system

Abstract

To provide a system that can efficiently manage order receipt and activation flows and prevent activation delays and increases in sales man-hours. [Solution] The system includes: a means for a user to input order information from a terminal; a means for a server to receive the order information and save it in a database; a means for the server to verify the order information and, if there are any deficiencies, generate an error message and send it to the terminal; a means for the terminal to display the error message to the user; a means for the user to check the error message and make any necessary corrections; a means for the server to automatically generate an adjustment request based on the order information without any deficiencies and send it to the relevant parties; a means for the server to receive responses from the relevant parties and save them in a database to verify duplications and inconsistencies; a means for the server to generate a summary result after verification is complete; a means for the server to notify the user of the adjustment result and receive final confirmation from the user; and a means for the server to share the final data with the relevant parties and proceed with preparations for opening.

Description

[Technical Field]

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

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

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

[0004] In the current order and activation flow for data-based products, many errors are likely to occur when sales personnel exchange line arrangement information and site information with PAM, customers, system engineers, and other schedulers, resulting in delays in the activation flow and increased sales workloads. These issues are likely to occur in multiple steps, such as entering order information, verifying it, notifying errors, sending adjustment requests, collecting and summarizing responses, and final confirmation, which reduces overall work efficiency and reduces customer satisfaction. Therefore, an efficient and reliable system to solve these problems is needed. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system that combines multiple methods. Specifically, the system includes a server that receives order information entered by a user from a terminal, stores it in a database, verifies it, and checks for any errors. If an error is found, an error message is sent to the terminal, prompting the user to correct the error. If no error is found, the server automatically generates an adjustment request based on the information required for each product and sends it to each relevant party. Furthermore, the server collects responses from the relevant parties, stores them in a database, verifies them, and aggregates them. After requesting final confirmation from the user, the server shares the final data with the relevant parties and, upon receiving OK, begins preparations for service activation. This series of processes efficiently manages the order and activation flow, preventing delays in activation and increased sales workloads.

[0006] "User" refers to a person who operates the system and performs various operations such as inputting and confirming order information.

[0007] "Terminal" refers to a device operated by a user to input order information and receive notifications from the system. Examples include personal computers and smartphones.

[0008] "Server" refers to the central computer system that receives, stores, and validates order information, generates and sends adjustment requests, collects responses, aggregates data, and receives final confirmation.

[0009] "Database" refers to an information management system for storing and managing order information, adjustment requests, responses from each party, etc.

[0010] "Order information" refers to information entered by the user regarding an order for a data-based product. Examples include the customer name, product name, desired activation date, and required line information.

[0011] An "error message" refers to a message that is sent to notify the user if there is an error in the order information.

[0012] An "adjustment request" is a request automatically generated by the server to request line arrangements, site information, etc. This includes specific request content to be sent to customers and internal stakeholders.

[0013] "Response" refers to the response information provided by each party (customer, PAM, SE, production scheduler, etc.) in response to an adjustment request.

[0014] "Aggregated Results" refers to the final data results generated after the server consolidates responses from each participant and verifies for duplicates and inconsistencies.

[0015] "Final confirmation" refers to an operation by which the user confirms and approves the content of the adjustment results notified to the user by the server.

[0016] "Opening preparation" refers to the process of opening the line, which begins after all responses and confirmations have been completed and the system shares the final information with the relevant parties. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system for smoothly sharing and processing information regarding orders and activation of data-related products, and achieves efficient data processing by combining multiple elements.

[0039] 1. User enters and submits order information

[0040] The user uses a terminal to input order information. The input information includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server, which then stores the received data in a database in the appropriate format.

[0041] 2. The server validates the order information and checks for errors

[0042] The server retrieves order information from the database and verifies it for any errors. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. The user checks the error message, makes any necessary corrections, and then resubmits the data.

[0043] 3. Automatic generation and submission of adjustment requests

[0044] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information, site information, etc. The server then sends these adjustment requests to all parties involved (customer, system engineer, production scheduler) via email or messenger.

[0045] 4. Collecting responses from stakeholders and managing data

[0046] Once the responses from each participant are sent to the server, the server receives them and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary and prepares it for final confirmation.

[0047] 5. Final confirmation by the user and preparation for opening

[0048] The server notifies the user of the results of the calculation. The user checks the results through their terminal and sends OK if there are no problems. The server then shares the final data with all parties involved and begins preparations for launch. This completes all steps, ensuring a smooth launch of the data-based product.

[0049] Specific examples

[0050] For example, when a user (salesperson) places an order for a high-speed data line for ABC Corporation, the following procedure is carried out.

[0051] 1. The user enters order information from the terminal

[0052] Customer Name: ABC Co., Ltd.

[0053] Product: High-speed data line

[0054] Desired opening date: December 1, 2023

[0055] Required line information: 1Gbps

[0056] The terminal sends this information to the server.

[0057] 2. The server verifies the order information

[0058] The server saves the order information in a database and verifies it for any errors.

[0059] If there is a problem, an error message is generated and sent to the terminal.

[0060] 3. Automatic generation and submission of adjustment requests

[0061] If there are no errors, the server generates the following reconciliation request and sends it to each party:

[0062] Customer: Please provide the site information required for activation.

[0063] SE: Please check the line arrangement details and proceed with preparations.

[0064] Scheduler: Please adjust the schedule.

[0065] 4. Response collection and data management

[0066] The server receives responses from each party and stores them in a database.

[0067] The server verifies the response data and generates aggregated results.

[0068] 5. Final checks and preparations for opening

[0069] The server notifies the user of the results of the calculation, and the user sends OK.

[0070] The server shares the final data with the relevant parties, and preparations for opening proceed.

[0071] In this way, this system automates multi-stage information processing and coordination, thereby streamlining the order and activation flow for data-related products.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. Once the input is complete, the terminal sends the information to the server as an HTTP POST request.

[0075] Step 2:

[0076] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and stores it as a new order record in the database. After saving is complete, the server returns a "Data saved successfully" response to the terminal.

[0077] Step 3:

[0078] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[0079] Step 4:

[0080] If the server detects any errors, it will summarize the details of the errors, generate an error message, and send it to the terminal. For example, an error message will be generated if a required field is not filled in or if the date format is incorrect.

[0081] Step 5:

[0082] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[0083] Step 6:

[0084] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[0085] Step 7:

[0086] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The adjustment request includes details of the information to be provided and the actions to be taken.

[0087] Step 8:

[0088] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[0089] Step 9:

[0090] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[0091] Step 10:

[0092] The server notifies the user of the tally results for final confirmation. The user checks the tally results through their terminal and sends OK if there are no problems.

[0093] Step 11:

[0094] The server receives the OK from the user and shares the final data with all parties involved. The shared information includes the adjustment results, the launch date, and other necessary information. The server then proceeds with the launch preparations and completes the entire system process.

[0095] Example 1

[0096] 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."

[0097] Existing order and activation management systems have the problem that the input of order information, error checking, and information sharing and coordination with related parties are cumbersome, resulting in human error and incomplete communication. Furthermore, while there is a demand for more efficient automatic generation and processing of adjustment requests, there is a lack of effective means to achieve this. Furthermore, there is also the issue of a heavy burden on users due to insufficient support for inputting order information.

[0098] 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.

[0099] In this invention, the server includes: means for a user to input order information from a terminal; means for the terminal to transmit the input data to the server; means for the server to receive the order information and store it in a database; means for the server to verify the order information and, if there is any deficiency, generate an error message and transmit it to the terminal; means for the terminal to display the error message to the user; means for the user to check the error message and make any necessary corrections; means for the server to automatically generate an adjustment request based on the order information without any deficiency and transmit it to the relevant parties; means for the server to receive responses from the relevant parties, store them in a database and verify duplications and inconsistencies; means for the server to generate a summary result after verification is complete; means for the server to notify the user of the adjustment result and receive final confirmation from the user; means for the server to share the final data with the relevant parties and proceed with preparations for opening; and means for supporting the input of order information using a generative AI model. This enables improved efficiency and accuracy of the entire order and opening management process.

[0100] A "user" is an individual or company employee who uses this system to input order information and perform various operations.

[0101] A "terminal" is an information input device used by a user, such as a computer, tablet, or smartphone.

[0102] "Server" means the central computer system that processes and stores order information and related data and manages instructions.

[0103] "Order information" is order-related data such as the customer name, product name, desired activation date, and required line information entered by the user.

[0104] A "database" is an information storage system for saving and managing order information and responses from each party.

[0105] An "error message" is a message that the server generates when there is a problem with the order information and notifies the user of the problem.

[0106] An "adjustment request" is information that is automatically generated by the server based on complete order information and contains necessary information and instructions to be sent to each party involved.

[0107] "Related parties" are people or positions such as customers, system engineers, and schedulers who are involved in order information or adjustment requests.

[0108] A "response" is information provided by each participant in response to a coordination request from the server.

[0109] "Aggregated Results" means the final statistical data or report generated by the Server after verifying and aggregating the responses from Participants.

[0110] A "generative AI model" is a machine learning model used to assist in the input of order information and for data processing.

[0111] "Input assistance" is a function that uses generative AI models to enable users to input order information accurately and quickly.

[0112] This invention aims to smoothly share and process information related to the order and activation of data-based products by having users input order information using a terminal and having that information managed and processed by a server. Specific embodiments of this invention are described below.

[0113] 1. Enter and submit order information

[0114] The user uses a terminal to enter order information into a dedicated form. Order information includes the customer name, product name, desired activation date, required line information, etc. For example, a user (sales representative) enters the following information to place an order for a high-speed data line.

[0115] Customer Name: ABC Co., Ltd.

[0116] Product: High-speed data line

[0117] Desired opening date: December 1, 2023

[0118] Required line information: 1Gbps

[0119] The user checks the input and clicks the send button. The device converts this information into JSON format and sends it to the server.

[0120] 2. Receiving and storing order information

[0121] The server receives the order information sent from the terminal and stores it in a database. The database can be a general relational database such as MySQL (registered trademark) or PostgreSQL. The server checks the integrity of the data and performs an initial check.

[0122] 3. Validate order information and generate error messages

[0123] The server verifies the order information stored in the database. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. For example, if the desired activation date is in the past, the server generates an error message stating, "The desired activation date must be a future date."

[0124] 4. Automatic generation and submission of adjustment requests

[0125] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. The adjustment requests include information on line arrangements and site information. The server then sends these adjustment requests to the relevant parties via email or messenger app. For example, the following prompts can be used:

[0126] Customers are asked to provide the site information necessary for activation.

[0127] To the system engineer, "Please confirm the details of the line arrangement and proceed with the preparations."

[0128] To the scheduler, "Please adjust your schedule."

[0129] 5. Response collection and data management

[0130] The server receives the responses sent by the parties in response to the adjustment request and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary result. For example, it generates a summary result saying, "Responses from all parties have been collected. Adjustment is complete."

[0131] 6. Final confirmation by the user and preparation for opening

[0132] The server notifies the user of the generated aggregated results. The user checks the results through their terminal and sends OK if there are no problems. The server receives final confirmation from the user and shares the final data with the relevant parties. This allows preparations for launch to proceed and ensures a smooth launch of data-related products.

[0133] Using generative AI models

[0134] Furthermore, the server uses a generative AI model to assist with input of order information. This AI model predicts and auto-completes input content, reducing the burden on users.

[0135] As described above, the present invention combines the elements of a server, terminal, and generative AI model to improve the efficiency and accuracy of the entire order and activation management process.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] The user enters and submits order information from the terminal. The user opens a dedicated form screen on the terminal and enters the customer name, product name, desired activation date, and necessary line information. An example of input is as follows:

[0139] Customer name: General company

[0140] Product: High-speed data line

[0141] Desired opening date: December 1, 2023

[0142] Required line information: 1Gbps

[0143] After completing the input, the user clicks the send button, which causes the device to convert the information into JSON format and send it to the server.

[0144] Input: Customer name, product name, desired activation date, necessary line information

[0145] Output: Order information converted to JSON format

[0146] Step 2:

[0147] The server performs initial validation of the received data and saves it in the database. The server receives the order information in JSON format sent from the terminal and performs the following initial validation:

[0148] Are all required fields filled in?

[0149] Is the format correct?

[0150] Once the initial validation is complete, the server stores the order information in a database.

[0151] Input: Order information in JSON format

[0152] Output: Order information stored in the database

[0153] Step 3:

[0154] The server performs detailed verification of the stored order information. The specific verification items are as follows:

[0155] Is the customer name formatted correctly?

[0156] Is the desired activation date in the past?

[0157] Does the product name match what is registered in the system?

[0158] If an error is found, the server generates an error message and sends it to the terminal. Example of an error message: "The desired activation date must be a future date."

[0159] Input: Order information stored in the database

[0160] Output: Error message (if any)

[0161] Step 4:

[0162] The terminal displays an error message to the user, and the user makes the necessary corrections. The terminal displays the error message received from the server to the user. The user makes the necessary corrections according to the displayed error message and resubmits the order information.

[0163] Input: Error message

[0164] Output: Corrected order information

[0165] Step 5:

[0166] The server verifies the corrected order information again, and if there are no problems, it automatically generates an adjustment request. The server receives the order information again and verifies it again. After confirming that there are no errors, it automatically generates an adjustment request like the one below:

[0167] For customers, please provide the site information necessary for activation.

[0168] For system engineers, please check the details of the line arrangement and proceed with the preparations.

[0169] For schedulers, "Please adjust your schedule."

[0170] The generated adjustment request is sent to each party via email or messenger app.

[0171] Input: Revalidated Order Information

[0172] Output: Adjustment requests sent to each party

[0173] Step 6:

[0174] The server receives the responses from each participant, stores them in a database, and verifies them. Each participant provides a response in response to the reconciliation request, which the server receives. The received responses are stored in a database and the following verifications are performed:

[0175] Check whether the answers match

[0176] Formatting accuracy check

[0177] For example, if the scheduler responds, "Installation is scheduled for December 1, 2023," the server stores and validates this.

[0178] Input: Response data from stakeholders

[0179] Output: Validated database entries

[0180] Step 7:

[0181] The server uses the verified data to generate a summary result and notify the user. The server aggregates all the verified data and generates a result stating, "Responses from all parties have been received. Reconciliation is complete." The user is notified of this result.

[0182] Input: Validated response data

[0183] Output: User notification

[0184] Step 8:

[0185] The user makes a final confirmation, and the server shares the final data to begin preparations for launch. The user checks the results of the survey on their device and clicks the OK button if there are no problems. The server receives the user's final confirmation and shares the final data with all parties again, thus beginning preparations for launch.

[0186] Input: User's final confirmation

[0187] Output: Final data shared with stakeholders

[0188] The above is the flow of processing for this system's program, showing the specific operations and data input / output at each step.

[0189] (Application example 1)

[0190] 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."

[0191] Managing and processing order information at logistics centers is complex, and doing it manually is time-consuming and prone to errors. Additionally, correcting errors and sharing information among stakeholders is cumbersome, resulting in reduced work efficiency. Furthermore, there is a need for a function that can quickly verify order information and automatically generate and send adjustment requests. To solve these issues, an efficient and reliable system is required.

[0192] 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.

[0193] In this invention, the server

[0194] A means for a user to input order information from a terminal;

[0195] A means for transmitting input data from the terminal to a server;

[0196] A means for the server to receive the order information and store it in a database;

[0197] A means for the server to verify the order information and, if there is any defect, to generate an error message and send it to the terminal;

[0198] means for the terminal to display an error message to the user;

[0199] A means for the user to view error messages and make any necessary corrections;

[0200] A means for the server to automatically generate an adjustment request based on complete order information and transmit it to the relevant parties;

[0201] a means by which the server receives responses from participants, stores them in a database and checks for duplications and inconsistencies;

[0202] A means for the server to generate a summary result after the verification is completed;

[0203] means for the server to notify the user of the adjustment result and receive a final confirmation from the user;

[0204] The server will share the final data with the relevant parties to prepare for the launch.

[0205] A means to send an email to the customer if the order information is correct;

[0206] This will enable the efficient handling of order information at the logistics center, the prompt notification and correction of error messages, and the smooth automatic generation and transmission of adjustment requests to relevant parties.

[0207] A "terminal" is a device through which a user inputs order information and communicates.

[0208] A "server" is a central computer system that receives order information, stores it in a database, and performs a series of processes such as validation, generating error messages, and automatically generating adjustment requests.

[0209] "Order information" is information including data items such as customer name, product name, desired activation date, and necessary line information that are input by the user from the terminal.

[0210] "Database" refers to data storage for systematically storing and managing order information received by the server.

[0211] An "error message" is a message that is generated when the server finds an error in the order information when verifying it, and is sent to the terminal to prompt the user to make corrections.

[0212] An "adjustment request" is a request for specific tasks or information that is automatically generated by the server based on complete order information and sent to the relevant parties.

[0213] "Stakeholders" are people with an interest in the order processing and opening preparations, such as customers, engineers, and schedulers.

[0214] A "response" is information or a response provided by a participant in response to a coordination request from the server.

[0215] "Aggregated Results" means the comprehensive results data generated by the Server after verifying and processing the Participant's responses.

[0216] "Final confirmation" is an action in which the user confirms the counting results notified by the server and makes a final decision.

[0217] "Opening preparation" is the process in which the server shares the final data with the relevant parties after final confirmation and all preparation work is carried out.

[0218] "Mail" is an email that the server sends to the customer if the order information is correct.

[0219] This invention provides a system that streamlines the management and processing of order information at a logistics center. Specifically, a user inputs and transmits order information using a terminal, and the server receives the information and automatically performs various verifications and processing.

[0220] The server can use the following hardware and software: The hardware includes a database server for receiving and storing order information, and an application server for executing processing. The software used is Flask (a Python web framework), SQLite (a database management system), and an SMTP library (for sending emails).

[0221] The user enters order information on a device such as a smartphone or PC and presses the send button. This information is sent to the server and first saved in a database. The server verifies the saved information and, if there are any errors, generates an error message and sends it to the terminal. The terminal displays this error message to the user, who then makes the necessary corrections and resubmits the order.

[0222] For order information that is complete, the server automatically generates an adjustment request and sends it to the relevant parties via email or messenger app. The relevant parties respond with the necessary information based on the adjustment request, and the server receives it and stores it in a database. It verifies whether there are any duplicates or inconsistencies and generates a summary. The server then notifies the user of the summary results, who then performs a final confirmation. Once the final confirmation is complete, the server shares the final data with the relevant parties and begins preparations for launch.

[0223] For example, consider a scenario where a user wants to enter a new order. The user enters the order information as follows:

[0224] "Please enter new order information in the following format: Customer Name, Product Name, Order Date, and required information. For example: ABC Co., Ltd., Logistics Solutions, 2023-12-15, 5 Trucks."

[0225] This system will significantly improve the efficiency of order information handling at logistics centers, speeding up work and reducing errors. Furthermore, by utilizing a generative AI model, prompts will also be standardized, reducing the burden on users.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user enters and sends order information from the terminal. The user enters the customer name, product name, order date, and other necessary information into the input form on the terminal, and sends the data to the server. The input data format uses data structures such as JSON or XML. When the user presses the send button, this input data is sent to the server.

[0229] Step 2:

[0230] The server receives the order information and saves it in the database. In this step, the server analyzes the received data and saves the contents in the SQLite database. To process the data, it converts the JSON format data into a format that can be applied to SQL queries and inserts it into the database. The input is the order information sent from the terminal, and the output is the order information recorded in the database.

[0231] Step 3:

[0232] The server validates the order information and generates an error message if there are any errors and sends it to the terminal. The server validates the order information for the following items: presence of required fields, data format, and validity of date (must be in the future). If an error is found, the server generates an appropriate error message and sends it to the terminal. The input is the order information stored in the database and the output is the error message.

[0233] Step 4:

[0234] The terminal displays an error message to the user, who then makes the necessary corrections. The user checks the error message displayed on the terminal screen and corrects any deficiencies. The corrected order information is then sent back to the server. The input is the error message sent from the server, and the output is the corrected order information.

