminal generates an HTTP request and sends a POST request to the server.

[1081] Output: The scale details are sent to the server.

[1082] Step 3:

[1083] The server receives the size details and accesses the database.

[1084] Input: Size details sent from the terminal

[1085] What happens: The server receives the HTTP request, connects to a database management system (e.g., MySQL), and generates an SQL query to check inventory status.

[1086] Output: The SQL query is sent to the database.

[1087] Step 4:

[1088] The server retrieves the inventory status from the database.

[1089] Input: SQL query

[1090] What happens: The database returns the query results, and the server receives them.

[1091] Output: Stock availability data is retrieved to the server.

[1092] Step 5:

[1093] The server analyzes the stock status obtained.

[1094] Input: Inventory status data

[1095] What happens: The server compares the quantity in stock with the quantity requested by the user to determine if the required item is available in stock.

[1096] Output: Analysis results are generated.

[1097] Step 6:

[1098] The server sends the analysis results to the device.

[1099] Input: Analysis results

[1100] Specific operation: The server generates an HTTP response and sends it to the device.

[1101] Output: The analysis results are sent to the terminal.

[1102] Step 7:

[1103] The terminal displays the analysis results to the user.

[1104] Input: Analysis results sent from the server

[1105] Specific operation: The device displays the analysis results on the screen so that the user can check them.

[1106] Output: The analysis results are displayed to the user.

[1107] (Application example 2)

[1108] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1109] In conventional logistics centers, it was difficult to check the inventory status of fixtures and event items according to the scale of the event in real time, which led to problems with shortages and excess inventory. In addition, the process of proposing the most suitable items based on the inventory status and quickly ordering them was complicated, making efficient operation difficult.

[1110] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting details of the scale of the event; means for checking the inventory status of event items such as fixtures based on the details of the scale; means for proposing items appropriate to the scale based on the inventory status; means for responding with information such as the arrival date of the items based on the proposal; means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing an order once confirmed; and means installed on a smartphone that allows a manager or staff member at a logistics center to check the inventory status in real time. This makes it possible to check the inventory status of fixtures and event items appropriate to the scale of the event in real time at the logistics center and quickly propose and order the most appropriate items.

[1111] "Event scale details" is information indicating the specific scale and conditions of the event, such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1112] "Event fixtures and other items" refers to items such as furniture, equipment, and decorations necessary for holding an event.

[1113] "Stock status" is information indicating how many event items such as fixtures are currently in stock and how many are available for use.

[1114] "Suggestion method" refers to the function that automatically selects the most suitable items based on stock availability and suggests items that suit the scale of the event.

[1115] "Item Arrival Date" means the date the proposed item is expected to ship from the distribution center and arrive at the specified location.

[1116] "Automatic ordering means" refers to a function that checks the contents of the proposed items, adds or deletes as necessary, and then automatically carries out the ordering procedure once the contents are confirmed.

[1117] "Means installed on smartphones" refers to applications that allow logistics center managers and staff to check inventory status in real time using their smartphones.

[1118] The system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures based on the details of the scale, a means for proposing items that suit the scale based on the stock status, a means for responding with the arrival date of the items etc. based on the proposal, a means for confirming the content of the response, making minor adjustments to added items or deleted items as necessary, and automatically placing an order once confirmed, and a means that is installed on a smartphone and allows managers and staff at the logistics center to check the stock status in real time.

[1119] The server provides an interface for users to input details about the scale of the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers. The server receives this information and stores it in a database.

[1120] The server then searches its database to see if there is enough fixtures and other event items available based on the size details entered. For example, it checks whether there is enough fixtures for 500 people available. This availability can come from cloud storage or a local database.

[1121] After checking the inventory status, the server uses a generative AI model to automatically select the most suitable items and suggest them to the user, including details about the items and their expected arrival dates. The user can review the suggestions and add or remove items as needed.

[1122] Once the proposal is confirmed, the server initiates an automated ordering process, which ensures the required items are shipped from the distribution center and delivered to the specified location.

[1123] The application installed on a smartphone allows managers and staff at the distribution center to check inventory status in real time. The application uses Python and the requests library to retrieve inventory information from the server and display it to the user.

[1124] For example, if a logistics center manager inputs the size of an event for 500 people, the system will search the database to see if there is enough fixtures available in stock for 500 people. It will then use a generative AI model to suggest the most suitable items, and once the user has confirmed and revised the suggestions, it will automatically process the order.

[1125] Example prompts to input to a generative AI model:

[1126] "If your event size is 500 people, check how many fixtures you have available in stock."

[1127] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1128] Step 1:

[1129] The user inputs details about the scale of the event. Using a smartphone application, the user inputs details such as the date and time of the event, location, number of people working, event space, sales target, and number of customers. The input data is sent to the server.

[1130] Input: Event date and time, location, number of people in operation, event space, sales target, number of customers

[1131] Output: Detailed data on the scale of events sent to the server

[1132] Step 2:

[1133] The server saves the detailed data of the scale of the received event in a database. The server converts the input data into an appropriate format and stores it in the database.

[1134] Input: Detailed data on the scale of the event

[1135] Output: Detailed event size data stored in the database

[1136] Step 3:

[1137] The server searches the database to check the stock status of event items such as fixtures. The server calculates the number of items required based on the details of the scale of the event and retrieves stock information from the database or cloud storage.

[1138] Input: Detailed data on the scale of the event

[1139] Output: Stock status data

[1140] Step 4:

[1141] The server uses a generative AI model to automatically select the most suitable items and suggest them to the user. The server inputs inventory status data into the generative AI model and generates a list of the most suitable items. The generated list is then suggested to the user.

[1142] Input: Inventory status data

[1143] Output: A list of the best items

[1144] Step 5:

[1145] The user checks the list of suggested items and adds or removes them as necessary. The user then uses a smartphone application to check and modify the suggestions. The modified data is then sent to the server.

[1146] Input: List of best items

[1147] Output: A list of modified items

[1148] Step 6:

[1149] The server receives the revised list of items and initiates an automated ordering process. The server then performs the process to order the required items based on the revised list. The ordering information is then sent to the logistics center.

[1150] Input: List of modified items

[1151] Output: Order information

[1152] Step 7:

[1153] An application installed on a smartphone displays real-time inventory status to the distribution center manager and staff. The application retrieves inventory information from the server and displays it to the user.

[1154] Input: Inventory information

[1155] Output: Stock status displayed on smartphone

[1156] Example 3

[1157] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1158] Conventional event item suggestion systems often required manual processes such as checking inventory, suggesting items, confirming arrival dates, and placing an order, resulting in inefficiencies. Furthermore, they lacked functionality to automatically suggest the optimal combination of items needed by the user, placing a significant burden on the user. Furthermore, there was an insufficient means for users to confirm the arrival dates of the suggested items, potentially resulting in delays in event preparations. To solve these issues, an efficient and automated system was needed.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1160] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items, means for proposing items appropriate to the scale, means for responding with information such as item arrival dates, means for confirming the responses, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the responses are confirmed, means for acquiring inventory data from a database, means for preprocessing the acquired inventory data, means for generating item suggestions using a machine learning model, means for confirming item arrival dates in cooperation with a delivery system, and means for providing information through a user interface. This makes it possible to propose optimal items based on inventory status, confirm arrival dates, and automate ordering.

[1161] "Event scale details" refers to information such as the date and time of the event, location, number of people working, event space, sales targets, and number of customers.

[1162] "Inventory status" refers to the current inventory quantity and status of an item as recorded in the database.

[1163] "Item suggestion" refers to automatically selecting and suggesting the optimal combination of items based on the details of the event's scale and inventory status.

[1164] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified date, time, and location.

[1165] "Automatic ordering" refers to the process where the system automatically orders the item once the user confirms the proposal and presses the confirm button.

[1166] "Database" means an information management system for storing inventory data and other related information.

[1167] "Preprocessing" refers to processes such as filling in missing values ​​and normalizing data before applying the acquired data to analysis or machine learning models.

[1168] A "machine learning model" refers to an algorithm that learns patterns based on data and makes predictions and classifications.

[1169] "Delivery System" refers to an external system or service that allows you to check the delivery status and arrival date of an item.

[1170] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[1171] MODE FOR CARRYING OUT THE INVENTION

[1172] This invention is a system that proposes optimal items based on the details of the scale of the event, confirms the arrival date of the items, and finally places an automatic order. A specific embodiment of this system will be described below.

[1173] System configuration

[1174] The server includes a means for inputting details of the scale of the event, a means for checking the stock status of event items, a means for proposing items that suit the scale, a means for responding with information such as the arrival date of items, a means for confirming the content of the responses, making minor adjustments to added items or deleted items as necessary, and automatically placing orders once the responses are confirmed, a means for acquiring inventory data from a database, a means for preprocessing the acquired inventory data, a means for generating item suggestions using a machine learning model, a means for confirming item arrival dates in cooperation with a delivery system, and a means for providing information through a user interface.

[1175] Hardware and software used

[1176] Database: Information management system for storing inventory data (e.g. MySQL)

[1177] Preprocessing software: A program for preprocessing data (e.g., the Pandas library in Python)

[1178] Machine learning models: Algorithms that learn patterns from data and make predictions or classifications (e.g., TensorFlow, Scikit-learn)

[1179] Delivery system: An external system to check the item's delivery status and arrival date (e.g. FedEx API)

[1180] User interface: The screens and controls that allow users to interact with the system (e.g., the Django framework)

[1181] System Operation

[1182] The server first retrieves inventory data from the database. The retrieved data is preprocessed using Python, specifically by filling in missing values ​​and normalizing the data. Next, a machine learning model is used to suggest the optimal combination of items based on the scale of the event. For example, it suggests the optimal combination of event items, such as fixtures for 500 people.

[1183] The server verifies that the proposed items will arrive at the user's specified date, time, and location. To do this, it communicates with the delivery system to confirm the arrival date. The confirmed arrival date and information about the proposed items are provided to the user through a user interface.

[1184] The user checks the list of suggested items and adds or removes items as needed. When the user presses the confirm button, the server automatically places an order for the items. This ordering process is performed in conjunction with the ERP system.

