system

Abstract

The system provides users with a simple way to apply for travel expenses while also improving the accuracy of the application details. [Solution] A system comprising: communication means for the user to input destination and date / time information; upload means for the user to upload an image of a receipt; storage means for temporarily storing received text messages and image files; transmission means for sending received text messages and image files to a server; optical character recognition means for analyzing received image files and extracting text data; matching means for comparing the extracted text data with text data entered by the user; calculation means for calculating transportation expenses; and notification means for notifying the user of the matching results and calculation results.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional transportation expense application systems have problems such as a large amount of manual input and management, low efficiency, and a high likelihood of human errors. Therefore, there is a need for a method and system that can reduce the burden on users and accurately and quickly apply for transportation expenses.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following means.

[0006] The system includes communication means for users to input destination and date / time information, and upload means for users to upload images of receipts. It also provides storage means for temporarily storing received text messages and image files, and transmission means for sending this data to the server. The server has optical character recognition means for analyzing received image files and extracting text data, and includes matching means for comparing the extracted text data with the text data entered by the user. Furthermore, the system includes calculation means for calculating travel expenses based on the matched data, and notification means for notifying the user of the matching results and calculation results. This configuration allows users to easily apply for travel expenses and improves the accuracy of the application content.

[0007] "Means of communication" refers to the interface used by users to input destination and date / time information, and includes chat screens and messaging applications.

[0008] "Uploading method" refers to a function that allows users to send image files of receipts, and includes features such as a camera function and a file selection dialog.

[0009] "Storage means" refers to the function of temporarily storing received text messages and image files in digital storage, and includes RAM, hard disks, cloud storage, etc.

[0010] "Transmission means" refers to communication functions for sending text messages and image files stored in the storage means to a server, and includes internet communication and mobile network communication.

[0011] A "server" refers to a computer system used for analyzing, processing, storing, and managing received data, and includes remote servers and cloud servers.

[0012] "Optical character recognition means" refers to technology for extracting text data from received image files, and includes OCR (Optical Character Recognition) software and services.

[0013] "Matching means" refers to a function that compares and matches text data entered by the user with text data extracted from an image, and includes matching confirmation and data integrity checks.

[0014] "Calculation means" refers to the function of calculating transportation expenses based on information obtained through the verification means, and includes fare calculation algorithms and referencing of transportation expense databases.

[0015] "Notification means" refers to a function for notifying the user of the matching results and calculation results, and includes functions for generating and sending notification messages. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[0038] Terminal operation

[0039] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[0040] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[0041] Server operation

[0042] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[0043] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[0044] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it can calculate that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[0045] The matching and calculation results are sent to the user via the server's notification system. The user receives the notification and reviews its contents. They can also make corrections as needed.

[0046] Specific example

[0047] 1. Example of user input

[0048] Destination: Tokyo

[0049] Date and time: October 5, 2023 10:00

[0050] Upload a photo of your travel expense receipt.

[0051] 2. Server Analysis and Matching Examples

[0052] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0053] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0054] 3. Calculation results

[0055] Transportation expenses: 500 yen

[0056] 4. Example of a notification to the user

[0057] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0058] User verification and application confirmation

[0059] Once the user receives a confirmation message and verifies its contents are correct, they press the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. Simultaneously, it sends a processing completion notification to the user. This entire process ensures that travel expense applications are completed efficiently and accurately.

[0060] This system configuration allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[0061] The following describes the processing flow.

[0062] Step 1:

[0063] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0064] Step 2:

[0065] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[0066] Step 3:

[0067] The terminal temporarily stores text messages and image files received from the user in a storage device.

[0068] Step 4:

[0069] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[0070] Step 5:

[0071] The server analyzes the received text data and extracts destination and date / time information.

[0072] Step 6:

[0073] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[0074] Step 7:

[0075] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[0076] Step 8:

[0077] The server calculates the transportation cost using a calculation tool based on the results of the matching tool. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0078] Step 9:

[0079] The server sends the matching results and calculation results to the user via a notification system.

