system

Abstract

Provide a system. A method for obtaining input from a user; means for transmitting the acquired input information to a server; a means for generating a document based on the data generated in the server; means for returning document generation results to the user; A system including:

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] Traditional business processes involve a lot of manual work, which means tasks like invoicing, data organization, and reservation management take a lot of time and there is a high risk of human error. This not only prevents employees from concentrating on important tasks, but also reduces the efficiency of the entire business. There is a need to introduce a new system to improve this situation, increase work efficiency, and reduce errors. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring input from a user, a means for transmitting the acquired input information to a server, a means for generating documents based on data generated on the server, and a means for returning the document generation results to the user. This system enables efficient and accurate automatic invoice generation, data organization, reservation management, and other operations. Specifically, in the invoice creation module, a generative AI model on the server generates accurate invoices based on information input from the user, and in the data organization module, AI automatically organizes and classifies acquired data. In addition, the reservation management module automatically checks for duplicate reservation information and notifies customers. This improves the efficiency of the entire business process and reduces the risk of human error.

[0006] A "user" is a person or organization that utilizes the system to provide input information and perform various business tasks.

[0007] "Input information" refers to data provided by users to the system, including information required for business tasks such as billing, data organization, and reservation management.

[0008] A "server" is a computer system that receives input information sent by a user, generates and processes data using a generative AI model, and returns the results to the user.

[0009] A "generative AI model" is an artificial intelligence algorithm that processes input data based on specific rules and templates to automatically generate documents, organized data, schedules, etc.

[0010] An "invoice" is a document used to bill a customer for goods or services provided to them, and is automatically generated by this system.

[0011] "Data organization" is the process of centralizing distributed data and classifying and tagging it according to specified standards and rules.

[0012] "Reservation management" is the process of managing schedules based on reservation information provided by users, checking for duplicates, and notifying customers.

[0013] "Duplicate check" is a function that detects overlapping reservations for the same time slot or resource during the reservation management process.

[0014] "Customer notification" is a function that allows the reservation management system to automatically notify customers of reservation confirmations and changes. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that receives input from a user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. A specific example of the present invention is described below.

[0037] 1. Invoicing module

[0038] The user provides the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) to an input form on the terminal. The terminal acquires the input information and sends it to the server. The server analyzes the received information and passes it to the generative AI model. The generative AI model selects an appropriate template based on the provided information and automatically generates an invoice. The generated invoice is sent from the server to the terminal, where the user can review it on the terminal and enter corrections or additional information as necessary.

[0039] Examples:

[0040] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information to the server, which uses a generative AI model to generate an invoice based on this information. The generated invoice is then displayed on the terminal for the user to confirm.

[0041] 2. Data reduction module

[0042] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents to the server. The server inputs the data into a generative AI model, which automatically organizes and classifies the data. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is then sent from the server to the device, where the user can review it and make any necessary corrections.

[0043] Examples:

[0044] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase history to the system. The device reads the file and sends it to the server. The server uses a generative AI model to classify and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[0045] 3. Reservation Management Module

[0046] The user provides reservation information (date and time, customer name, service details, etc.) in an input form on the device. The device acquires the input information and sends it to the server. The server passes the received information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservations and automatically sets an appropriate schedule. If necessary, a notification is automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it on the device.

[0047] Examples:

[0048] A user enters a meeting reservation with customer "Customer B" for 9:00 on October 15th. The device sends the information to the server. The server uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[0049] In this way, the present invention provides a system that efficiently generates invoices, organizes data, and manages reservations, reduces the risk of human error, and improves business efficiency.

[0050] The processing flow will be explained below.

[0051] Invoice Creation Module Processing Flow

[0052] Step 1:

[0053] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[0054] Step 2:

[0055] The device validates the information entered by the user, checking the format and mandatory fields, and if there are no errors, sends the information to the server.

[0056] Step 3:

[0057] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including supplementary information if necessary.

[0058] Step 4:

[0059] The server provides input data to the generative AI model and initiates the process of selecting and generating an invoice template.

[0060] Step 5:

[0061] A generative AI model automatically generates invoices using templates based on the data provided.

[0062] Step 6:

[0063] The server receives the generated invoice data and checks the format and content.

[0064] Step 7:

[0065] The server returns the generated bill data to the terminal.

[0066] Step 8:

[0067] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[0068] Data reduction module processing flow

[0069] Step 1:

[0070] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[0071] Step 2:

[0072] The device reads the uploaded file, verifies the format, and performs basic data formatting. If there are no errors, the data is sent to the server.

[0073] Step 3:

[0074] The server analyzes the received data and converts it into a data format that can be passed to the generative AI model.

[0075] Step 4:

[0076] The server feeds the data to the generative AI model, which begins the process of cleaning and classifying the data.

[0077] Step 5:

[0078] Generative AI models analyze the data and tag and categorize it according to specified rules and criteria.

[0079] Step 6:

[0080] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[0081] Step 7:

[0082] The server returns the organized data to the device.

[0083] Step 8:

[0084] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[0085] Reservation Management Module Processing Flow

[0086] Step 1:

[0087] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[0088] Step 2:

[0089] The terminal verifies the reservation information entered by the user, and if there are no errors, sends the information to the server.

[0090] Step 3:

[0091] The server analyzes the received reservation information and converts it into a data format to be passed to the generative AI model.

[0092] Step 4:

[0093] The server feeds the reservation information to the generative AI model and initiates the schedule management process.

[0094] Step 5:

[0095] A generative AI model analyzes reservation information and checks for overlaps with existing schedules.

[0096] Step 6:

[0097] The server receives the schedule data returned from the generative AI model and checks for duplicates and schedule consistency.

[0098] Step 7:

[0099] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[0100] Step 8:

[0101] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[0102] Example 1

[0103] 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."

[0104] Conventional systems require users to manually create documents, organize data, and manage reservation information, which requires a great deal of effort and time. Manual tasks also pose a risk of human error. As a result, operational efficiency declines and productivity is limited. While systems exist to automate these tasks, the quality of the generated data and documents is often inconsistent, making them impractical. A system that solves these problems is needed.

[0105] 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.

[0106] In this invention, the server includes a means for acquiring input from a user, a means for transmitting the acquired input information to the server, a means for generating a document based on the data using a generative AI model in the server, and a means for returning the generated document to the user. This allows users to easily automatically generate high-quality documents, reduces the risk of errors due to manual work, and significantly improves business efficiency.

[0107] This invention also includes a means for sending data files provided by a user to a server, organizing and classifying the data files using a generative AI model, and a means for returning the organized data to the user, thereby enabling the user to organize and classify data efficiently.

[0108] Furthermore, it includes a means for acquiring reservation information from users and managing reservation schedules using a generative AI model, a means for automatically checking for duplicate reservation information and notifying customers, and a means for returning schedule management results to users. This automates reservation management, prevents duplication and omissions, and improves user convenience.

[0109] A "user" is an entity that utilizes the system to input information and perform various functions.

[0110] A "terminal" is a device or local computer that is directly operated by a user, and is a device that has the role of transmitting input information to a server.

[0111] The "server" is a central computer that receives information sent by users and uses generative AI models to generate documents, organize data, and manage schedules.

[0112] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate documents based on provided data, organize and classify data, and manage schedules.

[0113] "Documents" are documents such as reports, invoices, contracts, etc. created by a generative AI model based on information provided by a user.

[0114] "Data file" means a file, such as a CSV file or Excel file, that contains data that a user provides to the system and that needs to be organized and categorized.

[0115] "Reservation information" refers to information relating to a reservation, such as the date and time, customer name, and service details, that the user provides to the system.

[0116] "Duplicate check" is the process of comparing existing schedules and data with newly acquired information to check for duplicates.

[0117] "Customer notification" is a function that automatically notifies customers of reservation information, data sorting results, etc.

[0118] "Schedule management" is the process of creating an optimal schedule based on reservation information and managing it to prevent duplication and omissions.

[0119] An "input form" is an interface provided for a user to input information on a terminal.

[0120] The present invention is a system that takes input information from a user, sends it to a server, and automatically generates documents using a generative AI model. The system includes a billing module, a data organization module, and a reservation management module, each of which performs a specific task.

[0121] Invoicing Module

[0122] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. This information is sent to the server via the device. The server formats the received data and passes it to a generative AI model (e.g., GPT-3 (registered trademark)). The generative AI model selects an appropriate invoice template based on the provided data, embeds the data, and generates a document. The generated invoice is sent from the server to the device, where the user can review it and modify it if necessary.

[0123] Examples:

[0124] When a user creates a new invoice, they enter the following information:

[0125] Customer name: "Customer A"

[0126] Product: "Product X"

[0127] Quantity: "10"

[0128] Unit price: 5,000 yen

[0129] Transaction date: "2023-10-01"

[0130] This information is sent from the device to a server, which uses a generative AI model to generate an invoice based on this information, which can then be viewed on the user's device.

[0131] Data Reduction Module

[0132] Users upload data files (e.g., CSV or Excel files) that need to be organized and classified to their device. The device then sends the uploaded data files to a server, which analyzes the data using a generative AI model. The generative AI model uses natural language processing technology to tag, classify, and organize the data. The organized data is then sent from the server to the device, where it can be viewed and modified by the user.

[0133] Examples:

[0134] The user uploads customer data (a CSV file containing customer names, addresses, purchase history, etc.) to the system. The device sends the file to the server, which uses a generative AI model to analyze the data and classify and tag customers based on "customer purchase frequency." The organized data is then sent back to the device for review by the user.

[0135] Reservation Management Module

[0136] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information to the server. The server passes the received reservation information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservation information and automatically sets an appropriate schedule. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can review and modify it.

[0137] Examples:

[0138] A user enters a meeting reservation with "Customer B" for 9:00 on October 15th. This information is sent from the device to the server, which uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is displayed on the device for the user to review.

[0139] In this way, the present invention provides a system that reduces user effort, prevents human error, and improves work efficiency. Users can simply use specific prompts to pass the required information to the generative AI model and obtain automated results.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Invoicing module processing steps

[0142] Step 1:

[0143] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the terminal. This input form is displayed on the web application and can be accessed through a browser.

[0144] Specific behavior:

[0145] The user enters data into each field and clicks the submit button.

[0146] Input: Customer name "Customer A", product "Product X", quantity "10", unit price "5,000 yen", transaction date "2023-10-01"

[0147] Output: Data entered in the input form

[0148] Step 2:

[0149] The device temporarily stores the data obtained from the input form and then sends it to the server using an HTTP POST request.

[0150] Specific behavior:

[0151] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[0152] Input: Data entered into an input form

[0153] Output: JSON format data (customer name, product name, quantity, unit price, transaction date)

[0154] Step 3:

[0155] The server receives the data sent from the terminal. After receiving it, it checks the integrity of the data and performs any necessary data processing. Data processing includes standardizing the data format and processing missing values.

[0156] Specific behavior:

[0157] The server parses the received JSON data and checks whether all required fields are present and whether the data format is correct.

[0158] Input: JSON format data (customer name, product name, quantity, unit price, transaction date)

[0159] Output: Formatted data

[0160] Step 4:

[0161] The server passes the formatted data to a generative AI model, which selects an appropriate invoice template based on the provided data and generates the document by filling in the data.

[0162] Specific behavior:

[0163] The generative AI model uses a template selection algorithm to choose the best template and fill in the data to create the invoice.

[0164] Input: Formatted data

[0165] Output: Generated invoice document

[0166] Step 5:

[0167] The server receives the generated invoice and sends it to the terminal, again using an HTTP response, as a PDF file.

[0168] Specific behavior:

[0169] The server retrieves the generated invoice and sends it to the terminal as an attachment to the HTTP response.

[0170] Input: Generated invoice document

[0171] Output: Invoice document in PDF format

[0172] Step 6:

[0173] The user checks the generated invoice displayed on the terminal, enters corrections or additional information as necessary, and performs a final check.

[0174] Specific behavior:

[0175] Users can view the PDF invoice on their device, make any necessary corrections, and then save or email the invoice.

[0176] Input: Invoice document in PDF format

[0177] Output: Final confirmed invoice

[0178] Data Reduction Module Processing Steps

[0179] Step 1:

[0180] The user uploads the data file (e.g., CSV or Excel file) that needs to be organized to the device. The data file is selected using a file selection dialog.

[0181] Specific behavior:

[0182] The user opens a file selection dialog on the device's browser, selects a data file, and clicks the upload button.

[0183] Input: Data file (CSV file or Excel file)

[0184] Output: Selected data file

[0185] Step 2:

[0186] The device reads the uploaded data file, temporarily stores all of the data, and then transmits this data to the server.

[0187] Specific behavior:

[0188] The device reads the file and sends its contents to the server's API endpoint.

[0189] Input: Uploaded data file

[0190] Output: File contents (CSV format data)

[0191] Step 3:

[0192] The server receives the data file sent from the terminal, then checks the integrity of the data and processes it.

[0193] Specific behavior:

[0194] The server analyzes the received data, processes missing values, and formats the data.

[0195] Input: CSV format data

[0196] Output: Formatted data

[0197] Step 4:

[0198] The server passes the formatted data to a generative AI model, which then analyzes the data and tags and classifies it according to specified rules and criteria.

[0199] Specific behavior:

[0200] Generative AI models analyze data and use classification algorithms to tag and categorize it.

[0201] Input: Formatted data

[0202] Output: Classified data

[0203] Step 5:

[0204] The server receives the classified data and sends it to the device using an HTTP response.

[0205] Specific behavior:

[0206] The server retrieves the classified data and sends it to the terminal.

[0207] Input: Classified data

[0208] Output: Data sent in the form of an HTTP response

[0209] Step 6:

[0210] The user can check the organized data on the terminal and make corrections as necessary.

[0211] Specific behavior:

[0212] Users can check the classification results on their device and perform filtering and corrections.

[0213] Input: Classified data

[0214] Output: Final confirmed data

[0215] Reservation Management Module Processing Steps

[0216] Step 1:

[0217] The user enters reservation information (date and time, customer name, and service details) into an input form on the terminal. The input information can be accessed through a browser.

[0218] Specific behavior:

[0219] The user enters data into each field and clicks the submit button.

[0220] Input: Date and time, customer name, service details

[0221] Output: Data entered in the input form

[0222] Step 2:

[0223] The terminal temporarily stores the entered reservation information and sends it to the server using an HTTP POST request.

[0224] Specific behavior:

[0225] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[0226] Input: Reservation information entered in the input form

[0227] Output: Reservation information in JSON format

[0228] Step 3:

[0229] The server receives the data sent from the device, verifies its contents, and passes it to the generative AI model.

[0230] Specific behavior:

[0231] The server parses the received JSON data and checks the integrity of the data.

[0232] Input: Reservation information in JSON format

[0233] Output: Formatted reservation information

[0234] Step 4:

[0235] The server uses a generative AI model to check for overlapping reservations, create appropriate schedules, and generate necessary notifications.

[0236] Specific behavior:

[0237] The generative AI model manages the schedule, checks for conflicts with other reservations, and generates necessary notifications.

[0238] Input: Formatted reservation information

[0239] Output: Duplicate check results and notification information

[0240] Step 5:

[0241] The server receives the generated schedule and sends it to the terminal. A notification email is also sent to the customer at the same time.

[0242] Specific behavior:

[0243] The server sends the generated schedule to the terminal in the form of an HTTP response and invokes the customer email sending function.

[0244] Input: Duplicate check results and notification information

[0245] Output: Generated schedule and notification email

[0246] Step 6:

[0247] The user checks the schedule information on the terminal and makes corrections as necessary.

[0248] Specific behavior:

[0249] The user checks the schedule information on the terminal and makes any necessary corrections.

[0250] Input: Generated schedule

[0251] Output: Final confirmed and revised schedule

[0252] (Application example 1)

[0253] 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."

[0254] Inventory management and resource optimization are becoming increasingly important in modern factories. However, conventional methods require a lot of manual data entry and confirmation, resulting in a high risk of human error. Real-time inventory monitoring and optimal resource allocation are also difficult, resulting in reduced operational efficiency. Furthermore, manually generating inventory management reports and resource allocation optimization proposals is cumbersome and hinders efficient business operations. The present invention aims to solve these problems and provide a system for efficiently managing inventory and optimizing resources within a factory.

[0255] 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.

[0256] In this invention, the server includes means for acquiring inventory information and resource information from a user, means for transmitting the acquired inventory information and resource information to the server, means for generating an inventory management report and an optimal resource allocation proposal in the server, and means for returning the generated report and proposal to the user. This enables real-time monitoring of inventory status and resource usage status within a factory, enabling optimal inventory management and resource allocation.

[0257] A "user" is a person or organization that uses the system to perform operations or input data.

[0258] "Input" refers to data or information that a user provides to a system.

[0259] A "server" is a computer system that processes acquired data and stores generated results.

[0260] "Data" means information received by the server for processing.

[0261] A "document" is a structured form of information provided by a generative AI model on a server.

[0262] "Inventory information" is data that indicates the quantity and status of products and parts in a factory.

[0263] "Resource information" refers to data related to resources such as personnel and machinery used or managed within a factory.

[0264] An "inventory management report" is a report on inventory status and management created by a generative AI model based on acquired inventory information.

[0265] A "resource optimal allocation proposal" is a proposal for optimal resource allocation created by a generative AI model based on acquired resource information.

[0266] A "generative AI model" is an artificial intelligence model that automatically generates new information and documents based on data.

[0267] "Real-time" refers to data processing and information provision occurring almost simultaneously.

[0268] A "tablet" is a type of mobile terminal, and is a device that allows a user to perform input operations.

[0269] A "PC terminal" is a personal computer, a general computing device that allows users to operate a system.

[0270] A "factory robot" is an autonomous mechanical device used to perform physical tasks and data processing in a factory.

[0271] This invention is a system for inventory management and resource optimization within a factory. The system allows users to input inventory and resource information within the factory using a tablet or PC terminal, and then uses a generative AI model based on that information to generate inventory management reports and optimal resource allocation proposals in real time.

[0272] The specific hardware configuration of this system includes tablets and PCs used by users, factory robots operating within the factory, and a server responsible for data processing. The software includes Python, the requests library, and a generative AI model.

[0273] Users use tablets or PC terminals to input inventory information (e.g., product name, quantity) and resource information (e.g., machine status, worker status) within the factory. This input information is sent to the server by the terminal. The server analyzes the received data and uses a generative AI model to generate an inventory management report and a proposal for optimal resource allocation. The generated report and proposal are then sent back to the terminal, where the user can review and modify them.

[0274] In addition, factory robots can automatically check and replenish inventory and optimize resource allocation based on the generated inventory management reports and resource allocation recommendations, significantly improving operational efficiency within the factory and reducing the risk of human error.

[0275] Here is a specific example. A user uses a tablet to input inventory information such as "Product A: 100 units, Product B: 50 units, Product C: 75 units" and resource information such as "Machine 1: In operation, Machine 2: Under maintenance, Worker 1: On break." This input information is sent to a server, which uses a generative AI model to generate an inventory management report and a resource allocation optimization proposal, which are then sent back to the tablet. As a result, the user can confirm the optimal method for inventory management and resource allocation, and factory robots will automatically perform their work based on that information.

[0276] Examples of prompt sentences are:

[0277] "A user inputs inventory and resource information for the factory. Inventory information: 100 units of product A, 50 units of product B, and 75 units of product C. Resource information: Machine 1 is in operation, machine 2 is undergoing maintenance, and worker 1 is on break. Based on this input information, the generative AI model generates an inventory management report and a proposal for optimal resource allocation."

[0278] This system enables effective inventory management and resource optimization within the factory, improving business efficiency.

[0279] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0280] Step 1:

[0281] The user inputs inventory and resource information from a tablet or PC terminal. Inventory information includes the name and quantity of each product, while resource information includes the status of machines and workers. Specifically, this input information takes the form of 100 units of product A, 50 units of product B, and 75 units of product C. The input information is prepared as data to be sent to the server in the next processing step.