[1185] Specific examples

[1186] As a concrete example, the following prompt sentence is input into the generative AI model:

[1187] Example prompt sentence:

[1188] "Please suggest the fixtures needed for an event for 500 people. The event will be held on December 25, 2023, in Shibuya Ward, Tokyo."

[1189] Based on this prompt, the server retrieves inventory data, performs preprocessing, and generates optimal item recommendations using a machine learning model. It then connects with the delivery system to confirm the item arrival date and provides the information to the user. Once the user confirms the recommendations and presses the confirm button, the server automatically places an order for the item.

[1190] In this way, the system of the present invention realizes the automation of proposing the most suitable item based on the inventory status, confirming the arrival date, and placing an order. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1191] Program processing flow

[1192] Step 1: Enter details about the size of your event

[1193] The user inputs details of the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.) through the terminal. The input data is sent to the server.

[1194] Input: Details of the event scale (date, time, location, number of people working, event space, sales target, number of customers, etc.)

[1195] Output: Detailed data on the scale of the event sent to the server

[1196] Step 2: Retrieving inventory data

[1197] The server retrieves inventory data from the database by executing SQL queries to extract the required inventory information.

[1198] Input: Event size details

[1199] Output: Inventory data retrieved from the database

[1200] Step 3: Preprocessing the data

[1201] The server preprocesses the acquired inventory data, specifically by using the Python Pandas library to impute missing values ​​and normalize the data.

[1202] Input: Inventory data retrieved from the database

[1203] Output: Preprocessed inventory data

[1204] Step 4: Generate item suggestions

[1205] The server uses machine learning models to suggest the optimal combination of items depending on the scale of the event, for example, from among event items such as fixtures for 500 people.

[1206] Input: Pre-processed inventory data, event size details

[1207] Output: A list of suggested items

[1208] Step 5: Confirm the item arrival date

[1209] The server verifies that the proposed item will arrive at the user's specified date and time, by interfacing with the delivery system to verify the arrival date.

[1210] Input: List of proposed items, details of the event size

[1211] Output: Confirmed item arrival date

[1212] Step 6: Inform users

[1213] The server provides the user with information on confirmed arrival dates and suggested items, using a web application framework to build a user interface.

[1214] Input: Confirmed item arrival date, list of proposed items

[1215] Output: Information provided to the user (item list, arrival date)

[1216] Step 7: Fine-tune and finalize the item

[1217] The user reviews the list of suggested items and adds or removes items as needed. Once the user presses the confirm button, the server automatically places an order for the items.

[1218] Input: User-generated item addition / deletion information, confirmation button press

[1219] Output: Confirmed item list

[1220] Step 8: Automated ordering

[1221] The server works in conjunction with the ERP system to automatically place an order for the confirmed items.

[1222] Input: Confirmed item list

[1223] Output: Sending order information, notification of order completion

[1224] In this way, the server, terminal, and user work together to carry out a series of processes from proposing items based on stock status to placing an order.

[1225] (Application example 3)

[1226] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1227] Conventional inventory management systems have problems with suggesting optimal items based on the scale and details of an event, and there is a lack of a way to check the arrival date of suggested items, which means users have to manually check and correct them. Furthermore, there is a lack of functionality to automatically order item lists revised by users, making efficient inventory management difficult.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1229] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for proposing an optimal combination of items using a generative AI model, means for confirming the arrival dates of the proposed items and notifying the user, and means for automatically ordering the item list modified by the user. This makes it possible to propose optimal items according to the scale and details of the event, confirm arrival dates, and automatically place orders after user adjustments.

[1230] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1231] "Event fixtures and other items" refers to items such as furniture and equipment used at events.

[1232] "Inventory status" refers to the current number and condition of event items such as fixtures in logistics centers and warehouses.

[1233] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to suggest optimal item combinations from data.

[1234] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified date, time, and location.

[1235] "Means for notifying the user" refers to a method for informing the user of the proposed item's arrival date and other important information.

[1236] "Means for automatic ordering" refers to the function in which the system automatically carries out the ordering procedure based on the item list confirmed and modified by the user.

[1237] A system for implementing this invention includes means for inputting details of the scale of the event, means for checking inventory status, means for proposing optimal items, means for responding with the arrival date of the items, means for confirming the response, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for proposing the optimal combination of items using a generative AI model, means for confirming the arrival dates of the proposed items and notifying the user, and means for automatically ordering the item list modified by the user.

[1238] Hardware and software used

[1239] Hardware: Smartphone

[1240] Software: Python, Requests library, Scikit-learn library

[1241] Data processing and calculation

[1242] The server first receives information from the user about the event's scale, such as the date and time, location, number of people working, event space, sales targets, and number of customers, through a means for inputting details about the scale of the event. Next, the server uses a means for checking inventory status to obtain the current number and status of event items, such as fixtures, in the distribution center or warehouse.

[1243] Using a generative AI model, the server proposes an optimal combination of items based on inventory data and details of the event scale. The server confirms the arrival date of the proposed items and notifies the user through a notification mechanism. After the user confirms the proposed item list and adds or removes items as necessary, the server automatically processes the order using an automatic ordering mechanism once the order is confirmed.

[1244] Specific examples

[1245] For example, if you want to suggest the fixtures needed for an event with 500 people, you can input the following prompt into the generative AI model:

[1246] Prompt Sentence Examples

[1247] Event size: 500 people

[1248] Required items: Furniture

[1249] Inventory Data: {Inventory Data JSON}

[1250] User Modification: {User Modification JSON}

[1251] Using this prompt, the generative AI model proposes the most suitable items, confirms the arrival date, and automatically orders the list as modified by the user. This makes it possible to propose the most suitable items based on the scale and details of the event, confirm the arrival date, and automatically order after the user has made modifications.

[1252] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1253] Step 1:

[1254] The user inputs details about the scale of the event. Specifically, the user inputs information such as the date and time of the event, location, number of people working, event space, sales target, and number of customers into the smartphone application. This sends the details of the scale of the event as input data to the server.

[1255] Step 2:

[1256] The server checks the inventory status of the logistics center and warehouse based on the details of the scale of the event that have been entered. Specifically, it calls an API to obtain inventory data and obtains the current number and status of event items such as fixtures. This inputs the inventory data into the server.

[1257] Step 3:

[1258] The server uses a generative AI model to suggest the optimal combination of items based on inventory data and details of the event scale. Specifically, it uses a machine learning algorithm (RandomForestClassifier) ​​to select items that best fit the event requirements. This generates a list of optimal item suggestions.

[1259] Step 4:

[1260] The server checks the arrival date of the suggested items. Specifically, it retrieves the arrival date of each item based on the suggestion list through the API and notifies the user. This provides the user with information on the item arrival date.

[1261] Step 5:

[1262] The user can check the proposed item list and add or delete items as necessary. Specifically, the user displays the item list through a smartphone application and makes modifications. The modified item list is then sent to the server.

[1263] Step 6:

[1264] The server automatically processes the order based on the item list modified by the user. Specifically, it sends the modified item list to the ordering system via API and completes the ordering process. This places an order for the final item list.

[1265] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1266] "Example 1"

[1267] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts item suggestions and automatic ordering based on those emotions. Specifically, when a user inputs details about the scale of an event, the system analyzes that information and the user's emotions. For example, if the user expresses joy, the system suggests more luxurious items. On the other hand, if the user expresses anxiety, the system suggests less expensive items. In this way, the system suggests optimal items according to the user's emotions, improving user satisfaction.

[1268] "Example 2"

[1269] Another embodiment of the present invention provides a system in which an emotion engine adjusts automatic ordering of items. Specifically, when a user confirms an item, the system analyzes the information and the user's emotion. For example, if the user expresses joy, the system immediately orders the item. On the other hand, if the user expresses anxiety, the system delays the order by, for example, asking the user for reconfirmation. In this way, the system adjusts the timing of ordering according to the user's emotion, improving user satisfaction.

[1270] "Example 3"

[1271] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts item suggestions and automatic ordering based on those emotions. Specifically, when a user inputs details about the scale of an event, the system analyzes that information and the user's emotions. For example, if the user expresses joy, the system suggests more luxurious items. On the other hand, if the user expresses anxiety, the system suggests less expensive items. In this way, the system suggests optimal items according to the user's emotions, improving user satisfaction.

[1272] The processing flow of each embodiment will be described below.

[1273] "Example 1"

[1274] Step 1: The user enters details of the scale of the event into the system.

[1275] Step 2: The system recognizes the user's emotion using the user's emotion engine.

[1276] Step 3: The system analyzes the user's emotions and the details of the event scale, and suggests the most suitable items. For users who show emotions of joy, it suggests luxury items, and for users who show emotions of anxiety, it suggests inexpensive items.

[1277] "Example 2"

[1278] Step 1: The user commits the item to the system.

[1279] Step 2: The system recognizes the user's emotion using the user's emotion engine.

[1280] Step 3: The system analyzes the user's emotions and item confirmation information and adjusts the timing of ordering. For users who express joy, the system orders the item immediately, and for users who express anxiety, the system delays the order.

[1281] Example 1

[1282] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1283] Conventional event management systems suggest items based on the details of the event scale entered by the user, but do not consider the user's emotions. This makes it difficult to suggest optimal items to improve user satisfaction. Another issue is that suggestions based solely on inventory status do not allow for flexible responses based on the user's emotions.

[1284] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1285] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, means for recognizing the user's emotions, means for adjusting the item proposals based on the emotions, means for generating optimal item proposals using a generative AI model, and means for displaying the generated item proposals to the user. This enables optimal item proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[1286] "Event scale details" refers to the specific conditions and requirements of the event, such as the date, time, location, number of people working, event space, sales targets, and number of customers.

[1287] "Event fixtures and other items" refers to the equipment, decorations, and facilities necessary for holding an event.

[1288] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[1289] "Item suggestion" refers to selecting the most suitable event items, such as fixtures, and presenting them to the user based on the details of the event scale and stock status entered by the user.