[0080] Step 10:

[0081] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[0082] Step 11:

[0083] Users can review the content and send correction messages if necessary.

[0084] Step 12:

[0085] The user presses the "Confirm Application" button.

[0086] Step 13:

[0087] The server receives the instruction to confirm the application and saves the application data to the database.

[0088] Step 14:

[0089] The server sends a processing completion notification to the user.

[0090] This processing step allows users to efficiently submit travel expense claims, and the server automatically performs data analysis, matching, and calculations, improving the accuracy of the claims.

[0091] (Example 1)

[0092] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] The current travel expense application system has problems such as the large amount of information that users have to manually enter, and the inefficiency of processing travel expense applications due to input errors and inaccuracies. In addition, manual data verification and calculation work is required, which is time-consuming and resource-intensive.

[0094] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0095] In this invention, the server includes a text analysis means for analyzing received text data and extracting destination and date / time information, an optical character recognition means for analyzing received image files and extracting text data, and a matching means for comparing the extracted text data with text data entered by the user. This makes it possible to automatically compare the information entered by the user with the information on the receipt, and to quickly and accurately calculate and notify the travel expenses.

[0096] "Communication means" refers to a device used by users to input destination and date / time information, and which communicates via a messaging interface.

[0097] An "uploading device" is a device that provides the functionality for a user to capture an image of a receipt and send it to a server.

[0098] A "storage device" is a storage device for temporarily saving received text messages and image files.

[0099] "Transmission means" refers to a communication device for sending text messages and image files stored in the storage means to a server.

[0100] "Text analysis means" refers to software or hardware used to analyze text data received by a server and extract destination and date / time information.

[0101] "Optical character recognition means" refers to a technology for extracting text data from image files received by a server, and can utilize external libraries.

[0102] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[0103] "Calculation means" refers to software or hardware that the server uses to calculate travel expenses based on the verified information.

[0104] "Notification means" refers to a device or system that has the function of notifying the user of the matching results and calculation results.

[0105] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[0106] Device operation:

[0107] The user enters destination and date / time information using the communication means on their device. For example, the user can enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload means on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application. The device temporarily stores the received text message and image file in its storage means. This data is then sent to the server via the transmission means.

[0108] Server operation:

[0109] The server receives text messages and image files sent from the terminal. The specific operating procedure of the server is as follows:

[0110] 1. Data reception:

[0111] The server receives text messages and image files sent from the terminal.

[0112] 2. Text data analysis:

[0113] The server parses the received text data and extracts destination and date / time information. This process can utilize parsing tools such as Python's Natural Language Toolkit (NLTK).

[0114] 3. OCR analysis:

[0115] The server extracts text data from the received image file using optical character recognition (OCR) technology. Specifically, it can use OCR services such as Google® Cloud Vision API.

[0116] 4. Data matching:

[0117] The server compares and matches the data extracted by OCR analysis with the text data entered by the user. This matching is performed using an SQL database and database queries.

[0118] 5. Transportation cost calculation:

[0119] The server calculates travel expenses based on the verified information. For example, it calculates 500 yen for travel between "Tokyo Station and Shinagawa Station". This calculation uses Python's pandas library and built-in calculation functions.

[0120] 6. Notification to users:

[0121] The server notifies the user of the calculation results and the comparison results. Notification methods include email (e.g., email services such as SendGrid) and push notifications (Firebase Cloud Messaging).

[0122] Specific example:

[0123] 1. Example of user input:

[0124] Destination: Tokyo

[0125] Date and time: October 5, 2023 10:00

[0126] Upload a photo of your travel expense receipt.

[0127] 2. Server analysis and matching examples:

[0128] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0129] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0130] 3. Calculation results:

[0131] Transportation expenses: 500 yen

[0132] 4. Example of a notification to the user:

[0133] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0134] Examples of input prompts for a generative AI model:

[0135] "To claim your travel expenses, please enter the following information: destination, date and time, and upload an image of your travel expense receipt."