[0282] Step 2:

[0283] The terminal acquires inventory and resource information entered by the user and sends the data to the server. The data is packetized in JSON or XML format and sent to the server using the HTTP protocol. This processing step also checks whether the data transmission was successful, and attempts to resend if an error occurs.

[0284] Step 3:

[0285] The server receives inventory and resource information sent from the device. The received data is stored and added to the analysis queue for later analysis. The server verifies the integrity of the data and checks for missing information or inconsistencies.

[0286] Step 4:

[0287] The server invokes a generative AI model to analyze the data based on the received inventory and resource information. The generative AI model includes a process that takes into account the current inventory status and past data trends to generate inventory management reports and resource allocation optimization suggestions. For example, if inventory is below a certain threshold, the model may include a reorder suggestion.

[0288] Step 5:

[0289] The server then sends the generated inventory management report and resource allocation optimization proposal to the terminal. At this time, the data is converted back to JSON or XML format and sent using the HTTP protocol. The transmitted data includes the specific report content and proposal.

[0290] Step 6:

[0291] The terminal receives inventory management reports and resource allocation optimization proposals sent from the server and displays them to the user, who can then check this information on the screen of their tablet or PC and make corrections or additional inputs as necessary.

[0292] Step 7:

[0293] The factory robot automatically checks inventory, replenishes resources, and optimizes resource allocation based on the inventory management report and resource allocation optimization proposals provided by the terminal. For example, if the inventory of product A is below 100 units, the robot will automatically begin replenishment work.

[0294] The above are the specific processing steps of the program for actually implementing the present invention. This system enables efficient real-time inventory management and resource optimization within a factory.

[0295] 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.

[0296] This system receives input from the user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. Furthermore, it incorporates a function that uses an emotion engine to analyze the user's emotions and optimizes document generation, data organization, and reservation management based on the analysis.

[0297] Invoicing Module

[0298] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. The device acquires the entered information and sends it, along with the user's emotional state, to the server. The server passes the received information and the emotion analysis results to the generative AI model. The generative AI model selects an appropriate template based on the provided information and analyzed emotions, and automatically generates the invoice. For example, if the user is feeling stressed, a concise and easy-to-read format may be selected. The generated invoice is then sent from the server to the device, where the user can review it and enter corrections or additional information as necessary.

[0299] Examples:

[0300] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to confirm.

[0301] Data Reduction Module

[0302] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents and the user's emotional state to the server. The server inputs the submitted data and the results of the emotion analysis into a generative AI model, which automatically organizes and classifies the data. For example, if the user is concentrating, detailed analysis and complex classification will be performed. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is sent from the server to the device, where the user can review it and make any necessary corrections.

[0303] Examples:

[0304] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[0305] Reservation Management Module

[0306] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information, along with the user's emotional state, to the server. The server passes the received information and the results of emotion analysis to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicates and sets the optimal schedule based on the reservation information and the results of the user's emotion analysis. For example, if the user is in a hurry, a schedule that is easy to check immediately is prioritized. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it.

[0307] Examples:

[0308] A user enters a meeting reservation with a customer named "Customer B" for 9:00 on October 15th. The device sends this information along with the user's emotional state (e.g., nervousness) to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[0309] In this way, the present invention utilizes an emotion engine to provide optimal document generation, data organization, and reservation management tailored to the user's condition, thereby realizing a system that improves business efficiency and user satisfaction.

[0310] The processing flow will be explained below.

[0311] Invoice Creation Module Processing Flow

[0312] Step 1:

[0313] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[0314] Step 2:

[0315] The device receives user input information and simultaneously analyzes the user's emotional state using an emotion engine, which is acquired through facial expression recognition and voice analysis.

[0316] Step 3:

[0317] The input information acquired by the device and the emotion analysis results are sent to the server.

[0318] Step 4:

[0319] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including the results of sentiment analysis.

[0320] Step 5:

[0321] The server provides the input data and sentiment analysis results to the generative AI model, initiating the process of selecting and generating an invoice template.

[0322] Step 6:

[0323] The generative AI model automatically generates invoices using appropriate templates based on the data provided and sentiment analysis results.

[0324] Step 7:

[0325] The server receives the generated invoice data and checks the format and content.

[0326] Step 8:

[0327] The server returns the generated bill data to the terminal.

[0328] Step 9:

[0329] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[0330] Data reduction module processing flow

[0331] Step 1:

[0332] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[0333] Step 2:

[0334] The device reads the uploaded file, verifies the format and performs some basic data formatting, while simultaneously analyzing the user's emotional state using an emotion engine.

[0335] Step 3:

[0336] The device transmits the analyzed emotional state and data to the server.

[0337] Step 4:

[0338] The server analyzes the received data and the emotion analysis results and converts them into a data format to be passed to the generative AI model.

[0339] Step 5:

[0340] The server feeds the data and sentiment analysis results to the generative AI model, which then begins the data organization and classification process.

[0341] Step 6:

[0342] A generative AI model analyzes the data, tags and categorizes it according to specified rules and criteria, and selects the appropriate organization method based on the user's emotional state.

[0343] Step 7:

[0344] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[0345] Step 8:

[0346] The server returns the organized data to the device.

[0347] Step 9:

[0348] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[0349] Reservation Management Module Processing Flow

[0350] Step 1:

[0351] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[0352] Step 2:

[0353] The terminal acquires the reservation information input by the user and simultaneously analyzes the user's emotional state using an emotion engine.

[0354] Step 3:

[0355] The terminal transmits the entered reservation information and emotion analysis results to the server.

[0356] Step 4:

[0357] The server analyzes the received reservation information and sentiment analysis results and converts them into a data format to be passed to the generative AI model.

[0358] Step 5:

[0359] The server feeds the reservation information and sentiment analysis results to the generated AI model, initiating the schedule management process.

[0360] Step 6:

[0361] The generative AI model analyzes reservation information, checks for overlaps with existing schedules, and sets the optimal schedule based on the user's emotional state.

[0362] Step 7:

[0363] The server receives the schedule data from the generated AI model and checks for duplicates and schedule consistency.

[0364] Step 8:

[0365] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[0366] Step 9:

[0367] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[0368] Example 2

[0369] 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."

[0370] Conventional document generation, data organization, and reservation management systems are not optimized to take into account the user's emotional state, making it difficult to respond flexibly to the user's state and needs, and creating challenges in improving work efficiency and user satisfaction.In addition, in data organization and reservation management, duplicate checks and customer notifications are often done manually, increasing the risk of errors and effort.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0372] In this invention, the server includes means for acquiring input information and emotional state from a user and generating documents using a generative AI model, means for acquiring data files and emotional state and organizing and classifying data using a generative AI model, and means for acquiring reservation information and emotional state and automatically processing schedule management, duplicate checks, and customer notifications using a generative AI model. This enables document generation, data organization, and reservation management that take the user's emotional state into consideration.

[0373] A "user" is an individual or corporation that uses the system to perform operations such as document generation, data organization, and reservation management.

[0374] "Input information" is a general term for information such as customer name, product name, quantity, unit price, transaction date, and reservation information that a user inputs into the system.

[0375] "Emotional state" refers to the user's mental and emotional state, which is analyzed through the emotion engine.

[0376] A "terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet, through which input information is acquired and sent to a server.

[0377] A "server" is a remote computer system that receives data sent by users and uses generative AI models to perform tasks such as document generation, data organization, and reservation management.

[0378] "Generative AI model" refers to artificial intelligence technology that generates documents, organizes and categorizes data, and manages schedules based on received input information and emotional states.

[0379] "Data file" is a general term for files, such as CSV files and Excel files, that store data that users provide to the system and that need to be organized and classified.

[0380] "Schedule management" refers to the process of setting an optimal schedule and checking for overlaps based on reservation information and emotional state obtained from the user.

[0381] "Duplicate check" refers to the process of checking whether there is any overlapping information between existing data and new data.

[0382] "Customer notification" refers to the process of automatically notifying customers of important information such as reservation information via email, message, or other means.

[0383] This invention is a system that efficiently generates documents, organizes data, and manages reservations based on user input and emotional states. This system combines a user terminal, a server, a generative AI model, and an emotion engine.

[0384] First, a user uses a terminal to input the information required for tasks such as generating invoices, organizing data, and managing reservations. The terminal acquires the user's input information using JavaScript (registered trademark) on the browser. The terminal also has a built-in emotion engine that analyzes the user's emotional state. This emotional state is inferred from data such as facial recognition cameras and input speed.

[0385] The acquired input information and emotional state are sent to the server using an HTTP POST request. The server temporarily stores the received information and provides it to the generative AI model. The generative AI model generates documents, organizes data, and manages reservations based on the received prompts.

[0386] As a concrete example, consider the operation of creating an invoice. To create a new invoice, the user enters the customer name, product name, quantity, unit price, and transaction date into an input form on the terminal, and the terminal sends this information and the emotional state (e.g., "fatigue") to the server. The server inputs the following prompt sentence into the generative AI model.

[0387] Customer Name: Customer A

[0388] Product: Product X

[0389] Quantity: 10

[0390] Unit price: 5000

[0391] Transaction Date: 2023-10-01

[0392] Emotional state: Fatigue

[0393] The generative AI model uses this information to select the optimal template and automatically generate an invoice, which is then sent from the server to the device where the user can review it and make any necessary corrections.

[0394] For data organization, users upload CSV or Excel files using their devices, along with their emotional state, and send them to the server, which then inputs the following prompts into the generative AI model:

[0395] Data file: [file contents]

[0396] Emotional state: Relaxed

[0397] The generative AI model organizes and classifies the data based on this information. The organized data is sent from the server to the device, where the user can review it and make corrections as necessary.

[0398] In reservation management, the user inputs reservation information using a terminal and sends it along with their emotional state to the server, which then inputs the following prompt sentence into the generative AI model:

[0399] Reservation information: Date and time: 2023-10-15 09:00

[0400] Customer Name: Customer B

[0401] Services: Meetings

[0402] Emotional state: Tension

[0403] The generative AI model manages schedules, checks for duplicates, and automatically handles customer notifications. The schedule management results are sent from the server to the device, where the user can review and make corrections as necessary.

[0404] As described above, the present invention is a system that utilizes a generative AI model and an emotion engine to realize optimal document generation, data organization, and reservation management that corresponds to the user's emotional state, thereby improving business efficiency and user satisfaction.

[0405] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0406] Invoicing Module

[0407] Step 1:

[0408] The user enters invoice information (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal. This information becomes the input data.

[0409] Step 2:

[0410] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[0411] Step 3:

[0412] The device sends the acquired input information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes a prompt like this:

[0413] Customer Name: Customer A

[0414] Product: Product X

[0415] Quantity: 10

[0416] Unit price: 5000

[0417] Transaction Date: 2023-10-01

[0418] Emotional state: Fatigue

[0419] Step 4:

[0420] The server temporarily stores the received information and provides it as input to the generative AI model, which then selects the optimal template and generates the document. The output is the generated invoice.

[0421] Step 5:

[0422] The server encodes the generated invoice in JSON format and sends it as an HTTP response to the terminal. The sent data becomes the terminal's input data.

[0423] Step 6:

[0424] The user checks the invoice displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[0425] Data Reduction Module

[0426] Step 1:

[0427] Users use the system's upload form to select a CSV or Excel file, which becomes the input data.

[0428] Step 2:

[0429] The device retrieves the file contents and uses its built-in emotion engine to analyze the user's emotional state. The device estimates the user's emotional state based on data from the facial recognition camera and input speed. The results are also retrieved as input data.

[0430] Step 3:

[0431] The device sends the file contents and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[0432] Data file: [file contents]

[0433] Emotional state: Relaxed

[0434] Step 4:

[0435] The server provides the received data as input to the generative AI model, which then analyzes the data and organizes and classifies it according to specified rules and criteria. The output is an organized data file.

[0436] Step 5:

[0437] The server encodes the organized data in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[0438] Step 6:

[0439] The user checks the organized data displayed on the terminal and makes corrections as necessary. If corrections are made, the process starts again from step 2.

[0440] Reservation Management Module

[0441] Step 1:

[0442] The user inputs information such as date and time, customer name, service details, etc. into the reservation form. This information becomes input data.

[0443] Step 2:

[0444] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[0445] Step 3:

[0446] The device sends the acquired reservation information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[0447] Reservation information: Date and time: 2023-10-15 09:00

[0448] Customer Name: Customer B

[0449] Services: Meetings

[0450] Emotional state: Tension

[0451] Step 4:

[0452] The server provides the received data as input to the generative AI model, which then manages schedules, checks for duplicates, and notifies customers. The output is the optimal schedule and notification content.

[0453] Step 5:

[0454] The server encodes the generated schedule in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[0455] Step 6:

[0456] The user checks the schedule displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[0457] (Application example 2)

[0458] 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."

[0459] Conventional systems were unable to take the user's emotional state into account when generating documents, organizing data, or managing reservations based on user input. This resulted in problems such as reduced work efficiency due to user stress and fatigue. Furthermore, particularly in factories, there is a need to provide work instructions and operation guides based on the operator's emotional state, but no system has been available that can achieve this. In these circumstances, a system is needed that analyzes the user's emotional state and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing document generation, means for optimizing data organization, means for optimizing schedule management, means for adjusting work instructions, means for adjusting the difficulty level of operation guidance, and means for optimizing inventory management and order planning. This makes it possible to take the emotional state of the user into consideration and provide optimal document generation, data organization, reservation management, work instructions, operation guidance, and inventory management for the user.

[0461] "Means for obtaining input from the user" refers to the function of receiving information provided by the system user through a digital device.

[0462] The "means for transmitting acquired input information to a server" is the process of transferring information acquired from a user to a server via a network.

[0463] The "means for generating a document based on data generated on the server" is a function that processes data on the server side and automatically creates a document based on the results.

[0464] "Means for analyzing a user's emotional state using an emotion analysis engine" refers to technology that analyzes a user's current emotions using facial recognition, biometric signals, etc.

[0465] "Means for optimizing document generation based on analysis results" refers to technology that reflects the results of sentiment analysis to create documents with formats and content that are appropriate for the user.

[0466] The "means for returning the document generation results to the user" is the process for delivering the generated document to the user.

[0467] "Means for organizing and classifying acquired data" refers to a function that systematically arranges and classifies input data according to certain rules.

[0468] A "means for returning organized data to a user" is a process for presenting or transmitting organized data to a user.

[0469] "Means for optimizing data organization based on the results of user emotion analysis" refers to technology that reflects the user's emotional state to streamline data organization and classification work.

[0470] "Means for managing schedules on the server based on the acquired reservation information" refers to a function that automatically creates and manages schedules on the server based on reservation information.

[0471] The "means for automatically checking for duplicates and notifying customers" is a technology that detects duplicate reservation information and automatically notifies customers as necessary.

[0472] "Means for optimizing schedule management based on the results of user emotion analysis" is a function that adjusts schedule placement and notification methods according to the user's emotional state.

[0473] The "means for generating work instructions" is a function that allows the system to automatically create instructions for the work to be performed by the operator.

[0474] The "means for displaying the generated work instructions to the operator" is a process for visually presenting the generated work instructions to the operator.

[0475] "Means for adjusting the content of work instructions based on the emotional state of the operator" refers to a technology that adjusts the content and difficulty of work instructions by reflecting the results of an analysis of the operator's emotions.

[0476] "Means for automatically generating machine operation guides" refers to a system function that automatically describes and generates machine operation methods.

[0477] The "means for providing the generated operation guide to the user" is a process for delivering the generated operation guide to the user and making it available for use.

[0478] The "means for adjusting the difficulty of the operation guide based on the emotional state of the user" is a technology for adjusting the level of detail and simplicity of the operation guide based on the results of an analysis of the user's emotions.

[0479] "Means of obtaining data for inventory management" is the process of collecting information about inventory.

[0480] "Means for generating inventory management and parts ordering plans based on acquired data" refers to a function that creates inventory management and ordering plans for necessary parts based on collected inventory data.

[0481] "Means for optimizing inventory management and ordering plans based on the results of user emotion analysis" refers to technology that improves the efficiency and optimization of inventory management and ordering plans by reflecting the user's emotional state.

[0482] This invention is a system that analyzes the emotional state of a user and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management. A specific embodiment of the invention is described below.

[0483] The server receives input from the user. This input is provided by operating a device such as a smartphone, tablet, or desktop. The received input information is sent to the server via the network.

[0484] The server first analyzes the user's emotional state using an emotion analysis engine. Software used for emotion analysis includes facial recognition and biometric signal analysis. Specifically, tools such as Affectiva and Amazon Rekognition can be used. The analysis results are input into a generative AI model (e.g., OpenAI® GPT-4®) and used to optimize document generation, data organization, and reservation management.

[0485] Document generation optimization:

[0486] Based on user input and sentiment analysis, the server uses a generative AI model to generate appropriate documents. For example, when generating an invoice, if the user is fatigued, a simple, easy-to-read format will be selected.

[0487] Examples:

[0488] To create a new invoice, a user enters the customer name, product name, quantity, unit price, and transaction date. The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to review.

[0489] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[0490] Optimizing data organization:

[0491] It reads data files provided by users and organizes and categorizes the data based on the results of sentiment analysis. For example, if the user is concentrating, it analyzes the data in more detail and performs more complex categorization.

[0492] Examples:

[0493] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[0494] Example prompt: "Operator's emotional state is relaxed. Please generate a detailed inventory management plan for the next 10 days."

[0495] Optimized booking management:

[0496] The server obtains reservation information from users and sets the optimal schedule based on the results of sentiment analysis. The server uses a generative AI model to check for duplicates and automatically notify customers if necessary.

[0497] Examples:

[0498] A user enters a meeting reservation for a specific date and time. The device sends that information, along with the user's emotional state (e.g., tension), to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to the customer. The generated schedule is then displayed on the device for the user to review.

[0499] Example prompt: "User's emotional state is tense. Please optimize their schedule for the next 10 days."

[0500] Generate work orders:

[0501] The server uses a generative AI model to automatically generate and display work instructions that reflect the emotional state of the operator.

[0502] Examples:

[0503] The tired operator is instructed, "Next, please perform a simple part inspection task."

[0504] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[0505] Generate a How-to Guide:

[0506] When machine operation is required, the generative AI model automatically generates appropriate operating instructions and provides them to the operator, adjusting the content and difficulty based on the operator's emotional state.

[0507] Examples:

[0508] Impatient operators are provided with a concise guide such as "Step 1: Turn on the power. Step 2: Press the settings button..."

[0509] Example prompt: "The operator's emotional state is impatient. Please generate a concise operating guide that will prevent mistakes."

[0510] Optimize inventory management:

[0511] The server acquires data for inventory management and parts ordering, optimizes ordering plans based on sentiment analysis results, and uses generative AI models to create detailed inventory management and ordering plans.

[0512] Examples:

[0513] When inventory data is input by a centralized operator, a plan is provided such as, "We will provide a detailed plan of the parts needed for the next 10 days and a list of parts that need to be ordered."

[0514] Example prompt: "Operator's emotional state is focused. Generate a detailed inventory management plan for the next 10 days."

[0515] As described above, the present invention is a system that places importance on the emotional state of the user and optimizes various business processes, thereby improving efficiency and user satisfaction.

[0516] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0517] Step 1:

[0518] Users provide input information using devices, such as smartphones or desktops, to enter information such as invoices, schedules, data files, and work instructions into input forms.

[0519] Input: Data entered by the user (e.g., customer name, product name, reservation date and time, etc.)

[0520] Output: User input information stored on the device

[0521] Specific operation: The user enters information into the input form on the terminal and presses the "Submit" button.

[0522] Step 2:

[0523] The device sends the acquired input information to the server, along with the following data: the user's emotional state.

