System

Abstract

A system is provided.SOLUTION: A system includes a means for inputting schedule data of a participant, a means for transmitting the inputted schedule data to a management server, a means for analyzing the schedule data by using an AI model generated by the management server, and a means for proposing an optimum schedule, and a means for transmitting the proposed optimum schedule to a terminal and displaying it.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] Conventionally, scheduling meetings and events has been done manually, requiring time and effort to check the schedules of all participants one by one and find the optimal date. This makes scheduling particularly difficult when participants have different schedules, and communication related to schedule adjustments is cumbersome and inefficient. The present invention aims to solve these problems and improve the efficiency of schedule adjustments. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for inputting participants' schedule data, a means for transmitting the input schedule data to a management server, a means for the management server to analyze the schedule data using a generative AI model and propose an optimal schedule, and a means for transmitting and displaying the proposed optimal schedule to a terminal. This system eliminates the need for users to manually coordinate schedules and improves the efficiency of communication required for schedule coordination. Furthermore, by inputting schedule data and using the generative AI model by the management server, it is possible to propose an optimal schedule that takes into account participants' work schedules and priorities.

[0006] "Participant schedule data" is information that describes the schedules of people who will be attending a conference or event.

[0007] "Means of input" refers to the functions of devices and software used to input schedule data into the system.

[0008] The "management server" is a computer system that receives schedule data and analyzes it using a generative AI model.

[0009] A "generative AI model" refers to artificial intelligence technology that uses data to perform analysis and predictions.

[0010] "Analysis" is the process of calculating the optimal schedule based on the input data.

[0011] The "optimal date" is the most appropriate date and time, taking into account the schedules of all participants.

[0012] "Transmitting means" refers to the function or method for sending data to other devices or systems.

[0013] The "means of suggestion" is a mechanism for showing the results analyzed by the generative AI model to the user.

[0014] "Displaying means" refers to a device or function for showing information on a terminal screen or display.

[0015] A "terminal" is a computer or mobile device that a user accesses and operates. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system for streamlining schedule adjustments for meetings and events. The system consists of a series of processes: participants' schedule data is input, sent to a management server, analyzed by the server using a generative AI model, and the server proposes an optimal schedule, which is then displayed on a terminal.

[0038] 1. Enter and submit schedule data

[0039] The user uses the terminal to input the schedule data of the participants. The input interface uses a web form, which includes fields such as participant name, date, time slot, etc. This allows the user to easily input the schedule of each participant.

[0040] The terminal sends the entered schedule data to the management server. The server receives this data and checks its consistency. If there are any errors or deficiencies in the data, it notifies the user by returning an error message to the terminal.

[0041] 2. Analysis using generative AI models and optimal schedule proposals

[0042] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for the meeting or event. This process also takes into account the participants' work schedules and priorities, making adjustments easier.

[0043] The management server transmits the optimum schedule obtained as a result of the analysis to the terminal, which receives the optimum schedule and displays it on the user interface.

[0044] 3. Display and confirm the results

[0045] The user checks the optimal schedule displayed on their device. The display interface is in calendar or list format, allowing the user to intuitively understand the proposed schedule. If necessary, the user can also modify the schedule. The final approved schedule is notified to all participants.

[0046] Specific examples

[0047] 1. Meeting setup example

[0048] A user accesses a terminal and inputs the schedule data of five participants.

[0049] The terminal transmits this data to the management server.

[0050] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[0051] The terminal receives the optimum schedule and displays it to the user.

[0052] The user checks the displayed optimum schedule and makes corrections if necessary.

[0053] The final dates will be approved and communicated to all participants.

[0054] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, by checking the schedules of all participants at once and automatically proposing the optimal date, significantly reducing the time and effort required. This system allows users to adjust schedules without stress and improves the efficiency of the entire business.

[0055] The processing flow will be explained below.

[0056] Step 1: User enters schedule data

[0057] The user uses the terminal interface to input schedule data for each participant, which is provided in the form of a web form and includes items such as participant name, date, and time slot.

[0058] Step 2: The device sends the schedule data

[0059] The terminal sends the schedule data entered by the user to the management server, which also performs basic checks to ensure the data is consistent.

[0060] Step 3: Server receives data

[0061] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any discrepancy, the server returns an error message to the terminal.

[0062] Step 4: The server passes the data to the generative AI model

[0063] The server inputs the confirmed schedule data into the generative AI model and begins analysis. At this time, the generative AI model calculates the optimal schedule taking into account the schedules of the participants.

[0064] Step 5: The server calculates the optimal schedule

[0065] A generative AI model performs the analysis and calculates the optimal itinerary, taking into account participants' work schedules and priorities.

[0066] Step 6: The server sends the optimal schedule to the device.

[0067] The server sends the optimal schedule calculated by the generative AI model to the terminal.

[0068] Step 7: Your device receives the best schedule

[0069] The terminal receives the optimal schedule sent from the server and also checks the consistency of the received schedule data.

[0070] Step 8: Your device will display the best dates

[0071] The terminal displays the received optimal schedule to the user in a display interface in a calendar or list format.

[0072] Step 9: User confirms optimal schedule

[0073] The user can check the optimal schedule displayed on the terminal and, if necessary, modify the schedule.

[0074] Step 10: User confirms final date

[0075] The user confirms the optimal date and finalizes it. The confirmed date is notified to all participants.

[0076] This process streamlines meeting and event scheduling, eliminating the need for manual scheduling, and leverages generative AI models to suggest optimal schedules.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] Conventional scheduling for meetings and events requires manual coordination among participants, which takes a lot of time and effort. It is also difficult to grasp the schedules of all participants at once, making it difficult to find the optimal date. Therefore, there is a need for a system that can improve the efficiency of schedule coordination and quickly and accurately adjust the schedules of all participants.

[0080] 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.

[0081] In this invention, the server includes a means for checking the consistency of schedule data, a means for analyzing the schedule data using a generative AI model and proposing an optimal schedule, and a means for notifying all participants of the final approved schedule, which enables users to efficiently adjust their schedules and quickly determine the optimal schedule.

[0082] A "user" is a person or entity that utilizes the system to enter, review, and modify schedule data.

[0083] A "terminal" is a device used by a user, and is a device for inputting schedule data and communicating with a server.

[0084] A "management server" is a central computer system that receives, analyzes, and notifies input schedule data.

[0085] "Schedule data" refers to information such as participant names, dates, and time slots entered for a meeting or event.

[0086] A "generative AI model" is an artificial intelligence model used to analyze input schedule data and propose optimal dates.

[0087] "Integrity checking" is the process by which the management server verifies that the schedule data it receives is complete and accurate.

[0088] The "proposed optimal schedule" refers to the most suitable schedule for all participants, as determined by the generative AI model as an analysis result.

[0089] A "user interface" is a screen or display format that allows a user to input and check schedule data on a terminal.

[0090] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[0091] "Notification" refers to the act of the management server informing all participants of the final approved schedule.

[0092] MODE FOR CARRYING OUT THE INVENTION

[0093] The present invention is a system for improving the efficiency of scheduling meetings and events.

[0094] System configuration

[0095] User:

[0096] Users use a terminal to input schedule data. A web form is used as the input interface. Specifically, information such as participant names, dates, and time slots is input.

[0097] Device:

[0098] The terminal sends the schedule data entered by the user to the management server using an HTTP POST request. The display interface on the terminal displays the analysis results to the user in calendar or list format.

[0099] Management Server:

[0100] The management server checks the consistency of the received schedule data, and if there is an error in the data, returns an error message to the terminal.

[0101] The schedule data whose consistency has been confirmed is then passed to the generative AI model, which uses frameworks such as PyTorch and TENSORFLOW (registered trademark).

[0102] The generative AI model analyzes schedule data and proposes optimal dates, taking into account participants' work schedules and priorities.

[0103] The optimum schedule is sent to the terminal so that the user can check and modify it.

[0104] The final approved date will be notified to all participants via email or push notification.

[0105] Specific examples

[0106] 1. Example of a meeting setup:

[0107] A user accesses a terminal and inputs the schedule data of five participants.

[0108] Participant name: "Mr. A", Date: "2023-12-01", Time: "14:00-16:00"

[0109] The terminal transmits this data to the management server.

[0110] The management server checks the integrity of the received data and analyzes it using a generative AI model.

[0111] The generative AI model calculates the optimal meeting schedule and returns the result to the server, using the following prompt:

[0112] "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[0113] The management server transmits the analysis results to the terminal, and the terminal displays the optimal schedule on the user interface.

[0114] The user checks the optimal date and makes any necessary changes, for example, changing it to "2023-12-02 10:00-11:00."

[0115] The final date is approved and all participants are notified. An example of a notification email would be: Subject: "Notification of Final Meeting Date" with the body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[0116] By using this system, users can significantly simplify the tedious task of manually adjusting schedules, enabling them to quickly and accurately determine optimal dates for meetings and events.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] User Action:

[0120] The user uses the terminal to input schedule data for the participants.

[0121] Specific actions: Use a browser to access a web form and enter information such as participant names, date, and time slot.

[0122] Input: Participant name: "Mr. A", Date: "2023-12-01", Time period: "14:00-16:00"

[0123] Output: Input data is saved to the terminal.

[0124] Step 2:

[0125] Device features:

[0126] The terminal transmits the schedule data input by the user to the management server.

[0127] Specific behavior: Sends data to the management server using an HTTP POST request.

[0128] Input: Schedule data in JSON format

[0129] json

[0130] {

[0131] "Participant Name": "Mr. A",

[0132] "Date": "2023-12-01",

[0133] "Time Zone": "14:00-16:00"

[0134] }

[0135] Output: Data received by the management server

[0136] Step 3:

[0137] Server Integrity Check:

[0138] The server checks the integrity of the received schedule data.

[0139] Specific operation: Validates the data format and required fields. If there is an error, generates an error message and returns it to the terminal.

[0140] Input: Received schedule data

[0141] Output: Schedule data whose consistency has been checked or an error message

[0142] Step 4:

[0143] Server execution of generative AI models:

[0144] The server passes the schedule data whose consistency has been confirmed to the generative AI model and begins analysis.

[0145] Specific operation: Analyze schedule data using frameworks such as PyTorch and TensorFlow.

[0146] Prompt: "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[0147] Input: Schedule data that has been checked for consistency

[0148] Output: Optimal schedule based on analysis

[0149] Step 5:

[0150] Server sends analysis results:

[0151] The server sends the optimal schedule obtained from the generative AI model to the terminal.

[0152] Specific operation: The analysis results are converted into JSON format and sent to the terminal as an HTTP response.

[0153] Input: Optimal schedule analysis results

[0154] json

[0155] {

[0156] "Best Date": "2023-12-01 15:00-16:00"

[0157] }

[0158] Output: The device where the analysis results are sent

[0159] Step 6:

[0160] Displaying the best dates on your device:

[0161] The terminal displays the received optimum schedule on the user interface.

[0162] Specific operation: Display analysis results in calendar or list format in the browser

[0163] Input: Analysis result data

[0164] Output: Optimal schedule displayed to the user

[0165] example:

[0166] December 1st

[0167] 15:00 - 16:00 Meeting (Participants: Mr. A, Mr. B)

[0168] Step 7:

[0169] User confirmation and modification of dates:

[0170] The user checks the displayed optimum schedule and makes corrections if necessary.

[0171] Action: Re-enter new dates using the web form

[0172] Input: Revised schedule data

[0173] Output: The final approved schedule is sent to the server.

[0174] Step 8:

[0175] Server notification of final date:

[0176] The server will notify all participants of the final approved dates.

[0177] What it does: Sends notifications via email and push notifications.

[0178] Input: Final Approved Date

[0179] Output: Notification sent to all participants

[0180] example:

[0181] Email subject: "Notification of final meeting schedule"

[0182] Email body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] In today's food delivery industry, it is difficult to efficiently coordinate the schedules of multiple delivery drivers. In particular, simultaneously considering delivery driver schedules and restaurant operation schedules is time-consuming and prone to errors. This issue can lead to delivery delays and reduced efficiency.

[0186] 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.

[0187] In this invention, the server includes a means for inputting delivery staff schedule data, a means for transmitting the input schedule data to the management server, and a means for proposing an optimal delivery schedule in consideration of the delivery staff schedule data. This allows business operators such as restaurants and cafes to easily manage delivery staff schedules and efficiently adjust delivery schedules.

[0188] A "participant" is an individual or group involved in a meeting, event, or delivery task that needs to be scheduled within the system.

[0189] "Schedule data" is information indicating the schedules and availability of participants and delivery personnel, and is digital information including details such as dates, time periods, and priorities.

[0190] "Input means" refers to a method and device for providing schedule data to the system via a terminal, a web interface, or a smartphone application.

[0191] The "means for transmitting to the management server" is a communication function for transferring input schedule data to a central management server.

[0192] The "management server" is a centralized computer system that analyzes schedule data and generates optimal dates and delivery schedules.

[0193] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and calculates the optimal schedule.

[0194] The "means for transmitting to the terminal and displaying" refers to a method and device for transferring the optimum schedule obtained as a result of the analysis to the user's terminal and displaying it.

[0195] A "delivery person" is an individual whose job it is to transport food and merchandise to customers in a food delivery service.

[0196] A "delivery schedule" is a plan that indicates the time periods and order of deliveries planned to enable delivery personnel to perform their work efficiently.

[0197] The "means for proposing" is a method and apparatus for presenting the optimal schedule analyzed by the generative AI model to the user.

[0198] This invention is a system for streamlining delivery staff schedule adjustments in food delivery services. The system proposes optimal delivery schedules by having users input delivery staff schedule data and transmitting it to a management server. Below, we will explain the details of each component and process of the system.

[0199] System configuration

[0200] 1. User Device

[0201] A smartphone application or web interface is used, through which users input delivery staff schedule data. For example, a restaurant owner inputs the delivery staff's availability and desired delivery times.

[0202] The schedule data includes details such as the date, time, and delivery person's ID.

[0203] 2. Management Server

[0204] After the data is sent, the management server receives it and checks its integrity. If there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[0205] The management server is equipped with a generative AI model that analyzes the received schedule data and generates an optimal delivery schedule.

[0206] The specific AI model used has an algorithm that simultaneously takes into account delivery personnel's schedule data and job priorities.

[0207] 3. Generative AI Models

[0208] The generative AI model installed on the management server analyzes each delivery person's schedule data and creates an efficient delivery schedule, taking into account the delivery person's availability and priority to calculate the optimal delivery time slot.

[0209] Operational procedures and processing details

[0210] 1. Enter schedule data

[0211] A restaurant owner inputs delivery staff schedule data from a user terminal through an application. For example, suppose delivery staff A's availability is "2023-10-15 10:00-12:00" and delivery staff B's ​​availability is "2023-10-15 14:00-16:00."

[0212] The input format is simple and provides an interface that users can operate intuitively.

[0213] 2. Sending and Receiving Data

[0214] The entered data is sent to the management server in real time. Data is sent in JSON format using requests.post.

[0215] The management server checks the integrity of the data and returns an error message if there are any discrepancies.

[0216] 3. Analysis using generative AI models

[0217] The generative AI model calculates the optimal delivery schedule based on the received schedule data, taking into account the work schedules and priorities of delivery personnel.

[0218] 4. Display and confirm the results

[0219] The optimal schedule generated by the management server is sent to the user terminal and displayed by the application.

[0220] The store owner reviews the proposed schedule and can make any necessary changes. The final approved schedule is notified to all delivery drivers.

[0221] Specific examples

[0222] Example input:

[0223] The restaurant owner opens the app and enters the following schedule data:

[0224] Delivery Person A: 2023-10-15 10:00-12:00

[0225] Delivery Person B: 2023-10-15 14:00-16:00

[0226] Delivery Person C: 2023-10-16 09:00-11:00

[0227] Example prompts used for analysis by the generative AI model:

[0228] Restaurant ID: 12345

[0229] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[0230] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[0231] Generate an optimal delivery schedule to output.

[0232] This method streamlines delivery staff scheduling and improves the efficiency of food delivery operations.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] Entering schedule data

[0236] The user inputs the delivery person's schedule data using a smartphone application or a web interface. Specifically, the user inputs the delivery person's ID, available time, priority, etc. into an input form.

[0237] input:

[0238] Schedule data such as delivery driver ID, availability, and priority.

[0239] output:

[0240] The schedule data entered.

[0241] Specific behavior:

[0242] The user enters the delivery person's ID and available time in the application's input form. For example, the available time of delivery person A is entered as "2023-10-15 10:00-12:00."

[0243] Step 2:

[0244] Sending data

[0245] The terminal sends the entered schedule data to the management server using requests.post in JSON format.

[0246] input:

[0247] Schedule data entered by the user.

[0248] output:

[0249] Schedule data sent to the management server.

[0250] Specific behavior:

[0251] The terminal generates schedule data in JSON format and sends a POST request to the management server. For example, send the following data:

[0252] json

[0253] {

[0254] "restaurant_id": "12345",

[0255] "delivery_ids": ["deliv_1", "deliv_2", "deliv_3"],

[0256] "dates_times": ["2023-10-15 10:00-12:00", "2023-10-15 14:00-16:00", "2023-10-16 09:00-11:00"]

[0257] }

[0258] Step 3:

[0259] Data reception and integrity check

[0260] The management server checks the consistency of the schedule data it receives, and if there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[0261] input:

[0262] Schedule data sent to the management server.

[0263] output:

[0264] Schedule data that has been checked for consistency or error messages.

[0265] Specific behavior:

[0266] The management server verifies the received data, and if any defects are found, it generates an error message and notifies the user terminal. For example, if the date and time format is invalid, it returns the message "The date and time format is invalid."

[0267] Step 4:

[0268] Analysis using generative AI models

[0269] The management server passes the confirmed schedule data to the generative AI model and begins analysis. The model generates an optimal delivery schedule based on the schedule data.

[0270] input:

[0271] Schedule data that has been checked for consistency.

[0272] output:

[0273] Optimal delivery schedule.

[0274] Specific behavior:

[0275] The management server passes a prompt to the generative AI model to start the analysis. The prompt used as an example is as follows:

[0276] Restaurant ID: 12345

[0277] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[0278] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[0279] Generate an optimal delivery schedule to output.

[0280] A generative AI model then analyzes the data and generates an optimal delivery schedule.

[0281] Step 5:

[0282] Sending and displaying the optimal schedule

[0283] The management server sends the optimal schedule obtained by the generative AI model to the user's terminal and displays it in the application.

[0284] input:

[0285] Optimal schedule analyzed by generative AI model.

[0286] output:

[0287] The optimal schedule is displayed on the user's terminal.