[0235] Step 5:

[0236] The server automatically generates adjustment requests based on complete order information and sends them to the relevant parties. Based on the verified order information, the server generates the necessary adjustment requests for each relevant party (customer, engineer, scheduler) and sends them via email or messenger. Here, the generative AI model creates prompt text. The input is complete order information, and the output is the adjustment requests sent to each relevant party.

[0237] Step 6:

[0238] The server receives responses from the participants, stores them in a database, and verifies whether there are any duplicates or inconsistencies. The server stores the responses received from the participants in a database and verifies whether there are any duplicates or inconsistencies. The input is the response data from the participants, and the output is the response data stored in the database and the verification results.

[0239] Step 7:

[0240] The server generates the aggregated results after the verification is complete and notifies the user. The server aggregates the response data from the participants, generates the aggregated results in text or graph format based on the verification results, and notifies the user. The input is the response data from the participants, and the output is the generated aggregated results.

[0241] Step 8:

[0242] The user makes a final confirmation, and the server shares the final data with the relevant parties to begin preparations for launch. The user checks the tabulated results on their device and makes a final confirmation. After OK is sent, the server shares the final data with the relevant parties to begin preparations for launch. The input is the final confirmation from the user, and the output is the sharing of the final data.

[0243] This series of processing flows will make the management and processing of order information at the logistics center more efficient, accurate, and quick.

[0244] 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.

[0245] This invention further improves the user experience by combining a system that streamlines the order and activation flow for data-based products with an emotion engine that recognizes user emotions. This system comprehensively manages the entry of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[0246] 1. User enters and submits order information

[0247] The user uses a terminal to input order information. The input form includes information such as the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. In addition, the emotion engine recognizes the user's emotions while they are inputting, and this information is also sent to the server.

[0248] 2. The server validates the order information and checks for errors

[0249] The server receives the order information and stores it in a database in an appropriate format.The server then retrieves the order information from the database and verifies it for any errors.If an error is found, the emotion engine detects the user's emotion and generates an error message in an appropriate tone, which is sent to the terminal.

[0250] 3. Customize and modify error messages

[0251] The terminal displays an error message to the user. The error message is customized by the emotion engine. For example, if the terminal recognizes that the user is in a hurry, a message for prompt action is displayed. The user checks the error message, makes any necessary corrections, and resends the message.

[0252] 4. Automatic generation and submission of adjustment requests

[0253] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties (customer, system engineer, production scheduler). The emotion engine conveys the requests in a tone that takes the relevant parties into consideration.

[0254] 5. Collecting and managing responses from stakeholders

[0255] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication or inconsistency.

[0256] 6. Generation and notification of aggregated results

[0257] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user in a tone that corresponds to their emotion. For example, if the server detects that the user is anxious, it sends a reassuring message.

[0258] 7. Final confirmation by the user and preparation for opening

[0259] The server notifies the user of the results. The user checks the results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with all parties involved, and begins preparations for opening.

[0260] Specific examples

[0261] For example, when a user (salesperson) places an order for a high-speed data line for a certain company, the following procedure is carried out.

[0262] 1. The user enters order information from the terminal

[0263] Customer Name: A Co., Ltd.

[0264] Product: High-speed data line

[0265] Desired opening date: December 1, 2023

[0266] Required line information: 1Gbps

[0267] The emotion engine analyzes facial expressions and voice input and recognizes that the user is in a hurry. The device then sends the information to the server.

[0268] 2. The server verifies the order information

[0269] The server saves the order information in a database and verifies it for any errors.

[0270] If there is a problem, the emotion engine generates an error message in a tone such as "Please correct it promptly" and sends it to the terminal.

[0271] 3. Viewing and correcting error messages

[0272] The terminal displays an error message to the user, who then makes the correction.

[0273] 4. Automatic generation and submission of adjustment requests

[0274] If there are no errors, the server generates an adjustment request such as "I apologize for bothering you during your busy schedule, but please arrange for the line and adjust the schedule," and sends it to each party involved.

[0275] 5. Collect and verify responses

[0276] The server receives responses from each party and stores them in a database.

[0277] The server verifies the response data and generates aggregated results.

[0278] 6. Final confirmation notice

[0279] The server notifies the user of the aggregated results, and the emotion engine generates a message such as, "Please confirm that there are no problems with the results."

[0280] 7. Final checks and preparations for opening

[0281] User sends OK.

[0282] The server shares the final information with the relevant parties and preparations for opening proceed.

[0283] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. At the same time, the emotion engine installed in the terminal analyzes the user's facial expression and tone of voice to detect the user's current emotion. Once the input is complete, the terminal sends the information and emotion data to the server.

[0287] Step 2:

[0288] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and saves it as a new order record in the database. The emotion data is also saved at the same time. Once the saving is complete, the server returns a "Data saving successful" response to the device.

[0289] Step 3:

[0290] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[0291] Step 4:

[0292] If the server detects any defects, it summarizes the details of the defects and generates an error message. The emotion engine analyzes the recorded user emotion data and customizes the error message with a tone that corresponds to the user's emotion. For example, if it detects that the user is in a hurry, it generates an error message that reads, "We apologize for the inconvenience, but the following items need to be corrected." It then sends the error message to the device.

[0293] Step 5:

[0294] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[0295] Step 6:

[0296] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[0297] Step 7:

[0298] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The emotion engine recognizes the emotions of the parties involved and sends the adjustment request in an appropriate tone.

[0299] Step 8:

[0300] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[0301] Step 9:

[0302] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[0303] Step 10:

[0304] The server notifies the user of the results for final confirmation. The emotion engine analyzes the user's current emotions and customizes the notification message with the most appropriate tone. For example, if the user is feeling anxious, the notification might say, "Don't worry, all the information has been collected accurately."

[0305] Step 11:

[0306] The user performs a final confirmation via their terminal. Once the user sends OK, the server receives this and shares the final data with all parties involved. This allows preparations for opening to proceed, and the entire system process is completed.

[0307] In this way, this system, which incorporates an emotion engine, improves the user experience and enables the order and activation flow to proceed smoothly.

[0308] Example 2

[0309] 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."

[0310] In the conventional order and activation flow, users often received error messages, which caused stress and frustration, resulting in a poor user experience. Furthermore, smooth communication was difficult because requests for adjustments to stakeholders were mechanical. Furthermore, there was a tendency for responses from stakeholders to be duplicated or contradictory, which reduced the efficiency of activation preparations.

[0311] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving order information and saving it in a database, means for verifying the order information and, if there is an error, generating an error message and sending it to the terminal, and means for customizing the tone of the error message or adjustment request based on emotion data. This makes it possible to generate and send error messages and adjustment requests that take the user's emotions into consideration, improving the user experience.

[0312] "User" refers to a person who uses this system to input order information.

[0313] "Terminal" refers to a device through which a user inputs order information and communicates with a server.

[0314] "Order information" refers to data such as the product name, customer name, desired activation date, and required line information that the user inputs via the terminal.

[0315] "Server" refers to the central processing unit that stores received order information in a database and verifies and processes the information.

[0316] "Database" refers to a storage device for storing order information and responses from related parties, and for retrieving and verifying data as necessary.

[0317] An "error message" refers to a notification message that is generated by the server and sent to the user via the terminal when there is an error in the order information.

[0318] "Emotion engine" refers to the algorithms and hardware / software that recognize the user's emotions and generate and process information based on them.

[0319] An "adjustment request" refers to a message that is generated by the server based on complete order information and sent to relevant parties to request the arrangement of merchandise or schedule adjustments.

[0320] "Stakeholders" refers to customers, engineers, schedulers, etc. involved in the order and activation flow.

[0321] "Aggregated results" refers to the final result data obtained by aggregating and verifying responses from stakeholders.

[0322] "Opening preparation" refers to the overall opening procedure for a product, which is carried out after the server notifies all relevant parties after final confirmation of the order information.

[0323] "Tone" refers to a manner for adjusting the presentation of a message based on the user's emotions detected by the emotion engine.

[0324] "Verification" refers to the process in which the server checks for any deficiencies or inconsistencies in the order information and responses from the parties involved.

[0325] "Notification" refers to the act of a server sending a message or information to a user or interested party.

[0326] This invention is a system that improves the efficiency of the order and activation flow for data-related products and further enhances the user experience by combining an emotion engine that recognizes user emotions. This system comprehensively manages the input of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[0327] Hardware and software used

[0328] Terminal: The device (PC, smartphone, tablet, etc.) through which the user enters order information

[0329] Server: A central processing unit (e.g., cloud server, on-premise server) for storing, validating and processing order information.

[0330] Database: A storage device for storing order information and responses from stakeholders (a relational database such as MySQL or PostgreSQL)

[0331] Emotion engine: Algorithms and APIs (e.g., Amazon Rekognition, Google® Cloud Vision API) to recognize user emotions and generate error messages or adjustment requests in an appropriate tone.

[0332] System Operation Overview

[0333] The user uses a terminal to input order information. The input form includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. The emotion engine recognizes the user's facial expressions and voice as they are entering the information, and the emotion data is also sent to the server.

[0334] The server stores the received order information in a database in an appropriate format. The server then retrieves the order information from the database and verifies it for any errors. If an error is found, the server uses an emotion engine to generate an error message based on the user's emotions and sends it to the terminal. The terminal displays the error message to the user, and after the user makes the necessary corrections, the message is resent.

[0335] Once the order information is complete and complete, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. At this time, the emotion engine conveys the requests in a tone that is considerate to the relevant parties. The relevant parties send their responses to the adjustment requests to the server, which receives them and stores them in a database. The server then verifies the response data for duplication and inconsistencies.

[0336] After the verification is complete, the server generates the aggregated results and uses an emotion engine to notify the user in a tone that corresponds to their emotion. For example, if the user is anxious, a message that provides reassurance will be sent. Finally, the user checks the aggregated results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch.

[0337] Examples of concrete examples and prompts

[0338] For example, when a user places an order for a high-speed data line for a company, the following procedure is performed: The user inputs the following order information into the terminal:

[0339] Customer Name: A Co., Ltd.

[0340] Product: High-speed data line

[0341] Desired opening date: December 1, 2023

[0342] Required line information: 1Gbps

[0343] The emotion engine analyzes facial expressions and voices when inputting information and recognizes that the user is in a hurry. The device then sends the input information to the server.

[0344] The server saves the order information in a database and verifies it for any errors. If there are any errors, the emotion engine generates an error message in a tone such as "Please correct this as soon as possible" and sends it to the terminal.

[0345] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1: User enters and submits order information

[0348] Description: The user uses the terminal to enter order information (customer name, product name, desired activation date, required line information, etc.).

[0349] Input: User-entered order information

[0350] Data processing / calculation: Convert the data from the input form into an appropriate format and recognize the user's emotions using an emotion engine.

[0351] Output: Formatted order information and sentiment data

[0352] Specific operation: Enter information into a browser form, retrieve data using JavaScript (registered trademark), and send it to the server as an HTTP POST request. Also, collect facial and voice data using a webcam and microphone and send it to the emotion engine.

[0353] Step 2: The server receives the order information and stores it in the database

[0354] Description: The server receives the order information and stores it in a database.

[0355] Input: Order information and emotion data sent from the terminal

[0356] Data Processing / Calculation: Generate and execute SQL insert statements to save order information to the database in the appropriate format.

[0357] Output: Order information stored in the database

[0358] Specific operation: Extracts data from an HTTP POST request, generates an SQL insert statement, and inserts the data into a database (e.g., MySQL or PostgreSQL).

[0359] Step 3: The server validates the order information and checks for errors

[0360] Description: The server retrieves order information from the database and verifies it for accuracy.

[0361] Input: Order information stored in the database

[0362] Data processing / calculation: Data is obtained using an SQL query and verified using a validation algorithm to ensure there are no errors.

[0363] Output: Error message or complete order information

[0364] What it does: Retrieves data from the database using SQL queries, performs validation checks in code, and generates error messages based on the emotion engine if there are any errors.

[0365] Step 4: Generate and send an error message

[0366] Description: If the server is faulty, it generates an error message based on the emotion engine and sends it to the terminal.

[0367] Input: Verification results (with imperfections) and emotion data

[0368] Data processing / calculation: Generates error messages in an appropriate tone based on emotional data.

[0369] Output: Error message displayed on the terminal

[0370] Specific operation: Calls the emotion engine API, applies the error message generation algorithm, and sends the error message as an HTTP response.

[0371] Step 5: View and fix error messages

[0372] Description: The terminal displays an error message to the user, who then makes the necessary corrections.

[0373] Input: Error message sent to the terminal

[0374] Data processing / calculation: Display an error message in the relevant form field and resubmit once corrections are made.

[0375] Output: Revised order information

[0376] Specific operation: An error message is displayed in a pop-up window or on a form using JavaScript, the user corrects the error, and the data is sent to the server again.

[0377] Step 6: Automatically generate and submit adjustment requests

[0378] Description: The server automatically generates an adjustment request based on complete order information and sends it to the relevant parties.

[0379] Input: Complete order information and emotion data

[0380] Data processing / calculation: Automatically generate adjustment requests based on order information and customize the tone of the message with an emotion engine.

[0381] Output: Coordination request message to stakeholders

[0382] Specific behavior: Applying automated generation rules based on product information to generate request content for stakeholders, and sending emails and notifications with the appropriate tone using an emotion engine.

[0383] Step 7: Collect and manage stakeholder responses

[0384] Description: Participants send responses to reconciliation requests to the server, which receives them and stores them in a database.

[0385] Input: Response data from stakeholders

[0386] Data processing / calculation: Receive the response data, store it in a database in the appropriate format, and apply algorithms to check for duplicates and inconsistencies.

[0387] Output: Verified response data

[0388] Specific operation: Receive response data via HTTP POST request and save it to the database using SQL insert statements. Check for duplicates and inconsistencies within the program.

[0389] Step 8: Generate and notify the results

[0390] Description: The server generates the aggregated results after the verification is completed, and notifies the user with a tone according to their emotion using the emotion engine.

[0391] Input: Verified response data and sentiment data

[0392] Data processing / calculation: Aggregate response data and generate summary results in text or graph format. Generate notification messages with customized tone using the emotion engine.

[0393] Output: Aggregation results notified to the user

[0394] What it does: It applies aggregation algorithms to generate text and graphs, uses an emotion engine to adjust tone, and notifies via HTTP response and email.

[0395] Step 9: Final confirmation by the user and preparation for opening

[0396] Description: The server notifies the user of the counting results, and the user performs a final check. If there are no problems, the server sends OK.

[0397] Input: Final confirmation from the user (OK)

[0398] Data processing / calculation: Share the final information with the relevant parties and make schedules and arrangements to proceed with preparations for opening.

[0399] Output: Progress of opening preparations

[0400] Specific operation: The user presses the OK button and notifies the server via an HTTP POST request. The server then sends the final information to the relevant parties via email or notification, and generates an implementation schedule.

[0401] As a result, the overall system processing is managed appropriately, improving the user experience.

[0402] (Application example 2)

[0403] 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."

[0404] In the conventional order and activation flow system, communication with users was standardized, making it difficult to respond in a way that took into account the user's feelings and circumstances. This resulted in frustration and stress when users entered order information, as well as inconvenience caused by requests for adjustments and receiving error messages. This led to lower customer satisfaction and process delays.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0406] In this invention, the server includes means for recognizing a user's emotion using an emotion engine and generating and providing feedback according to the emotion, means for using the emotion engine to communicate with related parties in a tone that takes the emotion into consideration, and means for the server to send an adjustment request to each related party via email or a messenger app, thereby improving the user experience and enabling a smooth and efficient order and activation flow.

[0407] A "terminal" is a device used by a user to input order information, such as a smartphone or a personal computer.

[0408] The "server" is a central processing unit that receives order information sent from users, stores it in a database, and performs various processes.

[0409] A "database" is an information system managed by a server for storing and managing order information and responses from related parties.

[0410] An "error message" is a message generated by the server to notify the user of any incomplete or incorrect order information.

[0411] The "emotion engine" is an analysis system that recognizes the user's emotions and generates feedback and messages based on those emotions.

[0412] An "adjustment request" is a request that is automatically generated by the server based on complete order information and sent to the relevant parties.

[0413] "Parties" are people or organizations involved in the order process, such as customers, technical personnel, schedulers, etc.

[0414] "Feedback" is information or messages provided in response to a user's input or action.

[0415] "Notification" is the act of sending messages or information from the server to users or related parties.

[0416] "Opening preparation" refers to the preparation work for installing products and starting services based on the final order information.

[0417] This invention is a system that streamlines the order and activation flow for data-related products by combining an emotion engine that recognizes user emotions. This system includes the following components.

[0418] Hardware and Software Configuration

[0419] 1. Terminal

[0420] A device used by users to input order information. Generally, this is a smartphone or PC.

[0421] 2. Server

[0422] This is a central processing unit that receives order information sent from the terminal and stores it in a database. It uses a web framework such as "Flask" to perform server-side processing.

[0423] 3. Database

[0424] This is an information system managed by a server, which stores and manages order information and responses from related parties.

[0425] 4. Emotion Engine

[0426] It is an analysis system for recognizing user emotions. For example, by using "Affectiva's SDK," it can analyze emotions in real time from the user's facial expressions and voice.

[0427] System processing flow

[0428] 1. Enter and submit order information

[0429] The user uses the terminal to input order information. The input form includes the customer name, product name, desired activation date, required line information, etc. While the user is inputting information, the emotion engine analyzes the user's emotions in real time through the terminal's camera and microphone. The terminal then sends the data together with this information to the server.

[0430] 2. Validating order information and generating error messages

[0431] The server saves the received order information in a database. The server then verifies the order information and, if there are any errors, generates an error message and sends it to the terminal. At this time, the error message is generated based on the analysis results of the emotion engine, taking into consideration the user's emotions.

[0432] 3. Automatic generation and submission of adjustment requests

[0433] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. The emotion engine also generates messages in a tone that takes into account the emotions of the relevant parties.

[0434] 4. Collecting and Managing Responses

[0435] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication and inconsistency.

[0436] 5. Generating the aggregated results and notifying the user

[0437] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user with a tone that corresponds to their emotion. Finally, the user sends the results they have confirmed to the server, and the server shares the final information with the relevant parties to begin preparations for launch.

[0438] Examples of concrete examples and prompts

[0439] Specific examples

[0440] "When a user orders a new product from an online store, they enter their order information. An emotion recognition engine analyzes the user's facial expressions and detects that the user is in a hurry. This allows error messages and feedback to be displayed quickly and concisely, improving the user experience."

[0441] Prompt Sentence Examples

[0442] "Can you give me an example of a server-side program written in Python that quickly checks for inaccuracies in the information entered when a user is ordering a new product from an online shopping site and provides appropriate feedback depending on the situation?"

[0443] In this way, the present invention provides a system that improves the user experience and smoothly advances the order and activation flow by combining an emotion recognition engine.

[0444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0445] Step 1:

[0446] The user uses the terminal to input order information. The input form prompts the user to enter information such as the customer name, product name, desired activation date, and necessary line information. The emotion engine analyzes the user's facial expressions and voice while they are entering the information, and acquires emotional data. The terminal then sends the acquired order information and emotional data to the server as an HTTP POST request.

[0447] Input: Customer name, product name, desired activation date, necessary line information, emotion data

[0448] Output: HTTP POST request (order information and emotion data)

[0449] Step 2:

[0450] The server receives the order information and emotion data sent from the terminal and stores them in a database. It verifies that the data has been saved in the correct format. If the server finds any errors, it uses the emotion engine to generate an error message, adjusts the tone, and sends it to the terminal. If there are no errors, it proceeds to the next step.