[0524] Input: User input information and emotional state

[0525] Output: Input information and emotional state data sent to the server

[0526] Specific operation: The device sends input information and emotional state data to the server using a secure communication protocol such as HTTPS.

[0527] Step 3:

[0528] The server analyzes the received data and uses an emotion analysis engine to analyze the user's emotional state.

[0529] Input: Input information and emotional state data sent to the server

[0530] Output: Parsed emotional state information

[0531] How it works: The server uses an emotion analysis engine (e.g., Affectiva, Amazon Rekognition) to analyze the user's emotional state from facial recognition and biometric signal data.

[0532] Step 4:

[0533] The server uses generative AI models based on the analysis results to generate and optimize documents and data.

[0534] Input: User input information and analyzed emotional state

[0535] Output: Generated documents and optimized data

[0536] Specific operation: User input information and sentiment analysis results are provided as prompts to a generative AI model (e.g., OpenAI GPT-4) to generate optimal documents and data.

[0537] Step 5:

[0538] The server returns the generated document or data to the user.

[0539] Input: Generated documents and optimized data

[0540] Output: Returned documents and data

[0541] Specific operation: The server sends the generated documents and data to the terminal, and the terminal displays the sent information.

[0542] Step 6:

[0543] The terminal displays the returned document or data to the user.

[0544] Input: Documents and data returned from the server

[0545] Output: Documents and data displayed on the device

[0546] Specific operation: The terminal reflects the received data on the display screen so that the user can check and modify it.

[0547] Step 7:

[0548] If necessary, the user can enter corrections or additional information into the generated documents and data and resubmit them.

[0549] Input: Information modified or added by the user

[0550] Output: Corrections and additional information sent again

[0551] Specific operation: The user corrects the document or data displayed on the terminal and presses the "Resend" button to resend it to the server.

[0552] Through the above steps, a system is realized that takes into account the emotional state of the user and can optimize various business processes.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] [Second embodiment]

[0557] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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."

[0569] The present invention is a system that receives input from a user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. A specific example of the present invention is described below.

[0570] 1. Invoicing module

[0571] The user provides the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) to an input form on the terminal. The terminal acquires the input information and sends it to the server. The server analyzes the received information and passes it to the generative AI model. The generative AI model selects an appropriate template based on the provided information and automatically generates an invoice. The generated invoice is sent from the server to the terminal, where the user can review it on the terminal and enter corrections or additional information as necessary.

[0572] Examples:

[0573] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information to the server, which uses a generative AI model to generate an invoice based on this information. The generated invoice is then displayed on the terminal for the user to confirm.

[0574] 2. Data reduction module

[0575] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents to the server. The server inputs the data into a generative AI model, which automatically organizes and classifies the data. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is then sent from the server to the device, where the user can review it and make any necessary corrections.

[0576] Examples:

[0577] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase history to the system. The device reads the file and sends it to the server. The server uses a generative AI model to classify and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[0578] 3. Reservation Management Module

[0579] The user provides reservation information (date and time, customer name, service details, etc.) in an input form on the device. The device acquires the input information and sends it to the server. The server passes the received information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservations and automatically sets an appropriate schedule. If necessary, a notification is automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it on the device.

[0580] Examples:

[0581] A user enters a meeting reservation with customer "Customer B" for 9:00 on October 15th. The device sends the information to the server. The server uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[0582] In this way, the present invention provides a system that efficiently generates invoices, organizes data, and manages reservations, reduces the risk of human error, and improves business efficiency.

[0583] The processing flow will be explained below.

[0584] Invoice Creation Module Processing Flow

[0585] Step 1:

[0586] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[0587] Step 2:

[0588] The device validates the information entered by the user, checking the format and mandatory fields, and if there are no errors, sends the information to the server.

[0589] Step 3:

[0590] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including supplementary information if necessary.

[0591] Step 4:

[0592] The server provides input data to the generative AI model and initiates the process of selecting and generating an invoice template.

[0593] Step 5:

[0594] A generative AI model automatically generates invoices using templates based on the data provided.

[0595] Step 6:

[0596] The server receives the generated invoice data and checks the format and content.

[0597] Step 7:

[0598] The server returns the generated bill data to the terminal.

[0599] Step 8:

[0600] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[0601] Data reduction module processing flow

[0602] Step 1:

[0603] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[0604] Step 2:

[0605] The device reads the uploaded file, verifies the format, and performs basic data formatting. If there are no errors, the data is sent to the server.

[0606] Step 3:

[0607] The server analyzes the received data and converts it into a data format that can be passed to the generative AI model.

[0608] Step 4:

[0609] The server feeds the data to the generative AI model, which begins the process of cleaning and classifying the data.

[0610] Step 5:

[0611] Generative AI models analyze the data and tag and categorize it according to specified rules and criteria.

[0612] Step 6:

[0613] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[0614] Step 7:

[0615] The server returns the organized data to the device.

[0616] Step 8:

[0617] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[0618] Reservation Management Module Processing Flow

[0619] Step 1:

[0620] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[0621] Step 2:

[0622] The terminal verifies the reservation information entered by the user, and if there are no errors, sends the information to the server.

[0623] Step 3:

[0624] The server analyzes the received reservation information and converts it into a data format to be passed to the generative AI model.

[0625] Step 4:

[0626] The server feeds the reservation information to the generative AI model and initiates the schedule management process.

[0627] Step 5:

[0628] A generative AI model analyzes reservation information and checks for overlaps with existing schedules.

[0629] Step 6:

[0630] The server receives the schedule data returned from the generative AI model and checks for duplicates and schedule consistency.

[0631] Step 7:

[0632] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[0633] Step 8:

[0634] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[0635] Example 1

[0636] 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."

[0637] Conventional systems require users to manually create documents, organize data, and manage reservation information, which requires a great deal of effort and time. Manual tasks also pose a risk of human error. As a result, operational efficiency declines and productivity is limited. While systems exist to automate these tasks, the quality of the generated data and documents is often inconsistent, making them impractical. A system that solves these problems is needed.

[0638] 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.

[0639] In this invention, the server includes a means for acquiring input from a user, a means for transmitting the acquired input information to the server, a means for generating a document based on the data using a generative AI model in the server, and a means for returning the generated document to the user. This allows users to easily automatically generate high-quality documents, reduces the risk of errors due to manual work, and significantly improves business efficiency.

[0640] This invention also includes a means for sending data files provided by a user to a server, organizing and classifying the data files using a generative AI model, and a means for returning the organized data to the user, thereby enabling the user to organize and classify data efficiently.

[0641] Furthermore, it includes a means for acquiring reservation information from users and managing reservation schedules using a generative AI model, a means for automatically checking for duplicate reservation information and notifying customers, and a means for returning schedule management results to users. This automates reservation management, prevents duplication and omissions, and improves user convenience.

[0642] A "user" is an entity that utilizes the system to input information and perform various functions.

[0643] A "terminal" is a device or local computer that is directly operated by a user, and is a device that has the role of transmitting input information to a server.

[0644] The "server" is a central computer that receives information sent by users and uses generative AI models to generate documents, organize data, and manage schedules.

[0645] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate documents based on provided data, organize and classify data, and manage schedules.

[0646] "Documents" are documents such as reports, invoices, contracts, etc. created by a generative AI model based on information provided by a user.

[0647] "Data file" means a file, such as a CSV file or Excel file, that contains data that a user provides to the system and that needs to be organized and categorized.

[0648] "Reservation information" refers to information relating to a reservation, such as the date and time, customer name, and service details, that the user provides to the system.

[0649] "Duplicate check" is the process of comparing existing schedules and data with newly acquired information to check for duplicates.

[0650] "Customer notification" is a function that automatically notifies customers of reservation information, data sorting results, etc.

[0651] "Schedule management" is the process of creating an optimal schedule based on reservation information and managing it to prevent duplication and omissions.

[0652] An "input form" is an interface provided for a user to input information on a terminal.

[0653] The present invention is a system that takes input information from a user, sends it to a server, and automatically generates documents using a generative AI model. The system includes a billing module, a data organization module, and a reservation management module, each of which performs a specific task.

[0654] Invoicing Module

[0655] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. This information is sent to the server via the device. The server formats the received data and passes it to a generative AI model (e.g., GPT-3). The generative AI model selects an appropriate invoice template based on the provided data, embeds the data, and generates a document. The generated invoice is sent from the server to the device, where the user can review it and modify it if necessary.

[0656] Examples:

[0657] When a user creates a new invoice, they enter the following information:

[0658] Customer name: "Customer A"

[0659] Product: "Product X"

[0660] Quantity: "10"

[0661] Unit price: 5,000 yen

[0662] Transaction date: "2023-10-01"

[0663] This information is sent from the device to a server, which uses a generative AI model to generate an invoice based on this information, which can then be viewed on the user's device.

[0664] Data Reduction Module

[0665] Users upload data files (e.g., CSV or Excel files) that need to be organized and classified to their device. The device then sends the uploaded data files to a server, which analyzes the data using a generative AI model. The generative AI model uses natural language processing technology to tag, classify, and organize the data. The organized data is then sent from the server to the device, where it can be viewed and modified by the user.

[0666] Examples:

[0667] The user uploads customer data (a CSV file containing customer names, addresses, purchase history, etc.) to the system. The device sends the file to the server, which uses a generative AI model to analyze the data and classify and tag customers based on "customer purchase frequency." The organized data is then sent back to the device for review by the user.

[0668] Reservation Management Module

[0669] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information to the server. The server passes the received reservation information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservation information and automatically sets an appropriate schedule. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can review and modify it.

[0670] Examples:

[0671] A user enters a meeting reservation with "Customer B" for 9:00 on October 15th. This information is sent from the device to the server, which uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is displayed on the device for the user to review.

[0672] In this way, the present invention provides a system that reduces user effort, prevents human error, and improves work efficiency. Users can simply use specific prompts to pass the required information to the generative AI model and obtain automated results.

[0673] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0674] Invoicing module processing steps

[0675] Step 1:

[0676] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the terminal. This input form is displayed on the web application and can be accessed through a browser.

[0677] Specific behavior:

[0678] The user enters data into each field and clicks the submit button.

[0679] Input: Customer name "Customer A", product "Product X", quantity "10", unit price "5,000 yen", transaction date "2023-10-01"

[0680] Output: Data entered in the input form

[0681] Step 2:

[0682] The device temporarily stores the data obtained from the input form and then sends it to the server using an HTTP POST request.

[0683] Specific behavior:

[0684] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[0685] Input: Data entered into an input form

[0686] Output: JSON format data (customer name, product name, quantity, unit price, transaction date)

[0687] Step 3:

[0688] The server receives the data sent from the terminal. After receiving it, it checks the integrity of the data and performs any necessary data processing. Data processing includes standardizing the data format and processing missing values.

[0689] Specific behavior:

[0690] The server parses the received JSON data and checks whether all required fields are present and whether the data format is correct.

[0691] Input: JSON format data (customer name, product name, quantity, unit price, transaction date)

[0692] Output: Formatted data

[0693] Step 4:

[0694] The server passes the formatted data to a generative AI model, which selects an appropriate invoice template based on the provided data and generates the document by filling in the data.

[0695] Specific behavior:

[0696] The generative AI model uses a template selection algorithm to choose the best template and fill in the data to create the invoice.

[0697] Input: Formatted data

[0698] Output: Generated invoice document

[0699] Step 5:

[0700] The server receives the generated invoice and sends it to the terminal, again using an HTTP response, as a PDF file.

[0701] Specific behavior:

[0702] The server retrieves the generated invoice and sends it to the terminal as an attachment to the HTTP response.

[0703] Input: Generated invoice document

[0704] Output: Invoice document in PDF format

[0705] Step 6:

[0706] The user checks the generated invoice displayed on the terminal, enters corrections or additional information as necessary, and performs a final check.

[0707] Specific behavior:

[0708] Users can view the PDF invoice on their device, make any necessary corrections, and then save or email the invoice.

[0709] Input: Invoice document in PDF format

[0710] Output: Final confirmed invoice

[0711] Data Reduction Module Processing Steps

[0712] Step 1:

[0713] The user uploads the data file (e.g., CSV or Excel file) that needs to be organized to the device. The data file is selected using a file selection dialog.

[0714] Specific behavior:

[0715] The user opens a file selection dialog on the device's browser, selects a data file, and clicks the upload button.

[0716] Input: Data file (CSV file or Excel file)

[0717] Output: Selected data file

[0718] Step 2:

[0719] The device reads the uploaded data file, temporarily stores all of the data, and then transmits this data to the server.

[0720] Specific behavior:

[0721] The device reads the file and sends its contents to the server's API endpoint.

[0722] Input: Uploaded data file

[0723] Output: File contents (CSV format data)

[0724] Step 3:

[0725] The server receives the data file sent from the terminal, then checks the integrity of the data and processes it.

[0726] Specific behavior:

[0727] The server analyzes the received data, processes missing values, and formats the data.

[0728] Input: CSV format data

[0729] Output: Formatted data

[0730] Step 4:

[0731] The server passes the formatted data to a generative AI model, which then analyzes the data and tags and classifies it according to specified rules and criteria.

[0732] Specific behavior:

[0733] Generative AI models analyze data and use classification algorithms to tag and categorize it.

[0734] Input: Formatted data

[0735] Output: Classified data

[0736] Step 5:

[0737] The server receives the classified data and sends it to the device using an HTTP response.

[0738] Specific behavior:

[0739] The server retrieves the classified data and sends it to the terminal.

[0740] Input: Classified data

[0741] Output: Data sent in the form of an HTTP response

[0742] Step 6:

[0743] The user can check the organized data on the terminal and make corrections as necessary.

[0744] Specific behavior:

[0745] Users can check the classification results on their device and perform filtering and corrections.

[0746] Input: Classified data

[0747] Output: Final confirmed data

[0748] Reservation Management Module Processing Steps

[0749] Step 1:

[0750] The user enters reservation information (date and time, customer name, and service details) into an input form on the terminal. The input information can be accessed through a browser.

[0751] Specific behavior:

[0752] The user enters data into each field and clicks the submit button.

[0753] Input: Date and time, customer name, service details

[0754] Output: Data entered in the input form

[0755] Step 2:

[0756] The terminal temporarily stores the entered reservation information and sends it to the server using an HTTP POST request.

[0757] Specific behavior:

[0758] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[0759] Input: Reservation information entered in the input form

[0760] Output: Reservation information in JSON format

[0761] Step 3:

[0762] The server receives the data sent from the device, verifies its contents, and passes it to the generative AI model.

[0763] Specific behavior:

[0764] The server parses the received JSON data and checks the integrity of the data.

[0765] Input: Reservation information in JSON format

[0766] Output: Formatted reservation information

[0767] Step 4:

[0768] The server uses a generative AI model to check for overlapping reservations, create appropriate schedules, and generate necessary notifications.

[0769] Specific behavior:

[0770] The generative AI model manages the schedule, checks for conflicts with other reservations, and generates necessary notifications.

[0771] Input: Formatted reservation information

[0772] Output: Duplicate check results and notification information

[0773] Step 5:

[0774] The server receives the generated schedule and sends it to the terminal. A notification email is also sent to the customer at the same time.

[0775] Specific behavior:

[0776] The server sends the generated schedule to the terminal in the form of an HTTP response and invokes the customer email sending function.

[0777] Input: Duplicate check results and notification information

[0778] Output: Generated schedule and notification email

[0779] Step 6:

[0780] The user checks the schedule information on the terminal and makes corrections as necessary.

[0781] Specific behavior:

[0782] The user checks the schedule information on the terminal and makes any necessary corrections.

[0783] Input: Generated schedule

[0784] Output: Final confirmed and revised schedule

[0785] (Application example 1)

[0786] 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."

[0787] Inventory management and resource optimization are becoming increasingly important in modern factories. However, conventional methods require a lot of manual data entry and confirmation, resulting in a high risk of human error. Real-time inventory monitoring and optimal resource allocation are also difficult, resulting in reduced operational efficiency. Furthermore, manually generating inventory management reports and resource allocation optimization proposals is cumbersome and hinders efficient business operations. The present invention aims to solve these problems and provide a system for efficiently managing inventory and optimizing resources within a factory.

[0788] 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.

[0789] In this invention, the server includes means for acquiring inventory information and resource information from a user, means for transmitting the acquired inventory information and resource information to the server, means for generating an inventory management report and an optimal resource allocation proposal in the server, and means for returning the generated report and proposal to the user. This enables real-time monitoring of inventory status and resource usage status within a factory, enabling optimal inventory management and resource allocation.

[0790] A "user" is a person or organization that uses the system to perform operations or input data.

[0791] "Input" refers to data or information that a user provides to a system.

[0792] A "server" is a computer system that processes acquired data and stores generated results.

[0793] "Data" means information received by the server for processing.

[0794] A "document" is a structured form of information provided by a generative AI model on a server.

[0795] "Inventory information" is data that indicates the quantity and status of products and parts in a factory.

[0796] "Resource information" refers to data related to resources such as personnel and machinery used or managed within a factory.

[0797] An "inventory management report" is a report on inventory status and management created by a generative AI model based on acquired inventory information.

[0798] A "resource optimal allocation proposal" is a proposal for optimal resource allocation created by a generative AI model based on acquired resource information.

[0799] A "generative AI model" is an artificial intelligence model that automatically generates new information and documents based on data.

[0800] "Real-time" refers to data processing and information provision occurring almost simultaneously.

[0801] A "tablet" is a type of mobile terminal, and is a device that allows a user to perform input operations.

[0802] A "PC terminal" is a personal computer, a general computing device that allows users to operate a system.

[0803] A "factory robot" is an autonomous mechanical device used to perform physical tasks and data processing in a factory.

[0804] This invention is a system for inventory management and resource optimization within a factory. The system allows users to input inventory and resource information within the factory using a tablet or PC terminal, and then uses a generative AI model based on that information to generate inventory management reports and optimal resource allocation proposals in real time.

[0805] The specific hardware configuration of this system includes tablets and PCs used by users, factory robots operating within the factory, and a server responsible for data processing. The software includes Python, the requests library, and a generative AI model.

[0806] Users use tablets or PC terminals to input inventory information (e.g., product name, quantity) and resource information (e.g., machine status, worker status) within the factory. This input information is sent to the server by the terminal. The server analyzes the received data and uses a generative AI model to generate an inventory management report and a proposal for optimal resource allocation. The generated report and proposal are then sent back to the terminal, where the user can review and modify them.

[0807] In addition, factory robots can automatically check and replenish inventory and optimize resource allocation based on the generated inventory management reports and resource allocation recommendations, significantly improving operational efficiency within the factory and reducing the risk of human error.

[0808] Here is a specific example. A user uses a tablet to input inventory information such as "Product A: 100 units, Product B: 50 units, Product C: 75 units" and resource information such as "Machine 1: In operation, Machine 2: Under maintenance, Worker 1: On break." This input information is sent to a server, which uses a generative AI model to generate an inventory management report and a resource allocation optimization proposal, which are then sent back to the tablet. As a result, the user can confirm the optimal method for inventory management and resource allocation, and factory robots will automatically perform their work based on that information.

[0809] Examples of prompt sentences are:

[0810] "A user inputs inventory and resource information for the factory. Inventory information: 100 units of product A, 50 units of product B, and 75 units of product C. Resource information: Machine 1 is in operation, machine 2 is undergoing maintenance, and worker 1 is on break. Based on this input information, the generative AI model generates an inventory management report and a proposal for optimal resource allocation."

[0811] This system enables effective inventory management and resource optimization within the factory, improving business efficiency.

[0812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0813] Step 1:

[0814] The user inputs inventory and resource information from a tablet or PC terminal. Inventory information includes the name and quantity of each product, while resource information includes the status of machines and workers. Specifically, this input information takes the form of 100 units of product A, 50 units of product B, and 75 units of product C. The input information is prepared as data to be sent to the server in the next processing step.