[0288] Specific behavior:

[0289] The management server sends the generated optimal schedule to the user terminal and displays it on the application interface. For example, the following schedule is displayed:

[0290] Date: 2023-10-15, Time: 10:00-12:00, Delivery Person: Delivery Person A

[0291] Date: 2023-10-15, Time: 14:00-16:00, Delivery Person: Delivery Person B

[0292] Date: 2023-10-16, Time: 09:00-11:00, Delivery Person: Delivery Person C

[0293] Step 6:

[0294] Check and correct the results

[0295] The user can check the displayed optimal schedule and make any necessary corrections. The final approved schedule is notified to all participants.

[0296] input:

[0297] The optimal schedule displayed on the user's terminal.

[0298] output:

[0299] The final schedule, as amended or approved.

[0300] Specific behavior:

[0301] The user checks the optimal schedule and makes any necessary corrections. If no corrections are necessary, the user simply presses the approval button, and the final schedule is notified to all delivery personnel.

[0302] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0303] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[0304] 1. Enter and submit schedule data

[0305] The user inputs the schedule data of the participants using a terminal. The input interface is provided as a web form, and includes fields such as participant name, date, and time slot. The input data is checked for consistency and then sent to the management server.

[0306] 2. Analysis using generative AI models and optimal schedule proposals

[0307] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account each participant's schedule and calculates the optimal date for the meeting or event. During this process, participants' work schedules and priorities are also taken into account.

[0308] 3. Emotion recognition and feedback using an emotion engine

[0309] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine detects the user's facial expressions and tone of voice and generates feedback according to the user's emotions based on the analysis of the schedule data. This feedback helps reduce the psychological stress that the optimal schedule causes to the user.

[0310] 4. Display and confirm the results

[0311] The management server sends the optimal schedule obtained as a result of the analysis to the terminal. The terminal receives this optimal schedule and displays it on the user interface. The display interface is provided in calendar or list format, allowing the user to intuitively check the presented optimal schedule.

[0312] 5. User confirmation and final schedule confirmation

[0313] The user can check the optimal schedule displayed on the device and make any necessary adjustments. Feedback from the emotion engine also provides guidance to help users select a better schedule. The final confirmed schedule is notified to all participants.

[0314] Specific examples

[0315] 1. Meeting setup example

[0316] A user accesses a terminal and inputs the schedule data of five participants.

[0317] The terminal transmits this data to the management server.

[0318] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[0319] An emotion engine built into the management server recognizes emotions from the user's facial expressions and tone of voice, and provides feedback based on the analysis results.

[0320] The terminal receives the optimal schedule and feedback and displays it to the user.

[0321] The user checks the displayed optimum schedule, makes corrections if necessary, and finalizes the schedule.

[0322] Confirmed dates will be communicated to all participants.

[0323] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, and enables optimal schedule adjustment that takes user emotions into consideration. The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[0324] The processing flow will be explained below.

[0325] Step 1: User enters schedule data

[0326] The user inputs the schedule data of each participant using the terminal interface. The input interface is provided in the form of a web form, and the user can enter the necessary information in fields such as participant name, date, time slot, etc.

[0327] Step 2: The device sends the schedule data

[0328] The terminal sends the schedule data entered by the user to the management server. During this sending process, the terminal performs basic checks (format checks and blank checks) to ensure the consistency of the data.

[0329] Step 3: Server receives data

[0330] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any error, the server returns an error message to the terminal and asks the user to correct it.

[0331] Step 4: The server passes the data to the generative AI model

[0332] Once the consistency of the schedule data has been confirmed, the server inputs it into a generative AI model and begins analysis. This generative AI model takes into account the schedules of each participant and calculates the optimal dates for meetings and events.

[0333] Step 5: The server recognizes the user's emotion using the emotion engine

[0334] The server is equipped with an emotion engine that analyzes facial expressions and tone of voice in real time when users input schedule data to recognize their emotions. Emotional data is reflected in schedule analysis.

[0335] Step 6: The server calculates the optimal schedule

[0336] The generative AI model analyzes and calculates an optimal itinerary that takes into account participants' schedules and the user's emotional data, and is tailored to reduce the user's psychological stress.

[0337] Step 7: The server sends the optimal schedule to the device.

[0338] The server combines the optimal schedule analyzed by the generative AI model with feedback from the emotion engine and sends it to the terminal.

[0339] Step 8: The device receives the optimal schedule

[0340] The terminal receives the optimal schedule sent from the server, checks its consistency, and if there are no problems with the data format, prepares it for display on the user interface.

[0341] Step 9: Your device will display the best date and give you feedback.

[0342] The device displays the received optimal schedule and feedback from the emotion engine to the user. The display interface is provided in calendar and list format, allowing users to intuitively check the information.

[0343] Step 10: User confirms optimal schedule

[0344] The user checks the optimal schedule displayed on the device and makes any necessary adjustments. Based on feedback from the emotion engine, the user also determines whether the proposed schedule will cause any psychological stress.

[0345] Step 11: User confirms final date

[0346] The user confirms the optimal schedule and, once any modifications are complete, confirms the final schedule. The confirmed schedule is notified to all participants. This step allows all participants to be aware of the new schedule and begin preparations.

[0347] Example 2

[0348] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] Traditionally, scheduling meetings and events has often been done manually, which is time-consuming and inefficient. Finding the optimal date while taking into account the schedules of multiple participants is extremely difficult, especially when multiple participants are involved. Furthermore, the psychological impact and emotions of the proposed schedule on the user were not taken into consideration. As a result, even when an optimal date was found, the user often felt stressed.

[0350] 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.

[0351] In this invention, the server includes a means for inputting schedule data of participants, a means for confirming the consistency of the input schedule data, a means for transmitting the schedule data whose consistency has been confirmed to the management server, a means for the management server to analyze the schedule data using a generative AI model and propose an optimal schedule, a means for recognizing the emotional state of the user using an emotion engine and generating feedback based on the analysis results, and a means for transmitting and displaying the proposed optimal schedule and feedback to the terminal. This not only improves the efficiency of schedule adjustment, but also enables the adjustment of an optimal schedule that takes the user's emotions into consideration.

[0352] "Schedule data" is information indicating the dates and times when each participant of a meeting or event is available.

[0353] "Input means" refers to an interface that allows a user to input schedule data into the system using a terminal. Specifically, this corresponds to a web form, etc.

[0354] "Consistency checking means" is a process or system function for checking the accuracy and consistency of input schedule data.

[0355] The "transmission means" is a process or system function for transmitting schedule data from a terminal to the management server. Specifically, HTTP or HTTPS protocols are used.

[0356] The "management server" is a central component of the system that stores received schedule data, analyzes it using a generative AI model, and proposes optimal schedules.

[0357] The "generative AI model" is an artificial intelligence algorithm that analyzes the received schedule data and calculates the optimal schedule by taking into account the available dates and times of each participant. Specifically, a natural language processing model is used.

[0358] The "emotion engine" is part of a system that analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotional state.

[0359] "Feedback" is some kind of reaction or guidance to the user that is generated based on the user's emotional state recognized by the emotion engine.

[0360] The "display means" is an interface that allows the user to visualize the proposed optimal schedule and feedback. Specifically, it is displayed in a calendar or list format on the terminal display.

[0361] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[0362] First, a user uses a terminal (such as a PC or smartphone) to enter the schedule data of participants. The input interface is provided as a web form, and includes items such as participant names, dates, and time slots. For example, a user can access a terminal and enter the schedule data of five participants. The data is verified by the terminal and then sent to the management server.

[0363] The management server passes the received schedule data to a generative AI model (e.g., a natural language processing model such as GPT-4 (registered trademark) or BERT) and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for a meeting or event. At this time, the work schedules and priorities of the participants are also taken into consideration. For example, a prompt such as "Based on the schedule data of the following five people, please propose the optimal meeting date that allows everyone to attend. Please also take into consideration various priorities" is input into the generative AI model.

[0364] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine uses the device's camera and microphone to detect the user's facial expressions and tone of voice, and generates feedback based on the analysis results. This feedback helps reduce the psychological stress that the proposed optimal schedule causes to the user. For example, positive feedback is provided to a user who is feeling stressed.

[0365] The optimal schedule obtained as a result of the analysis is sent from the management server to the terminal. The terminal displays this optimal schedule and feedback in a user interface in calendar or list format. The user can intuitively check the presented optimal schedule and make corrections as necessary. For example, if a user checks the presented optimal schedule and wants to change a specific schedule, they can make the corrections while also taking into account feedback from the emotion engine.

[0366] Finally, the final schedule confirmed by the user is sent to the management server and notified to all participants via email or push notification, so that all participants can check the final schedule.

[0367] This system not only significantly improves the efficiency of schedule adjustment work, which was previously done manually, but also enables optimal schedule adjustment that takes into account the user's emotions.The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[0368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0369] Program processing steps

[0370] Step 1: Enter schedule data

[0371] The user enters the schedule data.

[0372] The user uses a terminal to enter the schedule data of participants (participant name, date, time slot) via a web form. The entered data may include "Participant A: January 1st 10:00-12:00".

[0373] input:

[0374] Participant schedule (e.g. Participant A: January 1st 10:00-12:00)

[0375] output:

[0376] Input schedule data

[0377] Step 2: Data integrity check

[0378] The device checks the integrity of the data

[0379] The terminal checks the integrity of the data entered, specifically checking for missing required fields and validating date formats, such as whether the date entered is in a valid format and whether the time zone is consistent.

[0380] input:

[0381] Schedule data entered by the user

[0382] output:

[0383] Schedule data that has been checked for consistency or an error message

[0384] Step 3: Sending data

[0385] The device sends data to the management server

[0386] The data whose integrity has been confirmed is then sent to the management server using the HTTPS protocol to ensure data security.

[0387] input:

[0388] Consistency-checked schedule data

[0389] output:

[0390] Schedule data sent to the management server

[0391] Step 4: Analyze the data with a generative AI model

[0392] The server receives the data and passes it to the generative AI model

[0393] The management server passes the received schedule data to the generation AI model and begins analysis. The generated prompt is, "Based on the schedule data of the following five people, please suggest the optimal meeting date that everyone can attend."

[0394] input:

[0395] Schedule data stored on the management server

[0396] output:

[0397] A prompt to pass to the generative AI model

[0398] Generative AI model calculates optimal schedule

[0399] The generative AI model analyzes the prompt text and calculates the optimal meeting date, taking into account each participant's schedule and calculating the optimal non-overlapping time slot.

[0400] input:

[0401] Prompt: "Based on the schedule data of the following five people, please suggest the best meeting date that allows everyone to attend."

[0402] output:

[0403] Optimal meeting dates

[0404] Step 5: Emotion Recognition with the Emotion Engine

[0405] The server recognizes the user's emotions

[0406] The management server passes data obtained from the device's camera and microphone to the emotion engine, which analyzes the user's facial expressions and tone of voice. For example, the camera captures the user's facial expressions, and the microphone captures the user's tone of voice.

[0407] input:

[0408] User facial expression data, tone of voice data

[0409] output:

[0410] The user's emotional state (e.g., stress, joy)

[0411] The server generates feedback

[0412] Based on the analysis results of the emotion engine, feedback is generated that takes into account the impact of the optimal schedule on the user. For example, if the user feels stressed, a positive message will be sent to provide a sense of security.

[0413] input:

[0414] The user's emotional state

[0415] output:

[0416] Feedback Message

[0417] Step 6: Viewing the best dates and feedback

[0418] The server sends the optimal schedule and feedback to the device.

[0419] The management server sends the optimal schedule and feedback to the terminal.

[0420] input:

[0421] Optimal meeting schedule, feedback message

[0422] output:

[0423] Data sent to the device

[0424] The device displays the best time and gives feedback

[0425] The device displays the optimal schedule and feedback in a user interface in calendar or list format.

[0426] input:

[0427] Optimal meeting schedule, feedback message

[0428] output:

[0429] Display in user interface (calendar format, list format)

[0430] Step 7: Final confirmation and schedule confirmation

[0431] The user checks the optimal schedule and makes any necessary adjustments.

[0432] The user can review the displayed optimal schedule and make adjustments as needed, for example, by changing the date of a specific meeting to accommodate individual needs.

[0433] input:

[0434] Optimal meeting schedule, user-specified corrections

[0435] output:

[0436] Revised final schedule

[0437] The server will confirm and notify the final schedule.

[0438] The user then sends the final schedule to the management server, which then notifies all participants via email or push notification.

[0439] input:

[0440] Revised final schedule

[0441] output:

[0442] Notification from the server to all participants

[0443] (Application example 2)

[0444] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0445] Conventional schedule adjustment systems have difficulty efficiently consolidating the diverse schedules of participants. Furthermore, they do not consider the psychological burden or stress that the proposed schedule places on users, making it difficult to say that the schedule is optimal. In particular, in work environments such as factories, efficient schedule adjustment while reducing fatigue and stress among maintenance staff is required.

[0446] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting schedule data of participants, a means for transmitting the input schedule data to a management server, a means for the management server to analyze the schedule data using a generation AI model and propose an optimal schedule, a means for transmitting and displaying the proposed optimal schedule to a terminal, and a means including an emotion engine that analyzes user emotion data and providing feedback. This makes it possible to improve the efficiency of schedule adjustment for participants, provide feedback that takes into consideration the user's emotions, and achieve optimal schedule adjustment with reduced psychological burden.

[0447] "Participant schedule data" is information indicating the schedule of each individual who will participate in a conference or event, and is data including the date, time period, and work content.

[0448] The "management server" is a central computer system that comprehensively manages participants' schedule data, generative AI models, and emotion engines, and proposes optimal schedules.

[0449] A "generative AI model" is an artificial intelligence model that analyzes collected schedule data and calculates the optimal schedule.

[0450] An "emotion engine" is a software or hardware component for analyzing a user's emotional data and providing real-time feedback on schedule suggestions.

[0451] "Proposed optimal dates" are the most suitable dates calculated by the generative AI model based on the participants' schedules and other relevant information.

[0452] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to receive and display schedule suggestions and feedback from a server on the cloud.

[0453] "User emotion data" is information about emotions acquired from the user's facial expressions, tone of voice, etc., and is data that is analyzed by the emotion engine.

[0454] This invention relates to a system for efficiently managing the schedules of maintenance staff in a factory. The system includes functions for inputting, sending, analyzing, proposing, and displaying schedule data of participants, as well as emotion recognition feedback.

[0455] First, users (factory managers or maintenance staff) use terminals (smartphones or tablets) to enter their own and other staff members' schedule data. The schedule data includes details such as working hours, holidays, and job content. The data is entered via a web interface and sent to the management server.

[0456] The management server then passes the received schedule data to a generative AI model, which then proposes an optimal maintenance schedule. The generative AI model analyzes each staff member's schedule, as well as the maintenance requirements and priorities of robots and machines.

[0457] Furthermore, the emotion engine analyzes the user's emotional data for the proposed schedule. The emotion engine detects the user's facial expressions and tone of voice in real time to evaluate whether the proposed schedule will cause psychological stress. If necessary, it provides feedback and encourages schedule adjustments.

[0458] Finally, the management server sends the optimal schedule obtained as a result of the analysis to the terminal and displays it on the user interface in calendar or list format. The user can check the presented optimal schedule and make any necessary corrections. Once the final schedule is confirmed, all relevant parties are notified.

[0459] As a concrete example, let's look at a schedule management system for maintenance staff in a factory. A manager uses a terminal to input staff schedules, which are then sent to a management server. A generative AI model analyzes the data and proposes an optimal maintenance schedule, and an emotion engine detects the facial expressions and voices of staff members and provides feedback. Finally, the schedule is finalized and notified to everyone.

[0460] An example of a prompt is as follows:

[0461] 1. Enter staff schedule data and send it to the management server: "2023-11-01:09:00-17:00,2023-11-02:09:00-17:00"

[0462] 2. The generative AI model calculates and displays the optimal maintenance schedule.

[0463] 3. The emotion engine analyzes staff's facial expressions and tone of voice to provide the optimal schedule.

[0464] 4. Receive and display confirmed schedules and notify staff.

[0465]

[0466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0467] Step 1:

[0468] A user uses a terminal to input schedule data for participants, including working hours, vacations, specific work duties, etc. The input interface is a web interface, and the input data is packaged in JSON format.

[0469] input:

[0470] Schedule data for each participant (date, time, and work content)

[0471] output:

[0472] Schedule data in JSON format

[0473] Step 2:

[0474] The terminal checks the consistency of the entered schedule data and sends it to the management server. The consistency check includes checking the date format and duplicate data.

[0475] input:

[0476] Schedule data in JSON format

[0477] output:

[0478] Consistency-checked schedule data in JSON format

[0479] Step 3:

[0480] The management server passes the received schedule data to the generative AI model, which then takes into account staff working hours, work schedules, and priorities to generate the optimal maintenance schedule.

[0481] input:

[0482] Schedule data, working hours, work schedules, and priorities for each staff member

[0483] output:

[0484] Proposing optimal maintenance schedules

[0485] Step 4:

[0486] The management server sends the proposed optimal schedule to the terminal and displays it on the user interface in calendar or list format, allowing the user to check it in a visually easy-to-understand format.

[0487] input:

[0488] Proposing optimal maintenance schedules

[0489] output:

[0490] Calendar or list view of schedule

[0491] Step 5:

[0492] When the user provides feedback on the proposed schedule, the device activates an emotion engine, which analyzes the user's facial expressions and tone of voice in real time to evaluate the psychological burden.

[0493] input:

[0494] User facial expression data, tone of voice data

[0495] output:

[0496] Sentiment analysis results and feedback

[0497] Step 6:

[0498] Based on the results of the emotion analysis, the management server re-proposes a schedule as necessary, thereby proposing an optimal schedule that takes into account the user's psychological burden.

[0499] input:

[0500] Sentiment analysis results and optimal maintenance schedule suggestions

[0501] output:

[0502] Revised optimal maintenance schedule proposal

[0503] Step 7:

[0504] The final confirmed schedule is sent from the management server to the terminal and notified to the user, who then rechecks the presented schedule, makes any necessary corrections, and confirms it.

[0505] input:

[0506] Revised optimal maintenance schedule proposal

[0507] output:

[0508] Finalized maintenance schedule

[0509] Step 8:

[0510] The finalized schedule is notified to all relevant parties by the management server, allowing all participants to share the latest schedule.

[0511] input:

[0512] Finalized maintenance schedule

[0513] output:

[0514] Schedule notification (notification to all participants)

[0515] 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.

[0516] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0517] 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.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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. 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0530] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0531] This invention is a system for streamlining schedule adjustments for meetings and events. The system consists of a series of processes: participants' schedule data is input, sent to a management server, analyzed by the server using a generative AI model, and the server proposes an optimal schedule, which is then displayed on a terminal.