[0136] In this way, the entire system functions in a way that allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[0137] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0138] Step 1:

[0139] Enter destination and date / time

[0140] The user uses their device's communication method to enter destination and date / time information. For example, the user might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00 AM".

[0141] Input: The user enters the destination and date / time into a form on the device.

[0142] Output: The destination and date / time information are stored on the terminal.

[0143] Specific action: Enter text into the terminal's input field and press the send button.

[0144] Step 2:

[0145] Upload receipt image

[0146] The user uses their device's camera function to capture an image of their travel expense receipt and uploads it to a messaging application.

[0147] Input: The user takes a picture of their travel expense receipt and presses the upload button.

[0148] Output: The receipt image is saved to the device and uploaded.

[0149] Specific steps: Launch the camera app on your device, take a picture of the receipt, and then upload it.

[0150] Step 3:

[0151] Data is temporarily stored.

[0152] The terminal temporarily stores text data such as the destination and date / time entered by the user, as well as uploaded image files.

[0153] Input: Text data of destination and date / time, and an image file of the receipt.

[0154] Output: Text data and image files are saved to the device's storage.

[0155] Specific action: Saves data to the device's cache or temporary folder.

[0156] Step 4:

[0157] Sending data

[0158] The device sends the saved text data and image files to the server.

[0159] Input: Text data and image files.

[0160] Output: Data is sent to the server.

[0161] Specific operation: Send data to the server using the HTTPS protocol.

[0162] Step 5:

[0163] Data reception

[0164] The server receives text messages and image files sent from the terminal.

[0165] Input: Text data and image files.

[0166] Output: The received data is stored on the server.

[0167] Specific operation: Receive data and save it to local storage.

[0168] Step 6:

[0169] Text data analysis

[0170] The server analyzes the received text data and extracts destination and date / time information. Tools such as the Natural Language Toolkit (NLTK) are used for the analysis.

[0171] Input: Received text data.

[0172] Output: Destination and date / time are extracted.

[0173] Specific operation: The text analysis engine analyzes the data and extracts the necessary information.

[0174] Step 7:

[0175] OCR analysis

[0176] The server extracts text data from received image files using optical character recognition (OCR). It utilizes APIs such as the Google Cloud Vision API.

[0177] Input: Received receipt image file.

[0178] Output: Text data extracted from the image.

[0179] Specific operation: The OCR engine analyzes image data and extracts text data.

[0180] Step 8:

[0181] Data matching

[0182] The server compares and matches the data extracted by OCR analysis with the text data entered by the user, using database queries.

[0183] Input: Text data entered by the user and text data extracted by OCR.

[0184] Output: Matching results (data match / mismatch information).

[0185] Specific operation: Perform collation and verification using SQL queries.

[0186] Step 9:

[0187] Transportation expense calculation

[0188] The server calculates travel expenses based on the verified information, using Python's pandas library and built-in calculation functions.

[0189] Input: Matched, accurate destination information and transportation route.

[0190] Output: Calculated transportation costs.

[0191] Specific operation: Calculates transportation expenses based on the calculation logic.

[0192] Step 10:

[0193] Notification to the user

[0194] The server notifies the user of the calculation results and the comparison results. This is done using email services such as SendGrid or push notifications (Firebase Cloud Messaging).

[0195] Input: Calculation results and comparison results.

[0196] Output: Notification message to the user.

[0197] Specific action: Send a message to the user using the notification system.

[0198] (Application Example 1)

[0199] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0200] The application process for transportation costs at logistics centers involves manual data entry and receipt organization, which is time-consuming and prone to errors. Furthermore, many of the calculation and application processes for transportation costs are analog, making them inefficient. Therefore, there is a need for increased efficiency while simultaneously improving accuracy.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0202] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, transmission means for sending received text messages and image files to the server, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating transportation costs, notification means for notifying the user of the matching results and calculation results, storage means for saving transportation cost data to a database, and confirmation means for finalizing the transportation cost application. This makes it possible to improve the efficiency and accuracy of the transportation cost application process.

[0203] "Means of communication" refers to means by which users input destination and date / time information, and in particular includes chat interfaces.