[1290] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[1291] "Automatic ordering" refers to the system automatically placing an order for a suggested item after the user confirms it.

[1292] "Means for recognizing emotions" refers to technology that analyzes emotions from a user's facial expressions and input content and detects a specific emotional state.

[1293] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate optimal results for a specific task.

[1294] "Means for adjusting item suggestions" refers to technology that changes the content and type of suggested event items, such as fixtures, based on the user's emotions.

[1295] The "means for displaying to the user" refers to an interface that displays the generated item suggestions so that the user can visually confirm them.

[1296] The present invention is a system that inputs details of the scale of an event and proposes optimal items taking into consideration the emotions of the user. A specific embodiment of this system will be described below.

[1297] System configuration

[1298] User Interface

[1299] The user enters details of the event using the system's user interface, which can be implemented as a web or mobile application. The user enters information such as the date, time, location, number of people available, event space, sales target, and number of customers.

[1300] Data transmission

[1301] The device sends the details of the event scale entered by the user to the server, where the input data is converted to JSON format and sent using a secure communication protocol (e.g., HTTPS).

[1302] emotion recognition

[1303] The server starts an emotion engine to recognize the user's emotion along with the details of the scale of the received event. The emotion engine analyzes the emotion from the user's facial expression and input content. For example, if the user is smiling through the camera, it recognizes the emotion of joy.

[1304] Data Integration

[1305] The server integrates the emotion data obtained from the emotion engine with the magnitude details of the event entered by the user, and this integrated data is used in the next step.

[1306] Generate item suggestions

[1307] The server uses a generative AI model based on the integrated data to generate optimal item suggestions for the user. The generative AI model selects optimal items taking into account the user's emotions and the details of the scale of the event.

[1308] Submit your proposal

[1309] The server sends the generated item suggestions to the terminal, and the suggestions are displayed in a format that is easy for the user to understand.

[1310] Displaying suggestions to the user

[1311] The terminal displays the item suggestions received from the server to the user, who can then check the suggestions and make selections or modifications as necessary.

[1312] Specific examples

[1313] For example, if a user enters, "I'm holding an event for 500 people in Tokyo on December 25, 2022, and I need event items such as fixtures," and expresses joy, the system will suggest luxurious decorations and high-quality fixtures.

[1314] Prompt Sentence Examples

[1315] Examples of prompts to be input to a generative AI model include:

[1316] "A user has entered that they are planning an event for 500 people in Tokyo on December 25, 2022, and need event items such as fixtures. The user's emotion is joy. What items would you suggest?"

[1317] By inputting this prompt into a generative AI model, the system can suggest optimal items based on the user's emotions.

[1318] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1319] Step 1:

[1320] The user uses the system's user interface to input details about the scale of the event. Specifically, the user enters information such as the date and time, location, number of people working, event space, sales target, and number of customers. For example, the user might enter, "I'm holding an event for 500 people in Tokyo on December 25, 2022, and I need event items such as fixtures." The input data is divided into fields such as date and time, location, and number of people working.

[1321] Step 2:

[1322] The device sends the details of the event scale entered by the user to the server. At this time, the input data is converted to JSON format and sent using a secure communication protocol (e.g. HTTPS). For example, the following JSON data is sent: json{ "date": "2022-12-25", "location": "Tokyo", "staff": 500, "items_needed": true}

[1323] The input data is sent to the server, which receives it.

[1324] Step 3:

[1325] The server launches an emotion engine to recognize the user's emotion along with the details of the scale of the received event. The emotion engine analyzes the emotion from the user's facial expression and input content. For example, if the user is smiling through the camera, it recognizes the emotion of joy. The input data is the user's facial expression data and text data, and the output is the recognized emotion data.

[1326] Step 4:

[1327] The server combines the emotion data obtained from the emotion engine with the details of the scale of the event entered by the user. This combined data will be used in the next step. For example, the following combined data will be generated: json{ "date": "2022-12-25", "location": "Tokyo", "staff": 500, "items_needed": true, "emotion": "joy"}

[1328] The input data is emotion data and event detail data, and the output is the integrated data.

[1329] Step 5:

[1330] The server uses a generative AI model based on the integrated data to generate optimal item suggestions for the user. The generative AI model selects the optimal item taking into account the user's emotions and the details of the scale of the event. For example, the following prompt sentence is input to the generative AI model:

[1331] "A user has entered that they are planning an event for 500 people in Tokyo on December 25, 2022, and need event items such as fixtures. The user's emotion is joy. What items would you suggest?"

[1332] The input data are the synthesis data and the prompt statements, and the output is the generated item suggestions.

[1333] Step 6:

[1334] The server sends the generated item suggestions to the device. The suggestions are displayed in a format that is easy for the user to understand. For example, the following suggestions are sent: json{ "suggestions": [ "Luxurious decorations", "High-quality fixtures" ]}

[1335] The input data is the generated item suggestions, and the output is the submitted suggestions.

[1336] Step 7:

[1337] The terminal displays the item suggestions received from the server to the user. The user can review the suggestions and make selections or modifications as needed. For example, if the user selects "luxurious decorations," the system records the selection and proceeds to the next step. The input data is the received suggestions, and the output is the user's selection.

[1338] (Application example 1)

[1339] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1340] In conventional event planning systems, users can input details about the scale of the event, but items are not suggested based on the user's emotions, which means that user satisfaction cannot be fully enhanced. Also, while items are suggested based on stock availability, they are not adjusted according to the user's emotions, so the system may not be able to suggest the most appropriate items.

[1341] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for recognizing the user's emotions, and means for adjusting the proposed items based on the emotions. This makes it possible to propose optimal items taking the user's emotions into consideration, thereby improving user satisfaction.

[1342] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1343] "Event fixtures and other items" refers to items such as furniture, decorations, and equipment used at events.

[1344] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[1345] "Item suggestion" refers to the act of selecting the most suitable event items, such as fixtures, based on the details of the event's scale and stock availability, and presenting them to the user.

[1346] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[1347] "Automatic ordering" refers to the process where the system automatically orders a suggested item after the user confirms and confirms the item.

[1348] "User emotions" refers to the psychological states such as joy, anxiety, and excitement that users feel when planning and preparing for an event.

[1349] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions, tone of voice, etc. to identify their emotions.

[1350] "Means for adjusting item suggestions based on emotions" refers to technology that changes the type and quality of suggested event items, such as fixtures, depending on the recognized emotions of the user.

[1351] A system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures, a means for proposing items that suit the scale, a means for responding with information such as the date of arrival of items, a means for confirming the response and making minor adjustments to add or delete items as necessary, and automatically placing an order once the response is confirmed, a means for recognizing the user's emotions, and a means for adjusting the item suggestions based on the emotions.

[1352] Program processing explanation

[1353] Hardware and Software Configuration

[1354] Server: Enter details of the event size, check stock availability, suggest items, provide item arrival dates, and automatically place orders.

[1355] User device: A device such as a smartphone or tablet where the user enters event details and recognizes emotions.

[1356] Emotion recognition software: Software used to analyze a user's facial expressions and tone of voice (e.g., EmotionRecognizer).

[1357] Item suggestion algorithms: Algorithms for selecting the best items based on stock availability and user sentiment (e.g., ItemSuggester).

[1358] Data processing and calculation

[1359] 1. Entering details of the scale of the event: Detailed information such as the date and time of the event, location, number of people working, event space, sales target, and number of customers is sent from the user terminal to the server.

[1360] 2. Check inventory status: The server retrieves the current inventory status of event items such as fixtures from the database.

[1361] 3. Emotion recognition: Using the camera and microphone on the user's device, emotion recognition software analyzes the user's facial expressions and tone of voice to identify the user's emotions.

[1362] 4. Item suggestion: Based on the acquired inventory status and the recognized user sentiment, the server uses an item suggestion algorithm to select the most suitable event items, such as fixtures, and suggest them to the user.

[1363] 5. Item Arrival Date Answer: Calculate the arrival date of the proposed item and notify the user.

[1364] 6. Automatic ordering: After the user confirms and confirms the proposed item, the server will automatically order the item.

[1365] Specific examples

[1366] For example, a user uses a smartphone to enter details of an event such as:

[1367] Example prompt sentence:

[1368] Enter your event details:

[1369] Date: 2022-12-25

[1370] Location: Tokyo

[1371] Number of participants: 500 people

[1372] Working staff: 50

[1373] Event space: 100 square meters

[1374] Sales target: 1 million yen

[1375] Number of customers served: 200 people

[1376] Recognize user emotions:

[1377] Emotion: Joy

[1378] Suggested items:

[1379] Luxurious decorations

[1380] Luxury catering services

[1381] Premium Gift

[1382] In this way, a system can be realized that makes optimal suggestions based on the user's emotions and the details of the event.

[1383] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1384] Step 1:

[1385] The user uses a device such as a smartphone or tablet to enter details about the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.). The entered data is sent from the device to the server. The input data includes the event date, location, number of participants, number of people working, size of the event space, sales target amount, and number of customers.

[1386] Step 2:

[1387] The server checks the stock status of event items such as fixtures from the database based on the details of the scale of the received event. The server accesses the inventory database to obtain the current number of required items and their availability status. As an output, a list of stock status is generated.

[1388] Step 3:

[1389] Using the camera and microphone of the user's device, emotion recognition software (e.g., EmotionRecognizer) analyzes the user's facial expressions and tone of voice to identify the user's emotions. The input data is the user's facial expressions and tone of voice, and the output is the user's emotion (joy, anxiety, excitement, etc.).

[1390] Step 4:

[1391] The server uses an item suggestion algorithm (e.g., ItemSuggester) to select the most suitable event items, such as fixtures, based on the acquired inventory status and the recognized user sentiment. The input data are inventory status and user sentiment, and the output is a list of suggested items.

[1392] Step 5:

[1393] The server calculates the arrival date of the proposed items and notifies the user. The input data is the list of proposed items and delivery information, and the output is the item arrival date.

[1394] Step 6:

[1395] The user confirms the proposed items and makes minor modifications to add or remove items as necessary. After the user confirms, the server automatically orders the items. The input data are the user's confirmations and modifications, and the output is order information.