[0532] 1. Enter and submit schedule data

[0533] The user uses the terminal to input the schedule data of the participants. The input interface uses a web form, which includes fields such as participant name, date, time slot, etc. This allows the user to easily input the schedule of each participant.

[0534] The terminal sends the entered schedule data to the management server. The server receives this data and checks its consistency. If there are any errors or deficiencies in the data, it notifies the user by returning an error message to the terminal.

[0535] 2. Analysis using generative AI models and optimal schedule proposals

[0536] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for the meeting or event. This process also takes into account the participants' work schedules and priorities, making adjustments easier.

[0537] The management server transmits the optimum schedule obtained as a result of the analysis to the terminal, which receives the optimum schedule and displays it on the user interface.

[0538] 3. Display and confirm the results

[0539] The user checks the optimal schedule displayed on their device. The display interface is in calendar or list format, allowing the user to intuitively understand the proposed schedule. If necessary, the user can also modify the schedule. The final approved schedule is notified to all participants.

[0540] Specific examples

[0541] 1. Meeting setup example

[0542] A user accesses a terminal and inputs the schedule data of five participants.

[0543] The terminal transmits this data to the management server.

[0544] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[0545] The terminal receives the optimum schedule and displays it to the user.

[0546] The user checks the displayed optimum schedule and makes corrections if necessary.

[0547] The final dates will be approved and communicated to all participants.

[0548] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, by checking the schedules of all participants at once and automatically proposing the optimal date, significantly reducing the time and effort required. This system allows users to adjust schedules without stress and improves the efficiency of the entire business.

[0549] The processing flow will be explained below.

[0550] Step 1: User enters schedule data

[0551] The user uses the terminal interface to input schedule data for each participant, which is provided in the form of a web form and includes items such as participant name, date, and time slot.

[0552] Step 2: The device sends the schedule data

[0553] The terminal sends the schedule data entered by the user to the management server, which also performs basic checks to ensure the data is consistent.

[0554] Step 3: Server receives data

[0555] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any discrepancy, the server returns an error message to the terminal.

[0556] Step 4: The server passes the data to the generative AI model

[0557] The server inputs the confirmed schedule data into the generative AI model and begins analysis. At this time, the generative AI model calculates the optimal schedule taking into account the schedules of the participants.

[0558] Step 5: The server calculates the optimal schedule

[0559] A generative AI model performs the analysis and calculates the optimal itinerary, taking into account participants' work schedules and priorities.

[0560] Step 6: The server sends the optimal schedule to the device.

[0561] The server sends the optimal schedule calculated by the generative AI model to the terminal.

[0562] Step 7: Your device receives the best schedule

[0563] The terminal receives the optimal schedule sent from the server and also checks the consistency of the received schedule data.

[0564] Step 8: Your device will display the best dates

[0565] The terminal displays the received optimal schedule to the user in a display interface in a calendar or list format.

[0566] Step 9: User confirms optimal schedule

[0567] The user can check the optimal schedule displayed on the terminal and, if necessary, modify the schedule.

[0568] Step 10: User confirms final date

[0569] The user confirms the optimal date and finalizes it. The confirmed date is notified to all participants.

[0570] This process streamlines meeting and event scheduling, eliminating the need for manual scheduling, and leverages generative AI models to suggest optimal schedules.

[0571] Example 1

[0572] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0573] Conventional scheduling for meetings and events requires manual coordination among participants, which takes a lot of time and effort. It is also difficult to grasp the schedules of all participants at once, making it difficult to find the optimal date. Therefore, there is a need for a system that can improve the efficiency of schedule coordination and quickly and accurately adjust the schedules of all participants.

[0574] 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.

[0575] In this invention, the server includes a means for checking the consistency of schedule data, a means for analyzing the schedule data using a generative AI model and proposing an optimal schedule, and a means for notifying all participants of the final approved schedule, which enables users to efficiently adjust their schedules and quickly determine the optimal schedule.

[0576] A "user" is a person or entity that utilizes the system to enter, review, and modify schedule data.

[0577] A "terminal" is a device used by a user, and is a device for inputting schedule data and communicating with a server.

[0578] A "management server" is a central computer system that receives, analyzes, and notifies input schedule data.

[0579] "Schedule data" refers to information such as participant names, dates, and time slots entered for a meeting or event.

[0580] A "generative AI model" is an artificial intelligence model used to analyze input schedule data and propose optimal dates.

[0581] "Integrity checking" is the process by which the management server verifies that the schedule data it receives is complete and accurate.

[0582] The "proposed optimal schedule" refers to the most suitable schedule for all participants, as determined by the generative AI model as an analysis result.

[0583] A "user interface" is a screen or display format that allows a user to input and check schedule data on a terminal.

[0584] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[0585] "Notification" refers to the act of the management server informing all participants of the final approved schedule.

[0586] MODE FOR CARRYING OUT THE INVENTION

[0587] The present invention is a system for improving the efficiency of scheduling meetings and events.

[0588] System configuration

[0589] User:

[0590] Users use a terminal to input schedule data. A web form is used as the input interface. Specifically, information such as participant names, dates, and time slots is input.

[0591] Device:

[0592] The terminal sends the schedule data entered by the user to the management server using an HTTP POST request. The display interface on the terminal displays the analysis results to the user in calendar or list format.

[0593] Management Server:

[0594] The management server checks the consistency of the received schedule data, and if there is an error in the data, returns an error message to the terminal.

[0595] Once consistency is confirmed, the schedule data is passed to a generative AI model, which uses frameworks such as PyTorch and TensorFlow to begin analysis.

[0596] The generative AI model analyzes schedule data and proposes optimal dates, taking into account participants' work schedules and priorities.

[0597] The optimum schedule is sent to the terminal so that the user can check and modify it.

[0598] The final approved date will be notified to all participants via email or push notification.

[0599] Specific examples

[0600] 1. Example of a meeting setup:

[0601] A user accesses a terminal and inputs the schedule data of five participants.

[0602] Participant name: "Mr. A", Date: "2023-12-01", Time: "14:00-16:00"

[0603] The terminal transmits this data to the management server.

[0604] The management server checks the integrity of the received data and analyzes it using a generative AI model.

[0605] The generative AI model calculates the optimal meeting schedule and returns the result to the server, using the following prompt:

[0606] "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[0607] The management server transmits the analysis results to the terminal, and the terminal displays the optimal schedule on the user interface.

[0608] The user checks the optimal date and makes any necessary changes, for example, changing it to "2023-12-02 10:00-11:00."

[0609] The final date is approved and all participants are notified. An example of a notification email would be: Subject: "Notification of Final Meeting Date" with the body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[0610] By using this system, users can significantly simplify the tedious task of manually adjusting schedules, enabling them to quickly and accurately determine optimal dates for meetings and events.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] User Action:

[0614] The user uses the terminal to input schedule data for the participants.

[0615] Specific actions: Use a browser to access a web form and enter information such as participant names, date, and time slot.

[0616] Input: Participant name: "Mr. A", Date: "2023-12-01", Time period: "14:00-16:00"

[0617] Output: Input data is saved to the terminal.

[0618] Step 2:

[0619] Device features:

[0620] The terminal transmits the schedule data input by the user to the management server.

[0621] Specific behavior: Sends data to the management server using an HTTP POST request.

[0622] Input: Schedule data in JSON format

[0623] json

[0624] {

[0625] "Participant Name": "Mr. A",

[0626] "Date": "2023-12-01",

[0627] "Time Zone": "14:00-16:00"

[0628] }

[0629] Output: Data received by the management server

[0630] Step 3:

[0631] Server Integrity Check:

[0632] The server checks the integrity of the received schedule data.

[0633] Specific operation: Validates the data format and required fields. If there is an error, generates an error message and returns it to the terminal.

[0634] Input: Received schedule data

[0635] Output: Schedule data whose consistency has been checked or an error message

[0636] Step 4:

[0637] Server execution of generative AI models:

[0638] The server passes the schedule data whose consistency has been confirmed to the generative AI model and begins analysis.

[0639] Specific operation: Analyze schedule data using frameworks such as PyTorch and TensorFlow.

[0640] Prompt: "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[0641] Input: Schedule data that has been checked for consistency

[0642] Output: Optimal schedule based on analysis

[0643] Step 5:

[0644] Server sends analysis results:

[0645] The server sends the optimal schedule obtained from the generative AI model to the terminal.

[0646] Specific operation: The analysis results are converted into JSON format and sent to the terminal as an HTTP response.

[0647] Input: Optimal schedule analysis results

[0648] json

[0649] {

[0650] "Best Date": "2023-12-01 15:00-16:00"

[0651] }

[0652] Output: The device where the analysis results are sent

[0653] Step 6:

[0654] Displaying the best dates on your device:

[0655] The terminal displays the received optimum schedule on the user interface.

[0656] Specific operation: Display analysis results in calendar or list format in the browser

[0657] Input: Analysis result data

[0658] Output: Optimal schedule displayed to the user

[0659] example:

[0660] December 1st

[0661] 15:00 - 16:00 Meeting (Participants: Mr. A, Mr. B)

[0662] Step 7:

[0663] User confirmation and modification of dates:

[0664] The user checks the displayed optimum schedule and makes corrections if necessary.

[0665] Action: Re-enter new dates using the web form

[0666] Input: Revised schedule data

[0667] Output: The final approved schedule is sent to the server.

[0668] Step 8:

[0669] Server notification of final date:

[0670] The server will notify all participants of the final approved dates.

[0671] What it does: Sends notifications via email and push notifications.

[0672] Input: Final Approved Date

[0673] Output: Notification sent to all participants

[0674] example:

[0675] Email subject: "Notification of final meeting schedule"

[0676] Email body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[0677] (Application example 1)

[0678] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0679] In today's food delivery industry, it is difficult to efficiently coordinate the schedules of multiple delivery drivers. In particular, simultaneously considering delivery driver schedules and restaurant operation schedules is time-consuming and prone to errors. This issue can lead to delivery delays and reduced efficiency.

[0680] 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.

[0681] In this invention, the server includes a means for inputting delivery staff schedule data, a means for transmitting the input schedule data to the management server, and a means for proposing an optimal delivery schedule in consideration of the delivery staff schedule data. This allows business operators such as restaurants and cafes to easily manage delivery staff schedules and efficiently adjust delivery schedules.

[0682] A "participant" is an individual or group involved in a meeting, event, or delivery task that needs to be scheduled within the system.

[0683] "Schedule data" is information indicating the schedules and availability of participants and delivery personnel, and is digital information including details such as dates, time periods, and priorities.

[0684] "Input means" refers to a method and device for providing schedule data to the system via a terminal, a web interface, or a smartphone application.

[0685] The "means for transmitting to the management server" is a communication function for transferring input schedule data to a central management server.

[0686] The "management server" is a centralized computer system that analyzes schedule data and generates optimal dates and delivery schedules.

[0687] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and calculates the optimal schedule.

[0688] The "means for transmitting to the terminal and displaying" refers to a method and device for transferring the optimum schedule obtained as a result of the analysis to the user's terminal and displaying it.

[0689] A "delivery person" is an individual whose job it is to transport food and merchandise to customers in a food delivery service.

[0690] A "delivery schedule" is a plan that indicates the time periods and order of deliveries planned to enable delivery personnel to perform their work efficiently.

[0691] The "means for proposing" is a method and apparatus for presenting the optimal schedule analyzed by the generative AI model to the user.

[0692] This invention is a system for streamlining delivery staff schedule adjustments in food delivery services. The system proposes optimal delivery schedules by having users input delivery staff schedule data and transmitting it to a management server. Below, we will explain the details of each component and process of the system.

[0693] System configuration

[0694] 1. User Device

[0695] A smartphone application or web interface is used, through which users input delivery staff schedule data. For example, a restaurant owner inputs the delivery staff's availability and desired delivery times.

[0696] The schedule data includes details such as the date, time, and delivery person's ID.

[0697] 2. Management Server

[0698] After the data is sent, the management server receives it and checks its integrity. If there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[0699] The management server is equipped with a generative AI model that analyzes the received schedule data and generates an optimal delivery schedule.

[0700] The specific AI model used has an algorithm that simultaneously takes into account delivery personnel's schedule data and job priorities.

[0701] 3. Generative AI Models

[0702] The generative AI model installed on the management server analyzes each delivery person's schedule data and creates an efficient delivery schedule, taking into account the delivery person's availability and priority to calculate the optimal delivery time slot.

[0703] Operational procedures and processing details

[0704] 1. Enter schedule data

[0705] A restaurant owner inputs delivery staff schedule data from a user terminal through an application. For example, suppose delivery staff A's availability is "2023-10-15 10:00-12:00" and delivery staff B's ​​availability is "2023-10-15 14:00-16:00."

[0706] The input format is simple and provides an interface that users can operate intuitively.

[0707] 2. Sending and Receiving Data

[0708] The entered data is sent to the management server in real time. Data is sent in JSON format using requests.post.

[0709] The management server checks the integrity of the data and returns an error message if there are any discrepancies.

[0710] 3. Analysis using generative AI models

[0711] The generative AI model calculates the optimal delivery schedule based on the received schedule data, taking into account the work schedules and priorities of delivery personnel.

[0712] 4. Display and confirm the results

[0713] The optimal schedule generated by the management server is sent to the user terminal and displayed by the application.

[0714] The store owner reviews the proposed schedule and can make any necessary changes. The final approved schedule is notified to all delivery drivers.

[0715] Specific examples

[0716] Example input:

[0717] The restaurant owner opens the app and enters the following schedule data:

[0718] Delivery Person A: 2023-10-15 10:00-12:00

[0719] Delivery Person B: 2023-10-15 14:00-16:00

[0720] Delivery Person C: 2023-10-16 09:00-11:00

[0721] Example prompts used for analysis by the generative AI model:

[0722] Restaurant ID: 12345

[0723] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[0724] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[0725] Generate an optimal delivery schedule to output.

[0726] This method streamlines delivery staff scheduling and improves the efficiency of food delivery operations.

[0727] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0728] Step 1:

[0729] Entering schedule data

[0730] The user inputs the delivery person's schedule data using a smartphone application or a web interface. Specifically, the user inputs the delivery person's ID, available time, priority, etc. into an input form.

[0731] input:

[0732] Schedule data such as delivery driver ID, availability, and priority.

[0733] output:

[0734] The schedule data entered.

[0735] Specific behavior:

[0736] The user enters the delivery person's ID and available time in the application's input form. For example, the available time of delivery person A is entered as "2023-10-15 10:00-12:00."

[0737] Step 2:

[0738] Sending data

[0739] The terminal sends the entered schedule data to the management server using requests.post in JSON format.

[0740] input:

[0741] Schedule data entered by the user.

[0742] output:

[0743] Schedule data sent to the management server.

[0744] Specific behavior:

[0745] The terminal generates schedule data in JSON format and sends a POST request to the management server. For example, send the following data:

[0746] json

[0747] {

[0748] "restaurant_id": "12345",

[0749] "delivery_ids": ["deliv_1", "deliv_2", "deliv_3"],

[0750] "dates_times": ["2023-10-15 10:00-12:00", "2023-10-15 14:00-16:00", "2023-10-16 09:00-11:00"]

[0751] }

[0752] Step 3:

[0753] Data reception and integrity check

[0754] The management server checks the consistency of the schedule data it receives, and if there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[0755] input:

[0756] Schedule data sent to the management server.

[0757] output:

[0758] Schedule data that has been checked for consistency or error messages.

[0759] Specific behavior:

[0760] The management server verifies the received data, and if any defects are found, it generates an error message and notifies the user terminal. For example, if the date and time format is invalid, it returns the message "The date and time format is invalid."

[0761] Step 4:

[0762] Analysis using generative AI models

[0763] The management server passes the confirmed schedule data to the generative AI model and begins analysis. The model generates an optimal delivery schedule based on the schedule data.

[0764] input:

[0765] Schedule data that has been checked for consistency.

[0766] output:

[0767] Optimal delivery schedule.

[0768] Specific behavior:

[0769] The management server passes a prompt to the generative AI model to start the analysis. The prompt used as an example is as follows:

[0770] Restaurant ID: 12345

[0771] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[0772] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[0773] Generate an optimal delivery schedule to output.

[0774] A generative AI model then analyzes the data and generates an optimal delivery schedule.

[0775] Step 5:

[0776] Sending and displaying the optimal schedule

[0777] The management server sends the optimal schedule obtained by the generative AI model to the user's terminal and displays it in the application.

[0778] input:

[0779] Optimal schedule analyzed by generative AI model.

[0780] output:

[0781] The optimal schedule is displayed on the user's terminal.

[0782] Specific behavior:

[0783] The management server sends the generated optimal schedule to the user terminal and displays it on the application interface. For example, the following schedule is displayed:

[0784] Date: 2023-10-15, Time: 10:00-12:00, Delivery Person: Delivery Person A

[0785] Date: 2023-10-15, Time: 14:00-16:00, Delivery Person: Delivery Person B

[0786] Date: 2023-10-16, Time: 09:00-11:00, Delivery Person: Delivery Person C

[0787] Step 6:

[0788] Check and correct the results

[0789] The user can check the displayed optimal schedule and make any necessary corrections. The final approved schedule is notified to all participants.

[0790] input:

[0791] The optimal schedule displayed on the user's terminal.

[0792] output:

[0793] The final schedule, as amended or approved.

[0794] Specific behavior:

[0795] The user checks the optimal schedule and makes any necessary corrections. If no corrections are necessary, the user simply presses the approval button, and the final schedule is notified to all delivery personnel.

[0796] 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.

[0797] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[0798] 1. Enter and submit schedule data

[0799] The user inputs the schedule data of the participants using a terminal. The input interface is provided as a web form, and includes fields such as participant name, date, and time slot. The input data is checked for consistency and then sent to the management server.

[0800] 2. Analysis using generative AI models and optimal schedule proposals

[0801] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account each participant's schedule and calculates the optimal date for the meeting or event. During this process, participants' work schedules and priorities are also taken into account.

[0802] 3. Emotion recognition and feedback using an emotion engine

[0803] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine detects the user's facial expressions and tone of voice and generates feedback according to the user's emotions based on the analysis of the schedule data. This feedback helps reduce the psychological stress that the optimal schedule causes to the user.

[0804] 4. Display and confirm the results

[0805] The management server sends the optimal schedule obtained as a result of the analysis to the terminal. The terminal receives this optimal schedule and displays it on the user interface. The display interface is provided in calendar or list format, allowing the user to intuitively check the presented optimal schedule.