[0815] Step 2:

[0816] The terminal acquires inventory and resource information entered by the user and sends the data to the server. The data is packetized in JSON or XML format and sent to the server using the HTTP protocol. This processing step also checks whether the data transmission was successful, and attempts to resend if an error occurs.

[0817] Step 3:

[0818] The server receives inventory and resource information sent from the device. The received data is stored and added to the analysis queue for later analysis. The server verifies the integrity of the data and checks for missing information or inconsistencies.

[0819] Step 4:

[0820] The server invokes a generative AI model to analyze the data based on the received inventory and resource information. The generative AI model includes a process that takes into account the current inventory status and past data trends to generate inventory management reports and resource allocation optimization suggestions. For example, if inventory is below a certain threshold, the model may include a reorder suggestion.

[0821] Step 5:

[0822] The server then sends the generated inventory management report and resource allocation optimization proposal to the terminal. At this time, the data is converted back to JSON or XML format and sent using the HTTP protocol. The transmitted data includes the specific report content and proposal.

[0823] Step 6:

[0824] The terminal receives inventory management reports and resource allocation optimization proposals sent from the server and displays them to the user, who can then check this information on the screen of their tablet or PC and make corrections or additional inputs as necessary.

[0825] Step 7:

[0826] The factory robot automatically checks inventory, replenishes resources, and optimizes resource allocation based on the inventory management report and resource allocation optimization proposals provided by the terminal. For example, if the inventory of product A is below 100 units, the robot will automatically begin replenishment work.

[0827] The above are the specific processing steps of the program for actually implementing the present invention. This system enables efficient real-time inventory management and resource optimization within a factory.

[0828] 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.

[0829] This system receives input from the user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. Furthermore, it incorporates a function that uses an emotion engine to analyze the user's emotions and optimizes document generation, data organization, and reservation management based on the analysis.

[0830] Invoicing Module

[0831] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. The device acquires the entered information and sends it, along with the user's emotional state, to the server. The server passes the received information and the emotion analysis results to the generative AI model. The generative AI model selects an appropriate template based on the provided information and analyzed emotions, and automatically generates the invoice. For example, if the user is feeling stressed, a concise and easy-to-read format may be selected. The generated invoice is then sent from the server to the device, where the user can review it and enter corrections or additional information as necessary.

[0832] Examples:

[0833] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to confirm.

[0834] Data Reduction Module

[0835] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents and the user's emotional state to the server. The server inputs the submitted data and the results of the emotion analysis into a generative AI model, which automatically organizes and classifies the data. For example, if the user is concentrating, detailed analysis and complex classification will be performed. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is sent from the server to the device, where the user can review it and make any necessary corrections.

[0836] Examples:

[0837] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[0838] Reservation Management Module

[0839] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information, along with the user's emotional state, to the server. The server passes the received information and the results of emotion analysis to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicates and sets the optimal schedule based on the reservation information and the results of the user's emotion analysis. For example, if the user is in a hurry, a schedule that is easy to check immediately is prioritized. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it.

[0840] Examples:

[0841] A user enters a meeting reservation with a customer named "Customer B" for 9:00 on October 15th. The device sends this information along with the user's emotional state (e.g., nervousness) to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[0842] In this way, the present invention utilizes an emotion engine to provide optimal document generation, data organization, and reservation management tailored to the user's condition, thereby realizing a system that improves business efficiency and user satisfaction.

[0843] The processing flow will be explained below.

[0844] Invoice Creation Module Processing Flow

[0845] Step 1:

[0846] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[0847] Step 2:

[0848] The device receives user input information and simultaneously analyzes the user's emotional state using an emotion engine, which is acquired through facial expression recognition and voice analysis.

[0849] Step 3:

[0850] The input information acquired by the device and the emotion analysis results are sent to the server.

[0851] Step 4:

[0852] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including the results of sentiment analysis.

[0853] Step 5:

[0854] The server provides the input data and sentiment analysis results to the generative AI model, initiating the process of selecting and generating an invoice template.

[0855] Step 6:

[0856] The generative AI model automatically generates invoices using appropriate templates based on the data provided and sentiment analysis results.

[0857] Step 7:

[0858] The server receives the generated invoice data and checks the format and content.

[0859] Step 8:

[0860] The server returns the generated bill data to the terminal.

[0861] Step 9:

[0862] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[0863] Data reduction module processing flow

[0864] Step 1:

[0865] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[0866] Step 2:

[0867] The device reads the uploaded file, verifies the format and performs some basic data formatting, while simultaneously analyzing the user's emotional state using an emotion engine.

[0868] Step 3:

[0869] The device transmits the analyzed emotional state and data to the server.

[0870] Step 4:

[0871] The server analyzes the received data and the emotion analysis results and converts them into a data format to be passed to the generative AI model.

[0872] Step 5:

[0873] The server feeds the data and sentiment analysis results to the generative AI model, which then begins the data organization and classification process.

[0874] Step 6:

[0875] A generative AI model analyzes the data, tags and categorizes it according to specified rules and criteria, and selects the appropriate organization method based on the user's emotional state.

[0876] Step 7:

[0877] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[0878] Step 8:

[0879] The server returns the organized data to the device.

[0880] Step 9:

[0881] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[0882] Reservation Management Module Processing Flow

[0883] Step 1:

[0884] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[0885] Step 2:

[0886] The terminal acquires the reservation information input by the user and simultaneously analyzes the user's emotional state using an emotion engine.

[0887] Step 3:

[0888] The terminal transmits the entered reservation information and emotion analysis results to the server.

[0889] Step 4:

[0890] The server analyzes the received reservation information and sentiment analysis results and converts them into a data format to be passed to the generative AI model.

[0891] Step 5:

[0892] The server feeds the reservation information and sentiment analysis results to the generated AI model, initiating the schedule management process.

[0893] Step 6:

[0894] The generative AI model analyzes reservation information, checks for overlaps with existing schedules, and sets the optimal schedule based on the user's emotional state.

[0895] Step 7:

[0896] The server receives the schedule data from the generated AI model and checks for duplicates and schedule consistency.

[0897] Step 8:

[0898] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[0899] Step 9:

[0900] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[0901] Example 2

[0902] 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."

[0903] Conventional document generation, data organization, and reservation management systems are not optimized to take into account the user's emotional state, making it difficult to respond flexibly to the user's state and needs, and creating challenges in improving work efficiency and user satisfaction.In addition, in data organization and reservation management, duplicate checks and customer notifications are often done manually, increasing the risk of errors and effort.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0905] In this invention, the server includes means for acquiring input information and emotional state from a user and generating documents using a generative AI model, means for acquiring data files and emotional state and organizing and classifying data using a generative AI model, and means for acquiring reservation information and emotional state and automatically processing schedule management, duplicate checks, and customer notifications using a generative AI model. This enables document generation, data organization, and reservation management that take the user's emotional state into consideration.

[0906] A "user" is an individual or corporation that uses the system to perform operations such as document generation, data organization, and reservation management.

[0907] "Input information" is a general term for information such as customer name, product name, quantity, unit price, transaction date, and reservation information that a user inputs into the system.

[0908] "Emotional state" refers to the user's mental and emotional state, which is analyzed through the emotion engine.

[0909] A "terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet, through which input information is acquired and sent to a server.

[0910] A "server" is a remote computer system that receives data sent by users and uses generative AI models to perform tasks such as document generation, data organization, and reservation management.

[0911] "Generative AI model" refers to artificial intelligence technology that generates documents, organizes and categorizes data, and manages schedules based on received input information and emotional states.

[0912] "Data file" is a general term for files, such as CSV files and Excel files, that store data that users provide to the system and that need to be organized and classified.

[0913] "Schedule management" refers to the process of setting an optimal schedule and checking for overlaps based on reservation information and emotional state obtained from the user.

[0914] "Duplicate check" refers to the process of checking whether there is any overlapping information between existing data and new data.

[0915] "Customer notification" refers to the process of automatically notifying customers of important information such as reservation information via email, message, or other means.

[0916] This invention is a system that efficiently generates documents, organizes data, and manages reservations based on user input and emotional states. This system combines a user terminal, a server, a generative AI model, and an emotion engine.

[0917] First, a user uses a device to input the necessary information for tasks such as generating invoices, organizing data, and managing reservations. The device then uses JavaScript on the browser to obtain the user's input. The device also has a built-in emotion engine that analyzes the user's emotional state. This emotional state is inferred from data such as facial recognition cameras and input speed.

[0918] The acquired input information and emotional state are sent to the server using an HTTP POST request. The server temporarily stores the received information and provides it to the generative AI model. The generative AI model generates documents, organizes data, and manages reservations based on the received prompts.

[0919] As a concrete example, consider the operation of creating an invoice. To create a new invoice, the user enters the customer name, product name, quantity, unit price, and transaction date into an input form on the terminal, and the terminal sends this information and the emotional state (e.g., "fatigue") to the server. The server inputs the following prompt sentence into the generative AI model.

[0920] Customer Name: Customer A

[0921] Product: Product X

[0922] Quantity: 10

[0923] Unit price: 5000

[0924] Transaction Date: 2023-10-01

[0925] Emotional state: Fatigue

[0926] The generative AI model uses this information to select the optimal template and automatically generate an invoice, which is then sent from the server to the device where the user can review it and make any necessary corrections.

[0927] For data organization, users upload CSV or Excel files using their devices, along with their emotional state, and send them to the server, which then inputs the following prompts into the generative AI model:

[0928] Data file: [file contents]

[0929] Emotional state: Relaxed

[0930] The generative AI model organizes and classifies the data based on this information. The organized data is sent from the server to the device, where the user can review it and make corrections as necessary.

[0931] In reservation management, the user inputs reservation information using a terminal and sends it along with their emotional state to the server, which then inputs the following prompt sentence into the generative AI model:

[0932] Reservation information: Date and time: 2023-10-15 09:00

[0933] Customer Name: Customer B

[0934] Services: Meetings

[0935] Emotional state: Tension

[0936] The generative AI model manages schedules, checks for duplicates, and automatically handles customer notifications. The schedule management results are sent from the server to the device, where the user can review and make corrections as necessary.

[0937] As described above, the present invention is a system that utilizes a generative AI model and an emotion engine to realize optimal document generation, data organization, and reservation management that corresponds to the user's emotional state, thereby improving business efficiency and user satisfaction.

[0938] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0939] Invoicing Module

[0940] Step 1:

[0941] The user enters invoice information (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal. This information becomes the input data.

[0942] Step 2:

[0943] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[0944] Step 3:

[0945] The device sends the acquired input information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes a prompt like this:

[0946] Customer Name: Customer A

[0947] Product: Product X

[0948] Quantity: 10

[0949] Unit price: 5000

[0950] Transaction Date: 2023-10-01

[0951] Emotional state: Fatigue

[0952] Step 4:

[0953] The server temporarily stores the received information and provides it as input to the generative AI model, which then selects the optimal template and generates the document. The output is the generated invoice.

[0954] Step 5:

[0955] The server encodes the generated invoice in JSON format and sends it as an HTTP response to the terminal. The sent data becomes the terminal's input data.

[0956] Step 6:

[0957] The user checks the invoice displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[0958] Data Reduction Module

[0959] Step 1:

[0960] Users use the system's upload form to select a CSV or Excel file, which becomes the input data.

[0961] Step 2:

[0962] The device retrieves the file contents and uses its built-in emotion engine to analyze the user's emotional state. The device estimates the user's emotional state based on data from the facial recognition camera and input speed. The results are also retrieved as input data.

[0963] Step 3:

[0964] The device sends the file contents and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[0965] Data file: [file contents]

[0966] Emotional state: Relaxed

[0967] Step 4:

[0968] The server provides the received data as input to the generative AI model, which then analyzes the data and organizes and classifies it according to specified rules and criteria. The output is an organized data file.

[0969] Step 5:

[0970] The server encodes the organized data in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[0971] Step 6:

[0972] The user checks the organized data displayed on the terminal and makes corrections as necessary. If corrections are made, the process starts again from step 2.

[0973] Reservation Management Module

[0974] Step 1:

[0975] The user inputs information such as date and time, customer name, service details, etc. into the reservation form. This information becomes input data.

[0976] Step 2:

[0977] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[0978] Step 3:

[0979] The device sends the acquired reservation information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[0980] Reservation information: Date and time: 2023-10-15 09:00

[0981] Customer Name: Customer B

[0982] Services: Meetings

[0983] Emotional state: Tension

[0984] Step 4:

[0985] The server provides the received data as input to the generative AI model, which then manages schedules, checks for duplicates, and notifies customers. The output is the optimal schedule and notification content.

[0986] Step 5:

[0987] The server encodes the generated schedule in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[0988] Step 6:

[0989] The user checks the schedule displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[0990] (Application example 2)

[0991] 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."

[0992] Conventional systems were unable to take the user's emotional state into account when generating documents, organizing data, or managing reservations based on user input. This resulted in problems such as reduced work efficiency due to user stress and fatigue. Furthermore, particularly in factories, there is a need to provide work instructions and operation guides based on the operator's emotional state, but no system has been available that can achieve this. In these circumstances, a system is needed that analyzes the user's emotional state and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing document generation, means for optimizing data organization, means for optimizing schedule management, means for adjusting work instructions, means for adjusting the difficulty level of operation guidance, and means for optimizing inventory management and order planning. This makes it possible to take the emotional state of the user into consideration and provide optimal document generation, data organization, reservation management, work instructions, operation guidance, and inventory management for the user.

[0994] "Means for obtaining input from the user" refers to the function of receiving information provided by the system user through a digital device.

[0995] The "means for transmitting acquired input information to a server" is the process of transferring information acquired from a user to a server via a network.

[0996] The "means for generating a document based on data generated on the server" is a function that processes data on the server side and automatically creates a document based on the results.

[0997] "Means for analyzing a user's emotional state using an emotion analysis engine" refers to technology that analyzes a user's current emotions using facial recognition, biometric signals, etc.

[0998] "Means for optimizing document generation based on analysis results" refers to technology that reflects the results of sentiment analysis to create documents with formats and content that are appropriate for the user.

[0999] The "means for returning the document generation results to the user" is the process for delivering the generated document to the user.

[1000] "Means for organizing and classifying acquired data" refers to a function that systematically arranges and classifies input data according to certain rules.

[1001] A "means for returning organized data to a user" is a process for presenting or transmitting organized data to a user.

[1002] "Means for optimizing data organization based on the results of user emotion analysis" refers to technology that reflects the user's emotional state to streamline data organization and classification work.

[1003] "Means for managing schedules on the server based on the acquired reservation information" refers to a function that automatically creates and manages schedules on the server based on reservation information.

[1004] The "means for automatically checking for duplicates and notifying customers" is a technology that detects duplicate reservation information and automatically notifies customers as necessary.

[1005] "Means for optimizing schedule management based on the results of user emotion analysis" is a function that adjusts schedule placement and notification methods according to the user's emotional state.

[1006] The "means for generating work instructions" is a function that allows the system to automatically create instructions for the work to be performed by the operator.

[1007] The "means for displaying the generated work instructions to the operator" is a process for visually presenting the generated work instructions to the operator.

[1008] "Means for adjusting the content of work instructions based on the emotional state of the operator" refers to a technology that adjusts the content and difficulty of work instructions by reflecting the results of an analysis of the operator's emotions.

[1009] "Means for automatically generating machine operation guides" refers to a system function that automatically describes and generates machine operation methods.

[1010] The "means for providing the generated operation guide to the user" is a process for delivering the generated operation guide to the user and making it available for use.

[1011] The "means for adjusting the difficulty of the operation guide based on the emotional state of the user" is a technology for adjusting the level of detail and simplicity of the operation guide based on the results of an analysis of the user's emotions.

[1012] "Means of obtaining data for inventory management" is the process of collecting information about inventory.

[1013] "Means for generating inventory management and parts ordering plans based on acquired data" refers to a function that creates inventory management and ordering plans for necessary parts based on collected inventory data.

[1014] "Means for optimizing inventory management and ordering plans based on the results of user emotion analysis" refers to technology that improves the efficiency and optimization of inventory management and ordering plans by reflecting the user's emotional state.

[1015] This invention is a system that analyzes the emotional state of a user and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management. A specific embodiment of the invention is described below.

[1016] The server receives input from the user. This input is provided by operating a device such as a smartphone, tablet, or desktop. The received input information is sent to the server via the network.

[1017] The server first analyzes the user's emotional state using an emotion analysis engine. Software used for emotion analysis includes facial recognition and biometric signal analysis. Specifically, tools such as Affectiva and Amazon Rekognition can be used. The analysis results are input into a generative AI model (e.g., OpenAI GPT-4) and used to optimize document generation, data organization, and reservation management.

[1018] Document generation optimization:

[1019] Based on user input and sentiment analysis, the server uses a generative AI model to generate appropriate documents. For example, when generating an invoice, if the user is fatigued, a simple, easy-to-read format will be selected.

[1020] Examples:

[1021] To create a new invoice, a user enters the customer name, product name, quantity, unit price, and transaction date. The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to review.

[1022] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[1023] Optimizing data organization:

[1024] It reads data files provided by users and organizes and categorizes the data based on the results of sentiment analysis. For example, if the user is concentrating, it analyzes the data in more detail and performs more complex categorization.

[1025] Examples:

[1026] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1027] Example prompt: "Operator's emotional state is relaxed. Please generate a detailed inventory management plan for the next 10 days."

[1028] Optimized booking management:

[1029] The server obtains reservation information from users and sets the optimal schedule based on the results of sentiment analysis. The server uses a generative AI model to check for duplicates and automatically notify customers if necessary.

[1030] Examples:

[1031] A user enters a meeting reservation for a specific date and time. The device sends that information, along with the user's emotional state (e.g., tension), to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to the customer. The generated schedule is then displayed on the device for the user to review.

[1032] Example prompt: "User's emotional state is tense. Please optimize their schedule for the next 10 days."

[1033] Generate work orders:

[1034] The server uses a generative AI model to automatically generate and display work instructions that reflect the emotional state of the operator.

[1035] Examples:

[1036] The tired operator is instructed, "Next, please perform a simple part inspection task."

[1037] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[1038] Generate a How-to Guide:

[1039] When machine operation is required, the generative AI model automatically generates appropriate operating instructions and provides them to the operator, adjusting the content and difficulty based on the operator's emotional state.

[1040] Examples:

[1041] Impatient operators are provided with a concise guide such as "Step 1: Turn on the power. Step 2: Press the settings button..."

[1042] Example prompt: "The operator's emotional state is impatient. Please generate a concise operating guide that will prevent mistakes."

[1043] Optimize inventory management:

[1044] The server acquires data for inventory management and parts ordering, optimizes ordering plans based on sentiment analysis results, and uses generative AI models to create detailed inventory management and ordering plans.

[1045] Examples:

[1046] When inventory data is input by a centralized operator, a plan is provided such as, "We will provide a detailed plan of the parts needed for the next 10 days and a list of parts that need to be ordered."

[1047] Example prompt: "Operator's emotional state is focused. Generate a detailed inventory management plan for the next 10 days."

[1048] As described above, the present invention is a system that places importance on the emotional state of the user and optimizes various business processes, thereby improving efficiency and user satisfaction.

[1049] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1050] Step 1:

[1051] Users provide input information using devices, such as smartphones or desktops, to enter information such as invoices, schedules, data files, and work instructions into input forms.

[1052] Input: Data entered by the user (e.g., customer name, product name, reservation date and time, etc.)

[1053] Output: User input information stored on the device

[1054] Specific operation: The user enters information into the input form on the terminal and presses the "Submit" button.

[1055] Step 2:

[1056] The device sends the acquired input information to the server, along with the following data: the user's emotional state.