[1396] In this way, a system can be realized that makes optimal suggestions based on the user's emotions and the details of the event.

[1397] Example 2

[1398] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1399] Conventional event management systems lacked sufficient means to check the inventory status of fixtures and other items according to the scale of the event, which resulted in problems with shortages and excess inventory. Furthermore, automatic ordering was performed without considering the user's feelings, which could lead to a decrease in user satisfaction. There is a need to solve these problems and realize efficient event management that provides high user satisfaction.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1401] In this invention, the server includes means for inputting details of the scale of the event, means for checking the stock status of event items such as fixtures based on the details of the scale, means for proposing items that suit the scale based on the stock status, means for replying with the arrival date of the items based on the proposal, means for confirming the content of the reply, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, and means for analyzing the user's emotions and adjusting the timing of ordering based on the emotions, thereby making it possible to accurately check the stock status and adjust the timing of ordering according to the user's emotions.

[1402] "Details of the scale of the event" is specific information about the scale of the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers.

[1403] "Event fixtures and other items" refers to items such as chairs, tables, and display stands used at events.

[1404] "Stock status" is information indicating how many event items such as fixtures are currently in stock or available.

[1405] The "suggestion means" is a function that automatically selects the most suitable items for the scale of the event based on the inventory status and suggests them to the user.

[1406] "Item arrival date" is the date on which the event items, such as fixtures, ordered by the user are scheduled to arrive at the specified location.

[1407] "Automatic ordering means" is a function in which the system automatically orders an item after the user has confirmed the item.

[1408] "Means for analyzing emotions" is a function that analyzes the user's emotions and adjusts the system's operation based on the results.

[1409] The "means for adjusting the timing of ordering" is a function for adjusting the timing of ordering, such as whether to order an item immediately or to request reconfirmation, based on the user's feelings.

[1410] This invention is a system that checks the inventory status of event items such as fixtures based on the details of the scale of the event and adjusts the timing of ordering according to the user's emotions. A specific embodiment of this system will be described below.

[1411] Hardware and software used

[1412] The server manages inventory data using databases and cloud storage (e.g., Amazon RDS and Google Cloud Storage), and for sentiment analysis, it uses the Microsoft Azure Sentiment Analysis API and the Google Cloud Natural Language API.

[1413] The terminal provides an interface for users to input details of the event scale, check stock status and suggested items, and also acquires the user's emotions and sends them to the server.

[1414] The user inputs details of the event scale into the terminal, checks the system's suggestions and stock status, and proceeds with the ordering process by confirming the items and expressing their emotions.

[1415] System processing flow

[1416] 1. Enter the details of the scale

[1417] The user inputs details of the scale of the event (for example, "chairs and tables for 500 people") into the terminal.

[1418] 2. Obtaining inventory data

[1419] The terminal sends the entered size details to the server, which retrieves inventory data from a database or cloud storage.

[1420] 3. Check stock availability

[1421] The server checks the inventory data to see if the required items are in stock, for example, whether there are enough chairs and tables for 500 people.

[1422] 4. Returning the results

[1423] The server returns the results of the stock status check to the terminal, which then displays the results to the user.

[1424] 5. Confirmation of items

[1425] The user confirms the items they need on the terminal, for example, "100 chairs."

[1426] 6. Acquiring Emotion Data

[1427] The terminal uses an emotion engine to analyze the user's emotions.

[1428] 7. Sentiment Analysis

[1429] The device analyzes the user's emotions based on the acquired emotion data, for example, determining whether the user is expressing joy or anxiety.

[1430] 8. Order Adjustments

[1431] The server adjusts the timing of ordering based on the user's emotions: if the user expresses joy, the server orders the item immediately; if the user expresses anxiety, the server prompts the user for reconfirmation.

[1432] Specific examples

[1433] If a user inputs "500 people" as the size of the event, the device will send that information to the server. The server will search the database to see if there are enough chairs and tables in stock for 500 people. If the result is "in stock," the server will send that back to the device, which will then display "in stock" to the user.

[1434] When the user confirms "100 chairs," the device uses an emotion engine to analyze the user's emotions. If the user expresses joy, the server immediately orders 100 chairs. If the user expresses anxiety, the server asks, "Are you sure you want to order?" for confirmation.

[1435] Prompt Sentence Examples

[1436] "Please check the availability of event items for 500 people."

[1437] "Order 100 chairs. Adjust the order timing based on user sentiment."

[1438] In this way, the system can accurately check inventory status and adjust order timing according to the user's emotions, improving user satisfaction.

[1439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1440] Step 1:

[1441] The user inputs details of the scale of the event into the terminal, for example, "chairs and tables for 500 people."

[1442] Input: Details of the event size (e.g., "500 chairs and tables")

[1443] Output: Detailed scale data is saved to the terminal.

[1444] Step 2:

[1445] The terminal transmits the entered size details to the server.

[1446] Input: Detailed scale data

[1447] Output: Scale details data is sent to the server.

[1448] Step 3:

[1449] The server retrieves inventory data from a database or cloud storage, for example, Amazon RDS or Google Cloud Storage.

[1450] Input: Detailed scale data

[1451] Output: Inventory data is retrieved to the server.

[1452] Step 4:

[1453] The server checks the inventory data to see if the required items are in stock, for example, whether there are enough chairs and tables for 500 people.

[1454] Input: Inventory data, detailed size data

[1455] Output: Stock check result (e.g. "In stock")

[1456] Step 5:

[1457] The server returns the results of the stock status check to the terminal.

[1458] Input: Inventory check result

[1459] Output: The inventory check results are sent to the terminal.

[1460] Step 6:

[1461] The terminal displays the inventory check results to the user.

[1462] Input: Inventory check result

[1463] Output: The result of the stock check is displayed to the user (e.g. "In stock").

[1464] Step 7:

[1465] The user confirms the items they need on the terminal, for example, "100 chairs."

[1466] Input: Item confirmation information (e.g. "100 chairs")

[1467] Output: Item confirmation information is saved on the device.

[1468] Step 8:

[1469] The device uses an emotion engine to analyze the user's emotions, for example, using the Microsoft Azure Sentiment Analysis API or the Google Cloud Natural Language API.

[1470] Input: User emotion data (e.g., user facial expressions and text)

[1471] Output: Sentiment analysis result (e.g. "joy")

[1472] Step 9:

[1473] The device analyzes the user's emotions based on the acquired emotion data, for example, determining whether the user is expressing joy or anxiety.

[1474] Input: Emotion data

[1475] Output: Sentiment analysis result (e.g. "joy")

[1476] Step 10:

[1477] The server adjusts the timing of ordering based on the user's emotions: if the user expresses joy, the server orders the item immediately; if the user expresses anxiety, the server prompts the user for reconfirmation.

[1478] Input: Sentiment analysis results, item confirmation information

[1479] Output: Order instruction (e.g. "Order now" or "Reconfirm")

[1480] Specific examples of operation

[1481] When a user enters "chairs and tables for 500 people," the device sends that information to the server. The server searches the database to see if 500 chairs and tables are in stock. If the result is "in stock," the server sends the result back to the device, which displays "in stock" to the user.

[1482] When the user confirms "100 chairs," the device uses an emotion engine to analyze the user's emotions. If the user expresses joy, the server immediately orders 100 chairs. If the user expresses anxiety, the server asks, "Are you sure you want to order?" for confirmation.

[1483] (Application example 2)

[1484] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1485] In conventional inventory management systems, item suggestions and ordering based on the scale of the event are often done manually, resulting in inefficiencies. Furthermore, orders are placed without considering the user's feelings, which can lead to lower user satisfaction. This can affect the success of the event. Furthermore, there are issues with the system, such as difficulty in checking inventory status and appropriately adjusting the timing of orders.

[1486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1487] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items suitable for the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing an order when confirmed, means for analyzing user emotions, and means for adjusting the timing of ordering based on the emotion analysis. This enables more efficient inventory management and ordering, and allows the timing of ordering to be adjusted according to the user's emotions, thereby improving user satisfaction.

[1488] "Event scale details" is information indicating the specific scale and conditions of the event, such as the date and time, location, number of people working, event space, sales target, number of customers, etc.

[1489] "Event fixtures and other items" refers to items such as furniture, equipment, and decorations used at events.

[1490] "Stock status" is information that indicates the current quantity of a particular item in stock.

[1491] The "suggestion means" is a means having a function of automatically selecting the most suitable item based on the stock status and suggesting it to the user.

[1492] "Item Arrival Date" is the date on which the ordered item is scheduled to arrive at the specified location.

[1493] The "automatic ordering means" is a means having a function of automatically ordering an item after the user has confirmed the item.

[1494] The "means for analyzing the user's emotions" is a means having a function for determining the emotions of the user from facial expressions, voice, etc.

[1495] The "means for adjusting the timing of an order" is a means having a function for appropriately changing the timing of an order based on an analysis of the user's emotions.

[1496] A system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures, a means for proposing items that suit the scale, a means for responding with information such as the date of arrival of items, a means for confirming the responses, making minor adjustments to added or deleted items as necessary, and automatically placing an order once the responses are confirmed, a means for analyzing user emotions, and a means for adjusting the timing of ordering based on the emotion analysis.

[1497] Hardware and software used

[1498] Hardware: Smartphones, smart glasses

[1499] Software: Python, EmotionRecognizer library, InventoryManager library

[1500] Data processing and calculation

[1501] Stock Check

[1502] The server uses the InventoryManager library to retrieve inventory information from the database. For example, to check the stock status of a specific product (e.g., item123), the server sends a query to the database to retrieve the current stock quantity.

[1503] sentiment analysis

[1504] The device uses the EmotionRecognizer library to analyze the user's facial expressions and voice to determine their emotions. For example, if the user is happy to see a product, the device will recognize that emotion as "joy."

[1505] Automatic ordering

[1506] The server adjusts the timing of ordering based on the user's emotions. For example, if the user expresses "joy," the server immediately orders the product. On the other hand, if the user expresses "anxiety," the server asks the user for reconfirmation.