[0806] 5. User confirmation and final schedule confirmation

[0807] The user can check the optimal schedule displayed on the device and make any necessary adjustments. Feedback from the emotion engine also provides guidance to help users select a better schedule. The final confirmed schedule is notified to all participants.

[0808] Specific examples

[0809] 1. Meeting setup example

[0810] A user accesses a terminal and inputs the schedule data of five participants.

[0811] The terminal transmits this data to the management server.

[0812] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[0813] An emotion engine built into the management server recognizes emotions from the user's facial expressions and tone of voice, and provides feedback based on the analysis results.

[0814] The terminal receives the optimal schedule and feedback and displays it to the user.

[0815] The user checks the displayed optimum schedule, makes corrections if necessary, and finalizes the schedule.

[0816] Confirmed dates will be communicated to all participants.

[0817] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, and enables optimal schedule adjustment that takes user emotions into consideration. The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[0818] The processing flow will be explained below.

[0819] Step 1: User enters schedule data

[0820] The user inputs the schedule data of each participant using the terminal interface. The input interface is provided in the form of a web form, and the user can enter the necessary information in fields such as participant name, date, time slot, etc.

[0821] Step 2: The device sends the schedule data

[0822] The terminal sends the schedule data entered by the user to the management server. During this sending process, the terminal performs basic checks (format checks and blank checks) to ensure the consistency of the data.

[0823] Step 3: Server receives data

[0824] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any error, the server returns an error message to the terminal and asks the user to correct it.

[0825] Step 4: The server passes the data to the generative AI model

[0826] Once the consistency of the schedule data has been confirmed, the server inputs it into a generative AI model and begins analysis. This generative AI model takes into account the schedules of each participant and calculates the optimal dates for meetings and events.

[0827] Step 5: The server recognizes the user's emotion using the emotion engine

[0828] The server is equipped with an emotion engine that analyzes facial expressions and tone of voice in real time when users input schedule data to recognize their emotions. Emotional data is reflected in schedule analysis.

[0829] Step 6: The server calculates the optimal schedule

[0830] The generative AI model analyzes and calculates an optimal itinerary that takes into account participants' schedules and the user's emotional data, and is tailored to reduce the user's psychological stress.

[0831] Step 7: The server sends the optimal schedule to the device.

[0832] The server combines the optimal schedule analyzed by the generative AI model with feedback from the emotion engine and sends it to the terminal.

[0833] Step 8: The device receives the optimal schedule

[0834] The terminal receives the optimal schedule sent from the server, checks its consistency, and if there are no problems with the data format, prepares it for display on the user interface.

[0835] Step 9: Your device will display the best date and give you feedback.

[0836] The device displays the received optimal schedule and feedback from the emotion engine to the user. The display interface is provided in calendar and list format, allowing users to intuitively check the information.

[0837] Step 10: User confirms optimal schedule

[0838] The user checks the optimal schedule displayed on the device and makes any necessary adjustments. Based on feedback from the emotion engine, the user also determines whether the proposed schedule will cause any psychological stress.

[0839] Step 11: User confirms final date

[0840] The user confirms the optimal schedule and, once any modifications are complete, confirms the final schedule. The confirmed schedule is notified to all participants. This step allows all participants to be aware of the new schedule and begin preparations.

[0841] Example 2

[0842] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0843] Traditionally, scheduling meetings and events has often been done manually, which is time-consuming and inefficient. Finding the optimal date while taking into account the schedules of multiple participants is extremely difficult, especially when multiple participants are involved. Furthermore, the psychological impact and emotions of the proposed schedule on the user were not taken into consideration. As a result, even when an optimal date was found, the user often felt stressed.

[0844] 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.

[0845] In this invention, the server includes a means for inputting schedule data of participants, a means for confirming the consistency of the input schedule data, a means for transmitting the schedule data whose consistency has been confirmed to the management server, a means for the management server to analyze the schedule data using a generative AI model and propose an optimal schedule, a means for recognizing the emotional state of the user using an emotion engine and generating feedback based on the analysis results, and a means for transmitting and displaying the proposed optimal schedule and feedback to the terminal. This not only improves the efficiency of schedule adjustment, but also enables the adjustment of an optimal schedule that takes the user's emotions into consideration.

[0846] "Schedule data" is information indicating the dates and times when each participant of a meeting or event is available.

[0847] "Input means" refers to an interface that allows a user to input schedule data into the system using a terminal. Specifically, this corresponds to a web form, etc.

[0848] "Consistency checking means" is a process or system function for checking the accuracy and consistency of input schedule data.

[0849] The "transmission means" is a process or system function for transmitting schedule data from a terminal to the management server. Specifically, HTTP or HTTPS protocols are used.

[0850] The "management server" is a central component of the system that stores received schedule data, analyzes it using a generative AI model, and proposes optimal schedules.

[0851] The "generative AI model" is an artificial intelligence algorithm that analyzes the received schedule data and calculates the optimal schedule by taking into account the available dates and times of each participant. Specifically, a natural language processing model is used.

[0852] The "emotion engine" is part of a system that analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotional state.

[0853] "Feedback" is some kind of reaction or guidance to the user that is generated based on the user's emotional state recognized by the emotion engine.

[0854] The "display means" is an interface that allows the user to visualize the proposed optimal schedule and feedback. Specifically, it is displayed in a calendar or list format on the terminal display.

[0855] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[0856] First, a user uses a terminal (such as a PC or smartphone) to enter the schedule data of participants. The input interface is provided as a web form, and includes items such as participant names, dates, and time slots. For example, a user can access a terminal and enter the schedule data of five participants. The data is verified by the terminal and then sent to the management server.

[0857] The management server passes the received schedule data to a generative AI model (e.g., a natural language processing model such as GPT-4 or BERT) and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for a meeting or event. This also takes into account the participants' work schedules and priorities. For example, a prompt such as "Based on the schedule data of the following five people, please propose the optimal meeting date so that everyone can attend. Please also take into account various priorities" is input into the generative AI model.

[0858] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine uses the device's camera and microphone to detect the user's facial expressions and tone of voice, and generates feedback based on the analysis results. This feedback helps reduce the psychological stress that the proposed optimal schedule causes to the user. For example, positive feedback is provided to a user who is feeling stressed.

[0859] The optimal schedule obtained as a result of the analysis is sent from the management server to the terminal. The terminal displays this optimal schedule and feedback in a user interface in calendar or list format. The user can intuitively check the presented optimal schedule and make corrections as necessary. For example, if a user checks the presented optimal schedule and wants to change a specific schedule, they can make the corrections while also taking into account feedback from the emotion engine.

[0860] Finally, the final schedule confirmed by the user is sent to the management server and notified to all participants via email or push notification, so that all participants can check the final schedule.

[0861] This system not only significantly improves the efficiency of schedule adjustment work, which was previously done manually, but also enables optimal schedule adjustment that takes into account the user's emotions.The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Program processing steps

[0864] Step 1: Enter schedule data

[0865] The user enters the schedule data.

[0866] The user uses a terminal to enter the schedule data of participants (participant name, date, time slot) via a web form. The entered data may include "Participant A: January 1st 10:00-12:00".

[0867] input:

[0868] Participant schedule (e.g. Participant A: January 1st 10:00-12:00)

[0869] output:

[0870] Input schedule data

[0871] Step 2: Data integrity check

[0872] The device checks the integrity of the data

[0873] The terminal checks the integrity of the data entered, specifically checking for missing required fields and validating date formats, such as whether the date entered is in a valid format and whether the time zone is consistent.

[0874] input:

[0875] Schedule data entered by the user

[0876] output:

[0877] Schedule data that has been checked for consistency or an error message

[0878] Step 3: Sending data

[0879] The device sends data to the management server

[0880] The data whose integrity has been confirmed is then sent to the management server using the HTTPS protocol to ensure data security.

[0881] input:

[0882] Consistency-checked schedule data

[0883] output:

[0884] Schedule data sent to the management server

[0885] Step 4: Analyze the data with a generative AI model

[0886] The server receives the data and passes it to the generative AI model

[0887] The management server passes the received schedule data to the generation AI model and begins analysis. The generated prompt is, "Based on the schedule data of the following five people, please suggest the optimal meeting date that everyone can attend."

[0888] input:

[0889] Schedule data stored on the management server

[0890] output:

[0891] A prompt to pass to the generative AI model

[0892] Generative AI model calculates optimal schedule

[0893] The generative AI model analyzes the prompt text and calculates the optimal meeting date, taking into account each participant's schedule and calculating the optimal non-overlapping time slot.

[0894] input:

[0895] Prompt: "Based on the schedule data of the following five people, please suggest the best meeting date that allows everyone to attend."

[0896] output:

[0897] Optimal meeting dates

[0898] Step 5: Emotion Recognition with the Emotion Engine

[0899] The server recognizes the user's emotions

[0900] The management server passes data obtained from the device's camera and microphone to the emotion engine, which analyzes the user's facial expressions and tone of voice. For example, the camera captures the user's facial expressions, and the microphone captures the user's tone of voice.

[0901] input:

[0902] User facial expression data, tone of voice data

[0903] output:

[0904] The user's emotional state (e.g., stress, joy)

[0905] The server generates feedback

[0906] Based on the analysis results of the emotion engine, feedback is generated that takes into account the impact of the optimal schedule on the user. For example, if the user feels stressed, a positive message will be sent to provide a sense of security.

[0907] input:

[0908] The user's emotional state

[0909] output:

[0910] Feedback Message

[0911] Step 6: Viewing the best dates and feedback

[0912] The server sends the optimal schedule and feedback to the device.

[0913] The management server sends the optimal schedule and feedback to the terminal.

[0914] input:

[0915] Optimal meeting schedule, feedback message

[0916] output:

[0917] Data sent to the device

[0918] The device displays the best time and gives feedback

[0919] The device displays the optimal schedule and feedback in a user interface in calendar or list format.

[0920] input:

[0921] Optimal meeting schedule, feedback message

[0922] output:

[0923] Display in user interface (calendar format, list format)

[0924] Step 7: Final confirmation and schedule confirmation

[0925] The user checks the optimal schedule and makes any necessary adjustments.

[0926] The user can review the displayed optimal schedule and make adjustments as needed, for example, by changing the date of a specific meeting to accommodate individual needs.

[0927] input:

[0928] Optimal meeting schedule, user-specified corrections

[0929] output:

[0930] Revised final schedule

[0931] The server will confirm and notify the final schedule.

[0932] The user then sends the final schedule to the management server, which then notifies all participants via email or push notification.

[0933] input:

[0934] Revised final schedule

[0935] output:

[0936] Notification from the server to all participants

[0937] (Application example 2)

[0938] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0939] Conventional schedule adjustment systems have difficulty efficiently consolidating the diverse schedules of participants. Furthermore, they do not consider the psychological burden or stress that the proposed schedule places on users, making it difficult to say that the schedule is optimal. In particular, in work environments such as factories, efficient schedule adjustment while reducing fatigue and stress among maintenance staff is required.

[0940] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting schedule data of participants, a means for transmitting the input schedule data to a management server, a means for the management server to analyze the schedule data using a generation AI model and propose an optimal schedule, a means for transmitting and displaying the proposed optimal schedule to a terminal, and a means including an emotion engine that analyzes user emotion data and providing feedback. This makes it possible to improve the efficiency of schedule adjustment for participants, provide feedback that takes into consideration the user's emotions, and achieve optimal schedule adjustment with reduced psychological burden.

[0941] "Participant schedule data" is information indicating the schedule of each individual who will participate in a conference or event, and is data including the date, time period, and work content.

[0942] The "management server" is a central computer system that comprehensively manages participants' schedule data, generative AI models, and emotion engines, and proposes optimal schedules.

[0943] A "generative AI model" is an artificial intelligence model that analyzes collected schedule data and calculates the optimal schedule.

[0944] An "emotion engine" is a software or hardware component for analyzing a user's emotional data and providing real-time feedback on schedule suggestions.

[0945] "Proposed optimal dates" are the most suitable dates calculated by the generative AI model based on the participants' schedules and other relevant information.

[0946] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to receive and display schedule suggestions and feedback from a server on the cloud.

[0947] "User emotion data" is information about emotions acquired from the user's facial expressions, tone of voice, etc., and is data that is analyzed by the emotion engine.

[0948] This invention relates to a system for efficiently managing the schedules of maintenance staff in a factory. The system includes functions for inputting, sending, analyzing, proposing, and displaying schedule data of participants, as well as emotion recognition feedback.

[0949] First, users (factory managers or maintenance staff) use terminals (smartphones or tablets) to enter their own and other staff members' schedule data. The schedule data includes details such as working hours, holidays, and job content. The data is entered via a web interface and sent to the management server.

[0950] The management server then passes the received schedule data to a generative AI model, which then proposes an optimal maintenance schedule. The generative AI model analyzes each staff member's schedule, as well as the maintenance requirements and priorities of robots and machines.

[0951] Furthermore, the emotion engine analyzes the user's emotional data for the proposed schedule. The emotion engine detects the user's facial expressions and tone of voice in real time to evaluate whether the proposed schedule will cause psychological stress. If necessary, it provides feedback and encourages schedule adjustments.

[0952] Finally, the management server sends the optimal schedule obtained as a result of the analysis to the terminal and displays it on the user interface in calendar or list format. The user can check the presented optimal schedule and make any necessary corrections. Once the final schedule is confirmed, all relevant parties are notified.

[0953] As a concrete example, let's look at a schedule management system for maintenance staff in a factory. A manager uses a terminal to input staff schedules, which are then sent to a management server. A generative AI model analyzes the data and proposes an optimal maintenance schedule, and an emotion engine detects the facial expressions and voices of staff members and provides feedback. Finally, the schedule is finalized and notified to everyone.

[0954] An example of a prompt is as follows:

[0955] 1. Enter staff schedule data and send it to the management server: "2023-11-01:09:00-17:00,2023-11-02:09:00-17:00"

[0956] 2. The generative AI model calculates and displays the optimal maintenance schedule.

[0957] 3. The emotion engine analyzes staff's facial expressions and tone of voice to provide the optimal schedule.

[0958] 4. Receive and display confirmed schedules and notify staff.

[0959]

[0960] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0961] Step 1:

[0962] A user uses a terminal to input schedule data for participants, including working hours, vacations, specific work duties, etc. The input interface is a web interface, and the input data is packaged in JSON format.

[0963] input:

[0964] Schedule data for each participant (date, time, and work content)

[0965] output:

[0966] Schedule data in JSON format

[0967] Step 2:

[0968] The terminal checks the consistency of the entered schedule data and sends it to the management server. The consistency check includes checking the date format and duplicate data.

[0969] input:

[0970] Schedule data in JSON format

[0971] output:

[0972] Consistency-checked schedule data in JSON format

[0973] Step 3:

[0974] The management server passes the received schedule data to the generative AI model, which then takes into account staff working hours, work schedules, and priorities to generate the optimal maintenance schedule.

[0975] input:

[0976] Schedule data, working hours, work schedules, and priorities for each staff member

[0977] output:

[0978] Proposing optimal maintenance schedules

[0979] Step 4:

[0980] The management server sends the proposed optimal schedule to the terminal and displays it on the user interface in calendar or list format, allowing the user to check it in a visually easy-to-understand format.

[0981] input:

[0982] Proposing optimal maintenance schedules

[0983] output:

[0984] Calendar or list view of schedule

[0985] Step 5:

[0986] When the user provides feedback on the proposed schedule, the device activates an emotion engine, which analyzes the user's facial expressions and tone of voice in real time to evaluate the psychological burden.

[0987] input:

[0988] User facial expression data, tone of voice data

[0989] output:

[0990] Sentiment analysis results and feedback

[0991] Step 6:

[0992] Based on the results of the emotion analysis, the management server re-proposes a schedule as necessary, thereby proposing an optimal schedule that takes into account the user's psychological burden.

[0993] input:

[0994] Sentiment analysis results and optimal maintenance schedule suggestions

[0995] output:

[0996] Revised optimal maintenance schedule proposal

[0997] Step 7:

[0998] The final confirmed schedule is sent from the management server to the terminal and notified to the user, who then rechecks the presented schedule, makes any necessary corrections, and confirms it.

[0999] input:

[1000] Revised optimal maintenance schedule proposal

[1001] output:

[1002] Finalized maintenance schedule

[1003] Step 8:

[1004] The finalized schedule is notified to all relevant parties by the management server, allowing all participants to share the latest schedule.

[1005] input:

[1006] Finalized maintenance schedule

[1007] output:

[1008] Schedule notification (notification to all participants)

[1009] 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.

[1010] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1011] 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.

[1012] [Third embodiment]

[1013] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1014] 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.

[1015] 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).

[1016] 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.

[1017] 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.

[1018] 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).

[1019] 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. 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1025] This invention is a system for streamlining schedule adjustments for meetings and events. The system consists of a series of processes: participants' schedule data is input, sent to a management server, analyzed by the server using a generative AI model, and the server proposes an optimal schedule, which is then displayed on a terminal.

[1026] 1. Enter and submit schedule data

[1027] The user uses the terminal to input the schedule data of the participants. The input interface uses a web form, which includes fields such as participant name, date, time slot, etc. This allows the user to easily input the schedule of each participant.

[1028] The terminal sends the entered schedule data to the management server. The server receives this data and checks its consistency. If there are any errors or deficiencies in the data, it notifies the user by returning an error message to the terminal.

[1029] 2. Analysis using generative AI models and optimal schedule proposals

[1030] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for the meeting or event. This process also takes into account the participants' work schedules and priorities, making adjustments easier.

[1031] The management server transmits the optimum schedule obtained as a result of the analysis to the terminal, which receives the optimum schedule and displays it on the user interface.

[1032] 3. Display and confirm the results

[1033] The user checks the optimal schedule displayed on their device. The display interface is in calendar or list format, allowing the user to intuitively understand the proposed schedule. If necessary, the user can also modify the schedule. The final approved schedule is notified to all participants.

[1034] Specific examples

[1035] 1. Meeting setup example

[1036] A user accesses a terminal and inputs the schedule data of five participants.

[1037] The terminal transmits this data to the management server.

[1038] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[1039] The terminal receives the optimum schedule and displays it to the user.

[1040] The user checks the displayed optimum schedule and makes corrections if necessary.

[1041] The final dates will be approved and communicated to all participants.

[1042] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, by checking the schedules of all participants at once and automatically proposing the optimal date, significantly reducing the time and effort required. This system allows users to adjust schedules without stress and improves the efficiency of the entire business.

[1043] The processing flow will be explained below.

[1044] Step 1: User enters schedule data

[1045] The user uses the terminal interface to input schedule data for each participant, which is provided in the form of a web form and includes items such as participant name, date, and time slot.