[1057] Input: User input information and emotional state

[1058] Output: Input information and emotional state data sent to the server

[1059] Specific operation: The device sends input information and emotional state data to the server using a secure communication protocol such as HTTPS.

[1060] Step 3:

[1061] The server analyzes the received data and uses an emotion analysis engine to analyze the user's emotional state.

[1062] Input: Input information and emotional state data sent to the server

[1063] Output: Parsed emotional state information

[1064] How it works: The server uses an emotion analysis engine (e.g., Affectiva, Amazon Rekognition) to analyze the user's emotional state from facial recognition and biometric signal data.

[1065] Step 4:

[1066] The server uses generative AI models based on the analysis results to generate and optimize documents and data.

[1067] Input: User input information and analyzed emotional state

[1068] Output: Generated documents and optimized data

[1069] Specific operation: User input information and sentiment analysis results are provided as prompts to a generative AI model (e.g., OpenAI GPT-4) to generate optimal documents and data.

[1070] Step 5:

[1071] The server returns the generated document or data to the user.

[1072] Input: Generated documents and optimized data

[1073] Output: Returned documents and data

[1074] Specific operation: The server sends the generated documents and data to the terminal, and the terminal displays the sent information.

[1075] Step 6:

[1076] The terminal displays the returned document or data to the user.

[1077] Input: Documents and data returned from the server

[1078] Output: Documents and data displayed on the device

[1079] Specific operation: The terminal reflects the received data on the display screen so that the user can check and modify it.

[1080] Step 7:

[1081] If necessary, the user can enter corrections or additional information into the generated documents and data and resubmit them.

[1082] Input: Information modified or added by the user

[1083] Output: Corrections and additional information sent again

[1084] Specific operation: The user corrects the document or data displayed on the terminal and presses the "Resend" button to resend it to the server.

[1085] Through the above steps, a system is realized that takes into account the emotional state of the user and can optimize various business processes.

[1086] 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.

[1087] 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.

[1088] 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.

[1089] [Third embodiment]

[1090] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1091] 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.

[1092] 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).

[1093] 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.

[1094] 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.

[1095] 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).

[1096] 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.

[1097] 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.

[1098] 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.

[1099] 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.

[1100] 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.

[1101] 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."

[1102] The present invention is a system that receives input from a user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. A specific example of the present invention is described below.

[1103] 1. Invoicing module

[1104] The user provides the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) to an input form on the terminal. The terminal acquires the input information and sends it to the server. The server analyzes the received information and passes it to the generative AI model. The generative AI model selects an appropriate template based on the provided information and automatically generates an invoice. The generated invoice is sent from the server to the terminal, where the user can review it on the terminal and enter corrections or additional information as necessary.

[1105] Examples:

[1106] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information to the server, which uses a generative AI model to generate an invoice based on this information. The generated invoice is then displayed on the terminal for the user to confirm.

[1107] 2. Data reduction module

[1108] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents to the server. The server inputs the data into a generative AI model, which automatically organizes and classifies the data. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is then sent from the server to the device, where the user can review it and make any necessary corrections.

[1109] Examples:

[1110] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase history to the system. The device reads the file and sends it to the server. The server uses a generative AI model to classify and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1111] 3. Reservation Management Module

[1112] The user provides reservation information (date and time, customer name, service details, etc.) in an input form on the device. The device acquires the input information and sends it to the server. The server passes the received information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservations and automatically sets an appropriate schedule. If necessary, a notification is automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it on the device.

[1113] Examples:

[1114] A user enters a meeting reservation with customer "Customer B" for 9:00 on October 15th. The device sends the information to the server. The server uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[1115] In this way, the present invention provides a system that efficiently generates invoices, organizes data, and manages reservations, reduces the risk of human error, and improves business efficiency.

[1116] The processing flow will be explained below.

[1117] Invoice Creation Module Processing Flow

[1118] Step 1:

[1119] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[1120] Step 2:

[1121] The device validates the information entered by the user, checking the format and mandatory fields, and if there are no errors, sends the information to the server.

[1122] Step 3:

[1123] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including supplementary information if necessary.

[1124] Step 4:

[1125] The server provides input data to the generative AI model and initiates the process of selecting and generating an invoice template.

[1126] Step 5:

[1127] A generative AI model automatically generates invoices using templates based on the data provided.

[1128] Step 6:

[1129] The server receives the generated invoice data and checks the format and content.

[1130] Step 7:

[1131] The server returns the generated bill data to the terminal.

[1132] Step 8:

[1133] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[1134] Data reduction module processing flow

[1135] Step 1:

[1136] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[1137] Step 2:

[1138] The device reads the uploaded file, verifies the format, and performs basic data formatting. If there are no errors, the data is sent to the server.

[1139] Step 3:

[1140] The server analyzes the received data and converts it into a data format that can be passed to the generative AI model.

[1141] Step 4:

[1142] The server feeds the data to the generative AI model, which begins the process of cleaning and classifying the data.

[1143] Step 5:

[1144] Generative AI models analyze the data and tag and categorize it according to specified rules and criteria.

[1145] Step 6:

[1146] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[1147] Step 7:

[1148] The server returns the organized data to the device.

[1149] Step 8:

[1150] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[1151] Reservation Management Module Processing Flow

[1152] Step 1:

[1153] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[1154] Step 2:

[1155] The terminal verifies the reservation information entered by the user, and if there are no errors, sends the information to the server.

[1156] Step 3:

[1157] The server analyzes the received reservation information and converts it into a data format to be passed to the generative AI model.

[1158] Step 4:

[1159] The server feeds the reservation information to the generative AI model and initiates the schedule management process.

[1160] Step 5:

[1161] A generative AI model analyzes reservation information and checks for overlaps with existing schedules.

[1162] Step 6:

[1163] The server receives the schedule data returned from the generative AI model and checks for duplicates and schedule consistency.

[1164] Step 7:

[1165] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[1166] Step 8:

[1167] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[1168] Example 1

[1169] 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."

[1170] Conventional systems require users to manually create documents, organize data, and manage reservation information, which requires a great deal of effort and time. Manual tasks also pose a risk of human error. As a result, operational efficiency declines and productivity is limited. While systems exist to automate these tasks, the quality of the generated data and documents is often inconsistent, making them impractical. A system that solves these problems is needed.

[1171] 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.

[1172] In this invention, the server includes a means for acquiring input from a user, a means for transmitting the acquired input information to the server, a means for generating a document based on the data using a generative AI model in the server, and a means for returning the generated document to the user. This allows users to easily automatically generate high-quality documents, reduces the risk of errors due to manual work, and significantly improves business efficiency.

[1173] This invention also includes a means for sending data files provided by a user to a server, organizing and classifying the data files using a generative AI model, and a means for returning the organized data to the user, thereby enabling the user to organize and classify data efficiently.

[1174] Furthermore, it includes a means for acquiring reservation information from users and managing reservation schedules using a generative AI model, a means for automatically checking for duplicate reservation information and notifying customers, and a means for returning schedule management results to users. This automates reservation management, prevents duplication and omissions, and improves user convenience.

[1175] A "user" is an entity that utilizes the system to input information and perform various functions.

[1176] A "terminal" is a device or local computer that is directly operated by a user, and is a device that has the role of transmitting input information to a server.

[1177] The "server" is a central computer that receives information sent by users and uses generative AI models to generate documents, organize data, and manage schedules.

[1178] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate documents based on provided data, organize and classify data, and manage schedules.

[1179] "Documents" are documents such as reports, invoices, contracts, etc. created by a generative AI model based on information provided by a user.

[1180] "Data file" means a file, such as a CSV file or Excel file, that contains data that a user provides to the system and that needs to be organized and categorized.

[1181] "Reservation information" refers to information relating to a reservation, such as the date and time, customer name, and service details, that the user provides to the system.

[1182] "Duplicate check" is the process of comparing existing schedules and data with newly acquired information to check for duplicates.

[1183] "Customer notification" is a function that automatically notifies customers of reservation information, data sorting results, etc.

[1184] "Schedule management" is the process of creating an optimal schedule based on reservation information and managing it to prevent duplication and omissions.

[1185] An "input form" is an interface provided for a user to input information on a terminal.

[1186] The present invention is a system that takes input information from a user, sends it to a server, and automatically generates documents using a generative AI model. The system includes a billing module, a data organization module, and a reservation management module, each of which performs a specific task.

[1187] Invoicing Module

[1188] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. This information is sent to the server via the device. The server formats the received data and passes it to a generative AI model (e.g., GPT-3). The generative AI model selects an appropriate invoice template based on the provided data, embeds the data, and generates a document. The generated invoice is sent from the server to the device, where the user can review it and modify it if necessary.

[1189] Examples:

[1190] When a user creates a new invoice, they enter the following information:

[1191] Customer name: "Customer A"

[1192] Product: "Product X"

[1193] Quantity: "10"

[1194] Unit price: 5,000 yen

[1195] Transaction date: "2023-10-01"

[1196] This information is sent from the device to a server, which uses a generative AI model to generate an invoice based on this information, which can then be viewed on the user's device.

[1197] Data Reduction Module

[1198] Users upload data files (e.g., CSV or Excel files) that need to be organized and classified to their device. The device then sends the uploaded data files to a server, which analyzes the data using a generative AI model. The generative AI model uses natural language processing technology to tag, classify, and organize the data. The organized data is then sent from the server to the device, where it can be viewed and modified by the user.

[1199] Examples:

[1200] The user uploads customer data (a CSV file containing customer names, addresses, purchase history, etc.) to the system. The device sends the file to the server, which uses a generative AI model to analyze the data and classify and tag customers based on "customer purchase frequency." The organized data is then sent back to the device for review by the user.

[1201] Reservation Management Module

[1202] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information to the server. The server passes the received reservation information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservation information and automatically sets an appropriate schedule. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can review and modify it.

[1203] Examples:

[1204] A user enters a meeting reservation with "Customer B" for 9:00 on October 15th. This information is sent from the device to the server, which uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is displayed on the device for the user to review.

[1205] In this way, the present invention provides a system that reduces user effort, prevents human error, and improves work efficiency. Users can simply use specific prompts to pass the required information to the generative AI model and obtain automated results.

[1206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1207] Invoicing module processing steps

[1208] Step 1:

[1209] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the terminal. This input form is displayed on the web application and can be accessed through a browser.

[1210] Specific behavior:

[1211] The user enters data into each field and clicks the submit button.

[1212] Input: Customer name "Customer A", product "Product X", quantity "10", unit price "5,000 yen", transaction date "2023-10-01"

[1213] Output: Data entered in the input form

[1214] Step 2:

[1215] The device temporarily stores the data obtained from the input form and then sends it to the server using an HTTP POST request.

[1216] Specific behavior:

[1217] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[1218] Input: Data entered into an input form

[1219] Output: JSON format data (customer name, product name, quantity, unit price, transaction date)

[1220] Step 3:

[1221] The server receives the data sent from the terminal. After receiving it, it checks the integrity of the data and performs any necessary data processing. Data processing includes standardizing the data format and processing missing values.

[1222] Specific behavior:

[1223] The server parses the received JSON data and checks whether all required fields are present and whether the data format is correct.

[1224] Input: JSON format data (customer name, product name, quantity, unit price, transaction date)

[1225] Output: Formatted data

[1226] Step 4:

[1227] The server passes the formatted data to a generative AI model, which selects an appropriate invoice template based on the provided data and generates the document by filling in the data.

[1228] Specific behavior:

[1229] The generative AI model uses a template selection algorithm to choose the best template and fill in the data to create the invoice.

[1230] Input: Formatted data

[1231] Output: Generated invoice document

[1232] Step 5:

[1233] The server receives the generated invoice and sends it to the terminal, again using an HTTP response, as a PDF file.

[1234] Specific behavior:

[1235] The server retrieves the generated invoice and sends it to the terminal as an attachment to the HTTP response.

[1236] Input: Generated invoice document

[1237] Output: Invoice document in PDF format

[1238] Step 6:

[1239] The user checks the generated invoice displayed on the terminal, enters corrections or additional information as necessary, and performs a final check.

[1240] Specific behavior:

[1241] Users can view the PDF invoice on their device, make any necessary corrections, and then save or email the invoice.

[1242] Input: Invoice document in PDF format

[1243] Output: Final confirmed invoice

[1244] Data Reduction Module Processing Steps

[1245] Step 1:

[1246] The user uploads the data file (e.g., CSV or Excel file) that needs to be organized to the device. The data file is selected using a file selection dialog.

[1247] Specific behavior:

[1248] The user opens a file selection dialog on the device's browser, selects a data file, and clicks the upload button.

[1249] Input: Data file (CSV file or Excel file)

[1250] Output: Selected data file

[1251] Step 2:

[1252] The device reads the uploaded data file, temporarily stores all of the data, and then transmits this data to the server.

[1253] Specific behavior:

[1254] The device reads the file and sends its contents to the server's API endpoint.

[1255] Input: Uploaded data file

[1256] Output: File contents (CSV format data)

[1257] Step 3:

[1258] The server receives the data file sent from the terminal, then checks the integrity of the data and processes it.

[1259] Specific behavior:

[1260] The server analyzes the received data, processes missing values, and formats the data.

[1261] Input: CSV format data

[1262] Output: Formatted data

[1263] Step 4:

[1264] The server passes the formatted data to a generative AI model, which then analyzes the data and tags and classifies it according to specified rules and criteria.

[1265] Specific behavior:

[1266] Generative AI models analyze data and use classification algorithms to tag and categorize it.

[1267] Input: Formatted data

[1268] Output: Classified data

[1269] Step 5:

[1270] The server receives the classified data and sends it to the device using an HTTP response.

[1271] Specific behavior:

[1272] The server retrieves the classified data and sends it to the terminal.

[1273] Input: Classified data

[1274] Output: Data sent in the form of an HTTP response

[1275] Step 6:

[1276] The user can check the organized data on the terminal and make corrections as necessary.

[1277] Specific behavior:

[1278] Users can check the classification results on their device and perform filtering and corrections.

[1279] Input: Classified data

[1280] Output: Final confirmed data

[1281] Reservation Management Module Processing Steps

[1282] Step 1:

[1283] The user enters reservation information (date and time, customer name, and service details) into an input form on the terminal. The input information can be accessed through a browser.

[1284] Specific behavior:

[1285] The user enters data into each field and clicks the submit button.

[1286] Input: Date and time, customer name, service details

[1287] Output: Data entered in the input form

[1288] Step 2:

[1289] The terminal temporarily stores the entered reservation information and sends it to the server using an HTTP POST request.

[1290] Specific behavior:

[1291] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[1292] Input: Reservation information entered in the input form

[1293] Output: Reservation information in JSON format

[1294] Step 3:

[1295] The server receives the data sent from the device, verifies its contents, and passes it to the generative AI model.

[1296] Specific behavior:

[1297] The server parses the received JSON data and checks the integrity of the data.

[1298] Input: Reservation information in JSON format

[1299] Output: Formatted reservation information

[1300] Step 4:

[1301] The server uses a generative AI model to check for overlapping reservations, create appropriate schedules, and generate necessary notifications.

[1302] Specific behavior:

[1303] The generative AI model manages the schedule, checks for conflicts with other reservations, and generates necessary notifications.

[1304] Input: Formatted reservation information

[1305] Output: Duplicate check results and notification information

[1306] Step 5:

[1307] The server receives the generated schedule and sends it to the terminal. A notification email is also sent to the customer at the same time.

[1308] Specific behavior:

[1309] The server sends the generated schedule to the terminal in the form of an HTTP response and invokes the customer email sending function.

[1310] Input: Duplicate check results and notification information

[1311] Output: Generated schedule and notification email

[1312] Step 6:

[1313] The user checks the schedule information on the terminal and makes corrections as necessary.

[1314] Specific behavior:

[1315] The user checks the schedule information on the terminal and makes any necessary corrections.

[1316] Input: Generated schedule

[1317] Output: Final confirmed and revised schedule

[1318] (Application example 1)

[1319] 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."

[1320] Inventory management and resource optimization are becoming increasingly important in modern factories. However, conventional methods require a lot of manual data entry and confirmation, resulting in a high risk of human error. Real-time inventory monitoring and optimal resource allocation are also difficult, resulting in reduced operational efficiency. Furthermore, manually generating inventory management reports and resource allocation optimization proposals is cumbersome and hinders efficient business operations. The present invention aims to solve these problems and provide a system for efficiently managing inventory and optimizing resources within a factory.

[1321] 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.

[1322] In this invention, the server includes means for acquiring inventory information and resource information from a user, means for transmitting the acquired inventory information and resource information to the server, means for generating an inventory management report and an optimal resource allocation proposal in the server, and means for returning the generated report and proposal to the user. This enables real-time monitoring of inventory status and resource usage status within a factory, enabling optimal inventory management and resource allocation.

[1323] A "user" is a person or organization that uses the system to perform operations or input data.

[1324] "Input" refers to data or information that a user provides to a system.

[1325] A "server" is a computer system that processes acquired data and stores generated results.

[1326] "Data" means information received by the server for processing.

[1327] A "document" is a structured form of information provided by a generative AI model on a server.

[1328] "Inventory information" is data that indicates the quantity and status of products and parts in a factory.

[1329] "Resource information" refers to data related to resources such as personnel and machinery used or managed within a factory.

[1330] An "inventory management report" is a report on inventory status and management created by a generative AI model based on acquired inventory information.

[1331] A "resource optimal allocation proposal" is a proposal for optimal resource allocation created by a generative AI model based on acquired resource information.

[1332] A "generative AI model" is an artificial intelligence model that automatically generates new information and documents based on data.

[1333] "Real-time" refers to data processing and information provision occurring almost simultaneously.

[1334] A "tablet" is a type of mobile terminal, and is a device that allows a user to perform input operations.

[1335] A "PC terminal" is a personal computer, a general computing device that allows users to operate a system.

[1336] A "factory robot" is an autonomous mechanical device used to perform physical tasks and data processing in a factory.

[1337] This invention is a system for inventory management and resource optimization within a factory. The system allows users to input inventory and resource information within the factory using a tablet or PC terminal, and then uses a generative AI model based on that information to generate inventory management reports and optimal resource allocation proposals in real time.

[1338] The specific hardware configuration of this system includes tablets and PCs used by users, factory robots operating within the factory, and a server responsible for data processing. The software includes Python, the requests library, and a generative AI model.

[1339] Users use tablets or PC terminals to input inventory information (e.g., product name, quantity) and resource information (e.g., machine status, worker status) within the factory. This input information is sent to the server by the terminal. The server analyzes the received data and uses a generative AI model to generate an inventory management report and a proposal for optimal resource allocation. The generated report and proposal are then sent back to the terminal, where the user can review and modify them.

[1340] In addition, factory robots can automatically check and replenish inventory and optimize resource allocation based on the generated inventory management reports and resource allocation recommendations, significantly improving operational efficiency within the factory and reducing the risk of human error.

[1341] Here is a specific example. A user uses a tablet to input inventory information such as "Product A: 100 units, Product B: 50 units, Product C: 75 units" and resource information such as "Machine 1: In operation, Machine 2: Under maintenance, Worker 1: On break." This input information is sent to a server, which uses a generative AI model to generate an inventory management report and a resource allocation optimization proposal, which are then sent back to the tablet. As a result, the user can confirm the optimal method for inventory management and resource allocation, and factory robots will automatically perform their work based on that information.

[1342] Examples of prompt sentences are:

[1343] "A user inputs inventory and resource information for the factory. Inventory information: 100 units of product A, 50 units of product B, and 75 units of product C. Resource information: Machine 1 is in operation, machine 2 is undergoing maintenance, and worker 1 is on break. Based on this input information, the generative AI model generates an inventory management report and a proposal for optimal resource allocation."

[1344] This system enables effective inventory management and resource optimization within the factory, improving business efficiency.