[0451] Input: HTTP POST request (order information and emotion data)

[0452] Output: Error message or confirmation message

[0453] Step 3:

[0454] The terminal displays the error message received from the server to the user. The emotion engine customizes the tone of the error message to reflect the user's emotions. The user checks the error message, makes any necessary corrections, and resubmits the message. This process is repeated until the error is resolved.

[0455] Input: Error message

[0456] Output: Revised order information

[0457] Step 4:

[0458] After the server verifies the order information and finds no errors, it automatically generates an adjustment request and sends it to the relevant parties. The emotion engine generates messages in a tone that takes into account the emotions of the relevant parties. The message is sent via email or a messenger app.

[0459] Input: Complete order information

[0460] Output: Reconciliation request message

[0461] Step 5:

[0462] Each party sends a response to the reconciliation request to the server, which stores the received responses in a database and verifies the response data for duplication and inconsistency, thereby ensuring data consistency.

[0463] Input: Responses from stakeholders

[0464] Output: Validated answers stored in a database

[0465] Step 6:

[0466] The server generates the aggregated results after the verification is complete. The results are presented in text or graphical format. The emotion engine takes into account the user's emotions and generates a tone-adjusted message to notify the user of the results.

[0467] Input: Validated Answer

[0468] Output: Aggregation results (text or graph format)

[0469] Step 7:

[0470] The user checks the results of the calculations through their terminal. If there are no problems, they send a final confirmation of OK. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch. The emotion engine generates a final confirmation message and notifies the user in a way that takes their emotions into consideration.

[0471] Input: Check the summary results

[0472] Output: Final confirmation (OK) and preparation for opening

[0473] 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.

[0474] 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.

[0475] 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.

[0476] [Second embodiment]

[0477] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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).

[0483] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0488] 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."

[0489] This invention is a system for smoothly sharing and processing information regarding orders and activation of data-related products, and achieves efficient data processing by combining multiple elements.

[0490] 1. User enters and submits order information

[0491] The user uses a terminal to input order information. The input information includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server, which then stores the received data in a database in the appropriate format.

[0492] 2. The server validates the order information and checks for errors

[0493] The server retrieves order information from the database and verifies it for any errors. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. The user checks the error message, makes any necessary corrections, and then resubmits the data.

[0494] 3. Automatic generation and submission of adjustment requests

[0495] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information, site information, etc. The server then sends these adjustment requests to all parties involved (customer, system engineer, production scheduler) via email or messenger.

[0496] 4. Collecting responses from stakeholders and managing data

[0497] Once the responses from each participant are sent to the server, the server receives them and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary and prepares it for final confirmation.

[0498] 5. Final confirmation by the user and preparation for opening

[0499] The server notifies the user of the results of the calculation. The user checks the results through their terminal and sends OK if there are no problems. The server then shares the final data with all parties involved and begins preparations for launch. This completes all steps, ensuring a smooth launch of the data-based product.

[0500] Specific examples

[0501] For example, when a user (salesperson) places an order for a high-speed data line for ABC Corporation, the following procedure is carried out.

[0502] 1. The user enters order information from the terminal

[0503] Customer Name: ABC Co., Ltd.

[0504] Product: High-speed data line

[0505] Desired opening date: December 1, 2023

[0506] Required line information: 1Gbps

[0507] The terminal sends this information to the server.

[0508] 2. The server verifies the order information

[0509] The server saves the order information in a database and verifies it for any errors.

[0510] If there is a problem, an error message is generated and sent to the terminal.

[0511] 3. Automatic generation and submission of adjustment requests

[0512] If there are no errors, the server generates the following reconciliation request and sends it to each party:

[0513] Customer: Please provide the site information required for activation.

[0514] SE: Please check the line arrangement details and proceed with preparations.

[0515] Scheduler: Please adjust the schedule.

[0516] 4. Response collection and data management

[0517] The server receives responses from each party and stores them in a database.

[0518] The server verifies the response data and generates aggregated results.

[0519] 5. Final checks and preparations for opening

[0520] The server notifies the user of the results of the calculation, and the user sends OK.

[0521] The server shares the final data with the relevant parties, and preparations for opening proceed.

[0522] In this way, this system automates multi-stage information processing and coordination, thereby streamlining the order and activation flow for data-related products.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. Once the input is complete, the terminal sends the information to the server as an HTTP POST request.

[0526] Step 2:

[0527] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and stores it as a new order record in the database. After saving is complete, the server returns a "Data saved successfully" response to the terminal.

[0528] Step 3:

[0529] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[0530] Step 4:

[0531] If the server detects any errors, it will summarize the details of the errors, generate an error message, and send it to the terminal. For example, an error message will be generated if a required field is not filled in or if the date format is incorrect.

[0532] Step 5:

[0533] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[0534] Step 6:

[0535] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[0536] Step 7:

[0537] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The adjustment request includes details of the information to be provided and the actions to be taken.

[0538] Step 8:

[0539] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[0540] Step 9:

[0541] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[0542] Step 10:

[0543] The server notifies the user of the tally results for final confirmation. The user checks the tally results through their terminal and sends OK if there are no problems.

[0544] Step 11:

[0545] The server receives the OK from the user and shares the final data with all parties involved. The shared information includes the adjustment results, the launch date, and other necessary information. The server then proceeds with the launch preparations and completes the entire system process.

[0546] Example 1

[0547] 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."

[0548] Existing order and activation management systems have the problem that the input of order information, error checking, and information sharing and coordination with related parties are cumbersome, resulting in human error and incomplete communication. Furthermore, while there is a demand for more efficient automatic generation and processing of adjustment requests, there is a lack of effective means to achieve this. Furthermore, there is also the issue of a heavy burden on users due to insufficient support for inputting order information.

[0549] 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.

[0550] In this invention, the server includes: means for a user to input order information from a terminal; means for the terminal to transmit the input data to the server; means for the server to receive the order information and store it in a database; means for the server to verify the order information and, if there is any deficiency, generate an error message and transmit it to the terminal; means for the terminal to display the error message to the user; means for the user to check the error message and make any necessary corrections; means for the server to automatically generate an adjustment request based on the order information without any deficiency and transmit it to the relevant parties; means for the server to receive responses from the relevant parties, store them in a database and verify duplications and inconsistencies; means for the server to generate a summary result after verification is complete; means for the server to notify the user of the adjustment result and receive final confirmation from the user; means for the server to share the final data with the relevant parties and proceed with preparations for opening; and means for supporting the input of order information using a generative AI model. This enables improved efficiency and accuracy of the entire order and opening management process.

[0551] A "user" is an individual or company employee who uses this system to input order information and perform various operations.

[0552] A "terminal" is an information input device used by a user, such as a computer, tablet, or smartphone.

[0553] "Server" means the central computer system that processes and stores order information and related data and manages instructions.

[0554] "Order information" is order-related data such as the customer name, product name, desired activation date, and required line information entered by the user.

[0555] A "database" is an information storage system for saving and managing order information and responses from each party.

[0556] An "error message" is a message that the server generates when there is a problem with the order information and notifies the user of the problem.

[0557] An "adjustment request" is information that is automatically generated by the server based on complete order information and contains necessary information and instructions to be sent to each party involved.

[0558] "Related parties" are people or positions such as customers, system engineers, and schedulers who are involved in order information or adjustment requests.

[0559] A "response" is information provided by each participant in response to a coordination request from the server.

[0560] "Aggregated Results" means the final statistical data or report generated by the Server after verifying and aggregating the responses from Participants.

[0561] A "generative AI model" is a machine learning model used to assist in the input of order information and for data processing.

[0562] "Input assistance" is a function that uses generative AI models to enable users to input order information accurately and quickly.

[0563] This invention aims to smoothly share and process information related to the order and activation of data-based products by having users input order information using a terminal and having that information managed and processed by a server. Specific embodiments of this invention are described below.

[0564] 1. Enter and submit order information

[0565] The user uses a terminal to enter order information into a dedicated form. Order information includes the customer name, product name, desired activation date, required line information, etc. For example, a user (sales representative) enters the following information to place an order for a high-speed data line.

[0566] Customer Name: ABC Co., Ltd.

[0567] Product: High-speed data line

[0568] Desired opening date: December 1, 2023

[0569] Required line information: 1Gbps

[0570] The user checks the input and clicks the send button. The device converts this information into JSON format and sends it to the server.

[0571] 2. Receiving and storing order information

[0572] The server receives the order information sent from the terminal and stores it in a database. The database can be a common relational database such as MySQL or PostgreSQL. The server checks the integrity of the data and performs initial checks.

[0573] 3. Validate order information and generate error messages

[0574] The server verifies the order information stored in the database. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. For example, if the desired activation date is in the past, the server generates an error message stating, "The desired activation date must be a future date."

[0575] 4. Automatic generation and submission of adjustment requests

[0576] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. The adjustment requests include information on line arrangements and site information. The server then sends these adjustment requests to the relevant parties via email or messenger app. For example, the following prompts can be used:

[0577] Customers are asked to provide the site information necessary for activation.

[0578] To the system engineer, "Please confirm the details of the line arrangement and proceed with the preparations."

[0579] To the scheduler, "Please adjust your schedule."

[0580] 5. Response collection and data management

[0581] The server receives the responses sent by the parties in response to the adjustment request and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary result. For example, it generates a summary result saying, "Responses from all parties have been collected. Adjustment is complete."

[0582] 6. Final confirmation by the user and preparation for opening

[0583] The server notifies the user of the generated aggregated results. The user checks the results through their terminal and sends OK if there are no problems. The server receives final confirmation from the user and shares the final data with the relevant parties. This allows preparations for launch to proceed and ensures a smooth launch of data-related products.

[0584] Using generative AI models

[0585] Furthermore, the server uses a generative AI model to assist with input of order information. This AI model predicts and auto-completes input content, reducing the burden on users.

[0586] As described above, the present invention combines the elements of a server, terminal, and generative AI model to improve the efficiency and accuracy of the entire order and activation management process.

[0587] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0588] Step 1:

[0589] The user enters and submits order information from the terminal. The user opens a dedicated form screen on the terminal and enters the customer name, product name, desired activation date, and necessary line information. An example of input is as follows:

[0590] Customer name: General company

[0591] Product: High-speed data line

[0592] Desired opening date: December 1, 2023

[0593] Required line information: 1Gbps

[0594] After completing the input, the user clicks the send button, which causes the device to convert the information into JSON format and send it to the server.

[0595] Input: Customer name, product name, desired activation date, necessary line information

[0596] Output: Order information converted to JSON format

[0597] Step 2:

[0598] The server performs initial validation of the received data and saves it in the database. The server receives the order information in JSON format sent from the terminal and performs the following initial validation:

[0599] Are all required fields filled in?

[0600] Is the format correct?

[0601] Once the initial validation is complete, the server stores the order information in a database.

[0602] Input: Order information in JSON format

[0603] Output: Order information stored in the database

[0604] Step 3:

[0605] The server performs detailed verification of the stored order information. The specific verification items are as follows:

[0606] Is the customer name formatted correctly?

[0607] Is the desired activation date in the past?

[0608] Does the product name match what is registered in the system?

[0609] If an error is found, the server generates an error message and sends it to the terminal. Example of an error message: "The desired activation date must be a future date."

[0610] Input: Order information stored in the database

[0611] Output: Error message (if any)

[0612] Step 4:

[0613] The terminal displays an error message to the user, and the user makes the necessary corrections. The terminal displays the error message received from the server to the user. The user makes the necessary corrections according to the displayed error message and resubmits the order information.

[0614] Input: Error message

[0615] Output: Corrected order information

[0616] Step 5:

[0617] The server verifies the corrected order information again, and if there are no problems, it automatically generates an adjustment request. The server receives the order information again and verifies it again. After confirming that there are no errors, it automatically generates an adjustment request like the one below:

[0618] For customers, please provide the site information necessary for activation.

[0619] For system engineers, please check the details of the line arrangement and proceed with the preparations.

[0620] For schedulers, "Please adjust your schedule."

[0621] The generated adjustment request is sent to each party via email or messenger app.

[0622] Input: Revalidated Order Information

[0623] Output: Adjustment requests sent to each party

[0624] Step 6:

[0625] The server receives the responses from each participant, stores them in a database, and verifies them. Each participant provides a response in response to the reconciliation request, which the server receives. The received responses are stored in a database and the following verifications are performed:

[0626] Check whether the answers match

[0627] Formatting accuracy check

[0628] For example, if the scheduler responds, "Installation is scheduled for December 1, 2023," the server stores and validates this.

[0629] Input: Response data from stakeholders

[0630] Output: Validated database entries

[0631] Step 7:

[0632] The server uses the verified data to generate a summary result and notify the user. The server aggregates all the verified data and generates a result stating, "Responses from all parties have been received. Reconciliation is complete." The user is notified of this result.

[0633] Input: Validated response data

[0634] Output: User notification

[0635] Step 8:

[0636] The user makes a final confirmation, and the server shares the final data to begin preparations for launch. The user checks the results of the survey on their device and clicks the OK button if there are no problems. The server receives the user's final confirmation and shares the final data with all parties again, thus beginning preparations for launch.

[0637] Input: User's final confirmation

[0638] Output: Final data shared with stakeholders

[0639] The above is the flow of processing for this system's program, showing the specific operations and data input / output at each step.

[0640] (Application example 1)

[0641] 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."

[0642] Managing and processing order information at logistics centers is complex, and doing it manually is time-consuming and prone to errors. Additionally, correcting errors and sharing information among stakeholders is cumbersome, resulting in reduced work efficiency. Furthermore, there is a need for a function that can quickly verify order information and automatically generate and send adjustment requests. To solve these issues, an efficient and reliable system is required.

[0643] 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.

[0644] In this invention, the server

[0645] A means for a user to input order information from a terminal;

[0646] A means for transmitting input data from the terminal to a server;

[0647] A means for the server to receive the order information and store it in a database;

[0648] A means for the server to verify the order information and, if there is any defect, to generate an error message and send it to the terminal;

[0649] means for the terminal to display an error message to the user;

[0650] A means for the user to view error messages and make any necessary corrections;

[0651] A means for the server to automatically generate an adjustment request based on complete order information and transmit it to the relevant parties;

[0652] a means by which the server receives responses from participants, stores them in a database and checks for duplications and inconsistencies;

[0653] A means for the server to generate a summary result after the verification is completed;

[0654] means for the server to notify the user of the adjustment result and receive a final confirmation from the user;

[0655] The server will share the final data with the relevant parties to prepare for the launch.

[0656] A means to send an email to the customer if the order information is correct;

[0657] This will enable the efficient handling of order information at the logistics center, the prompt notification and correction of error messages, and the smooth automatic generation and transmission of adjustment requests to relevant parties.

[0658] A "terminal" is a device through which a user inputs order information and communicates.

[0659] A "server" is a central computer system that receives order information, stores it in a database, and performs a series of processes such as validation, generating error messages, and automatically generating adjustment requests.

[0660] "Order information" is information including data items such as customer name, product name, desired activation date, and necessary line information that are input by the user from the terminal.

[0661] "Database" refers to data storage for systematically storing and managing order information received by the server.

[0662] An "error message" is a message that is generated when the server finds an error in the order information when verifying it, and is sent to the terminal to prompt the user to make corrections.

[0663] An "adjustment request" is a request for specific tasks or information that is automatically generated by the server based on complete order information and sent to the relevant parties.

[0664] "Stakeholders" are people with an interest in the order processing and opening preparations, such as customers, engineers, and schedulers.

[0665] A "response" is information or a response provided by a participant in response to a coordination request from the server.

[0666] "Aggregated Results" means the comprehensive results data generated by the Server after verifying and processing the Participant's responses.

[0667] "Final confirmation" is an action in which the user confirms the counting results notified by the server and makes a final decision.

[0668] "Opening preparation" is the process in which the server shares the final data with the relevant parties after final confirmation and all preparation work is carried out.

[0669] "Mail" is an email that the server sends to the customer if the order information is correct.

[0670] This invention provides a system that streamlines the management and processing of order information at a logistics center. Specifically, a user inputs and transmits order information using a terminal, and the server receives the information and automatically performs various verifications and processing.

[0671] The server can use the following hardware and software: The hardware includes a database server for receiving and storing order information, and an application server for executing processing. The software used is Flask (a Python web framework), SQLite (a database management system), and an SMTP library (for sending emails).

[0672] The user enters order information on a device such as a smartphone or PC and presses the send button. This information is sent to the server and first saved in a database. The server verifies the saved information and, if there are any errors, generates an error message and sends it to the terminal. The terminal displays this error message to the user, who then makes the necessary corrections and resubmits the order.

[0673] For order information that is complete, the server automatically generates an adjustment request and sends it to the relevant parties via email or messenger app. The relevant parties respond with the necessary information based on the adjustment request, and the server receives it and stores it in a database. It verifies whether there are any duplicates or inconsistencies and generates a summary. The server then notifies the user of the summary results, who then performs a final confirmation. Once the final confirmation is complete, the server shares the final data with the relevant parties and begins preparations for launch.

[0674] For example, consider a scenario where a user wants to enter a new order. The user enters the order information as follows:

[0675] "Please enter new order information in the following format: Customer Name, Product Name, Order Date, and required information. For example: ABC Co., Ltd., Logistics Solutions, 2023-12-15, 5 Trucks."

[0676] This system will significantly improve the efficiency of order information handling at logistics centers, speeding up work and reducing errors. Furthermore, by utilizing a generative AI model, prompts will also be standardized, reducing the burden on users.

[0677] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0678] Step 1:

[0679] The user enters and sends order information from the terminal. The user enters the customer name, product name, order date, and other necessary information into the input form on the terminal, and sends the data to the server. The input data format uses data structures such as JSON or XML. When the user presses the send button, this input data is sent to the server.

[0680] Step 2:

[0681] The server receives the order information and saves it in the database. In this step, the server analyzes the received data and saves the contents in the SQLite database. To process the data, it converts the JSON format data into a format that can be applied to SQL queries and inserts it into the database. The input is the order information sent from the terminal, and the output is the order information recorded in the database.

[0682] Step 3:

[0683] The server validates the order information and generates an error message if there are any errors and sends it to the terminal. The server validates the order information for the following items: presence of required fields, data format, and validity of date (must be in the future). If an error is found, the server generates an appropriate error message and sends it to the terminal. The input is the order information stored in the database and the output is the error message.

[0684] Step 4:

[0685] The terminal displays an error message to the user, who then makes the necessary corrections. The user checks the error message displayed on the terminal screen and corrects any deficiencies. The corrected order information is then sent back to the server. The input is the error message sent from the server, and the output is the corrected order information.

[0686] Step 5:

[0687] The server automatically generates adjustment requests based on complete order information and sends them to the relevant parties. Based on the verified order information, the server generates the necessary adjustment requests for each relevant party (customer, engineer, scheduler) and sends them via email or messenger. Here, the generative AI model creates prompt text. The input is complete order information, and the output is the adjustment requests sent to each relevant party.

[0688] Step 6:

[0689] The server receives responses from the participants, stores them in a database, and verifies whether there are any duplicates or inconsistencies. The server stores the responses received from the participants in a database and verifies whether there are any duplicates or inconsistencies. The input is the response data from the participants, and the output is the response data stored in the database and the verification results.

[0690] Step 7:

[0691] The server generates the aggregated results after the verification is complete and notifies the user. The server aggregates the response data from the participants, generates the aggregated results in text or graph format based on the verification results, and notifies the user. The input is the response data from the participants, and the output is the generated aggregated results.

[0692] Step 8:

[0693] The user makes a final confirmation, and the server shares the final data with the relevant parties to begin preparations for launch. The user checks the tabulated results on their device and makes a final confirmation. After OK is sent, the server shares the final data with the relevant parties to begin preparations for launch. The input is the final confirmation from the user, and the output is the sharing of the final data.

[0694] This series of processing flows will make the management and processing of order information at the logistics center more efficient, accurate, and quick.

[0695] 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.