[1046] Step 2: The device sends the schedule data

[1047] The terminal sends the schedule data entered by the user to the management server, which also performs basic checks to ensure the data is consistent.

[1048] Step 3: Server receives data

[1049] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any discrepancy, the server returns an error message to the terminal.

[1050] Step 4: The server passes the data to the generative AI model

[1051] The server inputs the confirmed schedule data into the generative AI model and begins analysis. At this time, the generative AI model calculates the optimal schedule taking into account the schedules of the participants.

[1052] Step 5: The server calculates the optimal schedule

[1053] A generative AI model performs the analysis and calculates the optimal itinerary, taking into account participants' work schedules and priorities.

[1054] Step 6: The server sends the optimal schedule to the device.

[1055] The server sends the optimal schedule calculated by the generative AI model to the terminal.

[1056] Step 7: Your device receives the best schedule

[1057] The terminal receives the optimal schedule sent from the server and also checks the consistency of the received schedule data.

[1058] Step 8: Your device will display the best dates

[1059] The terminal displays the received optimal schedule to the user in a display interface in a calendar or list format.

[1060] Step 9: User confirms optimal schedule

[1061] The user can check the optimal schedule displayed on the terminal and, if necessary, modify the schedule.

[1062] Step 10: User confirms final date

[1063] The user confirms the optimal date and finalizes it. The confirmed date is notified to all participants.

[1064] This process streamlines meeting and event scheduling, eliminating the need for manual scheduling, and leverages generative AI models to suggest optimal schedules.

[1065] Example 1

[1066] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1067] Conventional scheduling for meetings and events requires manual coordination among participants, which takes a lot of time and effort. It is also difficult to grasp the schedules of all participants at once, making it difficult to find the optimal date. Therefore, there is a need for a system that can improve the efficiency of schedule coordination and quickly and accurately adjust the schedules of all participants.

[1068] 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.

[1069] In this invention, the server includes a means for checking the consistency of schedule data, a means for analyzing the schedule data using a generative AI model and proposing an optimal schedule, and a means for notifying all participants of the final approved schedule, which enables users to efficiently adjust their schedules and quickly determine the optimal schedule.

[1070] A "user" is a person or entity that utilizes the system to enter, review, and modify schedule data.

[1071] A "terminal" is a device used by a user, and is a device for inputting schedule data and communicating with a server.

[1072] A "management server" is a central computer system that receives, analyzes, and notifies input schedule data.

[1073] "Schedule data" refers to information such as participant names, dates, and time slots entered for a meeting or event.

[1074] A "generative AI model" is an artificial intelligence model used to analyze input schedule data and propose optimal dates.

[1075] "Integrity checking" is the process by which the management server verifies that the schedule data it receives is complete and accurate.

[1076] The "proposed optimal schedule" refers to the most suitable schedule for all participants, as determined by the generative AI model as an analysis result.

[1077] A "user interface" is a screen or display format that allows a user to input and check schedule data on a terminal.

[1078] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[1079] "Notification" refers to the act of the management server informing all participants of the final approved schedule.

[1080] MODE FOR CARRYING OUT THE INVENTION

[1081] The present invention is a system for improving the efficiency of scheduling meetings and events.

[1082] System configuration

[1083] User:

[1084] Users use a terminal to input schedule data. A web form is used as the input interface. Specifically, information such as participant names, dates, and time slots is input.

[1085] Device:

[1086] The terminal sends the schedule data entered by the user to the management server using an HTTP POST request. The display interface on the terminal displays the analysis results to the user in calendar or list format.

[1087] Management Server:

[1088] The management server checks the consistency of the received schedule data, and if there is an error in the data, returns an error message to the terminal.

[1089] Once consistency is confirmed, the schedule data is passed to a generative AI model, which uses frameworks such as PyTorch and TensorFlow to begin analysis.

[1090] The generative AI model analyzes schedule data and proposes optimal dates, taking into account participants' work schedules and priorities.

[1091] The optimum schedule is sent to the terminal so that the user can check and modify it.

[1092] The final approved date will be notified to all participants via email or push notification.

[1093] Specific examples

[1094] 1. Example of a meeting setup:

[1095] A user accesses a terminal and inputs the schedule data of five participants.

[1096] Participant name: "Mr. A", Date: "2023-12-01", Time: "14:00-16:00"

[1097] The terminal transmits this data to the management server.

[1098] The management server checks the integrity of the received data and analyzes it using a generative AI model.

[1099] The generative AI model calculates the optimal meeting schedule and returns the result to the server, using the following prompt:

[1100] "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[1101] The management server transmits the analysis results to the terminal, and the terminal displays the optimal schedule on the user interface.

[1102] The user checks the optimal date and makes any necessary changes, for example, changing it to "2023-12-02 10:00-11:00."

[1103] The final date is approved and all participants are notified. An example of a notification email would be: Subject: "Notification of Final Meeting Date" with the body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[1104] By using this system, users can significantly simplify the tedious task of manually adjusting schedules, enabling them to quickly and accurately determine optimal dates for meetings and events.

[1105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1106] Step 1:

[1107] User Action:

[1108] The user uses the terminal to input schedule data for the participants.

[1109] Specific actions: Use a browser to access a web form and enter information such as participant names, date, and time slot.

[1110] Input: Participant name: "Mr. A", Date: "2023-12-01", Time period: "14:00-16:00"

[1111] Output: Input data is saved to the terminal.

[1112] Step 2:

[1113] Device features:

[1114] The terminal transmits the schedule data input by the user to the management server.

[1115] Specific behavior: Sends data to the management server using an HTTP POST request.

[1116] Input: Schedule data in JSON format

[1117] json

[1118] {

[1119] "Participant Name": "Mr. A",

[1120] "Date": "2023-12-01",

[1121] "Time Zone": "14:00-16:00"

[1122] }

[1123] Output: Data received by the management server

[1124] Step 3:

[1125] Server Integrity Check:

[1126] The server checks the integrity of the received schedule data.

[1127] Specific operation: Validates the data format and required fields. If there is an error, generates an error message and returns it to the terminal.

[1128] Input: Received schedule data

[1129] Output: Schedule data whose consistency has been checked or an error message

[1130] Step 4:

[1131] Server execution of generative AI models:

[1132] The server passes the schedule data whose consistency has been confirmed to the generative AI model and begins analysis.

[1133] Specific operation: Analyze schedule data using frameworks such as PyTorch and TensorFlow.

[1134] Prompt: "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[1135] Input: Schedule data that has been checked for consistency

[1136] Output: Optimal schedule based on analysis

[1137] Step 5:

[1138] Server sends analysis results:

[1139] The server sends the optimal schedule obtained from the generative AI model to the terminal.

[1140] Specific operation: The analysis results are converted into JSON format and sent to the terminal as an HTTP response.

[1141] Input: Optimal schedule analysis results

[1142] json

[1143] {

[1144] "Best Date": "2023-12-01 15:00-16:00"

[1145] }

[1146] Output: The device where the analysis results are sent

[1147] Step 6:

[1148] Displaying the best dates on your device:

[1149] The terminal displays the received optimum schedule on the user interface.

[1150] Specific operation: Display analysis results in calendar or list format in the browser

[1151] Input: Analysis result data

[1152] Output: Optimal schedule displayed to the user

[1153] example:

[1154] December 1st

[1155] 15:00 - 16:00 Meeting (Participants: Mr. A, Mr. B)

[1156] Step 7:

[1157] User confirmation and modification of dates:

[1158] The user checks the displayed optimum schedule and makes corrections if necessary.

[1159] Action: Re-enter new dates using the web form

[1160] Input: Revised schedule data

[1161] Output: The final approved schedule is sent to the server.

[1162] Step 8:

[1163] Server notification of final date:

[1164] The server will notify all participants of the final approved dates.

[1165] What it does: Sends notifications via email and push notifications.

[1166] Input: Final Approved Date

[1167] Output: Notification sent to all participants

[1168] example:

[1169] Email subject: "Notification of final meeting schedule"

[1170] Email body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[1171] (Application example 1)

[1172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1173] In today's food delivery industry, it is difficult to efficiently coordinate the schedules of multiple delivery drivers. In particular, simultaneously considering delivery driver schedules and restaurant operation schedules is time-consuming and prone to errors. This issue can lead to delivery delays and reduced efficiency.

[1174] 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.

[1175] In this invention, the server includes a means for inputting delivery staff schedule data, a means for transmitting the input schedule data to the management server, and a means for proposing an optimal delivery schedule in consideration of the delivery staff schedule data. This allows business operators such as restaurants and cafes to easily manage delivery staff schedules and efficiently adjust delivery schedules.

[1176] A "participant" is an individual or group involved in a meeting, event, or delivery task that needs to be scheduled within the system.

[1177] "Schedule data" is information indicating the schedules and availability of participants and delivery personnel, and is digital information including details such as dates, time periods, and priorities.

[1178] "Input means" refers to a method and device for providing schedule data to the system via a terminal, a web interface, or a smartphone application.

[1179] The "means for transmitting to the management server" is a communication function for transferring input schedule data to a central management server.

[1180] The "management server" is a centralized computer system that analyzes schedule data and generates optimal dates and delivery schedules.

[1181] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and calculates the optimal schedule.

[1182] The "means for transmitting to the terminal and displaying" refers to a method and device for transferring the optimum schedule obtained as a result of the analysis to the user's terminal and displaying it.

[1183] A "delivery person" is an individual whose job it is to transport food and merchandise to customers in a food delivery service.

[1184] A "delivery schedule" is a plan that indicates the time periods and order of deliveries planned to enable delivery personnel to perform their work efficiently.

[1185] The "means for proposing" is a method and apparatus for presenting the optimal schedule analyzed by the generative AI model to the user.

[1186] This invention is a system for streamlining delivery staff schedule adjustments in food delivery services. The system proposes optimal delivery schedules by having users input delivery staff schedule data and transmitting it to a management server. Below, we will explain the details of each component and process of the system.

[1187] System configuration

[1188] 1. User Device

[1189] A smartphone application or web interface is used, through which users input delivery staff schedule data. For example, a restaurant owner inputs the delivery staff's availability and desired delivery times.

[1190] The schedule data includes details such as the date, time, and delivery person's ID.

[1191] 2. Management Server

[1192] After the data is sent, the management server receives it and checks its integrity. If there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[1193] The management server is equipped with a generative AI model that analyzes the received schedule data and generates an optimal delivery schedule.

[1194] The specific AI model used has an algorithm that simultaneously takes into account delivery personnel's schedule data and job priorities.

[1195] 3. Generative AI Models

[1196] The generative AI model installed on the management server analyzes each delivery person's schedule data and creates an efficient delivery schedule, taking into account the delivery person's availability and priority to calculate the optimal delivery time slot.

[1197] Operational procedures and processing details

[1198] 1. Enter schedule data

[1199] A restaurant owner inputs delivery staff schedule data from a user terminal through an application. For example, suppose delivery staff A's availability is "2023-10-15 10:00-12:00" and delivery staff B's ​​availability is "2023-10-15 14:00-16:00."

[1200] The input format is simple and provides an interface that users can operate intuitively.

[1201] 2. Sending and Receiving Data

[1202] The entered data is sent to the management server in real time. Data is sent in JSON format using requests.post.

[1203] The management server checks the integrity of the data and returns an error message if there are any discrepancies.

[1204] 3. Analysis using generative AI models

[1205] The generative AI model calculates the optimal delivery schedule based on the received schedule data, taking into account the work schedules and priorities of delivery personnel.

[1206] 4. Display and confirm the results

[1207] The optimal schedule generated by the management server is sent to the user terminal and displayed by the application.

[1208] The store owner reviews the proposed schedule and can make any necessary changes. The final approved schedule is notified to all delivery drivers.

[1209] Specific examples

[1210] Example input:

[1211] The restaurant owner opens the app and enters the following schedule data:

[1212] Delivery Person A: 2023-10-15 10:00-12:00

[1213] Delivery Person B: 2023-10-15 14:00-16:00

[1214] Delivery Person C: 2023-10-16 09:00-11:00

[1215] Example prompts used for analysis by the generative AI model:

[1216] Restaurant ID: 12345

[1217] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[1218] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[1219] Generate an optimal delivery schedule to output.

[1220] This method streamlines delivery staff scheduling and improves the efficiency of food delivery operations.

[1221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1222] Step 1:

[1223] Entering schedule data

[1224] The user inputs the delivery person's schedule data using a smartphone application or a web interface. Specifically, the user inputs the delivery person's ID, available time, priority, etc. into an input form.

[1225] input:

[1226] Schedule data such as delivery driver ID, availability, and priority.

[1227] output:

[1228] The schedule data entered.

[1229] Specific behavior:

[1230] The user enters the delivery person's ID and available time in the application's input form. For example, the available time of delivery person A is entered as "2023-10-15 10:00-12:00."

[1231] Step 2:

[1232] Sending data

[1233] The terminal sends the entered schedule data to the management server using requests.post in JSON format.

[1234] input:

[1235] Schedule data entered by the user.

[1236] output:

[1237] Schedule data sent to the management server.

[1238] Specific behavior:

[1239] The terminal generates schedule data in JSON format and sends a POST request to the management server. For example, send the following data:

[1240] json

[1241] {

[1242] "restaurant_id": "12345",

[1243] "delivery_ids": ["deliv_1", "deliv_2", "deliv_3"],

[1244] "dates_times": ["2023-10-15 10:00-12:00", "2023-10-15 14:00-16:00", "2023-10-16 09:00-11:00"]

[1245] }

[1246] Step 3:

[1247] Data reception and integrity check

[1248] The management server checks the consistency of the schedule data it receives, and if there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[1249] input:

[1250] Schedule data sent to the management server.

[1251] output:

[1252] Schedule data that has been checked for consistency or error messages.

[1253] Specific behavior:

[1254] The management server verifies the received data, and if any defects are found, it generates an error message and notifies the user terminal. For example, if the date and time format is invalid, it returns the message "The date and time format is invalid."

[1255] Step 4:

[1256] Analysis using generative AI models

[1257] The management server passes the confirmed schedule data to the generative AI model and begins analysis. The model generates an optimal delivery schedule based on the schedule data.

[1258] input:

[1259] Schedule data that has been checked for consistency.

[1260] output:

[1261] Optimal delivery schedule.

[1262] Specific behavior:

[1263] The management server passes a prompt to the generative AI model to start the analysis. The prompt used as an example is as follows:

[1264] Restaurant ID: 12345

[1265] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[1266] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[1267] Generate an optimal delivery schedule to output.

[1268] A generative AI model then analyzes the data and generates an optimal delivery schedule.

[1269] Step 5:

[1270] Sending and displaying the optimal schedule

[1271] The management server sends the optimal schedule obtained by the generative AI model to the user's terminal and displays it in the application.

[1272] input:

[1273] Optimal schedule analyzed by generative AI model.

[1274] output:

[1275] The optimal schedule is displayed on the user's terminal.

[1276] Specific behavior:

[1277] The management server sends the generated optimal schedule to the user terminal and displays it on the application interface. For example, the following schedule is displayed:

[1278] Date: 2023-10-15, Time: 10:00-12:00, Delivery Person: Delivery Person A

[1279] Date: 2023-10-15, Time: 14:00-16:00, Delivery Person: Delivery Person B

[1280] Date: 2023-10-16, Time: 09:00-11:00, Delivery Person: Delivery Person C

[1281] Step 6:

[1282] Check and correct the results

[1283] The user can check the displayed optimal schedule and make any necessary corrections. The final approved schedule is notified to all participants.

[1284] input:

[1285] The optimal schedule displayed on the user's terminal.

[1286] output:

[1287] The final schedule, as amended or approved.

[1288] Specific behavior:

[1289] The user checks the optimal schedule and makes any necessary corrections. If no corrections are necessary, the user simply presses the approval button, and the final schedule is notified to all delivery personnel.

[1290] 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.

[1291] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[1292] 1. Enter and submit schedule data

[1293] The user inputs the schedule data of the participants using a terminal. The input interface is provided as a web form, and includes fields such as participant name, date, and time slot. The input data is checked for consistency and then sent to the management server.

[1294] 2. Analysis using generative AI models and optimal schedule proposals

[1295] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account each participant's schedule and calculates the optimal date for the meeting or event. During this process, participants' work schedules and priorities are also taken into account.

[1296] 3. Emotion recognition and feedback using an emotion engine

[1297] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine detects the user's facial expressions and tone of voice and generates feedback according to the user's emotions based on the analysis of the schedule data. This feedback helps reduce the psychological stress that the optimal schedule causes to the user.

[1298] 4. Display and confirm the results

[1299] The management server sends the optimal schedule obtained as a result of the analysis to the terminal. The terminal receives this optimal schedule and displays it on the user interface. The display interface is provided in calendar or list format, allowing the user to intuitively check the presented optimal schedule.

[1300] 5. User confirmation and final schedule confirmation

[1301] The user can check the optimal schedule displayed on the device and make any necessary adjustments. Feedback from the emotion engine also provides guidance to help users select a better schedule. The final confirmed schedule is notified to all participants.

[1302] Specific examples

[1303] 1. Meeting setup example

[1304] A user accesses a terminal and inputs the schedule data of five participants.

[1305] The terminal transmits this data to the management server.

[1306] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[1307] An emotion engine built into the management server recognizes emotions from the user's facial expressions and tone of voice, and provides feedback based on the analysis results.

[1308] The terminal receives the optimal schedule and feedback and displays it to the user.

[1309] The user checks the displayed optimum schedule, makes corrections if necessary, and finalizes the schedule.

[1310] Confirmed dates will be communicated to all participants.

[1311] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, and enables optimal schedule adjustment that takes user emotions into consideration. The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[1312] The processing flow will be explained below.

[1313] Step 1: User enters schedule data

[1314] The user inputs the schedule data of each participant using the terminal interface. The input interface is provided in the form of a web form, and the user can enter the necessary information in fields such as participant name, date, time slot, etc.

[1315] Step 2: The device sends the schedule data

[1316] The terminal sends the schedule data entered by the user to the management server. During this sending process, the terminal performs basic checks (format checks and blank checks) to ensure the consistency of the data.

[1317] Step 3: Server receives data

[1318] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any error, the server returns an error message to the terminal and asks the user to correct it.

[1319] Step 4: The server passes the data to the generative AI model

[1320] Once the consistency of the schedule data has been confirmed, the server inputs it into a generative AI model and begins analysis. This generative AI model takes into account the schedules of each participant and calculates the optimal dates for meetings and events.

[1321] Step 5: The server recognizes the user's emotion using the emotion engine

[1322] The server is equipped with an emotion engine that analyzes facial expressions and tone of voice in real time when users input schedule data to recognize their emotions. Emotional data is reflected in schedule analysis.