[1345] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1346] Step 1:

[1347] The user inputs inventory and resource information from a tablet or PC terminal. Inventory information includes the name and quantity of each product, while resource information includes the status of machines and workers. Specifically, this input information takes the form of 100 units of product A, 50 units of product B, and 75 units of product C. The input information is prepared as data to be sent to the server in the next processing step.

[1348] Step 2:

[1349] The terminal acquires inventory and resource information entered by the user and sends the data to the server. The data is packetized in JSON or XML format and sent to the server using the HTTP protocol. This processing step also checks whether the data transmission was successful, and attempts to resend if an error occurs.

[1350] Step 3:

[1351] The server receives inventory and resource information sent from the device. The received data is stored and added to the analysis queue for later analysis. The server verifies the integrity of the data and checks for missing information or inconsistencies.

[1352] Step 4:

[1353] The server invokes a generative AI model to analyze the data based on the received inventory and resource information. The generative AI model includes a process that takes into account the current inventory status and past data trends to generate inventory management reports and resource allocation optimization suggestions. For example, if inventory is below a certain threshold, the model may include a reorder suggestion.

[1354] Step 5:

[1355] The server then sends the generated inventory management report and resource allocation optimization proposal to the terminal. At this time, the data is converted back to JSON or XML format and sent using the HTTP protocol. The transmitted data includes the specific report content and proposal.

[1356] Step 6:

[1357] The terminal receives inventory management reports and resource allocation optimization proposals sent from the server and displays them to the user, who can then check this information on the screen of their tablet or PC and make corrections or additional inputs as necessary.

[1358] Step 7:

[1359] The factory robot automatically checks inventory, replenishes resources, and optimizes resource allocation based on the inventory management report and resource allocation optimization proposals provided by the terminal. For example, if the inventory of product A is below 100 units, the robot will automatically begin replenishment work.

[1360] The above are the specific processing steps of the program for actually implementing the present invention. This system enables efficient real-time inventory management and resource optimization within a factory.

[1361] 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.

[1362] This system receives input from the user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. Furthermore, it incorporates a function that uses an emotion engine to analyze the user's emotions and optimizes document generation, data organization, and reservation management based on the analysis.

[1363] Invoicing Module

[1364] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. The device acquires the entered information and sends it, along with the user's emotional state, to the server. The server passes the received information and the emotion analysis results to the generative AI model. The generative AI model selects an appropriate template based on the provided information and analyzed emotions, and automatically generates the invoice. For example, if the user is feeling stressed, a concise and easy-to-read format may be selected. The generated invoice is then sent from the server to the device, where the user can review it and enter corrections or additional information as necessary.

[1365] Examples:

[1366] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to confirm.

[1367] Data Reduction Module

[1368] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents and the user's emotional state to the server. The server inputs the submitted data and the results of the emotion analysis into a generative AI model, which automatically organizes and classifies the data. For example, if the user is concentrating, detailed analysis and complex classification will be performed. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is sent from the server to the device, where the user can review it and make any necessary corrections.

[1369] Examples:

[1370] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1371] Reservation Management Module

[1372] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information, along with the user's emotional state, to the server. The server passes the received information and the results of emotion analysis to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicates and sets the optimal schedule based on the reservation information and the results of the user's emotion analysis. For example, if the user is in a hurry, a schedule that is easy to check immediately is prioritized. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it.

[1373] Examples:

[1374] A user enters a meeting reservation with a customer named "Customer B" for 9:00 on October 15th. The device sends this information along with the user's emotional state (e.g., nervousness) to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[1375] In this way, the present invention utilizes an emotion engine to provide optimal document generation, data organization, and reservation management tailored to the user's condition, thereby realizing a system that improves business efficiency and user satisfaction.

[1376] The processing flow will be explained below.

[1377] Invoice Creation Module Processing Flow

[1378] Step 1:

[1379] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[1380] Step 2:

[1381] The device receives user input information and simultaneously analyzes the user's emotional state using an emotion engine, which is acquired through facial expression recognition and voice analysis.

[1382] Step 3:

[1383] The input information acquired by the device and the emotion analysis results are sent to the server.

[1384] Step 4:

[1385] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including the results of sentiment analysis.

[1386] Step 5:

[1387] The server provides the input data and sentiment analysis results to the generative AI model, initiating the process of selecting and generating an invoice template.

[1388] Step 6:

[1389] The generative AI model automatically generates invoices using appropriate templates based on the data provided and sentiment analysis results.

[1390] Step 7:

[1391] The server receives the generated invoice data and checks the format and content.

[1392] Step 8:

[1393] The server returns the generated bill data to the terminal.

[1394] Step 9:

[1395] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[1396] Data reduction module processing flow

[1397] Step 1:

[1398] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[1399] Step 2:

[1400] The device reads the uploaded file, verifies the format and performs some basic data formatting, while simultaneously analyzing the user's emotional state using an emotion engine.

[1401] Step 3:

[1402] The device transmits the analyzed emotional state and data to the server.

[1403] Step 4:

[1404] The server analyzes the received data and the emotion analysis results and converts them into a data format to be passed to the generative AI model.

[1405] Step 5:

[1406] The server feeds the data and sentiment analysis results to the generative AI model, which then begins the data organization and classification process.

[1407] Step 6:

[1408] A generative AI model analyzes the data, tags and categorizes it according to specified rules and criteria, and selects the appropriate organization method based on the user's emotional state.

[1409] Step 7:

[1410] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[1411] Step 8:

[1412] The server returns the organized data to the device.

[1413] Step 9:

[1414] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[1415] Reservation Management Module Processing Flow

[1416] Step 1:

[1417] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[1418] Step 2:

[1419] The terminal acquires the reservation information input by the user and simultaneously analyzes the user's emotional state using an emotion engine.

[1420] Step 3:

[1421] The terminal transmits the entered reservation information and emotion analysis results to the server.

[1422] Step 4:

[1423] The server analyzes the received reservation information and sentiment analysis results and converts them into a data format to be passed to the generative AI model.

[1424] Step 5:

[1425] The server feeds the reservation information and sentiment analysis results to the generated AI model, initiating the schedule management process.

[1426] Step 6:

[1427] The generative AI model analyzes reservation information, checks for overlaps with existing schedules, and sets the optimal schedule based on the user's emotional state.

[1428] Step 7:

[1429] The server receives the schedule data from the generated AI model and checks for duplicates and schedule consistency.

[1430] Step 8:

[1431] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[1432] Step 9:

[1433] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[1434] Example 2

[1435] 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."

[1436] Conventional document generation, data organization, and reservation management systems are not optimized to take into account the user's emotional state, making it difficult to respond flexibly to the user's state and needs, and creating challenges in improving work efficiency and user satisfaction.In addition, in data organization and reservation management, duplicate checks and customer notifications are often done manually, increasing the risk of errors and effort.

[1437] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1438] In this invention, the server includes means for acquiring input information and emotional state from a user and generating documents using a generative AI model, means for acquiring data files and emotional state and organizing and classifying data using a generative AI model, and means for acquiring reservation information and emotional state and automatically processing schedule management, duplicate checks, and customer notifications using a generative AI model. This enables document generation, data organization, and reservation management that take the user's emotional state into consideration.

[1439] A "user" is an individual or corporation that uses the system to perform operations such as document generation, data organization, and reservation management.

[1440] "Input information" is a general term for information such as customer name, product name, quantity, unit price, transaction date, and reservation information that a user inputs into the system.

[1441] "Emotional state" refers to the user's mental and emotional state, which is analyzed through the emotion engine.

[1442] A "terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet, through which input information is acquired and sent to a server.

[1443] A "server" is a remote computer system that receives data sent by users and uses generative AI models to perform tasks such as document generation, data organization, and reservation management.

[1444] "Generative AI model" refers to artificial intelligence technology that generates documents, organizes and categorizes data, and manages schedules based on received input information and emotional states.

[1445] "Data file" is a general term for files, such as CSV files and Excel files, that store data that users provide to the system and that need to be organized and classified.

[1446] "Schedule management" refers to the process of setting an optimal schedule and checking for overlaps based on reservation information and emotional state obtained from the user.

[1447] "Duplicate check" refers to the process of checking whether there is any overlapping information between existing data and new data.

[1448] "Customer notification" refers to the process of automatically notifying customers of important information such as reservation information via email, message, or other means.

[1449] This invention is a system that efficiently generates documents, organizes data, and manages reservations based on user input and emotional states. This system combines a user terminal, a server, a generative AI model, and an emotion engine.

[1450] First, a user uses a device to input the necessary information for tasks such as generating invoices, organizing data, and managing reservations. The device then uses JavaScript on the browser to obtain the user's input. The device also has a built-in emotion engine that analyzes the user's emotional state. This emotional state is inferred from data such as facial recognition cameras and input speed.

[1451] The acquired input information and emotional state are sent to the server using an HTTP POST request. The server temporarily stores the received information and provides it to the generative AI model. The generative AI model generates documents, organizes data, and manages reservations based on the received prompts.

[1452] As a concrete example, consider the operation of creating an invoice. To create a new invoice, the user enters the customer name, product name, quantity, unit price, and transaction date into an input form on the terminal, and the terminal sends this information and the emotional state (e.g., "fatigue") to the server. The server inputs the following prompt sentence into the generative AI model.

[1453] Customer Name: Customer A

[1454] Product: Product X

[1455] Quantity: 10

[1456] Unit price: 5000

[1457] Transaction Date: 2023-10-01

[1458] Emotional state: Fatigue

[1459] The generative AI model uses this information to select the optimal template and automatically generate an invoice, which is then sent from the server to the device where the user can review it and make any necessary corrections.

[1460] For data organization, users upload CSV or Excel files using their devices, along with their emotional state, and send them to the server, which then inputs the following prompts into the generative AI model:

[1461] Data file: [file contents]

[1462] Emotional state: Relaxed

[1463] The generative AI model organizes and classifies the data based on this information. The organized data is sent from the server to the device, where the user can review it and make corrections as necessary.

[1464] In reservation management, the user inputs reservation information using a terminal and sends it along with their emotional state to the server, which then inputs the following prompt sentence into the generative AI model:

[1465] Reservation information: Date and time: 2023-10-15 09:00

[1466] Customer Name: Customer B

[1467] Services: Meetings

[1468] Emotional state: Tension

[1469] The generative AI model manages schedules, checks for duplicates, and automatically handles customer notifications. The schedule management results are sent from the server to the device, where the user can review and make corrections as necessary.

[1470] As described above, the present invention is a system that utilizes a generative AI model and an emotion engine to realize optimal document generation, data organization, and reservation management that corresponds to the user's emotional state, thereby improving business efficiency and user satisfaction.

[1471] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1472] Invoicing Module

[1473] Step 1:

[1474] The user enters invoice information (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal. This information becomes the input data.

[1475] Step 2:

[1476] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[1477] Step 3:

[1478] The device sends the acquired input information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes a prompt like this:

[1479] Customer Name: Customer A

[1480] Product: Product X

[1481] Quantity: 10

[1482] Unit price: 5000

[1483] Transaction Date: 2023-10-01

[1484] Emotional state: Fatigue

[1485] Step 4:

[1486] The server temporarily stores the received information and provides it as input to the generative AI model, which then selects the optimal template and generates the document. The output is the generated invoice.

[1487] Step 5:

[1488] The server encodes the generated invoice in JSON format and sends it as an HTTP response to the terminal. The sent data becomes the terminal's input data.

[1489] Step 6:

[1490] The user checks the invoice displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[1491] Data Reduction Module

[1492] Step 1:

[1493] Users use the system's upload form to select a CSV or Excel file, which becomes the input data.

[1494] Step 2:

[1495] The device retrieves the file contents and uses its built-in emotion engine to analyze the user's emotional state. The device estimates the user's emotional state based on data from the facial recognition camera and input speed. The results are also retrieved as input data.

[1496] Step 3:

[1497] The device sends the file contents and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[1498] Data file: [file contents]

[1499] Emotional state: Relaxed

[1500] Step 4:

[1501] The server provides the received data as input to the generative AI model, which then analyzes the data and organizes and classifies it according to specified rules and criteria. The output is an organized data file.

[1502] Step 5:

[1503] The server encodes the organized data in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[1504] Step 6:

[1505] The user checks the organized data displayed on the terminal and makes corrections as necessary. If corrections are made, the process starts again from step 2.

[1506] Reservation Management Module

[1507] Step 1:

[1508] The user inputs information such as date and time, customer name, service details, etc. into the reservation form. This information becomes input data.

[1509] Step 2:

[1510] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[1511] Step 3:

[1512] The device sends the acquired reservation information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[1513] Reservation information: Date and time: 2023-10-15 09:00

[1514] Customer Name: Customer B

[1515] Services: Meetings

[1516] Emotional state: Tension

[1517] Step 4:

[1518] The server provides the received data as input to the generative AI model, which then manages schedules, checks for duplicates, and notifies customers. The output is the optimal schedule and notification content.

[1519] Step 5:

[1520] The server encodes the generated schedule in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[1521] Step 6:

[1522] The user checks the schedule displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[1523] (Application example 2)

[1524] 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."

[1525] Conventional systems were unable to take the user's emotional state into account when generating documents, organizing data, or managing reservations based on user input. This resulted in problems such as reduced work efficiency due to user stress and fatigue. Furthermore, particularly in factories, there is a need to provide work instructions and operation guides based on the operator's emotional state, but no system has been available that can achieve this. In these circumstances, a system is needed that analyzes the user's emotional state and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing document generation, means for optimizing data organization, means for optimizing schedule management, means for adjusting work instructions, means for adjusting the difficulty level of operation guidance, and means for optimizing inventory management and order planning. This makes it possible to take the emotional state of the user into consideration and provide optimal document generation, data organization, reservation management, work instructions, operation guidance, and inventory management for the user.

[1527] "Means for obtaining input from the user" refers to the function of receiving information provided by the system user through a digital device.

[1528] The "means for transmitting acquired input information to a server" is the process of transferring information acquired from a user to a server via a network.

[1529] The "means for generating a document based on data generated on the server" is a function that processes data on the server side and automatically creates a document based on the results.

[1530] "Means for analyzing a user's emotional state using an emotion analysis engine" refers to technology that analyzes a user's current emotions using facial recognition, biometric signals, etc.

[1531] "Means for optimizing document generation based on analysis results" refers to technology that reflects the results of sentiment analysis to create documents with formats and content that are appropriate for the user.

[1532] The "means for returning the document generation results to the user" is the process for delivering the generated document to the user.

[1533] "Means for organizing and classifying acquired data" refers to a function that systematically arranges and classifies input data according to certain rules.

[1534] A "means for returning organized data to a user" is a process for presenting or transmitting organized data to a user.

[1535] "Means for optimizing data organization based on the results of user emotion analysis" refers to technology that reflects the user's emotional state to streamline data organization and classification work.

[1536] "Means for managing schedules on the server based on the acquired reservation information" refers to a function that automatically creates and manages schedules on the server based on reservation information.

[1537] The "means for automatically checking for duplicates and notifying customers" is a technology that detects duplicate reservation information and automatically notifies customers as necessary.

[1538] "Means for optimizing schedule management based on the results of user emotion analysis" is a function that adjusts schedule placement and notification methods according to the user's emotional state.

[1539] The "means for generating work instructions" is a function that allows the system to automatically create instructions for the work to be performed by the operator.

[1540] The "means for displaying the generated work instructions to the operator" is a process for visually presenting the generated work instructions to the operator.

[1541] "Means for adjusting the content of work instructions based on the emotional state of the operator" refers to a technology that adjusts the content and difficulty of work instructions by reflecting the results of an analysis of the operator's emotions.

[1542] "Means for automatically generating machine operation guides" refers to a system function that automatically describes and generates machine operation methods.

[1543] The "means for providing the generated operation guide to the user" is a process for delivering the generated operation guide to the user and making it available for use.

[1544] The "means for adjusting the difficulty of the operation guide based on the emotional state of the user" is a technology for adjusting the level of detail and simplicity of the operation guide based on the results of an analysis of the user's emotions.

[1545] "Means of obtaining data for inventory management" is the process of collecting information about inventory.

[1546] "Means for generating inventory management and parts ordering plans based on acquired data" refers to a function that creates inventory management and ordering plans for necessary parts based on collected inventory data.

[1547] "Means for optimizing inventory management and ordering plans based on the results of user emotion analysis" refers to technology that improves the efficiency and optimization of inventory management and ordering plans by reflecting the user's emotional state.

[1548] This invention is a system that analyzes the emotional state of a user and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management. A specific embodiment of the invention is described below.

[1549] The server receives input from the user. This input is provided by operating a device such as a smartphone, tablet, or desktop. The received input information is sent to the server via the network.

[1550] The server first analyzes the user's emotional state using an emotion analysis engine. Software used for emotion analysis includes facial recognition and biometric signal analysis. Specifically, tools such as Affectiva and Amazon Rekognition can be used. The analysis results are input into a generative AI model (e.g., OpenAI GPT-4) and used to optimize document generation, data organization, and reservation management.

[1551] Document generation optimization:

[1552] Based on user input and sentiment analysis, the server uses a generative AI model to generate appropriate documents. For example, when generating an invoice, if the user is fatigued, a simple, easy-to-read format will be selected.

[1553] Examples:

[1554] To create a new invoice, a user enters the customer name, product name, quantity, unit price, and transaction date. The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to review.

[1555] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[1556] Optimizing data organization:

[1557] It reads data files provided by users and organizes and categorizes the data based on the results of sentiment analysis. For example, if the user is concentrating, it analyzes the data in more detail and performs more complex categorization.

[1558] Examples:

[1559] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1560] Example prompt: "Operator's emotional state is relaxed. Please generate a detailed inventory management plan for the next 10 days."

[1561] Optimized booking management:

[1562] The server obtains reservation information from users and sets the optimal schedule based on the results of sentiment analysis. The server uses a generative AI model to check for duplicates and automatically notify customers if necessary.

[1563] Examples:

[1564] A user enters a meeting reservation for a specific date and time. The device sends that information, along with the user's emotional state (e.g., tension), to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to the customer. The generated schedule is then displayed on the device for the user to review.

[1565] Example prompt: "User's emotional state is tense. Please optimize their schedule for the next 10 days."

[1566] Generate work orders:

[1567] The server uses a generative AI model to automatically generate and display work instructions that reflect the emotional state of the operator.

[1568] Examples:

[1569] The tired operator is instructed, "Next, please perform a simple part inspection task."

[1570] Example prompt: "The operator's emotional state is fatigued. Please generate a brief instruction on what to do next."

[1571] Generate a How-to Guide:

[1572] When machine operation is required, the generative AI model automatically generates appropriate operating instructions and provides them to the operator, adjusting the content and difficulty based on the operator's emotional state.

[1573] Examples:

[1574] Impatient operators are provided with a concise guide such as "Step 1: Turn on the power. Step 2: Press the settings button..."

[1575] Example prompt: "The operator's emotional state is impatient. Please generate a concise operating guide that will prevent mistakes."

[1576] Optimize inventory management:

[1577] The server acquires data for inventory management and parts ordering, optimizes ordering plans based on sentiment analysis results, and uses generative AI models to create detailed inventory management and ordering plans.

[1578] Examples:

[1579] When inventory data is input by a centralized operator, a plan is provided such as, "We will provide a detailed plan of the parts needed for the next 10 days and a list of parts that need to be ordered."

[1580] Example prompt: "Operator's emotional state is focused. Generate a detailed inventory management plan for the next 10 days."

[1581] As described above, the present invention is a system that places importance on the emotional state of the user and optimizes various business processes, thereby improving efficiency and user satisfaction.

[1582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1583] Step 1:

[1584] Users provide input information using devices, such as smartphones or desktops, to enter information such as invoices, schedules, data files, and work instructions into input forms.