[0696] This invention further improves the user experience by combining a system that streamlines the order and activation flow for data-based products with an emotion engine that recognizes user emotions. This system comprehensively manages the entry of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[0697] 1. User enters and submits order information

[0698] The user uses a terminal to input order information. The input form includes information such as the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. In addition, the emotion engine recognizes the user's emotions while they are inputting, and this information is also sent to the server.

[0699] 2. The server validates the order information and checks for errors

[0700] The server receives the order information and stores it in a database in an appropriate format.The server then retrieves the order information from the database and verifies it for any errors.If an error is found, the emotion engine detects the user's emotion and generates an error message in an appropriate tone, which is sent to the terminal.

[0701] 3. Customize and modify error messages

[0702] The terminal displays an error message to the user. The error message is customized by the emotion engine. For example, if the terminal recognizes that the user is in a hurry, a message for prompt action is displayed. The user checks the error message, makes any necessary corrections, and resends the message.

[0703] 4. Automatic generation and submission of adjustment requests

[0704] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties (customer, system engineer, production scheduler). The emotion engine conveys the requests in a tone that takes the relevant parties into consideration.

[0705] 5. Collecting and managing responses from stakeholders

[0706] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication or inconsistency.

[0707] 6. Generation and notification of aggregated results

[0708] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user in a tone that corresponds to their emotion. For example, if the server detects that the user is anxious, it sends a reassuring message.

[0709] 7. Final confirmation by the user and preparation for opening

[0710] The server notifies the user of the results. The user checks the results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with all parties involved, and begins preparations for opening.

[0711] Specific examples

[0712] For example, when a user (salesperson) places an order for a high-speed data line for a certain company, the following procedure is carried out.

[0713] 1. The user enters order information from the terminal

[0714] Customer Name: A Co., Ltd.

[0715] Product: High-speed data line

[0716] Desired opening date: December 1, 2023

[0717] Required line information: 1Gbps

[0718] The emotion engine analyzes facial expressions and voice input and recognizes that the user is in a hurry. The device then sends the information to the server.

[0719] 2. The server verifies the order information

[0720] The server saves the order information in a database and verifies it for any errors.

[0721] If there is a problem, the emotion engine generates an error message in a tone such as "Please correct it promptly" and sends it to the terminal.

[0722] 3. Viewing and correcting error messages

[0723] The terminal displays an error message to the user, who then makes the correction.

[0724] 4. Automatic generation and submission of adjustment requests

[0725] If there are no errors, the server generates an adjustment request such as "I apologize for bothering you during your busy schedule, but please arrange for the line and adjust the schedule," and sends it to each party involved.

[0726] 5. Collect and verify responses

[0727] The server receives responses from each party and stores them in a database.

[0728] The server verifies the response data and generates aggregated results.

[0729] 6. Final confirmation notice

[0730] The server notifies the user of the aggregated results, and the emotion engine generates a message such as, "Please confirm that there are no problems with the results."

[0731] 7. Final checks and preparations for opening

[0732] User sends OK.

[0733] The server shares the final information with the relevant parties and preparations for opening proceed.

[0734] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. At the same time, the emotion engine installed in the terminal analyzes the user's facial expression and tone of voice to detect the user's current emotion. Once the input is complete, the terminal sends the information and emotion data to the server.

[0738] Step 2:

[0739] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and saves it as a new order record in the database. The emotion data is also saved at the same time. Once the saving is complete, the server returns a "Data saving successful" response to the device.

[0740] Step 3:

[0741] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[0742] Step 4:

[0743] If the server detects any defects, it summarizes the details of the defects and generates an error message. The emotion engine analyzes the recorded user emotion data and customizes the error message with a tone that corresponds to the user's emotion. For example, if it detects that the user is in a hurry, it generates an error message that reads, "We apologize for the inconvenience, but the following items need to be corrected." It then sends the error message to the device.

[0744] Step 5:

[0745] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[0746] Step 6:

[0747] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[0748] Step 7:

[0749] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The emotion engine recognizes the emotions of the parties involved and sends the adjustment request in an appropriate tone.

[0750] Step 8:

[0751] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[0752] Step 9:

[0753] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[0754] Step 10:

[0755] The server notifies the user of the results for final confirmation. The emotion engine analyzes the user's current emotions and customizes the notification message with the most appropriate tone. For example, if the user is feeling anxious, the notification might say, "Don't worry, all the information has been collected accurately."

[0756] Step 11:

[0757] The user performs a final confirmation via their terminal. Once the user sends OK, the server receives this and shares the final data with all parties involved. This allows preparations for opening to proceed, and the entire system process is completed.

[0758] In this way, this system, which incorporates an emotion engine, improves the user experience and enables the order and activation flow to proceed smoothly.

[0759] Example 2

[0760] 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."

[0761] In the conventional order and activation flow, users often received error messages, which caused stress and frustration, resulting in a poor user experience. Furthermore, smooth communication was difficult because requests for adjustments to stakeholders were mechanical. Furthermore, there was a tendency for responses from stakeholders to be duplicated or contradictory, which reduced the efficiency of activation preparations.

[0762] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving order information and saving it in a database, means for verifying the order information and, if there is an error, generating an error message and sending it to the terminal, and means for customizing the tone of the error message or adjustment request based on emotion data. This makes it possible to generate and send error messages and adjustment requests that take the user's emotions into consideration, improving the user experience.

[0763] "User" refers to a person who uses this system to input order information.

[0764] "Terminal" refers to a device through which a user inputs order information and communicates with a server.

[0765] "Order information" refers to data such as the product name, customer name, desired activation date, and required line information that the user inputs via the terminal.

[0766] "Server" refers to the central processing unit that stores received order information in a database and verifies and processes the information.

[0767] "Database" refers to a storage device for storing order information and responses from related parties, and for retrieving and verifying data as necessary.

[0768] An "error message" refers to a notification message that is generated by the server and sent to the user via the terminal when there is an error in the order information.

[0769] "Emotion engine" refers to the algorithms and hardware / software that recognize the user's emotions and generate and process information based on them.

[0770] An "adjustment request" refers to a message that is generated by the server based on complete order information and sent to relevant parties to request the arrangement of merchandise or schedule adjustments.

[0771] "Stakeholders" refers to customers, engineers, schedulers, etc. involved in the order and activation flow.

[0772] "Aggregated results" refers to the final result data obtained by aggregating and verifying responses from stakeholders.

[0773] "Opening preparation" refers to the overall opening procedure for a product, which is carried out after the server notifies all relevant parties after final confirmation of the order information.

[0774] "Tone" refers to a manner for adjusting the presentation of a message based on the user's emotions detected by the emotion engine.

[0775] "Verification" refers to the process in which the server checks for any deficiencies or inconsistencies in the order information and responses from the parties involved.

[0776] "Notification" refers to the act of a server sending a message or information to a user or interested party.

[0777] This invention is a system that improves the efficiency of the order and activation flow for data-related products and further enhances the user experience by combining an emotion engine that recognizes user emotions. This system comprehensively manages the input of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[0778] Hardware and software used

[0779] Terminal: The device (PC, smartphone, tablet, etc.) through which the user enters order information

[0780] Server: A central processing unit (e.g., cloud server, on-premise server) for storing, validating and processing order information.

[0781] Database: A storage device for storing order information and responses from stakeholders (a relational database such as MySQL or PostgreSQL)

[0782] Emotion engine: Algorithms and APIs (e.g., Amazon Rekognition, Google Cloud Vision API) to recognize user emotions and generate error messages or adjustment requests in an appropriate tone.

[0783] System Operation Overview

[0784] The user uses a terminal to input order information. The input form includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. The emotion engine recognizes the user's facial expressions and voice as they are entering the information, and the emotion data is also sent to the server.

[0785] The server stores the received order information in a database in an appropriate format. The server then retrieves the order information from the database and verifies it for any errors. If an error is found, the server uses an emotion engine to generate an error message based on the user's emotions and sends it to the terminal. The terminal displays the error message to the user, and after the user makes the necessary corrections, the message is resent.

[0786] Once the order information is complete and complete, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. At this time, the emotion engine conveys the requests in a tone that is considerate to the relevant parties. The relevant parties send their responses to the adjustment requests to the server, which receives them and stores them in a database. The server then verifies the response data for duplication and inconsistencies.

[0787] After the verification is complete, the server generates the aggregated results and uses an emotion engine to notify the user in a tone that corresponds to their emotion. For example, if the user is anxious, a message that provides reassurance will be sent. Finally, the user checks the aggregated results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch.

[0788] Examples of concrete examples and prompts

[0789] For example, when a user places an order for a high-speed data line for a company, the following procedure is performed: The user inputs the following order information into the terminal:

[0790] Customer Name: A Co., Ltd.

[0791] Product: High-speed data line

[0792] Desired opening date: December 1, 2023

[0793] Required line information: 1Gbps

[0794] The emotion engine analyzes facial expressions and voices when inputting information and recognizes that the user is in a hurry. The device then sends the input information to the server.

[0795] The server saves the order information in a database and verifies it for any errors. If there are any errors, the emotion engine generates an error message in a tone such as "Please correct this as soon as possible" and sends it to the terminal.

[0796] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] Step 1: User enters and submits order information

[0799] Description: The user uses the terminal to enter order information (customer name, product name, desired activation date, required line information, etc.).

[0800] Input: User-entered order information

[0801] Data processing / calculation: Convert the data from the input form into an appropriate format and recognize the user's emotions using an emotion engine.

[0802] Output: Formatted order information and sentiment data

[0803] Specific operation: Enter information into a browser form, retrieve the data using JavaScript, and send it to the server as an HTTP POST request. Also, collect facial and voice data using a webcam and microphone and send it to the emotion engine.

[0804] Step 2: The server receives the order information and stores it in the database

[0805] Description: The server receives the order information and stores it in a database.

[0806] Input: Order information and emotion data sent from the terminal

[0807] Data Processing / Calculation: Generate and execute SQL insert statements to save order information to the database in the appropriate format.

[0808] Output: Order information stored in the database

[0809] Specific operation: Extracts data from an HTTP POST request, generates an SQL insert statement, and inserts the data into a database (e.g., MySQL or PostgreSQL).

[0810] Step 3: The server validates the order information and checks for errors

[0811] Description: The server retrieves order information from the database and verifies it for accuracy.

[0812] Input: Order information stored in the database

[0813] Data processing / calculation: Data is obtained using an SQL query and verified using a validation algorithm to ensure there are no errors.

[0814] Output: Error message or complete order information

[0815] What it does: Retrieves data from the database using SQL queries, performs validation checks in code, and generates error messages based on the emotion engine if there are any errors.

[0816] Step 4: Generate and send an error message

[0817] Description: If the server is faulty, it generates an error message based on the emotion engine and sends it to the terminal.

[0818] Input: Verification results (with imperfections) and emotion data

[0819] Data processing / calculation: Generates error messages in an appropriate tone based on emotional data.

[0820] Output: Error message displayed on the terminal

[0821] Specific operation: Calls the emotion engine API, applies the error message generation algorithm, and sends the error message as an HTTP response.

[0822] Step 5: View and fix error messages

[0823] Description: The terminal displays an error message to the user, who then makes the necessary corrections.

[0824] Input: Error message sent to the terminal

[0825] Data processing / calculation: Display an error message in the relevant form field and resubmit once corrections are made.

[0826] Output: Revised order information

[0827] Specific operation: An error message is displayed in a pop-up window or on a form using JavaScript, the user corrects the error, and the data is sent to the server again.

[0828] Step 6: Automatically generate and submit adjustment requests

[0829] Description: The server automatically generates an adjustment request based on complete order information and sends it to the relevant parties.

[0830] Input: Complete order information and emotion data

[0831] Data processing / calculation: Automatically generate adjustment requests based on order information and customize the tone of the message with an emotion engine.

[0832] Output: Coordination request message to stakeholders

[0833] Specific behavior: Applying automated generation rules based on product information to generate request content for stakeholders, and sending emails and notifications with the appropriate tone using an emotion engine.

[0834] Step 7: Collect and manage stakeholder responses

[0835] Description: Participants send responses to reconciliation requests to the server, which receives them and stores them in a database.

[0836] Input: Response data from stakeholders

[0837] Data processing / calculation: Receive the response data, store it in a database in the appropriate format, and apply algorithms to check for duplicates and inconsistencies.

[0838] Output: Verified response data

[0839] Specific operation: Receive response data via HTTP POST request and save it to the database using SQL insert statements. Check for duplicates and inconsistencies within the program.

[0840] Step 8: Generate and notify the results

[0841] Description: The server generates the aggregated results after the verification is completed, and notifies the user with a tone according to their emotion using the emotion engine.

[0842] Input: Verified response data and sentiment data

[0843] Data processing / calculation: Aggregate response data and generate summary results in text or graph format. Generate notification messages with customized tone using the emotion engine.

[0844] Output: Aggregation results notified to the user

[0845] What it does: It applies aggregation algorithms to generate text and graphs, uses an emotion engine to adjust tone, and notifies via HTTP response and email.

[0846] Step 9: Final confirmation by the user and preparation for opening

[0847] Description: The server notifies the user of the counting results, and the user performs a final check. If there are no problems, the server sends OK.

[0848] Input: Final confirmation from the user (OK)

[0849] Data processing / calculation: Share the final information with the relevant parties and make schedules and arrangements to proceed with preparations for opening.

[0850] Output: Progress of opening preparations

[0851] Specific operation: The user presses the OK button and notifies the server via an HTTP POST request. The server then sends the final information to the relevant parties via email or notification, and generates an implementation schedule.

[0852] As a result, the overall system processing is managed appropriately, improving the user experience.

[0853] (Application example 2)

[0854] 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."

[0855] In the conventional order and activation flow system, communication with users was standardized, making it difficult to respond in a way that took into account the user's feelings and circumstances. This resulted in frustration and stress when users entered order information, as well as inconvenience caused by requests for adjustments and receiving error messages. This led to lower customer satisfaction and process delays.

[0856] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0857] In this invention, the server includes means for recognizing a user's emotion using an emotion engine and generating and providing feedback according to the emotion, means for using the emotion engine to communicate with related parties in a tone that takes the emotion into consideration, and means for the server to send an adjustment request to each related party via email or a messenger app, thereby improving the user experience and enabling a smooth and efficient order and activation flow.

[0858] A "terminal" is a device used by a user to input order information, such as a smartphone or a personal computer.

[0859] The "server" is a central processing unit that receives order information sent from users, stores it in a database, and performs various processes.

[0860] A "database" is an information system managed by a server for storing and managing order information and responses from related parties.

[0861] An "error message" is a message generated by the server to notify the user of any incomplete or incorrect order information.

[0862] The "emotion engine" is an analysis system that recognizes the user's emotions and generates feedback and messages based on those emotions.

[0863] An "adjustment request" is a request that is automatically generated by the server based on complete order information and sent to the relevant parties.

[0864] "Parties" are people or organizations involved in the order process, such as customers, technical personnel, schedulers, etc.

[0865] "Feedback" is information or messages provided in response to a user's input or action.

[0866] "Notification" is the act of sending messages or information from the server to users or related parties.

[0867] "Opening preparation" refers to the preparation work for installing products and starting services based on the final order information.

[0868] This invention is a system that streamlines the order and activation flow for data-related products by combining an emotion engine that recognizes user emotions. This system includes the following components.

[0869] Hardware and Software Configuration

[0870] 1. Terminal

[0871] A device used by users to input order information. Generally, this is a smartphone or PC.

[0872] 2. Server

[0873] This is a central processing unit that receives order information sent from the terminal and stores it in a database. It uses a web framework such as "Flask" to perform server-side processing.

[0874] 3. Database

[0875] This is an information system managed by a server, which stores and manages order information and responses from related parties.

[0876] 4. Emotion Engine

[0877] It is an analysis system for recognizing user emotions. For example, by using "Affectiva's SDK," it can analyze emotions in real time from the user's facial expressions and voice.

[0878] System processing flow

[0879] 1. Enter and submit order information

[0880] The user uses the terminal to input order information. The input form includes the customer name, product name, desired activation date, required line information, etc. While the user is inputting information, the emotion engine analyzes the user's emotions in real time through the terminal's camera and microphone. The terminal then sends the data together with this information to the server.

[0881] 2. Validating order information and generating error messages

[0882] The server saves the received order information in a database. The server then verifies the order information and, if there are any errors, generates an error message and sends it to the terminal. At this time, the error message is generated based on the analysis results of the emotion engine, taking into consideration the user's emotions.

[0883] 3. Automatic generation and submission of adjustment requests

[0884] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. The emotion engine also generates messages in a tone that takes into account the emotions of the relevant parties.

[0885] 4. Collecting and Managing Responses

[0886] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication and inconsistency.

[0887] 5. Generating the aggregated results and notifying the user

[0888] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user with a tone that corresponds to their emotion. Finally, the user sends the results they have confirmed to the server, and the server shares the final information with the relevant parties to begin preparations for launch.

[0889] Examples of concrete examples and prompts

[0890] Specific examples

[0891] "When a user orders a new product from an online store, they enter their order information. An emotion recognition engine analyzes the user's facial expressions and detects that the user is in a hurry. This allows error messages and feedback to be displayed quickly and concisely, improving the user experience."

[0892] Prompt Sentence Examples

[0893] "Can you give me an example of a server-side program written in Python that quickly checks for inaccuracies in the information entered when a user is ordering a new product from an online shopping site and provides appropriate feedback depending on the situation?"

[0894] In this way, the present invention provides a system that improves the user experience and smoothly advances the order and activation flow by combining an emotion recognition engine.

[0895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0896] Step 1:

[0897] The user uses the terminal to input order information. The input form prompts the user to enter information such as the customer name, product name, desired activation date, and necessary line information. The emotion engine analyzes the user's facial expressions and voice while they are entering the information, and acquires emotional data. The terminal then sends the acquired order information and emotional data to the server as an HTTP POST request.

[0898] Input: Customer name, product name, desired activation date, necessary line information, emotion data

[0899] Output: HTTP POST request (order information and emotion data)

[0900] Step 2:

[0901] The server receives the order information and emotion data sent from the terminal and stores them in a database. It verifies that the data has been saved in the correct format. If the server finds any errors, it uses the emotion engine to generate an error message, adjusts the tone, and sends it to the terminal. If there are no errors, it proceeds to the next step.

[0902] Input: HTTP POST request (order information and emotion data)

[0903] Output: Error message or confirmation message

[0904] Step 3:

[0905] The terminal displays the error message received from the server to the user. The emotion engine customizes the tone of the error message to reflect the user's emotions. The user checks the error message, makes any necessary corrections, and resubmits the message. This process is repeated until the error is resolved.

[0906] Input: Error message

[0907] Output: Revised order information

[0908] Step 4:

[0909] After the server verifies the order information and finds no errors, it automatically generates an adjustment request and sends it to the relevant parties. The emotion engine generates messages in a tone that takes into account the emotions of the relevant parties. The message is sent via email or a messenger app.

[0910] Input: Complete order information

[0911] Output: Reconciliation request message

[0912] Step 5:

[0913] Each party sends a response to the reconciliation request to the server, which stores the received responses in a database and verifies the response data for duplication and inconsistency, thereby ensuring data consistency.

[0914] Input: Responses from stakeholders

[0915] Output: Validated answers stored in a database

[0916] Step 6:

[0917] The server generates the aggregated results after the verification is complete. The results are presented in text or graphical format. The emotion engine takes into account the user's emotions and generates a tone-adjusted message to notify the user of the results.

[0918] Input: Validated Answer

[0919] Output: Aggregation results (text or graph format)

[0920] Step 7:

[0921] The user checks the results of the calculations through their terminal. If there are no problems, they send a final confirmation of OK. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch. The emotion engine generates a final confirmation message and notifies the user in a way that takes their emotions into consideration.

[0922] Input: Check the summary results

[0923] Output: Final confirmation (OK) and preparation for opening

[0924] 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.

[0925] 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.

[0926] 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.

[0927] [Third embodiment]

[0928] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0929] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0930] 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).