[1323] Step 6: The server calculates the optimal schedule

[1324] The generative AI model analyzes and calculates an optimal itinerary that takes into account participants' schedules and the user's emotional data, and is tailored to reduce the user's psychological stress.

[1325] Step 7: The server sends the optimal schedule to the device.

[1326] The server combines the optimal schedule analyzed by the generative AI model with feedback from the emotion engine and sends it to the terminal.

[1327] Step 8: The device receives the optimal schedule

[1328] The terminal receives the optimal schedule sent from the server, checks its consistency, and if there are no problems with the data format, prepares it for display on the user interface.

[1329] Step 9: Your device will display the best date and give you feedback.

[1330] The device displays the received optimal schedule and feedback from the emotion engine to the user. The display interface is provided in calendar and list format, allowing users to intuitively check the information.

[1331] Step 10: User confirms optimal schedule

[1332] The user checks the optimal schedule displayed on the device and makes any necessary adjustments. Based on feedback from the emotion engine, the user also determines whether the proposed schedule will cause any psychological stress.

[1333] Step 11: User confirms final date

[1334] The user confirms the optimal schedule and, once any modifications are complete, confirms the final schedule. The confirmed schedule is notified to all participants. This step allows all participants to be aware of the new schedule and begin preparations.

[1335] Example 2

[1336] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1337] Traditionally, scheduling meetings and events has often been done manually, which is time-consuming and inefficient. Finding the optimal date while taking into account the schedules of multiple participants is extremely difficult, especially when multiple participants are involved. Furthermore, the psychological impact and emotions of the proposed schedule on the user were not taken into consideration. As a result, even when an optimal date was found, the user often felt stressed.

[1338] 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.

[1339] In this invention, the server includes a means for inputting schedule data of participants, a means for confirming the consistency of the input schedule data, a means for transmitting the schedule data whose consistency has been confirmed to the management server, a means for the management server to analyze the schedule data using a generative AI model and propose an optimal schedule, a means for recognizing the emotional state of the user using an emotion engine and generating feedback based on the analysis results, and a means for transmitting and displaying the proposed optimal schedule and feedback to the terminal. This not only improves the efficiency of schedule adjustment, but also enables the adjustment of an optimal schedule that takes the user's emotions into consideration.

[1340] "Schedule data" is information indicating the dates and times when each participant of a meeting or event is available.

[1341] "Input means" refers to an interface that allows a user to input schedule data into the system using a terminal. Specifically, this corresponds to a web form, etc.

[1342] "Consistency checking means" is a process or system function for checking the accuracy and consistency of input schedule data.

[1343] The "transmission means" is a process or system function for transmitting schedule data from a terminal to the management server. Specifically, HTTP or HTTPS protocols are used.

[1344] The "management server" is a central component of the system that stores received schedule data, analyzes it using a generative AI model, and proposes optimal schedules.

[1345] The "generative AI model" is an artificial intelligence algorithm that analyzes the received schedule data and calculates the optimal schedule by taking into account the available dates and times of each participant. Specifically, a natural language processing model is used.

[1346] The "emotion engine" is part of a system that analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotional state.

[1347] "Feedback" is some kind of reaction or guidance to the user that is generated based on the user's emotional state recognized by the emotion engine.

[1348] The "display means" is an interface that allows the user to visualize the proposed optimal schedule and feedback. Specifically, it is displayed in a calendar or list format on the terminal display.

[1349] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[1350] First, a user uses a terminal (such as a PC or smartphone) to enter the schedule data of participants. The input interface is provided as a web form, and includes items such as participant names, dates, and time slots. For example, a user can access a terminal and enter the schedule data of five participants. The data is verified by the terminal and then sent to the management server.

[1351] The management server passes the received schedule data to a generative AI model (e.g., a natural language processing model such as GPT-4 or BERT) and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for a meeting or event. This also takes into account the participants' work schedules and priorities. For example, a prompt such as "Based on the schedule data of the following five people, please propose the optimal meeting date so that everyone can attend. Please also take into account various priorities" is input into the generative AI model.

[1352] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine uses the device's camera and microphone to detect the user's facial expressions and tone of voice, and generates feedback based on the analysis results. This feedback helps reduce the psychological stress that the proposed optimal schedule causes to the user. For example, positive feedback is provided to a user who is feeling stressed.

[1353] The optimal schedule obtained as a result of the analysis is sent from the management server to the terminal. The terminal displays this optimal schedule and feedback in a user interface in calendar or list format. The user can intuitively check the presented optimal schedule and make corrections as necessary. For example, if a user checks the presented optimal schedule and wants to change a specific schedule, they can make the corrections while also taking into account feedback from the emotion engine.

[1354] Finally, the final schedule confirmed by the user is sent to the management server and notified to all participants via email or push notification, so that all participants can check the final schedule.

[1355] This system not only significantly improves the efficiency of schedule adjustment work, which was previously done manually, but also enables optimal schedule adjustment that takes into account the user's emotions.The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[1356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1357] Program processing steps

[1358] Step 1: Enter schedule data

[1359] The user enters the schedule data.

[1360] The user uses a terminal to enter the schedule data of participants (participant name, date, time slot) via a web form. The entered data may include "Participant A: January 1st 10:00-12:00".

[1361] input:

[1362] Participant schedule (e.g. Participant A: January 1st 10:00-12:00)

[1363] output:

[1364] Input schedule data

[1365] Step 2: Data integrity check

[1366] The device checks the integrity of the data

[1367] The terminal checks the integrity of the data entered, specifically checking for missing required fields and validating date formats, such as whether the date entered is in a valid format and whether the time zone is consistent.

[1368] input:

[1369] Schedule data entered by the user

[1370] output:

[1371] Schedule data that has been checked for consistency or an error message

[1372] Step 3: Sending data

[1373] The device sends data to the management server

[1374] The data whose integrity has been confirmed is then sent to the management server using the HTTPS protocol to ensure data security.

[1375] input:

[1376] Consistency-checked schedule data

[1377] output:

[1378] Schedule data sent to the management server

[1379] Step 4: Analyze the data with a generative AI model

[1380] The server receives the data and passes it to the generative AI model

[1381] The management server passes the received schedule data to the generation AI model and begins analysis. The generated prompt is, "Based on the schedule data of the following five people, please suggest the optimal meeting date that everyone can attend."

[1382] input:

[1383] Schedule data stored on the management server

[1384] output:

[1385] A prompt to pass to the generative AI model

[1386] Generative AI model calculates optimal schedule

[1387] The generative AI model analyzes the prompt text and calculates the optimal meeting date, taking into account each participant's schedule and calculating the optimal non-overlapping time slot.

[1388] input:

[1389] Prompt: "Based on the schedule data of the following five people, please suggest the best meeting date that allows everyone to attend."

[1390] output:

[1391] Optimal meeting dates

[1392] Step 5: Emotion Recognition with the Emotion Engine

[1393] The server recognizes the user's emotions

[1394] The management server passes data obtained from the device's camera and microphone to the emotion engine, which analyzes the user's facial expressions and tone of voice. For example, the camera captures the user's facial expressions, and the microphone captures the user's tone of voice.

[1395] input:

[1396] User facial expression data, tone of voice data

[1397] output:

[1398] The user's emotional state (e.g., stress, joy)

[1399] The server generates feedback

[1400] Based on the analysis results of the emotion engine, feedback is generated that takes into account the impact of the optimal schedule on the user. For example, if the user feels stressed, a positive message will be sent to provide a sense of security.

[1401] input:

[1402] The user's emotional state

[1403] output:

[1404] Feedback Message

[1405] Step 6: Viewing the best dates and feedback

[1406] The server sends the optimal schedule and feedback to the device.

[1407] The management server sends the optimal schedule and feedback to the terminal.

[1408] input:

[1409] Optimal meeting schedule, feedback message

[1410] output:

[1411] Data sent to the device

[1412] The device displays the best time and gives feedback

[1413] The device displays the optimal schedule and feedback in a user interface in calendar or list format.

[1414] input:

[1415] Optimal meeting schedule, feedback message

[1416] output:

[1417] Display in user interface (calendar format, list format)

[1418] Step 7: Final confirmation and schedule confirmation

[1419] The user checks the optimal schedule and makes any necessary adjustments.

[1420] The user can review the displayed optimal schedule and make adjustments as needed, for example, by changing the date of a specific meeting to accommodate individual needs.

[1421] input:

[1422] Optimal meeting schedule, user-specified corrections

[1423] output:

[1424] Revised final schedule

[1425] The server will confirm and notify the final schedule.

[1426] The user then sends the final schedule to the management server, which then notifies all participants via email or push notification.

[1427] input:

[1428] Revised final schedule

[1429] output:

[1430] Notification from the server to all participants

[1431] (Application example 2)

[1432] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1433] Conventional schedule adjustment systems have difficulty efficiently consolidating the diverse schedules of participants. Furthermore, they do not consider the psychological burden or stress that the proposed schedule places on users, making it difficult to say that the schedule is optimal. In particular, in work environments such as factories, efficient schedule adjustment while reducing fatigue and stress among maintenance staff is required.

[1434] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting schedule data of participants, a means for transmitting the input schedule data to a management server, a means for the management server to analyze the schedule data using a generation AI model and propose an optimal schedule, a means for transmitting and displaying the proposed optimal schedule to a terminal, and a means including an emotion engine that analyzes user emotion data and providing feedback. This makes it possible to improve the efficiency of schedule adjustment for participants, provide feedback that takes into consideration the user's emotions, and achieve optimal schedule adjustment with reduced psychological burden.

[1435] "Participant schedule data" is information indicating the schedule of each individual who will participate in a conference or event, and is data including the date, time period, and work content.

[1436] The "management server" is a central computer system that comprehensively manages participants' schedule data, generative AI models, and emotion engines, and proposes optimal schedules.

[1437] A "generative AI model" is an artificial intelligence model that analyzes collected schedule data and calculates the optimal schedule.

[1438] An "emotion engine" is a software or hardware component for analyzing a user's emotional data and providing real-time feedback on schedule suggestions.

[1439] "Proposed optimal dates" are the most suitable dates calculated by the generative AI model based on the participants' schedules and other relevant information.

[1440] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to receive and display schedule suggestions and feedback from a server on the cloud.

[1441] "User emotion data" is information about emotions acquired from the user's facial expressions, tone of voice, etc., and is data that is analyzed by the emotion engine.

[1442] This invention relates to a system for efficiently managing the schedules of maintenance staff in a factory. The system includes functions for inputting, sending, analyzing, proposing, and displaying schedule data of participants, as well as emotion recognition feedback.

[1443] First, users (factory managers or maintenance staff) use terminals (smartphones or tablets) to enter their own and other staff members' schedule data. The schedule data includes details such as working hours, holidays, and job content. The data is entered via a web interface and sent to the management server.

[1444] The management server then passes the received schedule data to a generative AI model, which then proposes an optimal maintenance schedule. The generative AI model analyzes each staff member's schedule, as well as the maintenance requirements and priorities of robots and machines.

[1445] Furthermore, the emotion engine analyzes the user's emotional data for the proposed schedule. The emotion engine detects the user's facial expressions and tone of voice in real time to evaluate whether the proposed schedule will cause psychological stress. If necessary, it provides feedback and encourages schedule adjustments.

[1446] Finally, the management server sends the optimal schedule obtained as a result of the analysis to the terminal and displays it on the user interface in calendar or list format. The user can check the presented optimal schedule and make any necessary corrections. Once the final schedule is confirmed, all relevant parties are notified.

[1447] As a concrete example, let's look at a schedule management system for maintenance staff in a factory. A manager uses a terminal to input staff schedules, which are then sent to a management server. A generative AI model analyzes the data and proposes an optimal maintenance schedule, and an emotion engine detects the facial expressions and voices of staff members and provides feedback. Finally, the schedule is finalized and notified to everyone.

[1448] An example of a prompt is as follows:

[1449] 1. Enter staff schedule data and send it to the management server: "2023-11-01:09:00-17:00,2023-11-02:09:00-17:00"

[1450] 2. The generative AI model calculates and displays the optimal maintenance schedule.

[1451] 3. The emotion engine analyzes staff's facial expressions and tone of voice to provide the optimal schedule.

[1452] 4. Receive and display confirmed schedules and notify staff.

[1453]

[1454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1455] Step 1:

[1456] A user uses a terminal to input schedule data for participants, including working hours, vacations, specific work duties, etc. The input interface is a web interface, and the input data is packaged in JSON format.

[1457] input:

[1458] Schedule data for each participant (date, time, and work content)

[1459] output:

[1460] Schedule data in JSON format

[1461] Step 2:

[1462] The terminal checks the consistency of the entered schedule data and sends it to the management server. The consistency check includes checking the date format and duplicate data.

[1463] input:

[1464] Schedule data in JSON format

[1465] output:

[1466] Consistency-checked schedule data in JSON format

[1467] Step 3:

[1468] The management server passes the received schedule data to the generative AI model, which then takes into account staff working hours, work schedules, and priorities to generate the optimal maintenance schedule.

[1469] input:

[1470] Schedule data, working hours, work schedules, and priorities for each staff member

[1471] output:

[1472] Proposing optimal maintenance schedules

[1473] Step 4:

[1474] The management server sends the proposed optimal schedule to the terminal and displays it on the user interface in calendar or list format, allowing the user to check it in a visually easy-to-understand format.

[1475] input:

[1476] Proposing optimal maintenance schedules

[1477] output:

[1478] Calendar or list view of schedule

[1479] Step 5:

[1480] When the user provides feedback on the proposed schedule, the device activates an emotion engine, which analyzes the user's facial expressions and tone of voice in real time to evaluate the psychological burden.

[1481] input:

[1482] User facial expression data, tone of voice data

[1483] output:

[1484] Sentiment analysis results and feedback

[1485] Step 6:

[1486] Based on the results of the emotion analysis, the management server re-proposes a schedule as necessary, thereby proposing an optimal schedule that takes into account the user's psychological burden.

[1487] input:

[1488] Sentiment analysis results and optimal maintenance schedule suggestions

[1489] output:

[1490] Revised optimal maintenance schedule proposal

[1491] Step 7:

[1492] The final confirmed schedule is sent from the management server to the terminal and notified to the user, who then rechecks the presented schedule, makes any necessary corrections, and confirms it.

[1493] input:

[1494] Revised optimal maintenance schedule proposal

[1495] output:

[1496] Finalized maintenance schedule

[1497] Step 8:

[1498] The finalized schedule is notified to all relevant parties by the management server, allowing all participants to share the latest schedule.

[1499] input:

[1500] Finalized maintenance schedule

[1501] output:

[1502] Schedule notification (notification to all participants)

[1503] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1504] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1505] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1506] [Fourth embodiment]

[1507] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1508] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1509] 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).

[1510] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1511] 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.

[1512] 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).

[1513] 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. 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.

[1514] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1515] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1516] 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.

[1517] 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.

[1518] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1519] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1520] This invention is a system for streamlining schedule adjustments for meetings and events. The system consists of a series of processes: participants' schedule data is input, sent to a management server, analyzed by the server using a generative AI model, and the server proposes an optimal schedule, which is then displayed on a terminal.

[1521] 1. Enter and submit schedule data

[1522] The user uses the terminal to input the schedule data of the participants. The input interface uses a web form, which includes fields such as participant name, date, time slot, etc. This allows the user to easily input the schedule of each participant.

[1523] The terminal sends the entered schedule data to the management server. The server receives this data and checks its consistency. If there are any errors or deficiencies in the data, it notifies the user by returning an error message to the terminal.

[1524] 2. Analysis using generative AI models and optimal schedule proposals

[1525] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for the meeting or event. This process also takes into account the participants' work schedules and priorities, making adjustments easier.

[1526] The management server transmits the optimum schedule obtained as a result of the analysis to the terminal, which receives the optimum schedule and displays it on the user interface.

[1527] 3. Display and confirm the results

[1528] The user checks the optimal schedule displayed on their device. The display interface is in calendar or list format, allowing the user to intuitively understand the proposed schedule. If necessary, the user can also modify the schedule. The final approved schedule is notified to all participants.

[1529] Specific examples

[1530] 1. Meeting setup example

[1531] A user accesses a terminal and inputs the schedule data of five participants.

[1532] The terminal transmits this data to the management server.

[1533] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[1534] The terminal receives the optimum schedule and displays it to the user.

[1535] The user checks the displayed optimum schedule and makes corrections if necessary.

[1536] The final dates will be approved and communicated to all participants.

[1537] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, by checking the schedules of all participants at once and automatically proposing the optimal date, significantly reducing the time and effort required. This system allows users to adjust schedules without stress and improves the efficiency of the entire business.

[1538] The processing flow will be explained below.

[1539] Step 1: User enters schedule data

[1540] The user uses the terminal interface to input schedule data for each participant, which is provided in the form of a web form and includes items such as participant name, date, and time slot.

[1541] Step 2: The device sends the schedule data

[1542] The terminal sends the schedule data entered by the user to the management server, which also performs basic checks to ensure the data is consistent.

[1543] Step 3: Server receives data

[1544] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any discrepancy, the server returns an error message to the terminal.

[1545] Step 4: The server passes the data to the generative AI model

[1546] The server inputs the confirmed schedule data into the generative AI model and begins analysis. At this time, the generative AI model calculates the optimal schedule taking into account the schedules of the participants.

[1547] Step 5: The server calculates the optimal schedule

[1548] A generative AI model performs the analysis and calculates the optimal itinerary, taking into account participants' work schedules and priorities.

[1549] Step 6: The server sends the optimal schedule to the device.

[1550] The server sends the optimal schedule calculated by the generative AI model to the terminal.

[1551] Step 7: Your device receives the best schedule

[1552] The terminal receives the optimal schedule sent from the server and also checks the consistency of the received schedule data.

[1553] Step 8: Your device will display the best dates

[1554] The terminal displays the received optimal schedule to the user in a display interface in a calendar or list format.

[1555] Step 9: User confirms optimal schedule

[1556] The user can check the optimal schedule displayed on the terminal and, if necessary, modify the schedule.

[1557] Step 10: User confirms final date

[1558] The user confirms the optimal date and finalizes it. The confirmed date is notified to all participants.

[1559] This process streamlines meeting and event scheduling, eliminating the need for manual scheduling, and leverages generative AI models to suggest optimal schedules.

[1560] Example 1

[1561] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1562] Conventional scheduling for meetings and events requires manual coordination among participants, which takes a lot of time and effort. It is also difficult to grasp the schedules of all participants at once, making it difficult to find the optimal date. Therefore, there is a need for a system that can improve the efficiency of schedule coordination and quickly and accurately adjust the schedules of all participants.