[1507] Specific examples

[1508] Specific examples of inventory checks

[1509] A store clerk uses smart glasses to check the stock status of a specific product (e.g., item 123). The smart glasses display shows the current stock quantity in real time.

[1510] Sentiment analysis examples

[1511] If a customer is happy looking at a product, the device analyzes their facial expression and recognizes the emotion of "happiness." This allows the server to immediately order the product.

[1512] Prompt Sentence Examples

[1513] "Develop a system that analyzes customer sentiment and automatically orders products based on availability. If the customer is happy, place the order immediately; if they're anxious, ask for reassurance."

[1514] In this way, an inventory management and automatic ordering system can be realized in a physical store.

[1515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1516] Step 1:

[1517] The user enters details about the scale of the event.

[1518] Input: Details of the event scale, such as date, time, location, number of people available, event space, sales target, number of customers.

[1519] Output: Detailed scale data entered.

[1520] Specific operation: The user inputs details of the scale of the event using a smartphone or smart glasses, and the device sends this information to the server.

[1521] Step 2:

[1522] The server checks the inventory status of fixtures and other event items.

[1523] Input: Scale details data.

[1524] Output: Inventory status data.

[1525] Specific operation: The server uses the InventoryManager library to retrieve the inventory status of the relevant event items from the database. For example, it checks the stock quantity of a specific product (e.g., item 123).

[1526] Step 3:

[1527] The server will suggest items that fit the scale.

[1528] Input: Inventory availability data.

[1529] Output: A list of suggested items.

[1530] Specific operation: Based on the inventory status data, the server automatically selects the most suitable items for the scale of the event and generates a list of suggested items.

[1531] Step 4:

[1532] The server will respond with the item arrival date, etc.

[1533] Input: Suggested items list.

[1534] Output: Response data such as item arrival date.

[1535] Specific operation: Based on the suggested item list, the server calculates the arrival date and other related information for each item and returns the answer to the user.

[1536] Step 5:

[1537] The user checks the answers and makes minor corrections to add or delete items as necessary.

[1538] Input: Response data such as item arrival date.

[1539] Output: The modified item list.

[1540] Specific operation: The user uses a smartphone or smart glasses to check the response from the server and modify the item list as necessary. The modified item list is then sent back to the server.

[1541] Step 6:

[1542] The server analyzes the user's emotions.

[1543] Input: Emotional data such as the user's facial expressions and voice.

[1544] Output: Sentiment analysis results.

[1545] Specific operation: The device uses the EmotionRecognizer library to analyze the user's facial expressions and voice to determine their emotions. The determined emotion data is sent to the server.

[1546] Step 7:

[1547] The server adjusts the timing of orders based on sentiment analysis.

[1548] Input: Sentiment analysis results, modified item list.

[1549] Output: Order instruction.

[1550] Specific operation: The server adjusts the timing of ordering based on the results of emotion analysis. For example, if the user expresses "joy," the server immediately orders the product. On the other hand, if the user expresses "anxiety," the server asks the user for reconfirmation.

[1551] Example 3

[1552] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1553] Conventional event management systems suggest and automatically order items based on inventory status, but they cannot make suggestions that take user emotions into account, which means they are unable to fully improve user satisfaction. Additionally, minor adjustments to the arrival date of suggested items and additions / deletions must be made manually, which is inefficient.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1555] In this invention, the server includes means for inputting details of the scale of the event, means for checking the stock status of event items based on the details of the scale, means for proposing items appropriate to the scale based on the stock status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, and means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions. This enables optimal item suggestions and efficient automatic ordering that take the user's emotions into consideration.

[1556] "Event scale details" refers to the specific conditions and requirements of the event, such as the date and time of the event, location, number of people working, event space, sales targets, number of customers, etc.

[1557] "Stock status" refers to information indicating the current number of event items in stock and their availability.

[1558] "Item suggestion" refers to selecting the optimal combination of event items based on stock availability and details of the scale of the event, and presenting it to the user.

[1559] "Item Arrival Date" refers to the date that the proposed event item is scheduled to arrive at the user's specified date, time, and location.

[1560] "Automatic ordering" refers to the system automatically ordering event items based on the item list confirmed by the user.

[1561] "User emotion" refers to the emotional state, such as joy or anxiety, that the user exhibits when entering the event size details.

[1562] "Means for recognizing emotions" refers to technology that analyzes emotions from user input and behavior and identifies their emotional state.

[1563] "Means for adjusting item suggestions and automatic ordering based on emotions" refers to technology that changes the types of items suggested and the content of automatic ordering according to the recognized user emotions.

[1564] As an embodiment of the present invention, the following system is constructed.

[1565] The server provides a means for inputting details of the scale of the event. The user uses the terminal to input details of the scale of the event (e.g., number of participants, type of event, budget, etc.). The terminal transmits the input information to the server.

[1566] The server provides a means to check the stock status of event items based on the details of the scale of the received event. The server references the stock database to check the current stock status. For this process, general database software is used as the database management system (DBMS).

[1567] The server then provides a means to suggest items appropriate for the scale based on inventory status. Using software such as Python and TensorFlow, the server runs machine learning models to generate optimal combinations of items. For example, it selects the most suitable items from fixtures and decorations for 500 people.

[1568] Additionally, the server provides a means to respond with information such as the arrival date of the proposed item. The server checks the inventory status and delivery schedule of the proposed item and calculates the arrival date of the item. This information is provided to the user and can be viewed through the terminal.

[1569] The user can review the list of suggested items and make minor adjustments to add or remove items as needed. Once the user presses the confirm button, the server automatically places the order for the items. This process uses a common web framework (e.g., React) as the front end and a common server-side framework (e.g., Node.js) as the back end.

[1570] The system also provides a means to recognize the user's emotions and adjust item suggestions and automatic ordering based on those emotions. The server uses common emotion analysis software (e.g., IBM Watson) as an emotion recognition engine to analyze emotions from the user's input and behavior. For example, if the user expresses joy, the server will suggest more luxurious items. On the other hand, if the user expresses anxiety, the server will suggest less expensive items.

[1571] As a specific example, a user enters "500-person event, budget 1 million yen, type of event wedding" into an input form on their terminal and presses the submit button. The server queries the inventory database and selects the optimal combination of fixtures and decorations for 500 people. For example, it proposes "50 tables, 500 chairs, and a complete set of decorations." The server checks the delivery schedule and notifies the user that "the proposed items can arrive on the specified date and time." The user checks the proposed list and enters "10 additional tables and 100 chairs" to update the list. When the user presses the confirm button, the server automatically places an order for "60 tables, 600 chairs, and a complete set of decorations" and sends the user a notification that the order has been completed.

[1572] An example prompt is "Please suggest furniture for an event for 500 people. The user expresses happiness."

[1573] In this way, the system can suggest optimal items based on the user's input and emotions, thereby improving user satisfaction. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1574] Step 1:

[1575] The user enters details about the scale of the event.

[1576] The user enters details of the scale of the event (for example, the number of participants, the type of event, the budget, etc.) into the input form on the terminal and presses the send button. The entered information is sent from the terminal to the server.

[1577] Input: Details of the event size (number of participants, type of event, budget, etc.)

[1578] Output: Details of the scale of the event sent to the server

[1579] Step 2:

[1580] The server checks the inventory status.

[1581] The server queries the inventory database based on the details of the size of the received event to check the current inventory status. The server retrieves inventory data using a database management system (DBMS).

[1582] Input: Details of the event size

[1583] Output: Stock status data

[1584] Step 3:

[1585] The server will suggest the best combination of items.

[1586] The server uses Python and TensorFlow to run machine learning models based on inventory data and details of the event's scale to generate the optimal combination of items, such as selecting the most suitable fixtures and decorations for 500 people.

[1587] Input: Inventory status data, details of event size

[1588] Output: Optimal item combination

[1589] Step 4:

[1590] The server will confirm the item arrival date and provide it to the user.

[1591] The server checks the inventory status and delivery schedule of the proposed item and calculates the arrival date of the item. The calculation result is notified to the user, who can check it through their terminal.

[1592] Inputs: Optimal item mix, availability data, delivery schedule

[1593] Output: Item arrival date information

[1594] Step 5:

[1595] The user reviews the proposed items and adds or removes them as necessary.

[1596] The user uses the terminal to review the list of suggested items and add or remove items as needed. For example, if the user needs additional tables or chairs, they add them to the list.

[1597] Input: A list of suggested items

[1598] Output: A list of modified items

[1599] Step 6:

[1600] When the user presses the confirm button, the server automatically places an order for the item.

[1601] When the user presses the confirm button, the device sends that information to the server. The server then automatically places an order for the item based on the received information. This process uses a common web framework (e.g., React) as the front end and a common server-side framework (e.g., Node.js) as the back end.

[1602] Input: List of modified items, confirmation button press information

[1603] Output: Order completion notification

[1604] Step 7:

[1605] The server recognizes the user's emotions and adjusts suggestions and automatic ordering.

[1606] The server uses common emotion analysis software (e.g., IBM Watson) as an emotion recognition engine to analyze emotions from the user's input and behavior. For example, if the user expresses joy, the server will suggest more luxurious items. On the other hand, if the user expresses anxiety, the server will suggest less expensive items.

[1607] Input: User input and behavioral data

[1608] Output: Sentiment-based item suggestions and automated ordering adjustments

[1609] (Application example 3)

[1610] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1611] While conventional event planning systems have the ability to suggest items based on inventory status, they have problems in that they are unable to take user sentiment into account when making suggestions or adjust automatic ordering. Furthermore, there are insufficient methods for users to easily plan events using their smartphones. This has led to issues such as lower user satisfaction and a decline in the efficiency of event planning.

[1612] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1613] In this invention, the server includes a means for inputting details of the scale of the event, a means for checking the inventory status of event items such as fixtures based on the details of the scale, a means for proposing items appropriate to the scale based on the inventory status, a means for responding with information such as the arrival date of the items based on the proposal, a means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, a means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions, and an application installed on the smartphone. This enables optimal item suggestions and automatic ordering that take the user's emotions into consideration, improving the efficiency of event planning and user satisfaction.