[1585] Input: Data entered by the user (e.g., customer name, product name, reservation date and time, etc.)

[1586] Output: User input information stored on the device

[1587] Specific operation: The user enters information into the input form on the terminal and presses the "Submit" button.

[1588] Step 2:

[1589] The device sends the acquired input information to the server, along with the following data: the user's emotional state.

[1590] Input: User input information and emotional state

[1591] Output: Input information and emotional state data sent to the server

[1592] Specific operation: The device sends input information and emotional state data to the server using a secure communication protocol such as HTTPS.

[1593] Step 3:

[1594] The server analyzes the received data and uses an emotion analysis engine to analyze the user's emotional state.

[1595] Input: Input information and emotional state data sent to the server

[1596] Output: Parsed emotional state information

[1597] How it works: The server uses an emotion analysis engine (e.g., Affectiva, Amazon Rekognition) to analyze the user's emotional state from facial recognition and biometric signal data.

[1598] Step 4:

[1599] The server uses generative AI models based on the analysis results to generate and optimize documents and data.

[1600] Input: User input information and analyzed emotional state

[1601] Output: Generated documents and optimized data

[1602] Specific operation: User input information and sentiment analysis results are provided as prompts to a generative AI model (e.g., OpenAI GPT-4) to generate optimal documents and data.

[1603] Step 5:

[1604] The server returns the generated document or data to the user.

[1605] Input: Generated documents and optimized data

[1606] Output: Returned documents and data

[1607] Specific operation: The server sends the generated documents and data to the terminal, and the terminal displays the sent information.

[1608] Step 6:

[1609] The terminal displays the returned document or data to the user.

[1610] Input: Documents and data returned from the server

[1611] Output: Documents and data displayed on the device

[1612] Specific operation: The terminal reflects the received data on the display screen so that the user can check and modify it.

[1613] Step 7:

[1614] If necessary, the user can enter corrections or additional information into the generated documents and data and resubmit them.

[1615] Input: Information modified or added by the user

[1616] Output: Corrections and additional information sent again

[1617] Specific operation: The user corrects the document or data displayed on the terminal and presses the "Resend" button to resend it to the server.

[1618] Through the above steps, a system is realized that takes into account the emotional state of the user and can optimize various business processes.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] [Fourth embodiment]

[1623] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1624] 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.

[1625] 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).

[1626] 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.

[1627] 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.

[1628] 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).

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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."

[1636] The present invention is a system that receives input from a user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. A specific example of the present invention is described below.

[1637] 1. Invoicing module

[1638] The user provides the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) to an input form on the terminal. The terminal acquires the input information and sends it to the server. The server analyzes the received information and passes it to the generative AI model. The generative AI model selects an appropriate template based on the provided information and automatically generates an invoice. The generated invoice is sent from the server to the terminal, where the user can review it on the terminal and enter corrections or additional information as necessary.

[1639] Examples:

[1640] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information to the server, which uses a generative AI model to generate an invoice based on this information. The generated invoice is then displayed on the terminal for the user to confirm.

[1641] 2. Data reduction module

[1642] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents to the server. The server inputs the data into a generative AI model, which automatically organizes and classifies the data. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is then sent from the server to the device, where the user can review it and make any necessary corrections.

[1643] Examples:

[1644] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase history to the system. The device reads the file and sends it to the server. The server uses a generative AI model to classify and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1645] 3. Reservation Management Module

[1646] The user provides reservation information (date and time, customer name, service details, etc.) in an input form on the device. The device acquires the input information and sends it to the server. The server passes the received information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservations and automatically sets an appropriate schedule. If necessary, a notification is automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it on the device.

[1647] Examples:

[1648] A user enters a meeting reservation with customer "Customer B" for 9:00 on October 15th. The device sends the information to the server. The server uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[1649] In this way, the present invention provides a system that efficiently generates invoices, organizes data, and manages reservations, reduces the risk of human error, and improves business efficiency.

[1650] The processing flow will be explained below.

[1651] Invoice Creation Module Processing Flow

[1652] Step 1:

[1653] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[1654] Step 2:

[1655] The device validates the information entered by the user, checking the format and mandatory fields, and if there are no errors, sends the information to the server.

[1656] Step 3:

[1657] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including supplementary information if necessary.

[1658] Step 4:

[1659] The server provides input data to the generative AI model and initiates the process of selecting and generating an invoice template.

[1660] Step 5:

[1661] A generative AI model automatically generates invoices using templates based on the data provided.

[1662] Step 6:

[1663] The server receives the generated invoice data and checks the format and content.

[1664] Step 7:

[1665] The server returns the generated bill data to the terminal.

[1666] Step 8:

[1667] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[1668] Data reduction module processing flow

[1669] Step 1:

[1670] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[1671] Step 2:

[1672] The device reads the uploaded file, verifies the format, and performs basic data formatting. If there are no errors, the data is sent to the server.

[1673] Step 3:

[1674] The server analyzes the received data and converts it into a data format that can be passed to the generative AI model.

[1675] Step 4:

[1676] The server feeds the data to the generative AI model, which begins the process of cleaning and classifying the data.

[1677] Step 5:

[1678] Generative AI models analyze the data and tag and categorize it according to specified rules and criteria.

[1679] Step 6:

[1680] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[1681] Step 7:

[1682] The server returns the organized data to the device.

[1683] Step 8:

[1684] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[1685] Reservation Management Module Processing Flow

[1686] Step 1:

[1687] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[1688] Step 2:

[1689] The terminal verifies the reservation information entered by the user, and if there are no errors, sends the information to the server.

[1690] Step 3:

[1691] The server analyzes the received reservation information and converts it into a data format to be passed to the generative AI model.

[1692] Step 4:

[1693] The server feeds the reservation information to the generative AI model and initiates the schedule management process.

[1694] Step 5:

[1695] A generative AI model analyzes reservation information and checks for overlaps with existing schedules.

[1696] Step 6:

[1697] The server receives the schedule data returned from the generative AI model and checks for duplicates and schedule consistency.

[1698] Step 7:

[1699] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[1700] Step 8:

[1701] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[1702] Example 1

[1703] 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."

[1704] Conventional systems require users to manually create documents, organize data, and manage reservation information, which requires a great deal of effort and time. Manual tasks also pose a risk of human error. As a result, operational efficiency declines and productivity is limited. While systems exist to automate these tasks, the quality of the generated data and documents is often inconsistent, making them impractical. A system that solves these problems is needed.

[1705] 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.

[1706] In this invention, the server includes a means for acquiring input from a user, a means for transmitting the acquired input information to the server, a means for generating a document based on the data using a generative AI model in the server, and a means for returning the generated document to the user. This allows users to easily automatically generate high-quality documents, reduces the risk of errors due to manual work, and significantly improves business efficiency.

[1707] This invention also includes a means for sending data files provided by a user to a server, organizing and classifying the data files using a generative AI model, and a means for returning the organized data to the user, thereby enabling the user to organize and classify data efficiently.

[1708] Furthermore, it includes a means for acquiring reservation information from users and managing reservation schedules using a generative AI model, a means for automatically checking for duplicate reservation information and notifying customers, and a means for returning schedule management results to users. This automates reservation management, prevents duplication and omissions, and improves user convenience.

[1709] A "user" is an entity that utilizes the system to input information and perform various functions.

[1710] A "terminal" is a device or local computer that is directly operated by a user, and is a device that has the role of transmitting input information to a server.

[1711] The "server" is a central computer that receives information sent by users and uses generative AI models to generate documents, organize data, and manage schedules.

[1712] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate documents based on provided data, organize and classify data, and manage schedules.

[1713] "Documents" are documents such as reports, invoices, contracts, etc. created by a generative AI model based on information provided by a user.

[1714] "Data file" means a file, such as a CSV file or Excel file, that contains data that a user provides to the system and that needs to be organized and categorized.

[1715] "Reservation information" refers to information relating to a reservation, such as the date and time, customer name, and service details, that the user provides to the system.

[1716] "Duplicate check" is the process of comparing existing schedules and data with newly acquired information to check for duplicates.

[1717] "Customer notification" is a function that automatically notifies customers of reservation information, data sorting results, etc.

[1718] "Schedule management" is the process of creating an optimal schedule based on reservation information and managing it to prevent duplication and omissions.

[1719] An "input form" is an interface provided for a user to input information on a terminal.

[1720] The present invention is a system that takes input information from a user, sends it to a server, and automatically generates documents using a generative AI model. The system includes a billing module, a data organization module, and a reservation management module, each of which performs a specific task.

[1721] Invoicing Module

[1722] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. This information is sent to the server via the device. The server formats the received data and passes it to a generative AI model (e.g., GPT-3). The generative AI model selects an appropriate invoice template based on the provided data, embeds the data, and generates a document. The generated invoice is sent from the server to the device, where the user can review it and modify it if necessary.

[1723] Examples:

[1724] When a user creates a new invoice, they enter the following information:

[1725] Customer name: "Customer A"

[1726] Product: "Product X"

[1727] Quantity: "10"

[1728] Unit price: 5,000 yen

[1729] Transaction date: "2023-10-01"

[1730] This information is sent from the device to a server, which uses a generative AI model to generate an invoice based on this information, which can then be viewed on the user's device.

[1731] Data Reduction Module

[1732] Users upload data files (e.g., CSV or Excel files) that need to be organized and classified to their device. The device then sends the uploaded data files to a server, which analyzes the data using a generative AI model. The generative AI model uses natural language processing technology to tag, classify, and organize the data. The organized data is then sent from the server to the device, where it can be viewed and modified by the user.

[1733] Examples:

[1734] The user uploads customer data (a CSV file containing customer names, addresses, purchase history, etc.) to the system. The device sends the file to the server, which uses a generative AI model to analyze the data and classify and tag customers based on "customer purchase frequency." The organized data is then sent back to the device for review by the user.

[1735] Reservation Management Module

[1736] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information to the server. The server passes the received reservation information to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicate reservation information and automatically sets an appropriate schedule. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can review and modify it.

[1737] Examples:

[1738] A user enters a meeting reservation with "Customer B" for 9:00 on October 15th. This information is sent from the device to the server, which uses the generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is displayed on the device for the user to review.

[1739] In this way, the present invention provides a system that reduces user effort, prevents human error, and improves work efficiency. Users can simply use specific prompts to pass the required information to the generative AI model and obtain automated results.

[1740] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1741] Invoicing module processing steps

[1742] Step 1:

[1743] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the terminal. This input form is displayed on the web application and can be accessed through a browser.

[1744] Specific behavior:

[1745] The user enters data into each field and clicks the submit button.

[1746] Input: Customer name "Customer A", product "Product X", quantity "10", unit price "5,000 yen", transaction date "2023-10-01"

[1747] Output: Data entered in the input form

[1748] Step 2:

[1749] The device temporarily stores the data obtained from the input form and then sends it to the server using an HTTP POST request.

[1750] Specific behavior:

[1751] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[1752] Input: Data entered into an input form

[1753] Output: JSON format data (customer name, product name, quantity, unit price, transaction date)

[1754] Step 3:

[1755] The server receives the data sent from the terminal. After receiving it, it checks the integrity of the data and performs any necessary data processing. Data processing includes standardizing the data format and processing missing values.

[1756] Specific behavior:

[1757] The server parses the received JSON data and checks whether all required fields are present and whether the data format is correct.

[1758] Input: JSON format data (customer name, product name, quantity, unit price, transaction date)

[1759] Output: Formatted data

[1760] Step 4:

[1761] The server passes the formatted data to a generative AI model, which selects an appropriate invoice template based on the provided data and generates the document by filling in the data.

[1762] Specific behavior:

[1763] The generative AI model uses a template selection algorithm to choose the best template and fill in the data to create the invoice.

[1764] Input: Formatted data

[1765] Output: Generated invoice document

[1766] Step 5:

[1767] The server receives the generated invoice and sends it to the terminal, again using an HTTP response, as a PDF file.

[1768] Specific behavior:

[1769] The server retrieves the generated invoice and sends it to the terminal as an attachment to the HTTP response.

[1770] Input: Generated invoice document

[1771] Output: Invoice document in PDF format

[1772] Step 6:

[1773] The user checks the generated invoice displayed on the terminal, enters corrections or additional information as necessary, and performs a final check.

[1774] Specific behavior:

[1775] Users can view the PDF invoice on their device, make any necessary corrections, and then save or email the invoice.

[1776] Input: Invoice document in PDF format

[1777] Output: Final confirmed invoice

[1778] Data Reduction Module Processing Steps

[1779] Step 1:

[1780] The user uploads the data file (e.g., CSV or Excel file) that needs to be organized to the device. The data file is selected using a file selection dialog.

[1781] Specific behavior:

[1782] The user opens a file selection dialog on the device's browser, selects a data file, and clicks the upload button.

[1783] Input: Data file (CSV file or Excel file)

[1784] Output: Selected data file

[1785] Step 2:

[1786] The device reads the uploaded data file, temporarily stores all of the data, and then transmits this data to the server.

[1787] Specific behavior:

[1788] The device reads the file and sends its contents to the server's API endpoint.

[1789] Input: Uploaded data file

[1790] Output: File contents (CSV format data)

[1791] Step 3:

[1792] The server receives the data file sent from the terminal, then checks the integrity of the data and processes it.

[1793] Specific behavior:

[1794] The server analyzes the received data, processes missing values, and formats the data.

[1795] Input: CSV format data

[1796] Output: Formatted data

[1797] Step 4:

[1798] The server passes the formatted data to a generative AI model, which then analyzes the data and tags and classifies it according to specified rules and criteria.

[1799] Specific behavior:

[1800] Generative AI models analyze data and use classification algorithms to tag and categorize it.

[1801] Input: Formatted data

[1802] Output: Classified data

[1803] Step 5:

[1804] The server receives the classified data and sends it to the device using an HTTP response.

[1805] Specific behavior:

[1806] The server retrieves the classified data and sends it to the terminal.

[1807] Input: Classified data

[1808] Output: Data sent in the form of an HTTP response

[1809] Step 6:

[1810] The user can check the organized data on the terminal and make corrections as necessary.

[1811] Specific behavior:

[1812] Users can check the classification results on their device and perform filtering and corrections.

[1813] Input: Classified data

[1814] Output: Final confirmed data

[1815] Reservation Management Module Processing Steps

[1816] Step 1:

[1817] The user enters reservation information (date and time, customer name, and service details) into an input form on the terminal. The input information can be accessed through a browser.

[1818] Specific behavior:

[1819] The user enters data into each field and clicks the submit button.

[1820] Input: Date and time, customer name, service details

[1821] Output: Data entered in the input form

[1822] Step 2:

[1823] The terminal temporarily stores the entered reservation information and sends it to the server using an HTTP POST request.

[1824] Specific behavior:

[1825] The terminal converts the data obtained from the input form into JSON format and sends it to the server's API endpoint.

[1826] Input: Reservation information entered in the input form

[1827] Output: Reservation information in JSON format

[1828] Step 3:

[1829] The server receives the data sent from the device, verifies its contents, and passes it to the generative AI model.

[1830] Specific behavior:

[1831] The server parses the received JSON data and checks the integrity of the data.

[1832] Input: Reservation information in JSON format

[1833] Output: Formatted reservation information

[1834] Step 4:

[1835] The server uses a generative AI model to check for overlapping reservations, create appropriate schedules, and generate necessary notifications.

[1836] Specific behavior:

[1837] The generative AI model manages the schedule, checks for conflicts with other reservations, and generates necessary notifications.

[1838] Input: Formatted reservation information

[1839] Output: Duplicate check results and notification information

[1840] Step 5:

[1841] The server receives the generated schedule and sends it to the terminal. A notification email is also sent to the customer at the same time.

[1842] Specific behavior:

[1843] The server sends the generated schedule to the terminal in the form of an HTTP response and invokes the customer email sending function.

[1844] Input: Duplicate check results and notification information

[1845] Output: Generated schedule and notification email

[1846] Step 6:

[1847] The user checks the schedule information on the terminal and makes corrections as necessary.

[1848] Specific behavior:

[1849] The user checks the schedule information on the terminal and makes any necessary corrections.

[1850] Input: Generated schedule

[1851] Output: Final confirmed and revised schedule

[1852] (Application example 1)

[1853] 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."

[1854] Inventory management and resource optimization are becoming increasingly important in modern factories. However, conventional methods require a lot of manual data entry and confirmation, resulting in a high risk of human error. Real-time inventory monitoring and optimal resource allocation are also difficult, resulting in reduced operational efficiency. Furthermore, manually generating inventory management reports and resource allocation optimization proposals is cumbersome and hinders efficient business operations. The present invention aims to solve these problems and provide a system for efficiently managing inventory and optimizing resources within a factory.

[1855] 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.

[1856] In this invention, the server includes means for acquiring inventory information and resource information from a user, means for transmitting the acquired inventory information and resource information to the server, means for generating an inventory management report and an optimal resource allocation proposal in the server, and means for returning the generated report and proposal to the user. This enables real-time monitoring of inventory status and resource usage status within a factory, enabling optimal inventory management and resource allocation.

[1857] A "user" is a person or organization that uses the system to perform operations or input data.

[1858] "Input" refers to data or information that a user provides to a system.

[1859] A "server" is a computer system that processes acquired data and stores generated results.

[1860] "Data" means information received by the server for processing.

[1861] A "document" is a structured form of information provided by a generative AI model on a server.

[1862] "Inventory information" is data that indicates the quantity and status of products and parts in a factory.

[1863] "Resource information" refers to data related to resources such as personnel and machinery used or managed within a factory.

[1864] An "inventory management report" is a report on inventory status and management created by a generative AI model based on acquired inventory information.

[1865] A "resource optimal allocation proposal" is a proposal for optimal resource allocation created by a generative AI model based on acquired resource information.

[1866] A "generative AI model" is an artificial intelligence model that automatically generates new information and documents based on data.

[1867] "Real-time" refers to data processing and information provision occurring almost simultaneously.

[1868] A "tablet" is a type of mobile terminal, and is a device that allows a user to perform input operations.

[1869] A "PC terminal" is a personal computer, a general computing device that allows users to operate a system.

[1870] A "factory robot" is an autonomous mechanical device used to perform physical tasks and data processing in a factory.

[1871] This invention is a system for inventory management and resource optimization within a factory. The system allows users to input inventory and resource information within the factory using a tablet or PC terminal, and then uses a generative AI model based on that information to generate inventory management reports and optimal resource allocation proposals in real time.

[1872] The specific hardware configuration of this system includes tablets and PCs used by users, factory robots operating within the factory, and a server responsible for data processing. The software includes Python, the requests library, and a generative AI model.

[1873] Users use tablets or PC terminals to input inventory information (e.g., product name, quantity) and resource information (e.g., machine status, worker status) within the factory. This input information is sent to the server by the terminal. The server analyzes the received data and uses a generative AI model to generate an inventory management report and a proposal for optimal resource allocation. The generated report and proposal are then sent back to the terminal, where the user can review and modify them.

[1874] In addition, factory robots can automatically check and replenish inventory and optimize resource allocation based on the generated inventory management reports and resource allocation recommendations, significantly improving operational efficiency within the factory and reducing the risk of human error.

[1875] Here is a specific example. A user uses a tablet to input inventory information such as "Product A: 100 units, Product B: 50 units, Product C: 75 units" and resource information such as "Machine 1: In operation, Machine 2: Under maintenance, Worker 1: On break." This input information is sent to a server, which uses a generative AI model to generate an inventory management report and a resource allocation optimization proposal, which are then sent back to the tablet. As a result, the user can confirm the optimal method for inventory management and resource allocation, and factory robots will automatically perform their work based on that information.

[1876] Examples of prompt sentences are:

[1877] "A user inputs inventory and resource information for the factory. Inventory information: 100 units of product A, 50 units of product B, and 75 units of product C. Resource information: Machine 1 is in operation, machine 2 is undergoing maintenance, and worker 1 is on break. Based on this input information, the generative AI model generates an inventory management report and a proposal for optimal resource allocation."