[1563] 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.

[1564] In this invention, the server includes a means for checking the consistency of schedule data, a means for analyzing the schedule data using a generative AI model and proposing an optimal schedule, and a means for notifying all participants of the final approved schedule, which enables users to efficiently adjust their schedules and quickly determine the optimal schedule.

[1565] A "user" is a person or entity that utilizes the system to enter, review, and modify schedule data.

[1566] A "terminal" is a device used by a user, and is a device for inputting schedule data and communicating with a server.

[1567] A "management server" is a central computer system that receives, analyzes, and notifies input schedule data.

[1568] "Schedule data" refers to information such as participant names, dates, and time slots entered for a meeting or event.

[1569] A "generative AI model" is an artificial intelligence model used to analyze input schedule data and propose optimal dates.

[1570] "Integrity checking" is the process by which the management server verifies that the schedule data it receives is complete and accurate.

[1571] The "proposed optimal schedule" refers to the most suitable schedule for all participants, as determined by the generative AI model as an analysis result.

[1572] A "user interface" is a screen or display format that allows a user to input and check schedule data on a terminal.

[1573] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[1574] "Notification" refers to the act of the management server informing all participants of the final approved schedule.

[1575] MODE FOR CARRYING OUT THE INVENTION

[1576] The present invention is a system for improving the efficiency of scheduling meetings and events.

[1577] System configuration

[1578] User:

[1579] Users use a terminal to input schedule data. A web form is used as the input interface. Specifically, information such as participant names, dates, and time slots is input.

[1580] Device:

[1581] The terminal sends the schedule data entered by the user to the management server using an HTTP POST request. The display interface on the terminal displays the analysis results to the user in calendar or list format.

[1582] Management Server:

[1583] The management server checks the consistency of the received schedule data, and if there is an error in the data, returns an error message to the terminal.

[1584] Once consistency is confirmed, the schedule data is passed to a generative AI model, which uses frameworks such as PyTorch and TensorFlow to begin analysis.

[1585] The generative AI model analyzes schedule data and proposes optimal dates, taking into account participants' work schedules and priorities.

[1586] The optimum schedule is sent to the terminal so that the user can check and modify it.

[1587] The final approved date will be notified to all participants via email or push notification.

[1588] Specific examples

[1589] 1. Example of a meeting setup:

[1590] A user accesses a terminal and inputs the schedule data of five participants.

[1591] Participant name: "Mr. A", Date: "2023-12-01", Time: "14:00-16:00"

[1592] The terminal transmits this data to the management server.

[1593] The management server checks the integrity of the received data and analyzes it using a generative AI model.

[1594] The generative AI model calculates the optimal meeting schedule and returns the result to the server, using the following prompt:

[1595] "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[1596] The management server transmits the analysis results to the terminal, and the terminal displays the optimal schedule on the user interface.

[1597] The user checks the optimal date and makes any necessary changes, for example, changing it to "2023-12-02 10:00-11:00."

[1598] The final date is approved and all participants are notified. An example of a notification email would be: Subject: "Notification of Final Meeting Date" with the body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[1599] By using this system, users can significantly simplify the tedious task of manually adjusting schedules, enabling them to quickly and accurately determine optimal dates for meetings and events.

[1600] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1601] Step 1:

[1602] User Action:

[1603] The user uses the terminal to input schedule data for the participants.

[1604] Specific actions: Use a browser to access a web form and enter information such as participant names, date, and time slot.

[1605] Input: Participant name: "Mr. A", Date: "2023-12-01", Time period: "14:00-16:00"

[1606] Output: Input data is saved to the terminal.

[1607] Step 2:

[1608] Device features:

[1609] The terminal transmits the schedule data input by the user to the management server.

[1610] Specific behavior: Sends data to the management server using an HTTP POST request.

[1611] Input: Schedule data in JSON format

[1612] json

[1613] {

[1614] "Participant Name": "Mr. A",

[1615] "Date": "2023-12-01",

[1616] "Time Zone": "14:00-16:00"

[1617] }

[1618] Output: Data received by the management server

[1619] Step 3:

[1620] Server Integrity Check:

[1621] The server checks the integrity of the received schedule data.

[1622] Specific operation: Validates the data format and required fields. If there is an error, generates an error message and returns it to the terminal.

[1623] Input: Received schedule data

[1624] Output: Schedule data whose consistency has been checked or an error message

[1625] Step 4:

[1626] Server execution of generative AI models:

[1627] The server passes the schedule data whose consistency has been confirmed to the generative AI model and begins analysis.

[1628] Specific operation: Analyze schedule data using frameworks such as PyTorch and TensorFlow.

[1629] Prompt: "Please suggest the best meeting date using the following schedule data: Person A (2023-12-01 14:00-16:00), Person B (2023-12-01 10:00-12:00)"

[1630] Input: Schedule data that has been checked for consistency

[1631] Output: Optimal schedule based on analysis

[1632] Step 5:

[1633] Server sends analysis results:

[1634] The server sends the optimal schedule obtained from the generative AI model to the terminal.

[1635] Specific operation: The analysis results are converted into JSON format and sent to the terminal as an HTTP response.

[1636] Input: Optimal schedule analysis results

[1637] json

[1638] {

[1639] "Best Date": "2023-12-01 15:00-16:00"

[1640] }

[1641] Output: The device where the analysis results are sent

[1642] Step 6:

[1643] Displaying the best dates on your device:

[1644] The terminal displays the received optimum schedule on the user interface.

[1645] Specific operation: Display analysis results in calendar or list format in the browser

[1646] Input: Analysis result data

[1647] Output: Optimal schedule displayed to the user

[1648] example:

[1649] December 1st

[1650] 15:00 - 16:00 Meeting (Participants: Mr. A, Mr. B)

[1651] Step 7:

[1652] User confirmation and modification of dates:

[1653] The user checks the displayed optimum schedule and makes corrections if necessary.

[1654] Action: Re-enter new dates using the web form

[1655] Input: Revised schedule data

[1656] Output: The final approved schedule is sent to the server.

[1657] Step 8:

[1658] Server notification of final date:

[1659] The server will notify all participants of the final approved dates.

[1660] What it does: Sends notifications via email and push notifications.

[1661] Input: Final Approved Date

[1662] Output: Notification sent to all participants

[1663] example:

[1664] Email subject: "Notification of final meeting schedule"

[1665] Email body: "The meeting date has been confirmed as 2023-12-01 15:00-16:00."

[1666] (Application example 1)

[1667] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1668] In today's food delivery industry, it is difficult to efficiently coordinate the schedules of multiple delivery drivers. In particular, simultaneously considering delivery driver schedules and restaurant operation schedules is time-consuming and prone to errors. This issue can lead to delivery delays and reduced efficiency.

[1669] 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.

[1670] In this invention, the server includes a means for inputting delivery staff schedule data, a means for transmitting the input schedule data to the management server, and a means for proposing an optimal delivery schedule in consideration of the delivery staff schedule data. This allows business operators such as restaurants and cafes to easily manage delivery staff schedules and efficiently adjust delivery schedules.

[1671] A "participant" is an individual or group involved in a meeting, event, or delivery task that needs to be scheduled within the system.

[1672] "Schedule data" is information indicating the schedules and availability of participants and delivery personnel, and is digital information including details such as dates, time periods, and priorities.

[1673] "Input means" refers to a method and device for providing schedule data to the system via a terminal, a web interface, or a smartphone application.

[1674] The "means for transmitting to the management server" is a communication function for transferring input schedule data to a central management server.

[1675] The "management server" is a centralized computer system that analyzes schedule data and generates optimal dates and delivery schedules.

[1676] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and calculates the optimal schedule.

[1677] The "means for transmitting to the terminal and displaying" refers to a method and device for transferring the optimum schedule obtained as a result of the analysis to the user's terminal and displaying it.

[1678] A "delivery person" is an individual whose job it is to transport food and merchandise to customers in a food delivery service.

[1679] A "delivery schedule" is a plan that indicates the time periods and order of deliveries planned to enable delivery personnel to perform their work efficiently.

[1680] The "means for proposing" is a method and apparatus for presenting the optimal schedule analyzed by the generative AI model to the user.

[1681] This invention is a system for streamlining delivery staff schedule adjustments in food delivery services. The system proposes optimal delivery schedules by having users input delivery staff schedule data and transmitting it to a management server. Below, we will explain the details of each component and process of the system.

[1682] System configuration

[1683] 1. User Device

[1684] A smartphone application or web interface is used, through which users input delivery staff schedule data. For example, a restaurant owner inputs the delivery staff's availability and desired delivery times.

[1685] The schedule data includes details such as the date, time, and delivery person's ID.

[1686] 2. Management Server

[1687] After the data is sent, the management server receives it and checks its integrity. If there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[1688] The management server is equipped with a generative AI model that analyzes the received schedule data and generates an optimal delivery schedule.

[1689] The specific AI model used has an algorithm that simultaneously takes into account delivery personnel's schedule data and job priorities.

[1690] 3. Generative AI Models

[1691] The generative AI model installed on the management server analyzes each delivery person's schedule data and creates an efficient delivery schedule, taking into account the delivery person's availability and priority to calculate the optimal delivery time slot.

[1692] Operational procedures and processing details

[1693] 1. Enter schedule data

[1694] A restaurant owner inputs delivery staff schedule data from a user terminal through an application. For example, suppose delivery staff A's availability is "2023-10-15 10:00-12:00" and delivery staff B's ​​availability is "2023-10-15 14:00-16:00."

[1695] The input format is simple and provides an interface that users can operate intuitively.

[1696] 2. Sending and Receiving Data

[1697] The entered data is sent to the management server in real time. Data is sent in JSON format using requests.post.

[1698] The management server checks the integrity of the data and returns an error message if there are any discrepancies.

[1699] 3. Analysis using generative AI models

[1700] The generative AI model calculates the optimal delivery schedule based on the received schedule data, taking into account the work schedules and priorities of delivery personnel.

[1701] 4. Display and confirm the results

[1702] The optimal schedule generated by the management server is sent to the user terminal and displayed by the application.

[1703] The store owner reviews the proposed schedule and can make any necessary changes. The final approved schedule is notified to all delivery drivers.

[1704] Specific examples

[1705] Example input:

[1706] The restaurant owner opens the app and enters the following schedule data:

[1707] Delivery Person A: 2023-10-15 10:00-12:00

[1708] Delivery Person B: 2023-10-15 14:00-16:00

[1709] Delivery Person C: 2023-10-16 09:00-11:00

[1710] Example prompts used for analysis by the generative AI model:

[1711] Restaurant ID: 12345

[1712] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[1713] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[1714] Generate an optimal delivery schedule to output.

[1715] This method streamlines delivery staff scheduling and improves the efficiency of food delivery operations.

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Step 1:

[1718] Entering schedule data

[1719] The user inputs the delivery person's schedule data using a smartphone application or a web interface. Specifically, the user inputs the delivery person's ID, available time, priority, etc. into an input form.

[1720] input:

[1721] Schedule data such as delivery driver ID, availability, and priority.

[1722] output:

[1723] The schedule data entered.

[1724] Specific behavior:

[1725] The user enters the delivery person's ID and available time in the application's input form. For example, the available time of delivery person A is entered as "2023-10-15 10:00-12:00."

[1726] Step 2:

[1727] Sending data

[1728] The terminal sends the entered schedule data to the management server using requests.post in JSON format.

[1729] input:

[1730] Schedule data entered by the user.

[1731] output:

[1732] Schedule data sent to the management server.

[1733] Specific behavior:

[1734] The terminal generates schedule data in JSON format and sends a POST request to the management server. For example, send the following data:

[1735] json

[1736] {

[1737] "restaurant_id": "12345",

[1738] "delivery_ids": ["deliv_1", "deliv_2", "deliv_3"],

[1739] "dates_times": ["2023-10-15 10:00-12:00", "2023-10-15 14:00-16:00", "2023-10-16 09:00-11:00"]

[1740] }

[1741] Step 3:

[1742] Data reception and integrity check

[1743] The management server checks the consistency of the schedule data it receives, and if there are any errors or deficiencies in the data, it returns an error message to the user terminal.

[1744] input:

[1745] Schedule data sent to the management server.

[1746] output:

[1747] Schedule data that has been checked for consistency or error messages.

[1748] Specific behavior:

[1749] The management server verifies the received data, and if any defects are found, it generates an error message and notifies the user terminal. For example, if the date and time format is invalid, it returns the message "The date and time format is invalid."

[1750] Step 4:

[1751] Analysis using generative AI models

[1752] The management server passes the confirmed schedule data to the generative AI model and begins analysis. The model generates an optimal delivery schedule based on the schedule data.

[1753] input:

[1754] Schedule data that has been checked for consistency.

[1755] output:

[1756] Optimal delivery schedule.

[1757] Specific behavior:

[1758] The management server passes a prompt to the generative AI model to start the analysis. The prompt used as an example is as follows:

[1759] Restaurant ID: 12345

[1760] Delivery Person: Delivery Person A, Delivery Person B, Delivery Person C

[1761] Schedule: 2023-10-15 10:00-12:00, 2023-10-15 14:00-16:00, 2023-10-16 09:00-11:00

[1762] Generate an optimal delivery schedule to output.

[1763] A generative AI model then analyzes the data and generates an optimal delivery schedule.

[1764] Step 5:

[1765] Sending and displaying the optimal schedule

[1766] The management server sends the optimal schedule obtained by the generative AI model to the user's terminal and displays it in the application.

[1767] input:

[1768] Optimal schedule analyzed by generative AI model.

[1769] output:

[1770] The optimal schedule is displayed on the user's terminal.

[1771] Specific behavior:

[1772] The management server sends the generated optimal schedule to the user terminal and displays it on the application interface. For example, the following schedule is displayed:

[1773] Date: 2023-10-15, Time: 10:00-12:00, Delivery Person: Delivery Person A

[1774] Date: 2023-10-15, Time: 14:00-16:00, Delivery Person: Delivery Person B

[1775] Date: 2023-10-16, Time: 09:00-11:00, Delivery Person: Delivery Person C

[1776] Step 6:

[1777] Check and correct the results

[1778] The user can check the displayed optimal schedule and make any necessary corrections. The final approved schedule is notified to all participants.

[1779] input:

[1780] The optimal schedule displayed on the user's terminal.

[1781] output:

[1782] The final schedule, as amended or approved.

[1783] Specific behavior:

[1784] The user checks the optimal schedule and makes any necessary corrections. If no corrections are necessary, the user simply presses the approval button, and the final schedule is notified to all delivery personnel.

[1785] 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.

[1786] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[1787] 1. Enter and submit schedule data

[1788] The user inputs the schedule data of the participants using a terminal. The input interface is provided as a web form, and includes fields such as participant name, date, and time slot. The input data is checked for consistency and then sent to the management server.

[1789] 2. Analysis using generative AI models and optimal schedule proposals

[1790] The management server passes the received schedule data to the generative AI model and begins analysis. The generative AI model takes into account each participant's schedule and calculates the optimal date for the meeting or event. During this process, participants' work schedules and priorities are also taken into account.

[1791] 3. Emotion recognition and feedback using an emotion engine

[1792] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine detects the user's facial expressions and tone of voice and generates feedback according to the user's emotions based on the analysis of the schedule data. This feedback helps reduce the psychological stress that the optimal schedule causes to the user.

[1793] 4. Display and confirm the results

[1794] The management server sends the optimal schedule obtained as a result of the analysis to the terminal. The terminal receives this optimal schedule and displays it on the user interface. The display interface is provided in calendar or list format, allowing the user to intuitively check the presented optimal schedule.

[1795] 5. User confirmation and final schedule confirmation

[1796] The user can check the optimal schedule displayed on the device and make any necessary adjustments. Feedback from the emotion engine also provides guidance to help users select a better schedule. The final confirmed schedule is notified to all participants.

[1797] Specific examples

[1798] 1. Meeting setup example

[1799] A user accesses a terminal and inputs the schedule data of five participants.

[1800] The terminal transmits this data to the management server.

[1801] The management server analyzes the data using the generated AI model and proposes the optimal meeting date.

[1802] An emotion engine built into the management server recognizes emotions from the user's facial expressions and tone of voice, and provides feedback based on the analysis results.

[1803] The terminal receives the optimal schedule and feedback and displays it to the user.

[1804] The user checks the displayed optimum schedule, makes corrections if necessary, and finalizes the schedule.

[1805] Confirmed dates will be communicated to all participants.

[1806] This invention significantly improves the efficiency of schedule adjustment work, which was previously done manually, and enables optimal schedule adjustment that takes user emotions into consideration. The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[1807] The processing flow will be explained below.

[1808] Step 1: User enters schedule data

[1809] The user inputs the schedule data of each participant using the terminal interface. The input interface is provided in the form of a web form, and the user can enter the necessary information in fields such as participant name, date, time slot, etc.

[1810] Step 2: The device sends the schedule data

[1811] The terminal sends the schedule data entered by the user to the management server. During this sending process, the terminal performs basic checks (format checks and blank checks) to ensure the consistency of the data.

[1812] Step 3: Server receives data

[1813] The server receives the schedule data sent from the terminal and reconfirms the consistency of the data. If there is any error, the server returns an error message to the terminal and asks the user to correct it.

[1814] Step 4: The server passes the data to the generative AI model

[1815] Once the consistency of the schedule data has been confirmed, the server inputs it into a generative AI model and begins analysis. This generative AI model takes into account the schedules of each participant and calculates the optimal dates for meetings and events.

[1816] Step 5: The server recognizes the user's emotion using the emotion engine

[1817] The server is equipped with an emotion engine that analyzes facial expressions and tone of voice in real time when users input schedule data to recognize their emotions. Emotional data is reflected in schedule analysis.

[1818] Step 6: The server calculates the optimal schedule

[1819] The generative AI model analyzes and calculates an optimal itinerary that takes into account participants' schedules and the user's emotional data, and is tailored to reduce the user's psychological stress.

[1820] Step 7: The server sends the optimal schedule to the device.

[1821] The server combines the optimal schedule analyzed by the generative AI model with feedback from the emotion engine and sends it to the terminal.

[1822] Step 8: The device receives the optimal schedule

[1823] The terminal receives the optimal schedule sent from the server, checks its consistency, and if there are no problems with the data format, prepares it for display on the user interface.

[1824] Step 9: Your device will display the best date and give you feedback.

[1825] The device displays the received optimal schedule and feedback from the emotion engine to the user. The display interface is provided in calendar and list format, allowing users to intuitively check the information.