[1614] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1615] "Event fixtures and other items" refers to items such as furniture and equipment used at events.

[1616] "Inventory status" refers to information indicating how many of a particular item are currently in stock.

[1617] "Item suggestions" refers to presenting the optimal combination of items based on stock availability and details of the event scale.

[1618] "Item Arrival Date" refers to the date the proposed item will arrive at the date, time and location specified by the user.

[1619] "Automatic ordering" refers to the process where the system automatically orders an item when the user presses the confirm button.

[1620] "Means for recognizing emotions" refers to technology for analyzing a user's emotions and adjusting the system's behavior based on those emotions.

[1621] "Application installed on a smartphone" refers to software that runs on a smartphone and allows users to plan events and receive item suggestions.

[1622] A system for carrying out this invention is configured as follows: The server includes means for inputting details of the scale of the event, means for checking the stock status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the stock status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, means for recognizing the user's emotions and adjusting the item suggestions and automatic ordering based on the emotions, and an application installed on a smartphone.

[1623] Hardware and Software Configuration

[1624] Hardware: Smartphone

[1625] Software: Python, EmotionRecognizer library, InventoryManager library, OrderProcessor library

[1626] Processing flow

[1627] 1. Obtaining user input: The user inputs the date and scale of the event using their smartphone, which then sends the details of the event scale to the server.

[1628] 2. Emotion Analysis: The server recognizes emotions based on the user's input using the EmotionRecognizer library.

[1629] 3. Item Suggestion: The server checks the inventory status using the InventoryManager library and suggests items that fit the scale. Based on the results of sentiment analysis, if the user expresses joy, it suggests luxury items, and if the user expresses anxiety, it suggests inexpensive items.

[1630] 4. Item arrival date response: The server verifies that the proposed item will arrive at the date, time and location specified by the user and provides this information to the user.

[1631] 5. Order confirmation and automatic ordering: The user reviews the proposal and adds or removes items as needed. Once the user presses the confirm button, the items are automatically ordered using the OrderProcessor library.

[1632] Specific examples

[1633] For example, suppose a user is planning an event for 100 people on 2023-12-25. When the user enters this information using their smartphone, the server receives the information and analyzes the user's emotions using the EmotionRecognizer library. If the user expresses joy, the server suggests luxurious items using the InventoryManager library. When the user confirms the suggestions and presses the confirm button, the items are automatically ordered using the OrderProcessor library.

[1634] Prompt Sentence Examples

[1635] The user enters the date and size of the event. Perform sentiment analysis and suggest the best items based on stock availability. If the user expresses joy, suggest luxury items; if the user expresses anxiety, suggest cheaper items.

[1636] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1637] Step 1:

[1638] The user uses a smartphone to input the date and scale of the event. The input information is sent to the server as details of the event scale. Specifically, if the user is planning an event for "100 people" on "2023-12-25," that information is sent to the server. The input data is the date and scale of the event, and the output data is the details of the event scale sent to the server.

[1639] Step 2:

[1640] The server uses the EmotionRecognizer library to recognize emotions based on user input. The input data is the user's event details, and the output data is the user's emotion analysis result. Specifically, it analyzes whether the user is expressing joy or anxiety.

[1641] Step 3:

[1642] The server checks the inventory status using the InventoryManager library and suggests items that fit the scale. The input data is the details of the scale of the event and the result of emotion analysis, and the output data is a list of suggested items. Specifically, if the emotion is joyful, luxurious items are suggested, and if the emotion is anxiety, inexpensive items are suggested.

[1643] Step 4:

[1644] The server confirms that the proposed items will arrive at the user's specified date, time, and location, and provides that information to the user. The input data is the list of proposed items and the user's specified date, time, and location, and the output data is the confirmation result of the item arrival date. Specifically, it confirms that the proposed items will arrive at the specified location on "2023-12-25."

[1645] Step 5:

[1646] The user reviews the suggestions and adds or removes items as needed. The input data is a list of suggested items, and the output data is a list of revised items. Specifically, the user reviews the suggested items and adds or removes them as needed.

[1647] Step 6:

[1648] When the user presses the Confirm button, the server automatically places an order for the items using the OrderProcessor library. The input data is the list of modified items, and the output data is the order confirmation result. Specifically, when the user presses the Confirm button, the system automatically places an order for the items and notifies the user that the order has been completed.

[1649] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1650] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[1651] An example of a data generation model58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1652] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1653] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1654] [Third embodiment]

[1655] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1656] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1657] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1658] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1659] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1660] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1661] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1662] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1663] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1664] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1665] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1666] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1667] "Example 1"

[1668] The system of the present invention provides a means for inputting details of the scale of an event through a user interface. Examples of details of the scale include the date, time, location, number of people working, event space, sales target, number of attendees, etc. For example, a user may input that they are planning to hold an event for 500 people in Tokyo on December 25, 2022, and that they need event items such as fixtures for the event.

[1669] "Example 2"

[1670] The system then provides a means to check the availability of event items, such as fixtures, based on the input size details. The availability can be obtained from a database, cloud storage, etc. For example, the system can search the database and confirm that event items, such as fixtures for 500 people, are available in stock.

[1671] "Example 3"

[1672] Furthermore, the system of the present invention provides a means to suggest items appropriate for the scale based on inventory status. These suggestions are generated automatically using algorithms and machine learning models. For example, the system can suggest the optimal combination of event items, such as furniture for 500 people.

[1673] suggest.

[1674] "Example 4"

[1675] The system of the present invention also provides a means for responding with information such as the item arrival date based on the proposal. For example, the system may confirm that the proposed item will arrive at the date, time, and location specified by the user, and provide that information to the user.

[1676] "Example 5"

[1677] Finally, the system of the present invention provides a means for users to review their responses, fine-tune the items they add or remove as needed, and automatically place an order upon confirmation. For example, a user can review the list of suggested items and add or remove items as needed. After that, the user can press the confirm button, and the system will automatically place an order for the items.

[1678] The processing flow of each embodiment will be described below.

[1679] "Example 1"

[1680] Step 1: The user inputs details of the scale of the event through the user interface of the system of the present invention. Specifically, the user inputs that an event for 500 people will be held in Tokyo on December 25, 2022, and that event items such as fixtures are required.

[1681] "Example 2"

[1682] Step 1: The system checks the availability of event items such as fixtures based on the inputted size details. Specifically, the system searches the database and confirms that event items such as fixtures for 500 people are available in stock.

[1683] "Example 3"

[1684] Step 1: The system of the present invention proposes items appropriate for the scale based on inventory status. Specifically, the system proposes the optimal combination from event items such as fixtures for 500 people that are automatically generated using algorithms and machine learning models.

[1685] "Example 4"

[1686] Step 1: The system of the present invention responds with the item arrival date etc. based on the proposal. Specifically, the system confirms that the proposed item will arrive at the date, time and location specified by the user and provides that information to the user.

[1687] "Example 5"

[1688] Step 1: The user reviews the list of suggested items and adds or removes items as needed.

[1689] Step 2: When the user presses the confirm button, the system of the present invention automatically places an order for the item.

[1690] Example 1

[1691] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1692] In conventional event management systems, entering details about the scale of an event was time-consuming, and the process of checking the stock status of necessary fixtures and items and proposing the most suitable items was cumbersome. Furthermore, the instructions and suggestions generated based on the information entered by the user were insufficient, making it difficult to efficiently reflect user feedback. This resulted in the problem of event preparation and management taking a great deal of time and effort.

[1693] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1694] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate for the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the proposal, making minor adjustments to added or deleted items as necessary, and automatically placing orders once the response is confirmed, means for creating prompt text using a generative AI model, and means for presenting the prompt text to the user and receiving feedback. This allows the user to efficiently input details of the event, receive suggestions for necessary items, and confirm and modify the generated prompt text, thereby enabling quick and accurate event preparation and management.

[1695] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, location, number of people working, event space, sales targets, and number of customers.

[1696] "Event fixtures and other items" refers to the goods and equipment necessary for holding an event, such as chairs, tables, exhibition booths, and audio equipment.

[1697] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[1698] The "suggestion means" refers to a function that selects the most suitable items that match the details of the scale of the event based on the stock situation and suggests them to the user.

[1699] "Item arrival date" refers to the date on which the proposed event items, such as fixtures, are scheduled to arrive at the location specified by the user.

[1700] "Automatic ordering means" refers to the function of automatically ordering necessary event items such as fixtures after the user confirms and confirms the proposal content.

[1701] "Generative AI model" refers to a model that uses artificial intelligence technology to generate prompt sentences based on event details entered by a user.

[1702] "Prompt sentence" refers to a sentence created by a generative AI model that contains specific instructions or suggestions regarding event details and required items.

[1703] "Feedback" refers to corrections or additional input the user makes to the generated prompt sentence.

[1704] This invention is a system that inputs details of the scale of an event, checks the stock status of necessary fixtures and other event items, suggests optimal items, responds with information such as the item's arrival date, makes minor adjustments to add or remove items as necessary, and automatically places orders once confirmed. It also includes a function to create prompts using a generative AI model, present them to the user, and receive feedback.

[1705] Hardware and software used

[1706] Hardware

[1707] Server: Receives, stores, processes data, and runs generative AI models.

[1708] Terminal: Used by the user to enter details of the event and to check and modify the generated prompt text.

[1709] software

[1710] Database: Use a database such as MySQL to store details of the event size and inventory status.

[1711] Generative AI models: Use generative AI models, such as OpenAI's GPT-3, to create prompts.

[1712] Web application: Provides an interface for users to enter details of the event size and review / modify the generated prompt text.

[1713] Data processing and calculation

[1714] Enter details about the scale of your event

[1715] The user enters details of the scale of the event (date and time, location, number of people working, event space, sales target, number of customers, etc.) through the web application. The entered data is sent to the server and stored in a database.

[1716] Check availability and suggest items

[1717] The server checks the inventory status of necessary event items such as fixtures based on the details of the scale of the event stored in the database, and selects the most suitable items based on the inventory status and suggests them to the user.