[1878] This system enables effective inventory management and resource optimization within the factory, improving business efficiency.

[1879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1880] Step 1:

[1881] The user inputs inventory and resource information from a tablet or PC terminal. Inventory information includes the name and quantity of each product, while resource information includes the status of machines and workers. Specifically, this input information takes the form of 100 units of product A, 50 units of product B, and 75 units of product C. The input information is prepared as data to be sent to the server in the next processing step.

[1882] Step 2:

[1883] The terminal acquires inventory and resource information entered by the user and sends the data to the server. The data is packetized in JSON or XML format and sent to the server using the HTTP protocol. This processing step also checks whether the data transmission was successful, and attempts to resend if an error occurs.

[1884] Step 3:

[1885] The server receives inventory and resource information sent from the device. The received data is stored and added to the analysis queue for later analysis. The server verifies the integrity of the data and checks for missing information or inconsistencies.

[1886] Step 4:

[1887] The server invokes a generative AI model to analyze the data based on the received inventory and resource information. The generative AI model includes a process that takes into account the current inventory status and past data trends to generate inventory management reports and resource allocation optimization suggestions. For example, if inventory is below a certain threshold, the model may include a reorder suggestion.

[1888] Step 5:

[1889] The server then sends the generated inventory management report and resource allocation optimization proposal to the terminal. At this time, the data is converted back to JSON or XML format and sent using the HTTP protocol. The transmitted data includes the specific report content and proposal.

[1890] Step 6:

[1891] The terminal receives inventory management reports and resource allocation optimization proposals sent from the server and displays them to the user, who can then check this information on the screen of their tablet or PC and make corrections or additional inputs as necessary.

[1892] Step 7:

[1893] The factory robot automatically checks inventory, replenishes resources, and optimizes resource allocation based on the inventory management report and resource allocation optimization proposals provided by the terminal. For example, if the inventory of product A is below 100 units, the robot will automatically begin replenishment work.

[1894] The above are the specific processing steps of the program for actually implementing the present invention. This system enables efficient real-time inventory management and resource optimization within a factory.

[1895] 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.

[1896] This system receives input from the user, sends the input information to a server, where a generative AI model generates a document based on the data and returns the result to the user. Furthermore, it incorporates a function that uses an emotion engine to analyze the user's emotions and optimizes document generation, data organization, and reservation management based on the analysis.

[1897] Invoicing Module

[1898] The user enters the information required for the invoice (customer name, product name, quantity, unit price, transaction date, etc.) into an input form on the device. The device acquires the entered information and sends it, along with the user's emotional state, to the server. The server passes the received information and the emotion analysis results to the generative AI model. The generative AI model selects an appropriate template based on the provided information and analyzed emotions, and automatically generates the invoice. For example, if the user is feeling stressed, a concise and easy-to-read format may be selected. The generated invoice is then sent from the server to the device, where the user can review it and enter corrections or additional information as necessary.

[1899] Examples:

[1900] To create a new invoice, a user enters the customer name "Customer A," the product "Product X," the quantity "10," the unit price "5,000 yen," and the transaction date "2023-10-01." The terminal sends this information, along with the user's emotional state (e.g., fatigue), to the server. The server uses a generative AI model to generate an invoice that is easy to read and less burdensome. The generated invoice is then displayed on the terminal for the user to confirm.

[1901] Data Reduction Module

[1902] The user provides the system with a data file (e.g., a CSV or Excel file) that needs to be organized and classified. The device reads the data file and sends its contents and the user's emotional state to the server. The server inputs the submitted data and the results of the emotion analysis into a generative AI model, which automatically organizes and classifies the data. For example, if the user is concentrating, detailed analysis and complex classification will be performed. The generative AI model uses natural language processing technology to analyze the data and tag and classify it according to specified rules and criteria. The organized data is sent from the server to the device, where the user can review it and make any necessary corrections.

[1903] Examples:

[1904] To organize customer data, users upload a CSV file containing information such as customer names, addresses, and purchase histories to the system. The device then sends the file along with the user's emotional state (e.g., relaxed state) to the server. The server then uses a generative AI model to categorize and tag customers based on, for example, "customer purchasing frequency." The organized data is then sent back to the device for review by the user.

[1905] Reservation Management Module

[1906] The user enters reservation information (date and time, customer name, service details, etc.) into an input form on the device. The device sends the entered information, along with the user's emotional state, to the server. The server passes the received information and the results of emotion analysis to the generative AI model, which manages the reservation schedule. The generative AI model checks for duplicates and sets the optimal schedule based on the reservation information and the results of the user's emotion analysis. For example, if the user is in a hurry, a schedule that is easy to check immediately is prioritized. If necessary, a notification is also automatically sent to the customer. The generated schedule is sent from the server to the device, where the user can check and modify it.

[1907] Examples:

[1908] A user enters a meeting reservation with a customer named "Customer B" for 9:00 on October 15th. The device sends this information along with the user's emotional state (e.g., nervousness) to the server. The server uses a generative AI model to analyze the reservation information and check for overlaps with other reservations. After confirmation, the meeting is scheduled and a confirmation email is automatically sent to Customer B. The generated schedule is then displayed on the device for the user to review.

[1909] In this way, the present invention utilizes an emotion engine to provide optimal document generation, data organization, and reservation management tailored to the user's condition, thereby realizing a system that improves business efficiency and user satisfaction.

[1910] The processing flow will be explained below.

[1911] Invoice Creation Module Processing Flow

[1912] Step 1:

[1913] The user enters the necessary information for creating an invoice (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal.

[1914] Step 2:

[1915] The device receives user input information and simultaneously analyzes the user's emotional state using an emotion engine, which is acquired through facial expression recognition and voice analysis.

[1916] Step 3:

[1917] The input information acquired by the device and the emotion analysis results are sent to the server.

[1918] Step 4:

[1919] The server analyzes the received information and converts it into a data format to be passed to the generative AI model, including the results of sentiment analysis.

[1920] Step 5:

[1921] The server provides the input data and sentiment analysis results to the generative AI model, initiating the process of selecting and generating an invoice template.

[1922] Step 6:

[1923] The generative AI model automatically generates invoices using appropriate templates based on the data provided and sentiment analysis results.

[1924] Step 7:

[1925] The server receives the generated invoice data and checks the format and content.

[1926] Step 8:

[1927] The server returns the generated bill data to the terminal.

[1928] Step 9:

[1929] The terminal displays the returned bill data to the user, who then checks the contents and inputs corrections or additional information as necessary.

[1930] Data reduction module processing flow

[1931] Step 1:

[1932] The user uploads data files (CSV, Excel, etc.) that need to be organized and classified to the terminal.

[1933] Step 2:

[1934] The device reads the uploaded file, verifies the format and performs some basic data formatting, while simultaneously analyzing the user's emotional state using an emotion engine.

[1935] Step 3:

[1936] The device transmits the analyzed emotional state and data to the server.

[1937] Step 4:

[1938] The server analyzes the received data and the emotion analysis results and converts them into a data format to be passed to the generative AI model.

[1939] Step 5:

[1940] The server feeds the data and sentiment analysis results to the generative AI model, which then begins the data organization and classification process.

[1941] Step 6:

[1942] A generative AI model analyzes the data, tags and categorizes it according to specified rules and criteria, and selects the appropriate organization method based on the user's emotional state.

[1943] Step 7:

[1944] The server receives the organized data from the generative AI model and checks its consistency and coherence.

[1945] Step 8:

[1946] The server returns the organized data to the device.

[1947] Step 9:

[1948] The terminal displays the returned organized data to the user, who then checks it and inputs corrections or additional information as necessary.

[1949] Reservation Management Module Processing Flow

[1950] Step 1:

[1951] The user enters reservation information (date and time, customer name, service details, etc.) into the input form on the terminal.

[1952] Step 2:

[1953] The terminal acquires the reservation information input by the user and simultaneously analyzes the user's emotional state using an emotion engine.

[1954] Step 3:

[1955] The terminal transmits the entered reservation information and emotion analysis results to the server.

[1956] Step 4:

[1957] The server analyzes the received reservation information and sentiment analysis results and converts them into a data format to be passed to the generative AI model.

[1958] Step 5:

[1959] The server feeds the reservation information and sentiment analysis results to the generated AI model, initiating the schedule management process.

[1960] Step 6:

[1961] The generative AI model analyzes reservation information, checks for overlaps with existing schedules, and sets the optimal schedule based on the user's emotional state.

[1962] Step 7:

[1963] The server receives the schedule data from the generated AI model and checks for duplicates and schedule consistency.

[1964] Step 8:

[1965] The server returns the confirmation results to the terminal and automatically sends a confirmation email to the customer if necessary.

[1966] Step 9:

[1967] The terminal displays the returned schedule data to the user, who then checks it and inputs corrections or additional information as necessary.

[1968] Example 2

[1969] 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."

[1970] Conventional document generation, data organization, and reservation management systems are not optimized to take into account the user's emotional state, making it difficult to respond flexibly to the user's state and needs, and creating challenges in improving work efficiency and user satisfaction.In addition, in data organization and reservation management, duplicate checks and customer notifications are often done manually, increasing the risk of errors and effort.

[1971] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1972] In this invention, the server includes means for acquiring input information and emotional state from a user and generating documents using a generative AI model, means for acquiring data files and emotional state and organizing and classifying data using a generative AI model, and means for acquiring reservation information and emotional state and automatically processing schedule management, duplicate checks, and customer notifications using a generative AI model. This enables document generation, data organization, and reservation management that take the user's emotional state into consideration.

[1973] A "user" is an individual or corporation that uses the system to perform operations such as document generation, data organization, and reservation management.

[1974] "Input information" is a general term for information such as customer name, product name, quantity, unit price, transaction date, and reservation information that a user inputs into the system.

[1975] "Emotional state" refers to the user's mental and emotional state, which is analyzed through the emotion engine.

[1976] A "terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet, through which input information is acquired and sent to a server.

[1977] A "server" is a remote computer system that receives data sent by users and uses generative AI models to perform tasks such as document generation, data organization, and reservation management.

[1978] "Generative AI model" refers to artificial intelligence technology that generates documents, organizes and categorizes data, and manages schedules based on received input information and emotional states.

[1979] "Data file" is a general term for files, such as CSV files and Excel files, that store data that users provide to the system and that need to be organized and classified.

[1980] "Schedule management" refers to the process of setting an optimal schedule and checking for overlaps based on reservation information and emotional state obtained from the user.

[1981] "Duplicate check" refers to the process of checking whether there is any overlapping information between existing data and new data.

[1982] "Customer notification" refers to the process of automatically notifying customers of important information such as reservation information via email, message, or other means.

[1983] This invention is a system that efficiently generates documents, organizes data, and manages reservations based on user input and emotional states. This system combines a user terminal, a server, a generative AI model, and an emotion engine.

[1984] First, a user uses a device to input the necessary information for tasks such as generating invoices, organizing data, and managing reservations. The device then uses JavaScript on the browser to obtain the user's input. The device also has a built-in emotion engine that analyzes the user's emotional state. This emotional state is inferred from data such as facial recognition cameras and input speed.

[1985] The acquired input information and emotional state are sent to the server using an HTTP POST request. The server temporarily stores the received information and provides it to the generative AI model. The generative AI model generates documents, organizes data, and manages reservations based on the received prompts.

[1986] As a concrete example, consider the operation of creating an invoice. To create a new invoice, the user enters the customer name, product name, quantity, unit price, and transaction date into an input form on the terminal, and the terminal sends this information and the emotional state (e.g., "fatigue") to the server. The server inputs the following prompt sentence into the generative AI model.

[1987] Customer Name: Customer A

[1988] Product: Product X

[1989] Quantity: 10

[1990] Unit price: 5000

[1991] Transaction Date: 2023-10-01

[1992] Emotional state: Fatigue

[1993] The generative AI model uses this information to select the optimal template and automatically generate an invoice, which is then sent from the server to the device where the user can review it and make any necessary corrections.

[1994] For data organization, users upload CSV or Excel files using their devices, along with their emotional state, and send them to the server, which then inputs the following prompts into the generative AI model:

[1995] Data file: [file contents]

[1996] Emotional state: Relaxed

[1997] The generative AI model organizes and classifies the data based on this information. The organized data is sent from the server to the device, where the user can review it and make corrections as necessary.

[1998] In reservation management, the user inputs reservation information using a terminal and sends it along with their emotional state to the server, which then inputs the following prompt sentence into the generative AI model:

[1999] Reservation information: Date and time: 2023-10-15 09:00

[2000] Customer Name: Customer B

[2001] Services: Meetings

[2002] Emotional state: Tension

[2003] The generative AI model manages schedules, checks for duplicates, and automatically handles customer notifications. The schedule management results are sent from the server to the device, where the user can review and make corrections as necessary.

[2004] As described above, the present invention is a system that utilizes a generative AI model and an emotion engine to realize optimal document generation, data organization, and reservation management that corresponds to the user's emotional state, thereby improving business efficiency and user satisfaction.

[2005] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2006] Invoicing Module

[2007] Step 1:

[2008] The user enters invoice information (customer name, product name, quantity, unit price, transaction date) into the input form on the terminal. This information becomes the input data.

[2009] Step 2:

[2010] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[2011] Step 3:

[2012] The device sends the acquired input information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes a prompt like this:

[2013] Customer Name: Customer A

[2014] Product: Product X

[2015] Quantity: 10

[2016] Unit price: 5000

[2017] Transaction Date: 2023-10-01

[2018] Emotional state: Fatigue

[2019] Step 4:

[2020] The server temporarily stores the received information and provides it as input to the generative AI model, which then selects the optimal template and generates the document. The output is the generated invoice.

[2021] Step 5:

[2022] The server encodes the generated invoice in JSON format and sends it as an HTTP response to the terminal. The sent data becomes the terminal's input data.

[2023] Step 6:

[2024] The user checks the invoice displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[2025] Data Reduction Module

[2026] Step 1:

[2027] Users use the system's upload form to select a CSV or Excel file, which becomes the input data.

[2028] Step 2:

[2029] The device retrieves the file contents and uses its built-in emotion engine to analyze the user's emotional state. The device estimates the user's emotional state based on data from the facial recognition camera and input speed. The results are also retrieved as input data.

[2030] Step 3:

[2031] The device sends the file contents and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[2032] Data file: [file contents]

[2033] Emotional state: Relaxed

[2034] Step 4:

[2035] The server provides the received data as input to the generative AI model, which then analyzes the data and organizes and classifies it according to specified rules and criteria. The output is an organized data file.

[2036] Step 5:

[2037] The server encodes the organized data in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[2038] Step 6:

[2039] The user checks the organized data displayed on the terminal and makes corrections as necessary. If corrections are made, the process starts again from step 2.

[2040] Reservation Management Module

[2041] Step 1:

[2042] The user inputs information such as date and time, customer name, service details, etc. into the reservation form. This information becomes input data.

[2043] Step 2:

[2044] The device receives the input information and analyzes the user's emotional state using a built-in emotion engine. The device estimates the user's emotional state based on data from a facial recognition camera and input speed. The results are also collected as input data.

[2045] Step 3:

[2046] The device sends the acquired reservation information and emotional state to the server as an HTTP POST request. The sent data becomes the server's input data. This data includes the following prompt:

[2047] Reservation information: Date and time: 2023-10-15 09:00

[2048] Customer Name: Customer B

[2049] Services: Meetings

[2050] Emotional state: Tension

[2051] Step 4:

[2052] The server provides the received data as input to the generative AI model, which then manages schedules, checks for duplicates, and notifies customers. The output is the optimal schedule and notification content.

[2053] Step 5:

[2054] The server encodes the generated schedule in JSON format and sends it to the terminal as an HTTP response. The sent data becomes the terminal's input data.

[2055] Step 6:

[2056] The user checks the schedule displayed on the terminal and makes any necessary corrections. If any corrections are made, the process starts again from step 2.

[2057] (Application example 2)

[2058] 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."

[2059] Conventional systems were unable to take the user's emotional state into account when generating documents, organizing data, or managing reservations based on user input. This resulted in problems such as reduced work efficiency due to user stress and fatigue. Furthermore, particularly in factories, there is a need to provide work instructions and operation guides based on the operator's emotional state, but no system has been available that can achieve this. In these circumstances, a system is needed that analyzes the user's emotional state and, based on that, optimizes document generation, data organization, reservation management, work instruction generation, machine operation guide generation, and inventory management.

[2060] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing document generation, means for optimizing data organization, means for optimizing schedule management, means for adjusting work instructions, means for adjusting the difficulty level of operation guidance, and means for optimizing inventory management and order planning. This makes it possible to take the emotional state of the user into consideration and provide optimal document generation, data organization, reservation management, work instructions, operation guidance, and inventory management for the user.

[2061] "Means for obtaining input from the user" refers to the function of receiving information provided by the system user through a digital device.

[2062] The "means for transmitting acquired input information to a server" is the process of transferring information acquired from a user to a server via a network.

[2063] The "means for generating a document based on data generated on the server" is a function that processes data on the server side and automatically creates a document based on the results.

[2064] "Means for analyzing a user's emotional state using an emotion analysis engine" refers to technology that analyzes a user's current emotions using facial recognition, biometric signals, etc.

[2065] "Means for optimizing document generation based on analysis results" refers to technology that reflects the results of sentiment analysis to create documents with formats and content that are appropriate for the user.

[2066] The "means for returning the document generation results to the user" is the process for delivering the generated document to the user.

[2067] "Means for organizing and classifying acquired data" refers to a function that systematically arranges and classifies input data according to certain rules.

[2068] A "means for returning organized data to a user" is a process for presenting or transmitting organized data to a user.

[2069] "Means for optimizing data organization based on the results of user emotion analysis" refers to technology that reflects the user's emotional state to streamline data organization and classification work.

[2070] "Means for managing schedules on the server based on the acquired reservation information" refers to a function that automatically creates and manages schedules on the server based on reservation information.

[2071] The "means for automatically checking for duplicates and notifying customers" is a technology that detects duplicate reservation information and automatically notifies customers as necessary.

[2072] "Means for optimizing schedule management based on the results of user emotion analysis" is a function that adjusts schedule placement and notification methods according to the user's emotional state.

[2073] The "means for generating work instructions" is a function that allows the system to automatically create instructions for the work to be performed by the operator.

[2074] The "means for displaying the generated work instructions to the operator" is a process for visually presenting the generated work instructions to the operator.

[2075] "Means for adjusting the content of work instructions based on the emotional state of the operator" refers to a technology that adjusts the content and difficulty of work instructions by reflecting the results of an analysis of the operator's emotions.

[2076] "Means for automatically generating machine operation guides" refers to a system function that automatically describes and generates machine operation methods.

[2077] The "means for providing the generated operation guide to the user" is a process for delivering the generated operation guide to the user and making it available for use.

[2078] The "means for adjusting the difficulty of the operation guide based on the emotional state of the user" is a technology for adjusting the level of detail and simplicity of the operation guide based on the results of an analysis of the user's emotions.

[2079] "Means of obtaining data for inventory management" is the process of collecting information about inventory.

[2080] "Means for generating inventory management and parts ordering plans based on acquired data" refers to a function that creates inventory ma...

Claims

1. a means for obtaining input from a user; means for transmitting the acquired input information to a server; a means for generating a document based on the data generated in the server; means for returning document generation results to the user; A system including:

2. A means of organizing and classifying the acquired data; means for returning the organized data to the user; The system of claim 1 further comprising:

3. A means for obtaining reservation information from a user; A means for managing a schedule in a server based on the acquired reservation information; a means for automatically handling duplicate checks and customer notification; means for returning schedule management results to the user; The system of claim 1 further comprising:

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