[0931] 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.

[0932] 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.

[0933] 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).

[0934] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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."

[0940] This invention is a system for smoothly sharing and processing information regarding orders and activation of data-related products, and achieves efficient data processing by combining multiple elements.

[0941] 1. User enters and submits order information

[0942] The user uses a terminal to input order information. The input information includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server, which then stores the received data in a database in the appropriate format.

[0943] 2. The server validates the order information and checks for errors

[0944] The server retrieves order information from the database and verifies it for any errors. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. The user checks the error message, makes any necessary corrections, and then resubmits the data.

[0945] 3. Automatic generation and submission of adjustment requests

[0946] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information, site information, etc. The server then sends these adjustment requests to all parties involved (customer, system engineer, production scheduler) via email or messenger.

[0947] 4. Collecting responses from stakeholders and managing data

[0948] Once the responses from each participant are sent to the server, the server receives them and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary and prepares it for final confirmation.

[0949] 5. Final confirmation by the user and preparation for opening

[0950] The server notifies the user of the results of the calculation. The user checks the results through their terminal and sends OK if there are no problems. The server then shares the final data with all parties involved and begins preparations for launch. This completes all steps, ensuring a smooth launch of the data-based product.

[0951] Specific examples

[0952] For example, when a user (salesperson) places an order for a high-speed data line for ABC Corporation, the following procedure is carried out.

[0953] 1. The user enters order information from the terminal

[0954] Customer Name: ABC Co., Ltd.

[0955] Product: High-speed data line

[0956] Desired opening date: December 1, 2023

[0957] Required line information: 1Gbps

[0958] The terminal sends this information to the server.

[0959] 2. The server verifies the order information

[0960] The server saves the order information in a database and verifies it for any errors.

[0961] If there is a problem, an error message is generated and sent to the terminal.

[0962] 3. Automatic generation and submission of adjustment requests

[0963] If there are no errors, the server generates the following reconciliation request and sends it to each party:

[0964] Customer: Please provide the site information required for activation.

[0965] SE: Please check the line arrangement details and proceed with preparations.

[0966] Scheduler: Please adjust the schedule.

[0967] 4. Response collection and data management

[0968] The server receives responses from each party and stores them in a database.

[0969] The server verifies the response data and generates aggregated results.

[0970] 5. Final checks and preparations for opening

[0971] The server notifies the user of the results of the calculation, and the user sends OK.

[0972] The server shares the final data with the relevant parties, and preparations for opening proceed.

[0973] In this way, this system automates multi-stage information processing and coordination, thereby streamlining the order and activation flow for data-related products.

[0974] The processing flow will be explained below.

[0975] Step 1:

[0976] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. Once the input is complete, the terminal sends the information to the server as an HTTP POST request.

[0977] Step 2:

[0978] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and stores it as a new order record in the database. After saving is complete, the server returns a "Data saved successfully" response to the terminal.

[0979] Step 3:

[0980] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[0981] Step 4:

[0982] If the server detects any errors, it will summarize the details of the errors, generate an error message, and send it to the terminal. For example, an error message will be generated if a required field is not filled in or if the date format is incorrect.

[0983] Step 5:

[0984] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[0985] Step 6:

[0986] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[0987] Step 7:

[0988] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The adjustment request includes details of the information to be provided and the actions to be taken.

[0989] Step 8:

[0990] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[0991] Step 9:

[0992] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[0993] Step 10:

[0994] The server notifies the user of the tally results for final confirmation. The user checks the tally results through their terminal and sends OK if there are no problems.

[0995] Step 11:

[0996] The server receives the OK from the user and shares the final data with all parties involved. The shared information includes the adjustment results, the launch date, and other necessary information. The server then proceeds with the launch preparations and completes the entire system process.

[0997] Example 1

[0998] 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."

[0999] Existing order and activation management systems have the problem that the input of order information, error checking, and information sharing and coordination with related parties are cumbersome, resulting in human error and incomplete communication. Furthermore, while there is a demand for more efficient automatic generation and processing of adjustment requests, there is a lack of effective means to achieve this. Furthermore, there is also the issue of a heavy burden on users due to insufficient support for inputting order information.

[1000] 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.

[1001] In this invention, the server includes: means for a user to input order information from a terminal; means for the terminal to transmit the input data to the server; means for the server to receive the order information and store it in a database; means for the server to verify the order information and, if there is any deficiency, generate an error message and transmit it to the terminal; means for the terminal to display the error message to the user; means for the user to check the error message and make any necessary corrections; means for the server to automatically generate an adjustment request based on the order information without any deficiency and transmit it to the relevant parties; means for the server to receive responses from the relevant parties, store them in a database and verify duplications and inconsistencies; means for the server to generate a summary result after verification is complete; means for the server to notify the user of the adjustment result and receive final confirmation from the user; means for the server to share the final data with the relevant parties and proceed with preparations for opening; and means for supporting the input of order information using a generative AI model. This enables improved efficiency and accuracy of the entire order and opening management process.

[1002] A "user" is an individual or company employee who uses this system to input order information and perform various operations.

[1003] A "terminal" is an information input device used by a user, such as a computer, tablet, or smartphone.

[1004] "Server" means the central computer system that processes and stores order information and related data and manages instructions.

[1005] "Order information" is order-related data such as the customer name, product name, desired activation date, and required line information entered by the user.

[1006] A "database" is an information storage system for saving and managing order information and responses from each party.

[1007] An "error message" is a message that the server generates when there is a problem with the order information and notifies the user of the problem.

[1008] An "adjustment request" is information that is automatically generated by the server based on complete order information and contains necessary information and instructions to be sent to each party involved.

[1009] "Related parties" are people or positions such as customers, system engineers, and schedulers who are involved in order information or adjustment requests.

[1010] A "response" is information provided by each participant in response to a coordination request from the server.

[1011] "Aggregated Results" means the final statistical data or report generated by the Server after verifying and aggregating the responses from Participants.

[1012] A "generative AI model" is a machine learning model used to assist in the input of order information and for data processing.

[1013] "Input assistance" is a function that uses generative AI models to enable users to input order information accurately and quickly.

[1014] This invention aims to smoothly share and process information related to the order and activation of data-based products by having users input order information using a terminal and having that information managed and processed by a server. Specific embodiments of this invention are described below.

[1015] 1. Enter and submit order information

[1016] The user uses a terminal to enter order information into a dedicated form. Order information includes the customer name, product name, desired activation date, required line information, etc. For example, a user (sales representative) enters the following information to place an order for a high-speed data line.

[1017] Customer Name: ABC Co., Ltd.

[1018] Product: High-speed data line

[1019] Desired opening date: December 1, 2023

[1020] Required line information: 1Gbps

[1021] The user checks the input and clicks the send button. The device converts this information into JSON format and sends it to the server.

[1022] 2. Receiving and storing order information

[1023] The server receives the order information sent from the terminal and stores it in a database. The database can be a common relational database such as MySQL or PostgreSQL. The server checks the integrity of the data and performs initial checks.

[1024] 3. Validate order information and generate error messages

[1025] The server verifies the order information stored in the database. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. For example, if the desired activation date is in the past, the server generates an error message stating, "The desired activation date must be a future date."

[1026] 4. Automatic generation and submission of adjustment requests

[1027] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. The adjustment requests include information on line arrangements and site information. The server then sends these adjustment requests to the relevant parties via email or messenger app. For example, the following prompts can be used:

[1028] Customers are asked to provide the site information necessary for activation.

[1029] To the system engineer, "Please confirm the details of the line arrangement and proceed with the preparations."

[1030] To the scheduler, "Please adjust your schedule."

[1031] 5. Response collection and data management

[1032] The server receives the responses sent by the parties in response to the adjustment request and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary result. For example, it generates a summary result saying, "Responses from all parties have been collected. Adjustment is complete."

[1033] 6. Final confirmation by the user and preparation for opening

[1034] The server notifies the user of the generated aggregated results. The user checks the results through their terminal and sends OK if there are no problems. The server receives final confirmation from the user and shares the final data with the relevant parties. This allows preparations for launch to proceed and ensures a smooth launch of data-related products.

[1035] Using generative AI models

[1036] Furthermore, the server uses a generative AI model to assist with input of order information. This AI model predicts and auto-completes input content, reducing the burden on users.

[1037] As described above, the present invention combines the elements of a server, terminal, and generative AI model to improve the efficiency and accuracy of the entire order and activation management process.

[1038] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1039] Step 1:

[1040] The user enters and submits order information from the terminal. The user opens a dedicated form screen on the terminal and enters the customer name, product name, desired activation date, and necessary line information. An example of input is as follows:

[1041] Customer name: General company

[1042] Product: High-speed data line

[1043] Desired opening date: December 1, 2023

[1044] Required line information: 1Gbps

[1045] After completing the input, the user clicks the send button, which causes the device to convert the information into JSON format and send it to the server.

[1046] Input: Customer name, product name, desired activation date, necessary line information

[1047] Output: Order information converted to JSON format

[1048] Step 2:

[1049] The server performs initial validation of the received data and saves it in the database. The server receives the order information in JSON format sent from the terminal and performs the following initial validation:

[1050] Are all required fields filled in?

[1051] Is the format correct?

[1052] Once the initial validation is complete, the server stores the order information in a database.

[1053] Input: Order information in JSON format

[1054] Output: Order information stored in the database

[1055] Step 3:

[1056] The server performs detailed verification of the stored order information. The specific verification items are as follows:

[1057] Is the customer name formatted correctly?

[1058] Is the desired activation date in the past?

[1059] Does the product name match what is registered in the system?

[1060] If an error is found, the server generates an error message and sends it to the terminal. Example of an error message: "The desired activation date must be a future date."

[1061] Input: Order information stored in the database

[1062] Output: Error message (if any)

[1063] Step 4:

[1064] The terminal displays an error message to the user, and the user makes the necessary corrections. The terminal displays the error message received from the server to the user. The user makes the necessary corrections according to the displayed error message and resubmits the order information.

[1065] Input: Error message

[1066] Output: Corrected order information

[1067] Step 5:

[1068] The server verifies the corrected order information again, and if there are no problems, it automatically generates an adjustment request. The server receives the order information again and verifies it again. After confirming that there are no errors, it automatically generates an adjustment request like the one below:

[1069] For customers, please provide the site information necessary for activation.

[1070] For system engineers, please check the details of the line arrangement and proceed with the preparations.

[1071] For schedulers, "Please adjust your schedule."

[1072] The generated adjustment request is sent to each party via email or messenger app.

[1073] Input: Revalidated Order Information

[1074] Output: Adjustment requests sent to each party

[1075] Step 6:

[1076] The server receives the responses from each participant, stores them in a database, and verifies them. Each participant provides a response in response to the reconciliation request, which the server receives. The received responses are stored in a database and the following verifications are performed:

[1077] Check whether the answers match

[1078] Formatting accuracy check

[1079] For example, if the scheduler responds, "Installation is scheduled for December 1, 2023," the server stores and validates this.

[1080] Input: Response data from stakeholders

[1081] Output: Validated database entries

[1082] Step 7:

[1083] The server uses the verified data to generate a summary result and notify the user. The server aggregates all the verified data and generates a result stating, "Responses from all parties have been received. Reconciliation is complete." The user is notified of this result.

[1084] Input: Validated response data

[1085] Output: User notification

[1086] Step 8:

[1087] The user makes a final confirmation, and the server shares the final data to begin preparations for launch. The user checks the results of the survey on their device and clicks the OK button if there are no problems. The server receives the user's final confirmation and shares the final data with all parties again, thus beginning preparations for launch.

[1088] Input: User's final confirmation

[1089] Output: Final data shared with stakeholders

[1090] The above is the flow of processing for this system's program, showing the specific operations and data input / output at each step.

[1091] (Application example 1)

[1092] 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."

[1093] Managing and processing order information at logistics centers is complex, and doing it manually is time-consuming and prone to errors. Additionally, correcting errors and sharing information among stakeholders is cumbersome, resulting in reduced work efficiency. Furthermore, there is a need for a function that can quickly verify order information and automatically generate and send adjustment requests. To solve these issues, an efficient and reliable system is required.

[1094] 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.

[1095] In this invention, the server

[1096] A means for a user to input order information from a terminal;

[1097] A means for transmitting input data from the terminal to a server;

[1098] A means for the server to receive the order information and store it in a database;

[1099] A means for the server to verify the order information and, if there is any defect, to generate an error message and send it to the terminal;

[1100] means for the terminal to display an error message to the user;

[1101] A means for the user to view error messages and make any necessary corrections;

[1102] A means for the server to automatically generate an adjustment request based on complete order information and transmit it to the relevant parties;

[1103] a means by which the server receives responses from participants, stores them in a database and checks for duplications and inconsistencies;

[1104] A means for the server to generate a summary result after the verification is completed;

[1105] means for the server to notify the user of the adjustment result and receive a final confirmation from the user;

[1106] The server will share the final data with the relevant parties to prepare for the launch.

[1107] A means to send an email to the customer if the order information is correct;

[1108] This will enable the efficient handling of order information at the logistics center, the prompt notification and correction of error messages, and the smooth automatic generation and transmission of adjustment requests to relevant parties.

[1109] A "terminal" is a device through which a user inputs order information and communicates.

[1110] A "server" is a central computer system that receives order information, stores it in a database, and performs a series of processes such as validation, generating error messages, and automatically generating adjustment requests.

[1111] "Order information" is information including data items such as customer name, product name, desired activation date, and necessary line information that are input by the user from the terminal.

[1112] "Database" refers to data storage for systematically storing and managing order information received by the server.

[1113] An "error message" is a message that is generated when the server finds an error in the order information when verifying it, and is sent to the terminal to prompt the user to make corrections.

[1114] An "adjustment request" is a request for specific tasks or information that is automatically generated by the server based on complete order information and sent to the relevant parties.

[1115] "Stakeholders" are people with an interest in the order processing and opening preparations, such as customers, engineers, and schedulers.

[1116] A "response" is information or a response provided by a participant in response to a coordination request from the server.

[1117] "Aggregated Results" means the comprehensive results data generated by the Server after verifying and processing the Participant's responses.

[1118] "Final confirmation" is an action in which the user confirms the counting results notified by the server and makes a final decision.

[1119] "Opening preparation" is the process in which the server shares the final data with the relevant parties after final confirmation and all preparation work is carried out.

[1120] "Mail" is an email that the server sends to the customer if the order information is correct.

[1121] This invention provides a system that streamlines the management and processing of order information at a logistics center. Specifically, a user inputs and transmits order information using a terminal, and the server receives the information and automatically performs various verifications and processing.

[1122] The server can use the following hardware and software: The hardware includes a database server for receiving and storing order information, and an application server for executing processing. The software used is Flask (a Python web framework), SQLite (a database management system), and an SMTP library (for sending emails).

[1123] The user enters order information on a device such as a smartphone or PC and presses the send button. This information is sent to the server and first saved in a database. The server verifies the saved information and, if there are any errors, generates an error message and sends it to the terminal. The terminal displays this error message to the user, who then makes the necessary corrections and resubmits the order.

[1124] For order information that is complete, the server automatically generates an adjustment request and sends it to the relevant parties via email or messenger app. The relevant parties respond with the necessary information based on the adjustment request, and the server receives it and stores it in a database. It verifies whether there are any duplicates or inconsistencies and generates a summary. The server then notifies the user of the summary results, who then performs a final confirmation. Once the final confirmation is complete, the server shares the final data with the relevant parties and begins preparations for launch.

[1125] For example, consider a scenario where a user wants to enter a new order. The user enters the order information as follows:

[1126] "Please enter new order information in the following format: Customer Name, Product Name, Order Date, and required information. For example: ABC Co., Ltd., Logistics Solutions, 2023-12-15, 5 Trucks."

[1127] This system will significantly improve the efficiency of order information handling at logistics centers, speeding up work and reducing errors. Furthermore, by utilizing a generative AI model, prompts will also be standardized, reducing the burden on users.

[1128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1129] Step 1:

[1130] The user enters and sends order information from the terminal. The user enters the customer name, product name, order date, and other necessary information into the input form on the terminal, and sends the data to the server. The input data format uses data structures such as JSON or XML. When the user presses the send button, this input data is sent to the server.

[1131] Step 2:

[1132] The server receives the order information and saves it in the database. In this step, the server analyzes the received data and saves the contents in the SQLite database. To process the data, it converts the JSON format data into a format that can be applied to SQL queries and inserts it into the database. The input is the order information sent from the terminal, and the output is the order information recorded in the database.

[1133] Step 3:

[1134] The server validates the order information and generates an error message if there are any errors and sends it to the terminal. The server validates the order information for the following items: presence of required fields, data format, and validity of date (must be in the future). If an error is found, the server generates an appropriate error message and sends it to the terminal. The input is the order information stored in the database and the output is the error message.

[1135] Step 4:

[1136] The terminal displays an error message to the user, who then makes the necessary corrections. The user checks the error message displayed on the terminal screen and corrects any deficiencies. The corrected order information is then sent back to the server. The input is the error message sent from the server, and the output is the corrected order information.

[1137] Step 5:

[1138] The server automatically generates adjustment requests based on complete order information and sends them to the relevant parties. Based on the verified order information, the server generates the necessary adjustment requests for each relevant party (customer, engineer, scheduler) and sends them via email or messenger. Here, the generative AI model creates prompt text. The input is complete order information, and the output is the adjustment requests sent to each relevant party.

[1139] Step 6:

[1140] The server receives responses from the participants, stores them in a database, and verifies whether there are any duplicates or inconsistencies. The server stores the responses received from the participants in a database and verifies whether there are any duplicates or inconsistencies. The input is the response data from the participants, and the output is the response data stored in the database and the verification results.

[1141] Step 7:

[1142] The server generates the aggregated results after the verification is complete and notifies the user. The server aggregates the response data from the participants, generates the aggregated results in text or graph format based on the verification results, and notifies the user. The input is the response data from the participants, and the output is the generated aggregated results.

[1143] Step 8:

[1144] The user makes a final confirmation, and the server shares the final data with the relevant parties to begin preparations for launch. The user checks the tabulated results on their device and makes a final confirmation. After OK is sent, the server shares the final data with the relevant parties to begin preparations for launch. The input is the final confirmation from the user, and the output is the sharing of the final data.

[1145] This series of processing flows will make the management and processing of order information at the logistics center more efficient, accurate, and quick.

[1146] 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.

[1147] This invention further improves the user experience by combining a system that streamlines the order and activation flow for data-based products with an emotion engine that recognizes user emotions. This system comprehensively manages the entry of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[1148] 1. User enters and submits order information

[1149] The user uses a terminal to input order information. The input form includes information such as the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. In addition, the emotion engine recognizes the user's emotions while they are inputting, and this information is also sent to the server.

[1150] 2. The server validates the order information and checks for errors

[1151] The server receives the order information and stores it in a database in an appropriate format.The server then retrieves the order information from the database and verifies it for any errors.If an error is found, the emotion engine detects the user's emotion and generates an error message in an appropriate tone, which is sent to the terminal.

[1152] 3. Customize and modify error messages

[1153] The terminal displays an error message to the user. The error message is customized by the emotion engine. For example, if the terminal recognizes that the user is in a hurry, a message for prompt action is displayed. The user checks the error message, makes any necessary corrections, and resends the message.

[1154] 4. Automatic generation and submission of adjustment requests

[1155] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties (customer, system engineer, production scheduler). The emotion engine conveys the requests in a tone that takes the relevant parties into consideration.

[1156] 5. Collecting and managing responses from stakeholders

[1157] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication or inconsistency.

[1158] 6. Generation and notification of aggregated results

[1159] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user in a tone that corresponds to their emotion. For example, if the server detects that the user is anxious, it sends a reassuring message.

[1160] 7. Final confirmation by the user and preparation for opening

[1161] The server notifies the user of the results. The user checks the results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with all parties involved, and begins preparations for opening.