[1826] Step 10: User confirms optimal schedule

[1827] The user checks the optimal schedule displayed on the device and makes any necessary adjustments. Based on feedback from the emotion engine, the user also determines whether the proposed schedule will cause any psychological stress.

[1828] Step 11: User confirms final date

[1829] The user confirms the optimal schedule and, once any modifications are complete, confirms the final schedule. The confirmed schedule is notified to all participants. This step allows all participants to be aware of the new schedule and begin preparations.

[1830] Example 2

[1831] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1832] Traditionally, scheduling meetings and events has often been done manually, which is time-consuming and inefficient. Finding the optimal date while taking into account the schedules of multiple participants is extremely difficult, especially when multiple participants are involved. Furthermore, the psychological impact and emotions of the proposed schedule on the user were not taken into consideration. As a result, even when an optimal date was found, the user often felt stressed.

[1833] 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.

[1834] In this invention, the server includes a means for inputting schedule data of participants, a means for confirming the consistency of the input schedule data, a means for transmitting the schedule data whose consistency has been confirmed to the management server, a means for the management server to analyze the schedule data using a generative AI model and propose an optimal schedule, a means for recognizing the emotional state of the user using an emotion engine and generating feedback based on the analysis results, and a means for transmitting and displaying the proposed optimal schedule and feedback to the terminal. This not only improves the efficiency of schedule adjustment, but also enables the adjustment of an optimal schedule that takes the user's emotions into consideration.

[1835] "Schedule data" is information indicating the dates and times when each participant of a meeting or event is available.

[1836] "Input means" refers to an interface that allows a user to input schedule data into the system using a terminal. Specifically, this corresponds to a web form, etc.

[1837] "Consistency checking means" is a process or system function for checking the accuracy and consistency of input schedule data.

[1838] The "transmission means" is a process or system function for transmitting schedule data from a terminal to the management server. Specifically, HTTP or HTTPS protocols are used.

[1839] The "management server" is a central component of the system that stores received schedule data, analyzes it using a generative AI model, and proposes optimal schedules.

[1840] The "generative AI model" is an artificial intelligence algorithm that analyzes the received schedule data and calculates the optimal schedule by taking into account the available dates and times of each participant. Specifically, a natural language processing model is used.

[1841] The "emotion engine" is part of a system that analyzes the user's facial expressions and tone of voice in real time to recognize the user's emotional state.

[1842] "Feedback" is some kind of reaction or guidance to the user that is generated based on the user's emotional state recognized by the emotion engine.

[1843] The "display means" is an interface that allows the user to visualize the proposed optimal schedule and feedback. Specifically, it is displayed in a calendar or list format on the terminal display.

[1844] This invention combines a system for streamlining schedules for meetings and events with an emotion engine that recognizes user emotions. The system inputs participants' schedule data, sends it to a management server, analyzes it using a generative AI model, and proposes an optimal schedule. The system also displays the proposed optimal schedule on a device and provides feedback based on the user's emotions.

[1845] First, a user uses a terminal (such as a PC or smartphone) to enter the schedule data of participants. The input interface is provided as a web form, and includes items such as participant names, dates, and time slots. For example, a user can access a terminal and enter the schedule data of five participants. The data is verified by the terminal and then sent to the management server.

[1846] The management server passes the received schedule data to a generative AI model (e.g., a natural language processing model such as GPT-4 or BERT) and begins analysis. The generative AI model takes into account the schedules of each participant and calculates the optimal date for a meeting or event. This also takes into account the participants' work schedules and priorities. For example, a prompt such as "Based on the schedule data of the following five people, please propose the optimal meeting date so that everyone can attend. Please also take into account various priorities" is input into the generative AI model.

[1847] Furthermore, the management server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine uses the device's camera and microphone to detect the user's facial expressions and tone of voice, and generates feedback based on the analysis results. This feedback helps reduce the psychological stress that the proposed optimal schedule causes to the user. For example, positive feedback is provided to a user who is feeling stressed.

[1848] The optimal schedule obtained as a result of the analysis is sent from the management server to the terminal. The terminal displays this optimal schedule and feedback in a user interface in calendar or list format. The user can intuitively check the presented optimal schedule and make corrections as necessary. For example, if a user checks the presented optimal schedule and wants to change a specific schedule, they can make the corrections while also taking into account feedback from the emotion engine.

[1849] Finally, the final schedule confirmed by the user is sent to the management server and notified to all participants via email or push notification, so that all participants can check the final schedule.

[1850] This system not only significantly improves the efficiency of schedule adjustment work, which was previously done manually, but also enables optimal schedule adjustment that takes into account the user's emotions.The combination of a generative AI model and an emotion engine achieves more advanced optimization.

[1851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1852] Program processing steps

[1853] Step 1: Enter schedule data

[1854] The user enters the schedule data.

[1855] The user uses a terminal to enter the schedule data of participants (participant name, date, time slot) via a web form. The entered data may include "Participant A: January 1st 10:00-12:00".

[1856] input:

[1857] Participant schedule (e.g. Participant A: January 1st 10:00-12:00)

[1858] output:

[1859] Input schedule data

[1860] Step 2: Data integrity check

[1861] The device checks the integrity of the data

[1862] The terminal checks the integrity of the data entered, specifically checking for missing required fields and validating date formats, such as whether the date entered is in a valid format and whether the time zone is consistent.

[1863] input:

[1864] Schedule data entered by the user

[1865] output:

[1866] Schedule data that has been checked for consistency or an error message

[1867] Step 3: Sending data

[1868] The device sends data to the management server

[1869] The data whose integrity has been confirmed is then sent to the management server using the HTTPS protocol to ensure data security.

[1870] input:

[1871] Consistency-checked schedule data

[1872] output:

[1873] Schedule data sent to the management server

[1874] Step 4: Analyze the data with a generative AI model

[1875] The server receives the data and passes it to the generative AI model

[1876] The management server passes the received schedule data to the generation AI model and begins analysis. The generated prompt is, "Based on the schedule data of the following five people, please suggest the optimal meeting date that everyone can attend."

[1877] input:

[1878] Schedule data stored on the management server

[1879] output:

[1880] A prompt to pass to the generative AI model

[1881] Generative AI model calculates optimal schedule

[1882] The generative AI model analyzes the prompt text and calculates the optimal meeting date, taking into account each participant's schedule and calculating the optimal non-overlapping time slot.

[1883] input:

[1884] Prompt: "Based on the schedule data of the following five people, please suggest the best meeting date that allows everyone to attend."

[1885] output:

[1886] Optimal meeting dates

[1887] Step 5: Emotion Recognition with the Emotion Engine

[1888] The server recognizes the user's emotions

[1889] The management server passes data obtained from the device's camera and microphone to the emotion engine, which analyzes the user's facial expressions and tone of voice. For example, the camera captures the user's facial expressions, and the microphone captures the user's tone of voice.

[1890] input:

[1891] User facial expression data, tone of voice data

[1892] output:

[1893] The user's emotional state (e.g., stress, joy)

[1894] The server generates feedback

[1895] Based on the analysis results of the emotion engine, feedback is generated that takes into account the impact of the optimal schedule on the user. For example, if the user feels stressed, a positive message will be sent to provide a sense of security.

[1896] input:

[1897] The user's emotional state

[1898] output:

[1899] Feedback Message

[1900] Step 6: Viewing the best dates and feedback

[1901] The server sends the optimal schedule and feedback to the device.

[1902] The management server sends the optimal schedule and feedback to the terminal.

[1903] input:

[1904] Optimal meeting schedule, feedback message

[1905] output:

[1906] Data sent to the device

[1907] The device displays the best time and gives feedback

[1908] The device displays the optimal schedule and feedback in a user interface in calendar or list format.

[1909] input:

[1910] Optimal meeting schedule, feedback message

[1911] output:

[1912] Display in user interface (calendar format, list format)

[1913] Step 7: Final confirmation and schedule confirmation

[1914] The user checks the optimal schedule and makes any necessary adjustments.

[1915] The user can review the displayed optimal schedule and make adjustments as needed, for example, by changing the date of a specific meeting to accommodate individual needs.

[1916] input:

[1917] Optimal meeting schedule, user-specified corrections

[1918] output:

[1919] Revised final schedule

[1920] The server will confirm and notify the final schedule.

[1921] The user then sends the final schedule to the management server, which then notifies all participants via email or push notification.

[1922] input:

[1923] Revised final schedule

[1924] output:

[1925] Notification from the server to all participants

[1926] (Application example 2)

[1927] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1928] Conventional schedule adjustment systems have difficulty efficiently consolidating the diverse schedules of participants. Furthermore, they do not consider the psychological burden or stress that the proposed schedule places on users, making it difficult to say that the schedule is optimal. In particular, in work environments such as factories, efficient schedule adjustment while reducing fatigue and stress among maintenance staff is required.

[1929] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting schedule data of participants, a means for transmitting the input schedule data to a management server, a means for the management server to analyze the schedule data using a generation AI model and propose an optimal schedule, a means for transmitting and displaying the proposed optimal schedule to a terminal, and a means including an emotion engine that analyzes user emotion data and providing feedback. This makes it possible to improve the efficiency of schedule adjustment for participants, provide feedback that takes into consideration the user's emotions, and achieve optimal schedule adjustment with reduced psychological burden.

[1930] "Participant schedule data" is information indicating the schedule of each individual who will participate in a conference or event, and is data including the date, time period, and work content.

[1931] The "management server" is a central computer system that comprehensively manages participants' schedule data, generative AI models, and emotion engines, and proposes optimal schedules.

[1932] A "generative AI model" is an artificial intelligence model that analyzes collected schedule data and calculates the optimal schedule.

[1933] An "emotion engine" is a software or hardware component for analyzing a user's emotional data and providing real-time feedback on schedule suggestions.

[1934] "Proposed optimal dates" are the most suitable dates calculated by the generative AI model based on the participants' schedules and other relevant information.

[1935] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to receive and display schedule suggestions and feedback from a server on the cloud.

[1936] "User emotion data" is information about emotions acquired from the user's facial expressions, tone of voice, etc., and is data that is analyzed by the emotion engine.

[1937] This invention relates to a system for efficiently managing the schedules of maintenance staff in a factory. The system includes functions for inputting, sending, analyzing, proposing, and displaying schedule data of participants, as well as emotion recognition feedback.

[1938] First, users (factory managers or maintenance staff) use terminals (smartphones or tablets) to enter their own and other staff members' schedule data. The schedule data includes details such as working hours, holidays, and job content. The data is entered via a web interface and sent to the management server.

[1939] The management server then passes the received schedule data to a generative AI model, which then proposes an optimal maintenance schedule. The generative AI model analyzes each staff member's schedule, as well as the maintenance requirements and priorities of robots and machines.

[1940] Furthermore, the emotion engine analyzes the user's emotional data for the proposed schedule. The emotion engine detects the user's facial expressions and tone of voice in real time to evaluate whether the proposed schedule will cause psychological stress. If necessary, it provides feedback and encourages schedule adjustments.

[1941] Finally, the management server sends the optimal schedule obtained as a result of the analysis to the terminal and displays it on the user interface in calendar or list format. The user can check the presented optimal schedule and make any necessary corrections. Once the final schedule is confirmed, all relevant parties are notified.

[1942] As a concrete example, let's look at a schedule management system for maintenance staff in a factory. A manager uses a terminal to input staff schedules, which are then sent to a management server. A generative AI model analyzes the data and proposes an optimal maintenance schedule, and an emotion engine detects the facial expressions and voices of staff members and provides feedback. Finally, the schedule is finalized and notified to everyone.

[1943] An example of a prompt is as follows:

[1944] 1. Enter staff schedule data and send it to the management server: "2023-11-01:09:00-17:00,2023-11-02:09:00-17:00"

[1945] 2. The generative AI model calculates and displays the optimal maintenance schedule.

[1946] 3. The emotion engine analyzes staff's facial expressions and tone of voice to provide the optimal schedule.

[1947] 4. Receive and display confirmed schedules and notify staff.

[1948]

[1949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1950] Step 1:

[1951] A user uses a terminal to input schedule data for participants, including working hours, vacations, specific work duties, etc. The input interface is a web interface, and the input data is packaged in JSON format.

[1952] input:

[1953] Schedule data for each participant (date, time, and work content)

[1954] output:

[1955] Schedule data in JSON format

[1956] Step 2:

[1957] The terminal checks the consistency of the entered schedule data and sends it to the management server. The consistency check includes checking the date format and duplicate data.

[1958] input:

[1959] Schedule data in JSON format

[1960] output:

[1961] Consistency-checked schedule data in JSON format

[1962] Step 3:

[1963] The management server passes the received schedule data to the generative AI model, which then takes into account staff working hours, work schedules, and priorities to generate the optimal maintenance schedule.

[1964] input:

[1965] Schedule data, working hours, work schedules, and priorities for each staff member

[1966] output:

[1967] Proposing optimal maintenance schedules

[1968] Step 4:

[1969] The management server sends the proposed optimal schedule to the terminal and displays it on the user interface in calendar or list format, allowing the user to check it in a visually easy-to-understand format.

[1970] input:

[1971] Proposing optimal maintenance schedules

[1972] output:

[1973] Calendar or list view of schedule

[1974] Step 5:

[1975] When the user provides feedback on the proposed schedule, the device activates an emotion engine, which analyzes the user's facial expressions and tone of voice in real time to evaluate the psychological burden.

[1976] input:

[1977] User facial expression data, tone of voice data

[1978] output:

[1979] Sentiment analysis results and feedback

[1980] Step 6:

[1981] Based on the results of the emotion analysis, the management server re-proposes a schedule as necessary, thereby proposing an optimal schedule that takes into account the user's psychological burden.

[1982] input:

[1983] Sentiment analysis results and optimal maintenance schedule suggestions

[1984] output:

[1985] Revised optimal maintenance schedule proposal

[1986] Step 7:

[1987] The final confirmed schedule is sent from the management server to the terminal and notified to the user, who then rechecks the presented schedule, makes any necessary corrections, and confirms it.

[1988] input:

[1989] Revised optimal maintenance schedule proposal

[1990] output:

[1991] Finalized maintenance schedule

[1992] Step 8:

[1993] The finalized schedule is notified to all relevant parties by the management server, allowing all participants to share the latest schedule.

[1994] input:

[1995] Finalized maintenance schedule

[1996] output:

[1997] Schedule notification (notification to all participants)

[1998] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1999] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2000] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2001] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2002] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2003] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2004] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2005] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2006] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2007] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2008] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2009] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2010] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2011] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2012] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2013] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2014] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2015] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2016] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2017] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2018] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2019] The following is further disclosed regarding the above embodiment.

[2020] (Claim 1)

[2021] a means for inputting participant schedule data;

[2022] means for transmitting the input schedule data to a management server;

[2023] The management server uses the generated AI model to analyze schedule data and propose optimal schedules;

[2024] means for transmitting the proposed optimum schedule to a terminal and displaying it;

[2025] A system including:

[2026] (Claim 2)

[2027] 10. The system of claim 1, wherein the schedule data is entered via a web interface.

[2028] (Claim 3)

[2029] 2. The system of claim 1, wherein the work schedules and priorities of the participants are taken into consideration when analyzing the optimal schedule.

[2030] "Example 1"

[2031] (Claim 1)

[2032] a means for a user to input schedule data;

[2033] a means for transmitting input schedule data from the terminal to a management server;

[2034] a means for verifying the consistency of the schedule data received by the management server;

[2035] The management server uses the generated AI model to analyze schedule data and propose optimal schedules;

[2036] means for the terminal to display the proposed optimum schedule on a user interface;

[2037] a means for the user to modify the displayed optimal schedule;

[2038] A means for the management server to notify all participants of the final approved schedule;

[2039] A system including:

[2040] (Claim 2)

[2041] 10. The system of claim 1, wherein schedule data entry is performed via a web interface and data is submitted using HTTP POST requests.

[2042] (Claim 3)

[2043] 2. The system of claim 1, wherein the work schedules and priorities of the participants are taken into consideration when analyzing the optimal schedule.

[2044] "Application Example 1"

[2045] (Claim 1)

[2046] a means for inputting participant schedule data;

[2047] means for transmitting the input schedule data to a management server;

[2048] The management server uses the generated AI model to analyze schedule data and propose optimal schedules;

[2049] means for transmitting the proposed optimum schedule to a terminal and displaying it;

[2050] A means for proposing an optimal delivery schedule taking into account the delivery person's schedule data;

[2051] A system including:

[2052] (Claim 2)

[2053] 10. The system of claim 1, wherein schedule data is entered via a web interface and a smartphone application.

[2054] (Claim 3)

[2055] 2. The system of claim 1, wherein the work schedules and priorities of participants and delivery personnel are taken into consideration when analyzing the optimal schedule.

[2056] "Example 2: Combining Emotion Engines"

[2057] (Claim 1)

[2058] a means for inputting participant schedule data;

[2059] A means for checking the consistency of entered schedule data;

[2060] means for transmitting the schedule data whose consistency has been confirmed to the management server;

[2061] The management server uses the generated AI model to analyze schedule data and propose optimal schedules;

[2062] means for recognizing a user's emotional state using an emotion engine and generating feedback based on the analysis results;

[2063] a means for transmitting the proposed optimum schedule and feedback to a terminal and displaying the same;

[2064] A system including:

[2065] (Claim 2)

[2066] 10. The system of claim 1, wherein the schedule data is entered via a web interface.

[2067] (Claim 3)

[2068] 2. The system of claim 1, wherein the work schedules and priorities of the participants are taken into consideration when analyzing the optimal schedule.

[2069] "Application example 2 when combining emotion engines"

[2070] (Claim 1)

[2071] a means for inputting participant schedule data;

[2072] means for transmitting the input schedule data to a management server;

[2073] The management server uses the generated AI model to analyze schedule data and propose optimal schedules;

[2074] means for transmitting the proposed optimum schedule to a terminal and displaying it;

[2075] a means for providing feedback, the means including an emotion engine for analyzing the user's emotion data;

[2076] A system including:

[2077] (Claim 2)

[2078] 10. The system of claim 1, wherein the schedule data is entered via a web interface.

[2079] (Claim 3)

[2080] 2. The system of claim 1, wherein the work schedules and priorities of the participants are taken into consideration when analyzing the optimal schedule. [Explanation of symbols]

[2081] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting participant schedule data; means for transmitting the input schedule data to a management server; The management server uses the generated AI model to analyze schedule data and propose optimal schedules; means for transmitting the proposed optimum schedule to a terminal and displaying it; A system including:

2. 10. The system of claim 1, wherein the schedule data is entered via a web interface.

3. 2. The system of claim 1, wherein the work schedules and priorities of the participants are taken into consideration when analyzing the optimal schedule.

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