[1718] Item arrival date response and automatic ordering

[1719] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures.

[1720] Prompt creation using a generative AI model

[1721] The server uses a generative AI model to create a prompt based on the details of the event entered by the user, which is then presented to the user.

[1722] Receiving feedback

[1723] The user can check the generated prompt sentence and make corrections or add additional inputs as necessary. The server can receive the user's feedback and generate the prompt sentence again.

[1724] Specific examples

[1725] Example input

[1726] The user enters details about the event, such as:

[1727] Date: December 25, 2022

[1728] Location: Tokyo

[1729] Working staff: 50

[1730] Event space: 100 square meters

[1731] Sales target: 1 million yen

[1732] Number of customers: 500

[1733] Prompt Sentence Examples

[1734] An example of a prompt created by the server using a generative AI model:

[1735] Event details are as follows:

[1736] Date: December 25, 2022

[1737] Location: Tokyo

[1738] Working staff: 50

[1739] Event space: 100 square meters

[1740] Sales target: 1 million yen

[1741] Number of customers: 500

[1742] List the fixtures and items you need for this event, and also suggest a strategy for achieving your sales goals.

[1743] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[1744] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1745] Step 1:

[1746] The user accesses the system's web application using a terminal and enters details of the scale of the event, such as the date and time, location, number of people working, event space, sales target, number of customers, etc., into a form, and clicks the "Submit" button.

[1747] Input: Details of the event scale (date, time, location, number of people working, event space, sales target, number of customers)

[1748] Output: The input data is sent to the server

[1749] Step 2:

[1750] The server receives the details of the event size sent by the user and stores them in a database. The server receives the HTTP request, extracts the event details from the request body, establishes a database connection, executes an SQL query, and stores the data.

[1751] Input: Details of the event size sent by the user

[1752] Output: Details of the event size stored in the database

[1753] Step 3:

[1754] The server checks the stock status of necessary event items such as fixtures based on the details of the event scale stored in the database. The server accesses the stock database and executes an SQL query to obtain the stock status.

[1755] Input: Details of the event size stored in the database

[1756] Output: Stock status data

[1757] Step 4:

[1758] The server generates a list of items based on inventory availability that fit the size of the event, and uses a Python script to calculate the required number of fixtures and staff and select the most appropriate items.

[1759] Input: Inventory status data, details of event size

[1760] Output: A list of suggested items

[1761] Step 5:

[1762] The server uses the generative AI model to create a prompt based on the event details entered by the user. The server sends a request to the generative AI model's API to generate a prompt that includes the event details. The generated prompt is returned to the server.

[1763] Input: Details of the event size, list of proposed items

[1764] Output: Generated prompt statement

[1765] Step 6:

[1766] The server presents the generated prompt text to the user, who can then use the terminal to check the prompt text and make corrections or add additional input as necessary. When the user clicks the "Confirm" button, the server generates the prompt text again.

[1767] Input: Generated prompt text

[1768] Output: User feedback

[1769] Step 7:

[1770] Once the user confirms and confirms the proposal, the server responds with information such as the item arrival date and automatically places an order for the necessary event items, such as fixtures. The server then accesses the ordering system and places an order for the necessary items.

[1771] Input: User confirmed suggestion

[1772] Output: Item arrival date, order confirmation

[1773] In this way, the system receives user input, uses a generative AI model to create prompts, and presents them to the user. This allows users to efficiently enter event details, receive suggestions for necessary items, and review and modify the generated prompts, enabling quick and accurate event preparation and management.

[1774] (Application example 1)

[1775] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1776] Conventional event management systems provided a way to input details about the scale of an event and a way to check the inventory status of event items such as fixtures, but lacked a way to track the preparation status of the event and progress on the day in real time, or to generate reports after the event based on data such as the degree of sales target achievement and the number of customers served. This made overall event management cumbersome and made efficient operation difficult.

[1777] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1778] In this invention, the server includes means for inputting details of the scale of the event, means for checking the inventory status of event items such as fixtures based on the details of the scale, means for proposing items appropriate to the scale based on the inventory status, means for responding with information such as the arrival date of the items based on the proposal, means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing orders once confirmed, means for tracking the preparation status of the event and progress on the day in real time, and means for generating a report after the event based on data such as the degree of sales target achievement and the number of customers served. This allows for efficient overall event management and can increase the success rate of the event.

[1779] "Details of the scale of the event" refers to specific information such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1780] "Event fixtures and other items" refers to items such as tables, chairs, display shelves, posters, flyers, banners, etc. that are necessary for the event.

[1781] "Stock status" refers to information indicating the current number of event items such as fixtures and fixtures in stock and their availability.

[1782] "Suggestion means" refers to a function that automatically selects the most suitable item based on stock availability and suggests it to the user.

[1783] "Item Arrival Date" refers to the date the proposed item is scheduled to arrive at the user's specified location.

[1784] "Automatic ordering means" refers to a function that allows the user to check the proposed content, add or delete items as necessary, and then automatically order the confirmed items.

[1785] "Preparation status" refers to information indicating how far preparations for an event have progressed.

[1786] "Progress" refers to real-time tracking of how activities and plans are progressing on the day of the event.

[1787] "Sales target achievement rate" refers to an indicator that shows the degree to which actual sales were achieved relative to the sales target set after the event ended.

[1788] "Number of customers served" refers to the total number of customers served during the event.

[1789] "Report generation means" refers to a function that automatically generates a report after the event based on data such as the degree of sales target achievement and the number of customers served.

[1790] A system for implementing the present invention includes an event management application installed on a terminal such as a smartphone, a tablet, etc. A specific embodiment of this system will be described below.

[1791] Hardware and software used

[1792] Hardware: Smartphones, tablets

[1793] Software: Event management applications, inventory management systems, report generation tools

[1794] Database: MySQL

[1795] Generative AI model: GPT-4

[1796] Data processing and calculation

[1797] Enter event details

[1798] Users enter details about their event into a form in the event management application, which then stores the information in a MySQL database, including the date, time, location, staffing, event space, sales targets, and number of attendees.

[1799] Resource Suggestions

[1800] The server sends prompts to a generative AI model (GPT-4) based on the input event details, which then suggests the necessary fixtures, promotional items, and staffing. The suggested resources are then displayed in the application's UI.

[1801] Example prompt sentence:

[1802] Event Details:

[1803] Date: December 25, 2022

[1804] Location: Tokyo

[1805] Operating staff: 10

[1806] Event space: 50 square meters

[1807] Sales target: 1 million yen

[1808] Number of customers: 500

[1809] Please suggest the fixtures, promotional items, and staffing required for this event.

[1810] Inventory management

[1811] The server retrieves the stock status of the suggested items from the inventory management system and, if there is a shortage, gives the user the option to reorder.

[1812] Progress management

[1813] The server tracks the preparation status and progress of the event in real time and displays it on the application's dashboard, allowing users to see the progress of the event at a glance.

[1814] Report Generation

[1815] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[1816] Specific examples

[1817] For example, if a user is planning an event for 500 people in Tokyo on December 25, 2022, they would use the system as follows:

[1818] 1. The user enters the details of the event into the application.

[1819] 2. The server sends prompts to the generative AI model to suggest the necessary fixtures, promotional items, and staff placement.

[1820] 3. Check the availability of the suggested items and place an additional order if there is a shortage.

[1821] 4. Track event preparation and on-the-day progress in real time.

[1822] 5. After the event, generate a report based on data such as sales target achievement and number of customers served.

[1823] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[1824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1825] Step 1:

[1826] The user enters details of the event into a form in the event management application. The input items include the date and time, location, number of people available, event space, sales target, number of customers, etc. The input data is saved in a MySQL database. This registers the basic information of the event in the system.

[1827] Input: Date and time, location, number of employees, event space, sales target, number of customers

[1828] Output: Event details stored in a MySQL database

[1829] Step 2:

[1830] The server retrieves event details from a MySQL database and sends prompts to a generative AI model (GPT-4), which includes detailed information about the event. The model then suggests the necessary fixtures, promotional items, and staffing.

[1831] Input: Event details retrieved from a MySQL database

[1832] Output: Proposals from the generative AI model (furniture, promotional items, staff placement)

[1833] Step 3:

[1834] The server receives suggestions from the generative AI model and displays them in the application's UI. The user reviews the suggested items and adds or removes them as needed. Once the user confirms the suggestions, the server orders the items using an automated ordering mechanism.

[1835] Input: Suggestions from the generative AI model, user confirmation and corrections

[1836] Output: Confirmed item list, automatic ordering

[1837] Step 4:

[1838] The server retrieves the stock status of the suggested item from the inventory management system. If the item is out of stock, the server gives the user the option to reorder. When the user places an order, the server again places an order with the inventory management system.

[1839] Input: Confirmed item list, stock status from inventory management system

[1840] Output: Stock check results, additional order options

[1841] Step 5:

[1842] The server tracks the preparation and progress of the event in real time, including the arrival status of each item, staffing status, etc. The server displays this information on the application's dashboard so that users can see the progress.

[1843] Input: Arrival status of each item, staff allocation status

[1844] Output: Real-time progress information, dashboard display

[1845] Step 6:

[1846] After the event, the server generates a report based on data such as the degree of sales target achievement and the number of customers served, etc. The report can be exported in PDF format, allowing users to evaluate the event and analyze areas for improvement next time.

[1847] Input: Sales target achievement rate, number of customers

[1848] Output: Report in PDF format

[1849] In this way, the overall management of the event can be carried out efficiently, and the success rate of the event can be increased.

[1850] Example 2

[1851] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1852] There is a need for a system that can quickly and accurately check the inventory status of fixtures and other event items according to the scale of the event, and can suggest and automatically order the most suitable items. However, conventional systems require users to manually check inventory status and select the necessary items, which is time-consuming and labor-intensive. Furthermore, if the inventory status confirmation and item suggestions are inaccurate, it can hinder event preparations.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1854] In this invention, the server includes means for transmitting the details of the scale entered by the user to the server, means for the server to receive the details of the scale and access the database to obtain the stock status, and means for the server to analyze the obtained stock status and transmit the analysis results to the terminal. This allows the user to simply enter the details of the scale of the event, and the server will automatically check the stock status, suggest the most suitable items, and automatically place an order.