[1162] Specific examples

[1163] For example, when a user (salesperson) places an order for a high-speed data line for a certain company, the following procedure is carried out.

[1164] 1. The user enters order information from the terminal

[1165] Customer Name: A Co., Ltd.

[1166] Product: High-speed data line

[1167] Desired opening date: December 1, 2023

[1168] Required line information: 1Gbps

[1169] The emotion engine analyzes facial expressions and voice input and recognizes that the user is in a hurry. The device then sends the information to the server.

[1170] 2. The server verifies the order information

[1171] The server saves the order information in a database and verifies it for any errors.

[1172] If there is a problem, the emotion engine generates an error message in a tone such as "Please correct it promptly" and sends it to the terminal.

[1173] 3. Viewing and correcting error messages

[1174] The terminal displays an error message to the user, who then makes the correction.

[1175] 4. Automatic generation and submission of adjustment requests

[1176] If there are no errors, the server generates an adjustment request such as "I apologize for bothering you during your busy schedule, but please arrange for the line and adjust the schedule," and sends it to each party involved.

[1177] 5. Collect and verify responses

[1178] The server receives responses from each party and stores them in a database.

[1179] The server verifies the response data and generates aggregated results.

[1180] 6. Final confirmation notice

[1181] The server notifies the user of the aggregated results, and the emotion engine generates a message such as, "Please confirm that there are no problems with the results."

[1182] 7. Final checks and preparations for opening

[1183] User sends OK.

[1184] The server shares the final information with the relevant parties and preparations for opening proceed.

[1185] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. At the same time, the emotion engine installed in the terminal analyzes the user's facial expression and tone of voice to detect the user's current emotion. Once the input is complete, the terminal sends the information and emotion data to the server.

[1189] Step 2:

[1190] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and saves it as a new order record in the database. The emotion data is also saved at the same time. Once the saving is complete, the server returns a "Data saving successful" response to the device.

[1191] Step 3:

[1192] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[1193] Step 4:

[1194] If the server detects any defects, it summarizes the details of the defects and generates an error message. The emotion engine analyzes the recorded user emotion data and customizes the error message with a tone that corresponds to the user's emotion. For example, if it detects that the user is in a hurry, it generates an error message that reads, "We apologize for the inconvenience, but the following items need to be corrected." It then sends the error message to the device.

[1195] Step 5:

[1196] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[1197] Step 6:

[1198] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[1199] Step 7:

[1200] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The emotion engine recognizes the emotions of the parties involved and sends the adjustment request in an appropriate tone.

[1201] Step 8:

[1202] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[1203] Step 9:

[1204] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[1205] Step 10:

[1206] The server notifies the user of the results for final confirmation. The emotion engine analyzes the user's current emotions and customizes the notification message with the most appropriate tone. For example, if the user is feeling anxious, the notification might say, "Don't worry, all the information has been collected accurately."

[1207] Step 11:

[1208] The user performs a final confirmation via their terminal. Once the user sends OK, the server receives this and shares the final data with all parties involved. This allows preparations for opening to proceed, and the entire system process is completed.

[1209] In this way, this system, which incorporates an emotion engine, improves the user experience and enables the order and activation flow to proceed smoothly.

[1210] Example 2

[1211] 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."

[1212] In the conventional order and activation flow, users often received error messages, which caused stress and frustration, resulting in a poor user experience. Furthermore, smooth communication was difficult because requests for adjustments to stakeholders were mechanical. Furthermore, there was a tendency for responses from stakeholders to be duplicated or contradictory, which reduced the efficiency of activation preparations.

[1213] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving order information and saving it in a database, means for verifying the order information and, if there is an error, generating an error message and sending it to the terminal, and means for customizing the tone of the error message or adjustment request based on emotion data. This makes it possible to generate and send error messages and adjustment requests that take the user's emotions into consideration, improving the user experience.

[1214] "User" refers to a person who uses this system to input order information.

[1215] "Terminal" refers to a device through which a user inputs order information and communicates with a server.

[1216] "Order information" refers to data such as the product name, customer name, desired activation date, and required line information that the user inputs via the terminal.

[1217] "Server" refers to the central processing unit that stores received order information in a database and verifies and processes the information.

[1218] "Database" refers to a storage device for storing order information and responses from related parties, and for retrieving and verifying data as necessary.

[1219] An "error message" refers to a notification message that is generated by the server and sent to the user via the terminal when there is an error in the order information.

[1220] "Emotion engine" refers to the algorithms and hardware / software that recognize the user's emotions and generate and process information based on them.

[1221] An "adjustment request" refers to a message that is generated by the server based on complete order information and sent to relevant parties to request the arrangement of merchandise or schedule adjustments.

[1222] "Stakeholders" refers to customers, engineers, schedulers, etc. involved in the order and activation flow.

[1223] "Aggregated results" refers to the final result data obtained by aggregating and verifying responses from stakeholders.

[1224] "Opening preparation" refers to the overall opening procedure for a product, which is carried out after the server notifies all relevant parties after final confirmation of the order information.

[1225] "Tone" refers to a manner for adjusting the presentation of a message based on the user's emotions detected by the emotion engine.

[1226] "Verification" refers to the process in which the server checks for any deficiencies or inconsistencies in the order information and responses from the parties involved.

[1227] "Notification" refers to the act of a server sending a message or information to a user or interested party.

[1228] This invention is a system that improves the efficiency of the order and activation flow for data-related products and further enhances the user experience by combining an emotion engine that recognizes user emotions. This system comprehensively manages the input of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[1229] Hardware and software used

[1230] Terminal: The device (PC, smartphone, tablet, etc.) through which the user enters order information

[1231] Server: A central processing unit (e.g., cloud server, on-premise server) for storing, validating and processing order information.

[1232] Database: A storage device for storing order information and responses from stakeholders (a relational database such as MySQL or PostgreSQL)

[1233] Emotion engine: Algorithms and APIs (e.g., Amazon Rekognition, Google Cloud Vision API) to recognize user emotions and generate error messages or adjustment requests in an appropriate tone.

[1234] System Operation Overview

[1235] The user uses a terminal to input order information. The input form includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. The emotion engine recognizes the user's facial expressions and voice as they are entering the information, and the emotion data is also sent to the server.

[1236] The server stores the received order information in a database in an appropriate format. The server then retrieves the order information from the database and verifies it for any errors. If an error is found, the server uses an emotion engine to generate an error message based on the user's emotions and sends it to the terminal. The terminal displays the error message to the user, and after the user makes the necessary corrections, the message is resent.

[1237] Once the order information is complete and complete, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. At this time, the emotion engine conveys the requests in a tone that is considerate to the relevant parties. The relevant parties send their responses to the adjustment requests to the server, which receives them and stores them in a database. The server then verifies the response data for duplication and inconsistencies.

[1238] After the verification is complete, the server generates the aggregated results and uses an emotion engine to notify the user in a tone that corresponds to their emotion. For example, if the user is anxious, a message that provides reassurance will be sent. Finally, the user checks the aggregated results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch.

[1239] Examples of concrete examples and prompts

[1240] For example, when a user places an order for a high-speed data line for a company, the following procedure is performed: The user inputs the following order information into the terminal:

[1241] Customer Name: A Co., Ltd.

[1242] Product: High-speed data line

[1243] Desired opening date: December 1, 2023

[1244] Required line information: 1Gbps

[1245] The emotion engine analyzes facial expressions and voices when inputting information and recognizes that the user is in a hurry. The device then sends the input information to the server.

[1246] The server saves the order information in a database and verifies it for any errors. If there are any errors, the emotion engine generates an error message in a tone such as "Please correct this as soon as possible" and sends it to the terminal.

[1247] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[1248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1249] Step 1: User enters and submits order information

[1250] Description: The user uses the terminal to enter order information (customer name, product name, desired activation date, required line information, etc.).

[1251] Input: User-entered order information

[1252] Data processing / calculation: Convert the data from the input form into an appropriate format and recognize the user's emotions using an emotion engine.

[1253] Output: Formatted order information and sentiment data

[1254] Specific operation: Enter information into a browser form, retrieve the data using JavaScript, and send it to the server as an HTTP POST request. Also, collect facial and voice data using a webcam and microphone and send it to the emotion engine.

[1255] Step 2: The server receives the order information and stores it in the database

[1256] Description: The server receives the order information and stores it in a database.

[1257] Input: Order information and emotion data sent from the terminal

[1258] Data Processing / Calculation: Generate and execute SQL insert statements to save order information to the database in the appropriate format.

[1259] Output: Order information stored in the database

[1260] Specific operation: Extracts data from an HTTP POST request, generates an SQL insert statement, and inserts the data into a database (e.g., MySQL or PostgreSQL).

[1261] Step 3: The server validates the order information and checks for errors

[1262] Description: The server retrieves order information from the database and verifies it for accuracy.

[1263] Input: Order information stored in the database

[1264] Data processing / calculation: Data is obtained using an SQL query and verified using a validation algorithm to ensure there are no errors.

[1265] Output: Error message or complete order information

[1266] What it does: Retrieves data from the database using SQL queries, performs validation checks in code, and generates error messages based on the emotion engine if there are any errors.

[1267] Step 4: Generate and send an error message

[1268] Description: If the server is faulty, it generates an error message based on the emotion engine and sends it to the terminal.

[1269] Input: Verification results (with imperfections) and emotion data

[1270] Data processing / calculation: Generates error messages in an appropriate tone based on emotional data.

[1271] Output: Error message displayed on the terminal

[1272] Specific operation: Calls the emotion engine API, applies the error message generation algorithm, and sends the error message as an HTTP response.

[1273] Step 5: View and fix error messages

[1274] Description: The terminal displays an error message to the user, who then makes the necessary corrections.

[1275] Input: Error message sent to the terminal

[1276] Data processing / calculation: Display an error message in the relevant form field and resubmit once corrections are made.

[1277] Output: Revised order information

[1278] Specific operation: An error message is displayed in a pop-up window or on a form using JavaScript, the user corrects the error, and the data is sent to the server again.

[1279] Step 6: Automatically generate and submit adjustment requests

[1280] Description: The server automatically generates an adjustment request based on complete order information and sends it to the relevant parties.

[1281] Input: Complete order information and emotion data

[1282] Data processing / calculation: Automatically generate adjustment requests based on order information and customize the tone of the message with an emotion engine.

[1283] Output: Coordination request message to stakeholders

[1284] Specific behavior: Applying automated generation rules based on product information to generate request content for stakeholders, and sending emails and notifications with the appropriate tone using an emotion engine.

[1285] Step 7: Collect and manage stakeholder responses

[1286] Description: Participants send responses to reconciliation requests to the server, which receives them and stores them in a database.

[1287] Input: Response data from stakeholders

[1288] Data processing / calculation: Receive the response data, store it in a database in the appropriate format, and apply algorithms to check for duplicates and inconsistencies.

[1289] Output: Verified response data

[1290] Specific operation: Receive response data via HTTP POST request and save it to the database using SQL insert statements. Check for duplicates and inconsistencies within the program.

[1291] Step 8: Generate and notify the results

[1292] Description: The server generates the aggregated results after the verification is completed, and notifies the user with a tone according to their emotion using the emotion engine.

[1293] Input: Verified response data and sentiment data

[1294] Data processing / calculation: Aggregate response data and generate summary results in text or graph format. Generate notification messages with customized tone using the emotion engine.

[1295] Output: Aggregation results notified to the user

[1296] What it does: It applies aggregation algorithms to generate text and graphs, uses an emotion engine to adjust tone, and notifies via HTTP response and email.

[1297] Step 9: Final confirmation by the user and preparation for opening

[1298] Description: The server notifies the user of the counting results, and the user performs a final check. If there are no problems, the server sends OK.

[1299] Input: Final confirmation from the user (OK)

[1300] Data processing / calculation: Share the final information with the relevant parties and make schedules and arrangements to proceed with preparations for opening.

[1301] Output: Progress of opening preparations

[1302] Specific operation: The user presses the OK button and notifies the server via an HTTP POST request. The server then sends the final information to the relevant parties via email or notification, and generates an implementation schedule.

[1303] As a result, the overall system processing is managed appropriately, improving the user experience.

[1304] (Application example 2)

[1305] 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."

[1306] In the conventional order and activation flow system, communication with users was standardized, making it difficult to respond in a way that took into account the user's feelings and circumstances. This resulted in frustration and stress when users entered order information, as well as inconvenience caused by requests for adjustments and receiving error messages. This led to lower customer satisfaction and process delays.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1308] In this invention, the server includes means for recognizing a user's emotion using an emotion engine and generating and providing feedback according to the emotion, means for using the emotion engine to communicate with related parties in a tone that takes the emotion into consideration, and means for the server to send an adjustment request to each related party via email or a messenger app, thereby improving the user experience and enabling a smooth and efficient order and activation flow.

[1309] A "terminal" is a device used by a user to input order information, such as a smartphone or a personal computer.

[1310] The "server" is a central processing unit that receives order information sent from users, stores it in a database, and performs various processes.

[1311] A "database" is an information system managed by a server for storing and managing order information and responses from related parties.

[1312] An "error message" is a message generated by the server to notify the user of any incomplete or incorrect order information.

[1313] The "emotion engine" is an analysis system that recognizes the user's emotions and generates feedback and messages based on those emotions.

[1314] An "adjustment request" is a request that is automatically generated by the server based on complete order information and sent to the relevant parties.

[1315] "Parties" are people or organizations involved in the order process, such as customers, technical personnel, schedulers, etc.

[1316] "Feedback" is information or messages provided in response to a user's input or action.

[1317] "Notification" is the act of sending messages or information from the server to users or related parties.

[1318] "Opening preparation" refers to the preparation work for installing products and starting services based on the final order information.

[1319] This invention is a system that streamlines the order and activation flow for data-related products by combining an emotion engine that recognizes user emotions. This system includes the following components.

[1320] Hardware and Software Configuration

[1321] 1. Terminal

[1322] A device used by users to input order information. Generally, this is a smartphone or PC.

[1323] 2. Server

[1324] This is a central processing unit that receives order information sent from the terminal and stores it in a database. It uses a web framework such as "Flask" to perform server-side processing.

[1325] 3. Database

[1326] This is an information system managed by a server, which stores and manages order information and responses from related parties.

[1327] 4. Emotion Engine

[1328] It is an analysis system for recognizing user emotions. For example, by using "Affectiva's SDK," it can analyze emotions in real time from the user's facial expressions and voice.

[1329] System processing flow

[1330] 1. Enter and submit order information

[1331] The user uses the terminal to input order information. The input form includes the customer name, product name, desired activation date, required line information, etc. While the user is inputting information, the emotion engine analyzes the user's emotions in real time through the terminal's camera and microphone. The terminal then sends the data together with this information to the server.

[1332] 2. Validating order information and generating error messages

[1333] The server saves the received order information in a database. The server then verifies the order information and, if there are any errors, generates an error message and sends it to the terminal. At this time, the error message is generated based on the analysis results of the emotion engine, taking into consideration the user's emotions.

[1334] 3. Automatic generation and submission of adjustment requests

[1335] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. The emotion engine also generates messages in a tone that takes into account the emotions of the relevant parties.

[1336] 4. Collecting and Managing Responses

[1337] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication and inconsistency.

[1338] 5. Generating the aggregated results and notifying the user

[1339] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user with a tone that corresponds to their emotion. Finally, the user sends the results they have confirmed to the server, and the server shares the final information with the relevant parties to begin preparations for launch.

[1340] Examples of concrete examples and prompts

[1341] Specific examples

[1342] "When a user orders a new product from an online store, they enter their order information. An emotion recognition engine analyzes the user's facial expressions and detects that the user is in a hurry. This allows error messages and feedback to be displayed quickly and concisely, improving the user experience."

[1343] Prompt Sentence Examples

[1344] "Can you give me an example of a server-side program written in Python that quickly checks for inaccuracies in the information entered when a user is ordering a new product from an online shopping site and provides appropriate feedback depending on the situation?"

[1345] In this way, the present invention provides a system that improves the user experience and smoothly advances the order and activation flow by combining an emotion recognition engine.

[1346] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1347] Step 1:

[1348] The user uses the terminal to input order information. The input form prompts the user to enter information such as the customer name, product name, desired activation date, and necessary line information. The emotion engine analyzes the user's facial expressions and voice while they are entering the information, and acquires emotional data. The terminal then sends the acquired order information and emotional data to the server as an HTTP POST request.

[1349] Input: Customer name, product name, desired activation date, necessary line information, emotion data

[1350] Output: HTTP POST request (order information and emotion data)

[1351] Step 2:

[1352] The server receives the order information and emotion data sent from the terminal and stores them in a database. It verifies that the data has been saved in the correct format. If the server finds any errors, it uses the emotion engine to generate an error message, adjusts the tone, and sends it to the terminal. If there are no errors, it proceeds to the next step.

[1353] Input: HTTP POST request (order information and emotion data)

[1354] Output: Error message or confirmation message

[1355] Step 3:

[1356] The terminal displays the error message received from the server to the user. The emotion engine customizes the tone of the error message to reflect the user's emotions. The user checks the error message, makes any necessary corrections, and resubmits the message. This process is repeated until the error is resolved.

[1357] Input: Error message

[1358] Output: Revised order information

[1359] Step 4:

[1360] After the server verifies the order information and finds no errors, it automatically generates an adjustment request and sends it to the relevant parties. The emotion engine generates messages in a tone that takes into account the emotions of the relevant parties. The message is sent via email or a messenger app.

[1361] Input: Complete order information

[1362] Output: Reconciliation request message

[1363] Step 5:

[1364] Each party sends a response to the reconciliation request to the server, which stores the received responses in a database and verifies the response data for duplication and inconsistency, thereby ensuring data consistency.

[1365] Input: Responses from stakeholders

[1366] Output: Validated answers stored in a database

[1367] Step 6:

[1368] The server generates the aggregated results after the verification is complete. The results are presented in text or graphical format. The emotion engine takes into account the user's emotions and generates a tone-adjusted message to notify the user of the results.

[1369] Input: Validated Answer

[1370] Output: Aggregation results (text or graph format)

[1371] Step 7:

[1372] The user checks the results of the calculations through their terminal. If there are no problems, they send a final confirmation of OK. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch. The emotion engine generates a final confirmation message and notifies the user in a way that takes their emotions into consideration.

[1373] Input: Check the summary results

[1374] Output: Final confirmation (OK) and preparation for opening

[1375] 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.

[1376] 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.

[1377] 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.

[1378] [Fourth embodiment]

[1379] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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).

[1385] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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."

[1392] This invention is a system for smoothly sharing and processing information regarding orders and activation of data-related products, and achieves efficient data processing by combining multiple elements.

[1393] 1. User enters and submits order information

[1394] The user uses a terminal to input order information. The input information includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server, which then stores the received data in a database in the appropriate format.

[1395] 2. The server validates the order information and checks for errors

[1396] The server retrieves order information from the database and verifies it for any errors. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. The user checks the error message, makes any necessary corrections, and then resubmits the data.

[1397] 3. Automatic generation and submission of adjustment requests

[1398] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information, site information, etc. The server then sends these adjustment requests to all parties involved (customer, system engineer, production scheduler) via email or messenger.

[1399] 4. Collecting responses from stakeholders and managing data

[1400] Once the responses from each participant are sent to the server, the server receives them and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary and prepares it for final confirmation.

[1401] 5. Final confirmation by the user and preparation for opening

[1402] The server notifies the user of the results of the calculation. The user checks the results through their terminal and sends OK if there are no problems. The server then shares the final data with all parties involved and begins preparations for launch. This completes all steps, ensuring a smooth launch of the data-based product.

[1403] Specific examples

[1404] For example, when a user (salesperson) places an order for a high-speed data line for ABC Corporation, the following procedure is carried out.

[1405] 1. The user enters order information from the terminal

[1406] Customer Name: ABC Co., Ltd.

[1407] Product: High-speed data line

[1408] Desired opening date: December 1, 2023

[1409] Required line information: 1Gbps

[1410] The terminal sends this information to the server.

[1411] 2. The server verifies the order information

[1412] The server saves the order information in a database and verifies it for any errors.

[1413] If there is a problem, an error message is generated and sent to the terminal.

[1414] 3. Automatic generation and submission of adjustment requests

[1415] If there are no errors, the server generates the following reconciliation request and sends it to each party:

[1416] Customer: Please provide the site information required for activation.

[1417] SE: Please check the line arrangement details and proceed with preparations.

[1418] Scheduler: Please adjust the schedule.

[1419] 4. Response collection and data management

[1420] The server receives responses from each party and stores them in a database.

[1421] The server verifies the response data and generates aggregated results.

[1422] 5. Final checks and preparations for opening

[1423] The server notifies the user of the results of the calculation, and the user sends OK.

[1424] The server shares the final data with the relevant parties, and preparations for opening proceed.

[1425] In this way, this system automates multi-stage information processing and coordination, thereby streamlining the order and activation flow for data-related products.

[1426] The processing flow will be explained below.

[1427] Step 1:

[1428] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. Once the input is complete, the terminal sends the information to the server as an HTTP POST request.

[1429] Step 2:

[1430] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and stores it as a new order record in the database. After saving is complete, the server returns a "Data saved successfully" response to the terminal.

[1431] Step 3:

[1432] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[1433] Step 4:

[1434] If the server detects any errors, it will summarize the details of the errors, generate an error message, and send it to the terminal. For example, an error message will be generated if a required field is not filled in or if the date format is incorrect.

[1435] Step 5:

[1436] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[1437] Step 6:

[1438] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[1439] Step 7:

[1440] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The adjustment request includes details of the information to be provided and the actions to be taken.

[1441] Step 8:

[1442] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[1443] Step 9:

[1444] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[1445] Step 10:

[1446] The server notifies the user of the tally results for final confirmation. The user checks the tally results through their terminal and sends OK if there are no problems.

[1447] Step 11:

[1448] The server receives the OK from the user and shares the final data with all parties involved. The shared information includes the adjustment results, the launch date, and other necessary information. The server then proceeds with the launch preparations and completes the entire system process.

[1449] Example 1

[1450] 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."

[1451] Existing order and activation management systems have the problem that the input of order information, error checking, and information sharing and coordination with related parties are cumbersome, resulting in human error and incomplete communication. Furthermore, while there is a demand for more efficient automatic generation and processing of adjustment requests, there is a lack of effective means to achieve this. Furthermore, there is also the issue of a heavy burden on users due to insufficient support for inputting order information.

[1452] 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.

[1453] In this invention, the server includes: means for a user to input order information from a terminal; means for the terminal to transmit the input data to the server; means for the server to receive the order information and store it in a database; means for the server to verify the order information and, if there is any deficiency, generate an error message and transmit it to the terminal; means for the terminal to display the error message to the user; means for the user to check the error message and make any necessary corrections; means for the server to automatically generate an adjustment request based on the order information without any deficiency and transmit it to the relevant parties; means for the server to receive responses from the relevant parties, store them in a database and verify duplications and inconsistencies; means for the server to generate a summary result after verification is complete; means for the server to notify the user of the adjustment result and receive final confirmation from the user; means for the server to share the final data with the relevant parties and proceed with preparations for opening; and means for supporting the input of order information using a generative AI model. This enables improved efficiency and accuracy of the entire order and opening management process.

[1454] A "user" is an individual or company employee who uses this system to input order information and perform various operations.

[1455] A "terminal" is an information input device used by a user, such as a computer, tablet, or smartphone.

[1456] "Server" means the central computer system that processes and stores order information and related data and manages instructions.

[1457] "Order information" is order-related data such as the customer name, product name, desired activation date, and required line information entered by the user.

[1458] A "database" is an information storage system for saving and managing order information and responses from each party.

[1459] An "error message" is a message that the server generates when there is a problem with the order information and notifies the user of the problem.

[1460] An "adjustment request" is information that is automatically generated by the server based on complete order information and contains necessary information and instructions to be sent to each party involved.

[1461] "Related parties" are people or positions such as customers, system engineers, and schedulers who are involved in order information or adjustment requests.

[1462] A "response" is information provided by each participant in response to a coordination request from the server.

[1463] "Aggregated Results" means the final statistical data or report generated by the Server after verifying and aggregating the responses from Participants.

[1464] A "generative AI model" is a machine learning model used to assist in the input of order information and for data processing.

[1465] "Input assistance" is a function that uses generative AI models to enable users to input order information accurately and quickly.

[1466] This invention aims to smoothly share and process information related to the order and activation of data-based products by having users input order information using a terminal and having that information managed and processed by a server. Specific embodiments of this invention are described below.

[1467] 1. Enter and submit order information

[1468] The user uses a terminal to enter order information into a dedicated form. Order information includes the customer name, product name, desired activation date, required line information, etc. For example, a user (sales representative) enters the following information to place an order for a high-speed data line.

[1469] Customer Name: ABC Co., Ltd.

[1470] Product: High-speed data line

[1471] Desired opening date: December 1, 2023

[1472] Required line information: 1Gbps

[1473] The user checks the input and clicks the send button. The device converts this information into JSON format and sends it to the server.

[1474] 2. Receiving and storing order information

[1475] The server receives the order information sent from the terminal and stores it in a database. The database can be a common relational database such as MySQL or PostgreSQL. The server checks the integrity of the data and performs initial checks.

[1476] 3. Validate order information and generate error messages

[1477] The server verifies the order information stored in the database. Verification items include the format of the input data, whether or not there are required fields, and whether the desired activation date is in the past. If an error is found, the server generates an error message and sends it to the terminal. For example, if the desired activation date is in the past, the server generates an error message stating, "The desired activation date must be a future date."

[1478] 4. Automatic generation and submission of adjustment requests

[1479] After all order information is ready, the server automatically generates the necessary adjustment requests for each product. The adjustment requests include information on line arrangements and site information. The server then sends these adjustment requests to the relevant parties via email or messenger app. For example, the following prompts can be used:

[1480] Customers are asked to provide the site information necessary for activation.

[1481] To the system engineer, "Please confirm the details of the line arrangement and proceed with the preparations."

[1482] To the scheduler, "Please adjust your schedule."

[1483] 5. Response collection and data management

[1484] The server receives the responses sent by the parties in response to the adjustment request and stores them in a database. The server then verifies the response data for duplication or inconsistency. If there are no problems, the server generates a summary result. For example, it generates a summary result saying, "Responses from all parties have been collected. Adjustment is complete."

[1485] 6. Final confirmation by the user and preparation for opening

[1486] The server notifies the user of the generated aggregated results. The user checks the results through their terminal and sends OK if there are no problems. The server receives final confirmation from the user and shares the final data with the relevant parties. This allows preparations for launch to proceed and ensures a smooth launch of data-related products.

[1487] Using generative AI models

[1488] Furthermore, the server uses a generative AI model to assist with input of order information. This AI model predicts and auto-completes input content, reducing the burden on users.

[1489] As described above, the present invention combines the elements of a server, terminal, and generative AI model to improve the efficiency and accuracy of the entire order and activation management process.

[1490] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1491] Step 1:

[1492] The user enters and submits order information from the terminal. The user opens a dedicated form screen on the terminal and enters the customer name, product name, desired activation date, and necessary line information. An example of input is as follows:

[1493] Customer name: General company

[1494] Product: High-speed data line

[1495] Desired opening date: December 1, 2023

[1496] Required line information: 1Gbps

[1497] After completing the input, the user clicks the send button, which causes the device to convert the information into JSON format and send it to the server.

[1498] Input: Customer name, product name, desired activation date, necessary line information

[1499] Output: Order information converted to JSON format

[1500] Step 2:

[1501] The server performs initial validation of the received data and saves it in the database. The server receives the order information in JSON format sent from the terminal and performs the following initial validation:

[1502] Are all required fields filled in?

[1503] Is the format correct?

[1504] Once the initial validation is complete, the server stores the order information in a database.

[1505] Input: Order information in JSON format

[1506] Output: Order information stored in the database

[1507] Step 3:

[1508] The server performs detailed verification of the stored order information. The specific verification items are as follows:

[1509] Is the customer name formatted correctly?

[1510] Is the desired activation date in the past?

[1511] Does the product name match what is registered in the system?

[1512] If an error is found, the server generates an error message and sends it to the terminal. Example of an error message: "The desired activation date must be a future date."

[1513] Input: Order information stored in the database

[1514] Output: Error message (if any)

[1515] Step 4:

[1516] The terminal displays an error message to the user, and the user makes the necessary corrections. The terminal displays the error message received from the server to the user. The user makes the necessary corrections according to the displayed error message and resubmits the order information.

[1517] Input: Error message

[1518] Output: Corrected order information

[1519] Step 5:

[1520] The server verifies the corrected order information again, and if there are no problems, it automatically generates an adjustment request. The server receives the order information again and verifies it again. After confirming that there are no errors, it automatically generates an adjustment request like the one below:

[1521] For customers, please provide the site information necessary for activation.

[1522] For system engineers, please check the details of the line arrangement and proceed with the preparations.

[1523] For schedulers, "Please adjust your schedule."

[1524] The generated adjustment request is sent to each party via email or messenger app.

[1525] Input: Revalidated Order Information

[1526] Output: Adjustment requests sent to each party

[1527] Step 6:

[1528] The server receives the responses from each participant, stores them in a database, and verifies them. Each participant provides a response in response to the reconciliation request, which the server receives. The received responses are stored in a database and the following verifications are performed:

[1529] Check whether the answers match

[1530] Formatting accuracy check

[1531] For example, if the scheduler responds, "Installation is scheduled for December 1, 2023," the server stores and validates this.

[1532] Input: Response data from stakeholders

[1533] Output: Validated database entries

[1534] Step 7:

[1535] The server uses the verified data to generate a summary result and notify the user. The server aggregates all the verified data and generates a result stating, "Responses from all parties have been received. Reconciliation is complete." The user is notified of this result.

[1536] Input: Validated response data

[1537] Output: User notification

[1538] Step 8:

[1539] The user makes a final confirmation, and the server shares the final data to begin preparations for launch. The user checks the results of the survey on their device and clicks the OK button if there are no problems. The server receives the user's final confirmation and shares the final data with all parties again, thus beginning preparations for launch.

[1540] Input: User's final confirmation

[1541] Output: Final data shared with stakeholders

[1542] The above is the flow of processing for this system's program, showing the specific operations and data input / output at each step.

[1543] (Application example 1)

[1544] 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."

[1545] Managing and processing order information at logistics centers is complex, and doing it manually is time-consuming and prone to errors. Additionally, correcting errors and sharing information among stakeholders is cumbersome, resulting in reduced work efficiency. Furthermore, there is a need for a function that can quickly verify order information and automatically generate and send adjustment requests. To solve these issues, an efficient and reliable system is required.

[1546] 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.

[1547] In this invention, the server

[1548] A means for a user to input order information from a terminal;

[1549] A means for transmitting input data from the terminal to a server;

[1550] A means for the server to receive the order information and store it in a database;

[1551] A means for the server to verify the order information and, if there is any defect, to generate an error message and send it to the terminal;

[1552] means for the terminal to display an error message to the user;

[1553] A means for the user to view error messages and make any necessary corrections;

[1554] A means for the server to automatically generate an adjustment request based on complete order information and transmit it to the relevant parties;

[1555] a means by which the server receives responses from participants, stores them in a database and checks for duplications and inconsistencies;

[1556] A means for the server to generate a summary result after the verification is completed;

[1557] means for the server to notify the user of the adjustment result and receive a final confirmation from the user;

[1558] The server will share the final data with the relevant parties to prepare for the launch.

[1559] A means to send an email to the customer if the order information is correct;

[1560] This will enable the efficient handling of order information at the logistics center, the prompt notification and correction of error messages, and the smooth automatic generation and transmission of adjustment requests to relevant parties.

[1561] A "terminal" is a device through which a user inputs order information and communicates.

[1562] A "server" is a central computer system that receives order information, stores it in a database, and performs a series of processes such as validation, generating error messages, and automatically generating adjustment requests.

[1563] "Order information" is information including data items such as customer name, product name, desired activation date, and necessary line information that are input by the user from the terminal.

[1564] "Database" refers to data storage for systematically storing and managing order information received by the server.

[1565] An "error message" is a message that is generated when the server finds an error in the order information when verifying it, and is sent to the terminal to prompt the user to make corrections.

[1566] An "adjustment request" is a request for specific tasks or information that is automatically generated by the server based on complete order information and sent to the relevant parties.

[1567] "Stakeholders" are people with an interest in the order processing and opening preparations, such as customers, engineers, and schedulers.

[1568] A "response" is information or a response provided by a participant in response to a coordination request from the server.

[1569] "Aggregated Results" means the comprehensive results data generated by the Server after verifying and processing the Participant's responses.

[1570] "Final confirmation" is an action in which the user confirms the counting results notified by the server and makes a final decision.

[1571] "Opening preparation" is the process in which the server shares the final data with the relevant parties after final confirmation and all preparation work is carried out.

[1572] "Mail" is an email that the server sends to the customer if the order information is correct.

[1573] This invention provides a system that streamlines the management and processing of order information at a logistics center. Specifically, a user inputs and transmits order information using a terminal, and the server receives the information and automatically performs various verifications and processing.

[1574] The server can use the following hardware and software: The hardware includes a database server for receiving and storing order information, and an application server for executing processing. The software used is Flask (a Python web framework), SQLite (a database management system), and an SMTP library (for sending emails).

[1575] The user enters order information on a device such as a smartphone or PC and presses the send button. This information is sent to the server and first saved in a database. The server verifies the saved information and, if there are any errors, generates an error message and sends it to the terminal. The terminal displays this error message to the user, who then makes the necessary corrections and resubmits the order.

[1576] For order information that is complete, the server automatically generates an adjustment request and sends it to the relevant parties via email or messenger app. The relevant parties respond with the necessary information based on the adjustment request, and the server receives it and stores it in a database. It verifies whether there are any duplicates or inconsistencies and generates a summary. The server then notifies the user of the summary results, who then performs a final confirmation. Once the final confirmation is complete, the server shares the final data with the relevant parties and begins preparations for launch.

[1577] For example, consider a scenario where a user wants to enter a new order. The user enters the order information as follows:

[1578] "Please enter new order information in the following format: Customer Name, Product Name, Order Date, and required information. For example: ABC Co., Ltd., Logistics Solutions, 2023-12-15, 5 Trucks."

[1579] This system will significantly improve the efficiency of order information handling at logistics centers, speeding up work and reducing errors. Furthermore, by utilizing a generative AI model, prompts will also be standardized, reducing the burden on users.

[1580] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1581] Step 1:

[1582] The user enters and sends order information from the terminal. The user enters the customer name, product name, order date, and other necessary information into the input form on the terminal, and sends the data to the server. The input data format uses data structures such as JSON or XML. When the user presses the send button, this input data is sent to the server.

[1583] Step 2:

[1584] The server receives the order information and saves it in the database. In this step, the server analyzes the received data and saves the contents in the SQLite database. To process the data, it converts the JSON format data into a format that can be applied to SQL queries and inserts it into the database. The input is the order information sent from the terminal, and the output is the order information recorded in the database.

[1585] Step 3:

[1586] The server validates the order information and generates an error message if there are any errors and sends it to the terminal. The server validates the order information for the following items: presence of required fields, data format, and validity of date (must be in the future). If an error is found, the server generates an appropriate error message and sends it to the terminal. The input is the order information stored in the database and the output is the error message.

[1587] Step 4:

[1588] The terminal displays an error message to the user, who then makes the necessary corrections. The user checks the error message displayed on the terminal screen and corrects any deficiencies. The corrected order information is then sent back to the server. The input is the error message sent from the server, and the output is the corrected order information.

[1589] Step 5:

[1590] The server automatically generates adjustment requests based on complete order information and sends them to the relevant parties. Based on the verified order information, the server generates the necessary adjustment requests for each relevant party (customer, engineer, scheduler) and sends them via email or messenger. Here, the generative AI model creates prompt text. The input is complete order information, and the output is the adjustment requests sent to each relevant party.

[1591] Step 6:

[1592] The server receives responses from the participants, stores them in a database, and verifies whether there are any duplicates or inconsistencies. The server stores the responses received from the participants in a database and verifies whether there are any duplicates or inconsistencies. The input is the response data from the participants, and the output is the response data stored in the database and the verification results.

[1593] Step 7:

[1594] The server generates the aggregated results after the verification is complete and notifies the user. The server aggregates the response data from the participants, generates the aggregated results in text or graph format based on the verification results, and notifies the user. The input is the response data from the participants, and the output is the generated aggregated results.

[1595] Step 8:

[1596] The user makes a final confirmation, and the server shares the final data with the relevant parties to begin preparations for launch. The user checks the tabulated results on their device and makes a final confirmation. After OK is sent, the server shares the final data with the relevant parties to begin preparations for launch. The input is the final confirmation from the user, and the output is the sharing of the final data.

[1597] This series of processing flows will make the management and processing of order information at the logistics center more efficient, accurate, and quick.

[1598] 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.

[1599] This invention further improves the user experience by combining a system that streamlines the order and activation flow for data-based products with an emotion engine that recognizes user emotions. This system comprehensively manages the entry of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[1600] 1. User enters and submits order information

[1601] The user uses a terminal to input order information. The input form includes information such as the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. In addition, the emotion engine recognizes the user's emotions while they are inputting, and this information is also sent to the server.

[1602] 2. The server validates the order information and checks for errors

[1603] The server receives the order information and stores it in a database in an appropriate format.The server then retrieves the order information from the database and verifies it for any errors.If an error is found, the emotion engine detects the user's emotion and generates an error message in an appropriate tone, which is sent to the terminal.

[1604] 3. Customize and modify error messages

[1605] The terminal displays an error message to the user. The error message is customized by the emotion engine. For example, if the terminal recognizes that the user is in a hurry, a message for prompt action is displayed. The user checks the error message, makes any necessary corrections, and resends the message.

[1606] 4. Automatic generation and submission of adjustment requests

[1607] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties (customer, system engineer, production scheduler). The emotion engine conveys the requests in a tone that takes the relevant parties into consideration.

[1608] 5. Collecting and managing responses from stakeholders

[1609] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication or inconsistency.

[1610] 6. Generation and notification of aggregated results

[1611] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user in a tone that corresponds to their emotion. For example, if the server detects that the user is anxious, it sends a reassuring message.

[1612] 7. Final confirmation by the user and preparation for opening

[1613] The server notifies the user of the results. The user checks the results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with all parties involved, and begins preparations for opening.

[1614] Specific examples

[1615] For example, when a user (salesperson) places an order for a high-speed data line for a certain company, the following procedure is carried out.

[1616] 1. The user enters order information from the terminal

[1617] Customer Name: A Co., Ltd.

[1618] Product: High-speed data line

[1619] Desired opening date: December 1, 2023

[1620] Required line information: 1Gbps

[1621] The emotion engine analyzes facial expressions and voice input and recognizes that the user is in a hurry. The device then sends the information to the server.

[1622] 2. The server verifies the order information

[1623] The server saves the order information in a database and verifies it for any errors.

[1624] If there is a problem, the emotion engine generates an error message in a tone such as "Please correct it promptly" and sends it to the terminal.

[1625] 3. Viewing and correcting error messages

[1626] The terminal displays an error message to the user, who then makes the correction.

[1627] 4. Automatic generation and submission of adjustment requests

[1628] If there are no errors, the server generates an adjustment request such as "I apologize for bothering you during your busy schedule, but please arrange for the line and adjust the schedule," and sends it to each party involved.

[1629] 5. Collect and verify responses

[1630] The server receives responses from each party and stores them in a database.

[1631] The server verifies the response data and generates aggregated results.