[1855] "Details of the scale of the event" refers to specific information necessary for carrying out the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers.

[1856] "Inventory status" refers to information stored in a database or cloud storage that indicates the current stock and availability of fixtures and other event items.

[1857] The "suggestion means" is a function for automatically selecting the most suitable items for the scale of the event based on the stock situation and proposing them to the user.

[1858] The "automatic ordering means" is a function that allows the user to check the proposed items, add or delete items as necessary, and then automatically order the confirmed items.

[1859] A "server" is a computer system that receives input from a user, accesses a database to obtain inventory status, and transmits the analysis results to a terminal.

[1860] A "terminal" is a device through which a user inputs details of the scale of an event and receives and displays the analysis results from the server.

[1861] The "database" is a system for storing the inventory status of fixtures and other event items and providing information in response to queries from the server.

[1862] The "analysis result" is information indicating whether the item required for the user's request is available in stock, based on the inventory status acquired by the server.

[1863] This invention is a system that checks the inventory status of event items such as fixtures based on the details of the scale of the event, and then proposes and automatically orders the most suitable items. This system is composed of multiple components, including users, terminals, and a server.

[1864] Hardware and software used

[1865] Hardware: Servers, database servers, cloud storage, user devices (PCs, smartphones, tablets, etc.)

[1866] Software: Database management system (e.g., MySQL, PostgreSQL), cloud storage service (e.g., Amazon S3, Google Cloud Storage), web browser or dedicated application

[1867] System Operation Overview

[1868] User operations

[1869] The user enters details of the scale of the event (e.g., furniture for 500 people is required) into an input form on the terminal. The entered information is sent from the terminal to the server.

[1870] Server Processing

[1871] The server receives the size details sent from the terminal and connects to the database management system to execute a query to check the stock status. The server analyzes the stock status retrieved from the database and checks whether the required items for the user's request are available in stock. The analysis result is sent from the server to the terminal.

[1872] Terminal display

[1873] The terminal displays the analysis results received from the server to the user. The user can check the proposed items and add or delete them as necessary. The finalized items are then ordered by the automatic ordering means.

[1874] Specific examples

[1875] For example, if a user inputs "We need furniture for 500 people," the process will proceed as follows:

[1876] 1. The user enters "I need furniture for 500 people" into the input form on the terminal.

[1877] 2. The device sends this information to the server.

[1878] 3. The server receives this information and connects to the MySQL database to query the inventory.

[1879] 4. The server retrieves from the database the information that "furniture for 500 people is available in stock."

[1880] 5. The server analyzes this information and verifies that there is sufficient inventory for the user's request.

[1881] 6. The server sends the analysis results to the device.

[1882] 7. The terminal displays the analysis results to the user, informing them that "furniture for 500 people is available in stock."

[1883] Prompt Sentence Examples

[1884] An example of a prompt sentence to input to the generative AI model is as follows:

[1885] "We need furniture for 500 people. Please check availability."

[1886] By inputting this prompt into the generative AI model, the system performs the above process and checks the inventory status.

[1887] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1888] Step 1:

[1889] The user inputs the size details.

[1890] Input: Details of the event size (e.g., furniture for 500 people required)

[1891] Specific behavior: The user uses a web browser or a dedicated application to enter the quantity of the item they want into an input form.

[1892] Output: The scale details are entered into the terminal.

[1893] Step 2:

[1894] The terminal sends the entered size details to the server.

[1895] Input: User-entered size details

[1896] Specific operation: The terminal generates an HTTP request and sends a POST request to the server.

[1897] Output: The scale details are sent to the server.

[1898] Step 3:

[1899] The server receives the size details and accesses the database.

[1900] Input: Size details sent from the terminal

[1901] What happens: The server receives the HTTP request, connects to a database management system (e.g., MySQL), and generates an SQL query to check inventory status.

[1902] Output: The SQL query is sent to the database.

[1903] Step 4:

[1904] The server retrieves the inventory status from the database.

[1905] Input: SQL query

[1906] What happens: The database returns the query results, and the server receives them.

[1907] Output: Stock availability data is retrieved to the server.

[1908] Step 5:

[1909] The server analyzes the stock status obtained.

[1910] Input: Inventory status data

[1911] What happens: The server compares the quantity in stock with the quantity requested by the user to determine if the required item is available in stock.

[1912] Output: Analysis results are generated.

[1913] Step 6:

[1914] The server sends the analysis results to the device.

[1915] Input: Analysis results

[1916] Specific operation: The server generates an HTTP response and sends it to the device.

[1917] Output: The analysis results are sent to the terminal.

[1918] Step 7:

[1919] The terminal displays the analysis results to the user.

[1920] Input: Analysis results sent from the server

[1921] Specific operation: The device displays the analysis results on the screen so that the user can check them.

[1922] Output: The analysis results are displayed to the user.

[1923] (Application example 2)

[1924] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1925] In conventional logistics centers, it was difficult to check the inventory status of fixtures and event items according to the scale of the event in real time, which led to problems with shortages and excess inventory. In addition, the process of proposing the most suitable items based on the inventory status and quickly ordering them was complicated, making efficient operation difficult.

[1926] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting details of the scale of the event; means for checking the inventory status of event items such as fixtures based on the details of the scale; means for proposing items appropriate to the scale based on the inventory status; means for responding with information such as the arrival date of the items based on the proposal; means for confirming the content of the response, making minor adjustments to added or deleted items as necessary, and automatically placing an order once confirmed; and means installed on a smartphone that allows a manager or staff member at a logistics center to check the inventory status in real time. This makes it possible to check the inventory status of fixtures and event items appropriate to the scale of the event in real time at the logistics center and quickly propose and order the most appropriate items.

[1927] "Event scale details" is information indicating the specific scale and conditions of the event, such as the date and time of the event, location, number of people working, event space, sales target, number of customers, etc.

[1928] "Event fixtures and other items" refers to items such as furniture, equipment, and decorations necessary for holding an event.

[1929] "Stock status" is information indicating how many event items such as fixtures are currently in stock and how many are available for use.

[1930] "Suggestion method" refers to the function that automatically selects the most suitable items based on stock availability and suggests items that suit the scale of the event.

[1931] "Item Arrival Date" means the date the proposed item is expected to ship from the distribution center and arrive at the specified location.

[1932] "Automatic ordering means" refers to a function that checks the contents of the proposed items, adds or deletes as necessary, and then automatically carries out the ordering procedure once the contents are confirmed.

[1933] "Means installed on smartphones" refers to applications that allow logistics center managers and staff to check inventory status in real time using their smartphones.

[1934] The system for implementing this invention includes a means for inputting details of the scale of the event, a means for checking the stock status of event items such as fixtures based on the details of the scale, a means for proposing items that suit the scale based on the stock status, a means for responding with the arrival date of the items etc. based on the proposal, a means for confirming the content of the response, making minor adjustments to added items or deleted items as necessary, and automatically placing an order once confirmed, and a means that is installed on a smartphone and allows managers and staff at the logistics center to check the stock status in real time.

[1935] The server provides an interface for users to input details about the scale of the event, such as the date and time of the event, the location, the number of people working, the event space, sales targets, and the number of customers. The server receives this information and stores it in a database.

[1936] The server then searches its database to see if there is enough fixtures and other event items available based on the size details entered. For example, it checks whether there is enough fixtures for 500 people available. This availability can come from cloud storage or a local database.

[1937] After checking the inventory status, the server uses a generative AI model to automatically select the most suitable items and suggest them to the user, including details about the items and their expected arrival dates. The user can review the suggestions and add or remove items as needed.

[1938] Once the proposal is confirmed, the server initiates an automated ordering process, which ensures the required items are shipped from the distribution center and delivered to the specified location.

[1939] The application installed on a smartphone allows managers and staff at the distribution center to check inventory status in real time. The application uses Python and the requests library to retrieve inventory information from the server and display it to the user.

[1940] For example, if a logistics center manager inputs the size of an event for 500 people, the system will search the database to see if there is enough fixtures available in stock for 500 people. It will then use a generative AI model to suggest the most suitable items, and once the user has confirmed and revised the suggestions, it will automatically process the order.

[1941] Example prompts to input to a generative AI model:

[1942] "If your event size is 500 people, check how many fixtures you have available in stock."

[1943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1944] Step 1:

[1945] The user inputs details about the scale of the event. Using a smartphone application, the user inputs details such as the date and time of the event, location, number of people working, event space, sales target, and number of customers. The input data is sent to the server.

[1946] Input: Event date and time, location, number of people in operation, event space, sales target, number of customers

[1947] Output: Detailed data on the scale of events sent to the server

[1948] Step 2:

[1949] The server saves the detailed data of the scale of the received event in a database. The server converts the input data into an appropriate format and stores it in the database.

[1950] Input: Detailed data on the scale of the event

[1951] Output: Detailed event size data stored in the database

[1952] Step 3:

[1953] The server searches the database to check the stock status of event items such as fixtures. The server calculates the number of items required based on the details of the scale of the event and retrieves stock information from the database or cloud storage.

[1954] Input: Detailed data on the scale of the event

[1955] Output: Stock status data

[1956] Step 4:

[1957] The server uses a generative AI model to automatically select the most suitable items and suggest them to the user. The server inputs inventory status data into the generat...

Claims

1. A processor is provided, The processor: determining whether the required event items are available in stock by comparing the number of required event items calculated based on the input details of the scale of the event with the availability of the event items retrieved from a database; Recognizes user emotions using an emotion engine, inputting the inventory status and the scale details into a generative AI model, and having the generative AI model suggest event items; Based on the proposal, respond with the item arrival date output from the delivery system; When the user confirms the content of the proposal, modifies and confirms the added items and deleted items, the timing of the order is adjusted based on the recognized emotion of the user and an automatic order is placed. system.

2. The processor, Track the event preparation status and progress on the day in real time, Generate a report after the event based on data including sales target achievement and number of customers. The system of claim 1 .

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