[1632] 6. Final confirmation notice

[1633] The server notifies the user of the aggregated results, and the emotion engine generates a message such as, "Please confirm that there are no problems with the results."

[1634] 7. Final checks and preparations for opening

[1635] User sends OK.

[1636] The server shares the final information with the relevant parties and preparations for opening proceed.

[1637] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[1638] The processing flow will be explained below.

[1639] Step 1:

[1640] The user enters order information from the terminal. An input form is displayed on the terminal, and the user enters the customer name, product name, desired activation date, necessary line information, etc. At the same time, the emotion engine installed in the terminal analyzes the user's facial expression and tone of voice to detect the user's current emotion. Once the input is complete, the terminal sends the information and emotion data to the server.

[1641] Step 2:

[1642] The server receives the order information and stores it in the database in the appropriate format. The server first parses the received data into JSON format and saves it as a new order record in the database. The emotion data is also saved at the same time. Once the saving is complete, the server returns a "Data saving successful" response to the device.

[1643] Step 3:

[1644] The server retrieves the order information from the database and verifies it for any errors, specifically checking that each item has been entered correctly, that all required fields have been filled in, and that the desired activation date is not in the past.

[1645] Step 4:

[1646] If the server detects any defects, it summarizes the details of the defects and generates an error message. The emotion engine analyzes the recorded user emotion data and customizes the error message with a tone that corresponds to the user's emotion. For example, if it detects that the user is in a hurry, it generates an error message that reads, "We apologize for the inconvenience, but the following items need to be corrected." It then sends the error message to the device.

[1647] Step 5:

[1648] The terminal receives the error message and displays it to the user. The user checks the displayed error message and makes any necessary corrections. Once the corrections are complete, the corrected data is sent to the server again.

[1649] Step 6:

[1650] If the server receives complete order information, it proceeds to the next step. Specifically, it automatically generates the necessary adjustment requests for each product. These adjustment requests include line arrangement information and site information.

[1651] Step 7:

[1652] The server sends an adjustment request to each party involved, such as the customer, system engineer, or production planner, via email or internal messenger. The emotion engine recognizes the emotions of the parties involved and sends the adjustment request in an appropriate tone.

[1653] Step 8:

[1654] Each party submits a response to the reconciliation request to the server, which then stores the response data in a database and verifies it for duplicates and inconsistencies.

[1655] Step 9:

[1656] The server generates a summary after validation is complete. The server analyzes each response to ensure it is consistent and contains all required information. This summary is generated in preparation for a final validation.

[1657] Step 10:

[1658] The server notifies the user of the results for final confirmation. The emotion engine analyzes the user's current emotions and customizes the notification message with the most appropriate tone. For example, if the user is feeling anxious, the notification might say, "Don't worry, all the information has been collected accurately."

[1659] Step 11:

[1660] The user performs a final confirmation via their terminal. Once the user sends OK, the server receives this and shares the final data with all parties involved. This allows preparations for opening to proceed, and the entire system process is completed.

[1661] In this way, this system, which incorporates an emotion engine, improves the user experience and enables the order and activation flow to proceed smoothly.

[1662] Example 2

[1663] 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."

[1664] In the conventional order and activation flow, users often received error messages, which caused stress and frustration, resulting in a poor user experience. Furthermore, smooth communication was difficult because requests for adjustments to stakeholders were mechanical. Furthermore, there was a tendency for responses from stakeholders to be duplicated or contradictory, which reduced the efficiency of activation preparations.

[1665] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving order information and saving it in a database, means for verifying the order information and, if there is an error, generating an error message and sending it to the terminal, and means for customizing the tone of the error message or adjustment request based on emotion data. This makes it possible to generate and send error messages and adjustment requests that take the user's emotions into consideration, improving the user experience.

[1666] "User" refers to a person who uses this system to input order information.

[1667] "Terminal" refers to a device through which a user inputs order information and communicates with a server.

[1668] "Order information" refers to data such as the product name, customer name, desired activation date, and required line information that the user inputs via the terminal.

[1669] "Server" refers to the central processing unit that stores received order information in a database and verifies and processes the information.

[1670] "Database" refers to a storage device for storing order information and responses from related parties, and for retrieving and verifying data as necessary.

[1671] An "error message" refers to a notification message that is generated by the server and sent to the user via the terminal when there is an error in the order information.

[1672] "Emotion engine" refers to the algorithms and hardware / software that recognize the user's emotions and generate and process information based on them.

[1673] An "adjustment request" refers to a message that is generated by the server based on complete order information and sent to relevant parties to request the arrangement of merchandise or schedule adjustments.

[1674] "Stakeholders" refers to customers, engineers, schedulers, etc. involved in the order and activation flow.

[1675] "Aggregated results" refers to the final result data obtained by aggregating and verifying responses from stakeholders.

[1676] "Opening preparation" refers to the overall opening procedure for a product, which is carried out after the server notifies all relevant parties after final confirmation of the order information.

[1677] "Tone" refers to a manner for adjusting the presentation of a message based on the user's emotions detected by the emotion engine.

[1678] "Verification" refers to the process in which the server checks for any deficiencies or inconsistencies in the order information and responses from the parties involved.

[1679] "Notification" refers to the act of a server sending a message or information to a user or interested party.

[1680] This invention is a system that improves the efficiency of the order and activation flow for data-related products and further enhances the user experience by combining an emotion engine that recognizes user emotions. This system comprehensively manages the input of order information, verification, correction of defects, sending of adjustment requests, collection and verification of responses from related parties, final confirmation, and preparation for activation.

[1681] Hardware and software used

[1682] Terminal: The device (PC, smartphone, tablet, etc.) through which the user enters order information

[1683] Server: A central processing unit (e.g., cloud server, on-premise server) for storing, validating and processing order information.

[1684] Database: A storage device for storing order information and responses from stakeholders (a relational database such as MySQL or PostgreSQL)

[1685] Emotion engine: Algorithms and APIs (e.g., Amazon Rekognition, Google Cloud Vision API) to recognize user emotions and generate error messages or adjustment requests in an appropriate tone.

[1686] System Operation Overview

[1687] The user uses a terminal to input order information. The input form includes the customer name, product name, desired activation date, and required line information. The terminal sends this information to the server as an HTTP POST request. The emotion engine recognizes the user's facial expressions and voice as they are entering the information, and the emotion data is also sent to the server.

[1688] The server stores the received order information in a database in an appropriate format. The server then retrieves the order information from the database and verifies it for any errors. If an error is found, the server uses an emotion engine to generate an error message based on the user's emotions and sends it to the terminal. The terminal displays the error message to the user, and after the user makes the necessary corrections, the message is resent.

[1689] Once the order information is complete and complete, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. At this time, the emotion engine conveys the requests in a tone that is considerate to the relevant parties. The relevant parties send their responses to the adjustment requests to the server, which receives them and stores them in a database. The server then verifies the response data for duplication and inconsistencies.

[1690] After the verification is complete, the server generates the aggregated results and uses an emotion engine to notify the user in a tone that corresponds to their emotion. For example, if the user is anxious, a message that provides reassurance will be sent. Finally, the user checks the aggregated results on their device and sends an OK if there are no problems. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch.

[1691] Examples of concrete examples and prompts

[1692] For example, when a user places an order for a high-speed data line for a company, the following procedure is performed: The user inputs the following order information into the terminal:

[1693] Customer Name: A Co., Ltd.

[1694] Product: High-speed data line

[1695] Desired opening date: December 1, 2023

[1696] Required line information: 1Gbps

[1697] The emotion engine analyzes facial expressions and voices when inputting information and recognizes that the user is in a hurry. The device then sends the input information to the server.

[1698] The server saves the order information in a database and verifies it for any errors. If there are any errors, the emotion engine generates an error message in a tone such as "Please correct this as soon as possible" and sends it to the terminal.

[1699] In this way, by combining the emotion engine, a system is provided that improves the user experience and smoothly advances the order and activation flow.

[1700] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1701] Step 1: User enters and submits order information

[1702] Description: The user uses the terminal to enter order information (customer name, product name, desired activation date, required line information, etc.).

[1703] Input: User-entered order information

[1704] Data processing / calculation: Convert the data from the input form into an appropriate format and recognize the user's emotions using an emotion engine.

[1705] Output: Formatted order information and sentiment data

[1706] Specific operation: Enter information into a browser form, retrieve the data using JavaScript, and send it to the server as an HTTP POST request. Also, collect facial and voice data using a webcam and microphone and send it to the emotion engine.

[1707] Step 2: The server receives the order information and stores it in the database

[1708] Description: The server receives the order information and stores it in a database.

[1709] Input: Order information and emotion data sent from the terminal

[1710] Data Processing / Calculation: Generate and execute SQL insert statements to save order information to the database in the appropriate format.

[1711] Output: Order information stored in the database

[1712] Specific operation: Extracts data from an HTTP POST request, generates an SQL insert statement, and inserts the data into a database (e.g., MySQL or PostgreSQL).

[1713] Step 3: The server validates the order information and checks for errors

[1714] Description: The server retrieves order information from the database and verifies it for accuracy.

[1715] Input: Order information stored in the database

[1716] Data processing / calculation: Data is obtained using an SQL query and verified using a validation algorithm to ensure there are no errors.

[1717] Output: Error message or complete order information

[1718] What it does: Retrieves data from the database using SQL queries, performs validation checks in code, and generates error messages based on the emotion engine if there are any errors.

[1719] Step 4: Generate and send an error message

[1720] Description: If the server is faulty, it generates an error message based on the emotion engine and sends it to the terminal.

[1721] Input: Verification results (with imperfections) and emotion data

[1722] Data processing / calculation: Generates error messages in an appropriate tone based on emotional data.

[1723] Output: Error message displayed on the terminal

[1724] Specific operation: Calls the emotion engine API, applies the error message generation algorithm, and sends the error message as an HTTP response.

[1725] Step 5: View and fix error messages

[1726] Description: The terminal displays an error message to the user, who then makes the necessary corrections.

[1727] Input: Error message sent to the terminal

[1728] Data processing / calculation: Display an error message in the relevant form field and resubmit once corrections are made.

[1729] Output: Revised order information

[1730] Specific operation: An error message is displayed in a pop-up window or on a form using JavaScript, the user corrects the error, and the data is sent to the server again.

[1731] Step 6: Automatically generate and submit adjustment requests

[1732] Description: The server automatically generates an adjustment request based on complete order information and sends it to the relevant parties.

[1733] Input: Complete order information and emotion data

[1734] Data processing / calculation: Automatically generate adjustment requests based on order information and customize the tone of the message with an emotion engine.

[1735] Output: Coordination request message to stakeholders

[1736] Specific behavior: Applying automated generation rules based on product information to generate request content for stakeholders, and sending emails and notifications with the appropriate tone using an emotion engine.

[1737] Step 7: Collect and manage stakeholder responses

[1738] Description: Participants send responses to reconciliation requests to the server, which receives them and stores them in a database.

[1739] Input: Response data from stakeholders

[1740] Data processing / calculation: Receive the response data, store it in a database in the appropriate format, and apply algorithms to check for duplicates and inconsistencies.

[1741] Output: Verified response data

[1742] Specific operation: Receive response data via HTTP POST request and save it to the database using SQL insert statements. Check for duplicates and inconsistencies within the program.

[1743] Step 8: Generate and notify the results

[1744] Description: The server generates the aggregated results after the verification is completed, and notifies the user with a tone according to their emotion using the emotion engine.

[1745] Input: Verified response data and sentiment data

[1746] Data processing / calculation: Aggregate response data and generate summary results in text or graph format. Generate notification messages with customized tone using the emotion engine.

[1747] Output: Aggregation results notified to the user

[1748] What it does: It applies aggregation algorithms to generate text and graphs, uses an emotion engine to adjust tone, and notifies via HTTP response and email.

[1749] Step 9: Final confirmation by the user and preparation for opening

[1750] Description: The server notifies the user of the counting results, and the user performs a final check. If there are no problems, the server sends OK.

[1751] Input: Final confirmation from the user (OK)

[1752] Data processing / calculation: Share the final information with the relevant parties and make schedules and arrangements to proceed with preparations for opening.

[1753] Output: Progress of opening preparations

[1754] Specific operation: The user presses the OK button and notifies the server via an HTTP POST request. The server then sends the final information to the relevant parties via email or notification, and generates an implementation schedule.

[1755] As a result, the overall system processing is managed appropriately, improving the user experience.

[1756] (Application example 2)

[1757] 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."

[1758] In the conventional order and activation flow system, communication with users was standardized, making it difficult to respond in a way that took into account the user's feelings and circumstances. This resulted in frustration and stress when users entered order information, as well as inconvenience caused by requests for adjustments and receiving error messages. This led to lower customer satisfaction and process delays.

[1759] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1760] In this invention, the server includes means for recognizing a user's emotion using an emotion engine and generating and providing feedback according to the emotion, means for using the emotion engine to communicate with related parties in a tone that takes the emotion into consideration, and means for the server to send an adjustment request to each related party via email or a messenger app, thereby improving the user experience and enabling a smooth and efficient order and activation flow.

[1761] A "terminal" is a device used by a user to input order information, such as a smartphone or a personal computer.

[1762] The "server" is a central processing unit that receives order information sent from users, stores it in a database, and performs various processes.

[1763] A "database" is an information system managed by a server for storing and managing order information and responses from related parties.

[1764] An "error message" is a message generated by the server to notify the user of any incomplete or incorrect order information.

[1765] The "emotion engine" is an analysis system that recognizes the user's emotions and generates feedback and messages based on those emotions.

[1766] An "adjustment request" is a request that is automatically generated by the server based on complete order information and sent to the relevant parties.

[1767] "Parties" are people or organizations involved in the order process, such as customers, technical personnel, schedulers, etc.

[1768] "Feedback" is information or messages provided in response to a user's input or action.

[1769] "Notification" is the act of sending messages or information from the server to users or related parties.

[1770] "Opening preparation" refers to the preparation work for installing products and starting services based on the final order information.

[1771] This invention is a system that streamlines the order and activation flow for data-related products by combining an emotion engine that recognizes user emotions. This system includes the following components.

[1772] Hardware and Software Configuration

[1773] 1. Terminal

[1774] A device used by users to input order information. Generally, this is a smartphone or PC.

[1775] 2. Server

[1776] This is a central processing unit that receives order information sent from the terminal and stores it in a database. It uses a web framework such as "Flask" to perform server-side processing.

[1777] 3. Database

[1778] This is an information system managed by a server, which stores and manages order information and responses from related parties.

[1779] 4. Emotion Engine

[1780] It is an analysis system for recognizing user emotions. For example, by using "Affectiva's SDK," it can analyze emotions in real time from the user's facial expressions and voice.

[1781] System processing flow

[1782] 1. Enter and submit order information

[1783] The user uses the terminal to input order information. The input form includes the customer name, product name, desired activation date, required line information, etc. While the user is inputting information, the emotion engine analyzes the user's emotions in real time through the terminal's camera and microphone. The terminal then sends the data together with this information to the server.

[1784] 2. Validating order information and generating error messages

[1785] The server saves the received order information in a database. The server then verifies the order information and, if there are any errors, generates an error message and sends it to the terminal. At this time, the error message is generated based on the analysis results of the emotion engine, taking into consideration the user's emotions.

[1786] 3. Automatic generation and submission of adjustment requests

[1787] After all order information is ready, the server automatically generates the necessary adjustment requests for each product and sends them to the relevant parties. The emotion engine also generates messages in a tone that takes into account the emotions of the relevant parties.

[1788] 4. Collecting and Managing Responses

[1789] Each party sends a response to the reconciliation request to the server, which receives it and stores it in a database. The server then verifies the response data for duplication and inconsistency.

[1790] 5. Generating the aggregated results and notifying the user

[1791] The server generates a summary of the results after the verification is complete. The summary results are generated in text or graph format, and the emotion engine notifies the user with a tone that corresponds to their emotion. Finally, the user sends the results they have confirmed to the server, and the server shares the final information with the relevant parties to begin preparations for launch.

[1792] Examples of concrete examples and prompts

[1793] Specific examples

[1794] "When a user orders a new product from an online store, they enter their order information. An emotion recognition engine analyzes the user's facial expressions and detects that the user is in a hurry. This allows error messages and feedback to be displayed quickly and concisely, improving the user experience."

[1795] Prompt Sentence Examples

[1796] "Can you give me an example of a server-side program written in Python that quickly checks for inaccuracies in the information entered when a user is ordering a new product from an online shopping site and provides appropriate feedback depending on the situation?"

[1797] In this way, the present invention provides a system that improves the user experience and smoothly advances the order and activation flow by combining an emotion recognition engine.

[1798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1799] Step 1:

[1800] The user uses the terminal to input order information. The input form prompts the user to enter information such as the customer name, product name, desired activation date, and necessary line information. The emotion engine analyzes the user's facial expressions and voice while they are entering the information, and acquires emotional data. The terminal then sends the acquired order information and emotional data to the server as an HTTP POST request.

[1801] Input: Customer name, product name, desired activation date, necessary line information, emotion data

[1802] Output: HTTP POST request (order information and emotion data)

[1803] Step 2:

[1804] The server receives the order information and emotion data sent from the terminal and stores them in a database. It verifies that the data has been saved in the correct format. If the server finds any errors, it uses the emotion engine to generate an error message, adjusts the tone, and sends it to the terminal. If there are no errors, it proceeds to the next step.

[1805] Input: HTTP POST request (order information and emotion data)

[1806] Output: Error message or confirmation message

[1807] Step 3:

[1808] The terminal displays the error message received from the server to the user. The emotion engine customizes the tone of the error message to reflect the user's emotions. The user checks the error message, makes any necessary corrections, and resubmits the message. This process is repeated until the error is resolved.

[1809] Input: Error message

[1810] Output: Revised order information

[1811] Step 4:

[1812] After the server verifies the order information and finds no errors, it automatically generates an adjustment request and sends it to the relevant parties. The emotion engine generates messages in a tone that takes into account the emotions of the relevant parties. The message is sent via email or a messenger app.

[1813] Input: Complete order information

[1814] Output: Reconciliation request message

[1815] Step 5:

[1816] Each party sends a response to the reconciliation request to the server, which stores the received responses in a database and verifies the response data for duplication and inconsistency, thereby ensuring data consistency.

[1817] Input: Responses from stakeholders

[1818] Output: Validated answers stored in a database

[1819] Step 6:

[1820] The server generates the aggregated results after the verification is complete. The results are presented in text or graphical format. The emotion engine takes into account the user's emotions and generates a tone-adjusted message to notify the user of the results.

[1821] Input: Validated Answer

[1822] Output: Aggregation results (text or graph format)

[1823] Step 7:

[1824] The user checks the results of the calculations through their terminal. If there are no problems, they send a final confirmation of OK. The server receives the OK from the user, shares the final information with the relevant parties, and begins preparations for launch. The emotion engine generates a final confirmation message and notifies the user in a way that takes their emotions into consideration.

[1825] Input: Check the summary results

[1826] Output: Final confirmation (OK) and preparation for opening

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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. ...

Claims

1. A means for a user to input order information from a terminal; A means for transmitting input data from the terminal to a server; A means for the server to receive the order information and store it in a database; A means for the server to verify the order information and, if there is any defect, to generate an error message and send it to the terminal; means for the terminal to display an error message to the user; A means for the user to view error messages and make any necessary corrections; A means for the server to automatically generate an adjustment request based on complete order information and transmit it to the relevant parties; a means by which the server receives responses from participants, stores them in a database and checks for duplications and inconsistencies; A means for the server to generate a summary result after the verification is completed; means for the server to notify the user of the adjustment result and receive a final confirmation from the user; The server will share the final data with the relevant parties to prepare for the launch. A system including:

2. The system of claim 1 , further comprising means for the server to send the adjustment request to each of the parties via email or a messenger app.

3. 2. The system according to claim 1, further comprising means for the server to generate the tabulated results after the verification is completed in text or graph format and notify the user of the results.

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