[0204] "Upload method" refers to the means by which users upload images of receipts to the system.

[0205] "Storage means" refers to means for temporarily saving received text messages and image files.

[0206] "Transmission means" refers to the means for sending received text messages and image files to the server.

[0207] "Optical character recognition means" refers to means that use optical character recognition technology to extract text data from an image file.

[0208] "The matching means" refers to the means of comparing the extracted text data with the text data entered by the user.

[0209] "Calculation means" refers to the means used to calculate transportation costs.

[0210] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[0211] "Means of storage for saving to a database" refers to means of saving transportation cost data to a database.

[0212] "Confirmation method" refers to the means by which users can finalize their shipping cost claims.

[0213] This invention is a system aimed at improving the efficiency and accuracy of the application process for transportation costs at a logistics center. The system includes communication means, upload means, storage means, transmission means, optical character recognition means, matching means, calculation means, notification means, storage means for saving to a database, and verification means.

[0214] To use the system, users first enter destination and date / time information using their smartphones and upload images of their shipping expense receipts. A chat interface is used for communication, allowing users to intuitively input data.

[0215] Uploaded images are temporarily stored on the device using a storage method and then sent to the server via a transmission method. The server receives the image file and extracts text data from the image using optical character recognition (Tesseract OCR). This optical character recognition method is implemented using a Python library and recognizes the characters after converting the image to grayscale.

[0216] The extracted text data is compared with the destination and date / time information entered by the user using a matching mechanism. This matching is performed using a text analysis mechanism to confirm that the user's input data matches the OCR data. Once the matching is complete, the transportation cost is calculated using a calculation mechanism. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0217] The calculation and verification results are notified to the user using a notification system. The user receives the notification and is asked to confirm it. The user confirms the contents through the confirmation system and, if correct, confirms the application. Once this confirmation process is complete, the shipping cost data is stored in the database using a storage system.

[0218] As a concrete example, consider the following prompt:

[0219] "The destination is Tokyo. I have uploaded an image of the shipping receipt taken on October 5, 2023 at 10:00 AM. Please automatically calculate the shipping cost."

[0220] This series of processes effectively automates the application process for transportation costs, reducing the burden on users and improving the accuracy of application data.

[0221] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0222] Step 1:

[0223] The user opens the chat interface using their smartphone and enters destination and date / time information. The entered information is saved to the device via communication. The data saved as input is "Destination: Tokyo, Date / Time: October 5, 2023, 10:00".

[0224] Step 2:

[0225] The user uses the upload method to take a picture of the shipping receipt with their smartphone and upload it to the chat interface. The image file is saved on the device. The uploaded image file is temporarily stored on the device using a temporary storage method.

[0226] Step 3:

[0227] The terminal sends the saved input data and image files to the server using a transmission device. The transmitted data includes destination and date / time information, as well as an image file of the receipt. The server receives the transmitted data and temporarily stores it using a storage device.

[0228] Step 4:

[0229] The server uses optical character recognition (OCR) to extract text data from the received image file. Specifically, it uses Tesseract OCR to convert the image to grayscale and then reads the text from it. The extracted text data might be something like "Tokyo Station to Shinagawa Station" and "500 yen".

[0230] Step 5:

[0231] The server uses a matching mechanism to compare the extracted text data with the destination and date / time information entered by the user. Text analysis is performed to confirm that the input data and OCR data match. If the matching is successful, the transportation cost from "Tokyo Station to Shinagawa Station" is calculated.

[0232] Step 6:

[0233] The server uses a calculation tool to calculate the shipping cost based on the verified information. It verifies that the calculated shipping cost is 500 yen. The calculation result is notified to the user via a notification tool.

[0234] Step 7:

[0235] The user reviews the calculated shipping cost received as a notification. The notification includes details of the destination, date and time, and shipping costs. The user uses the verification method to confirm the details and finalize the application.

[0236] Step 8:

[0237] The server receives confirmation instructions from the user and stores the shipping cost data in the database using a storage method. This completes the shipping cost application and saves it to the database.

[0238] Step 9:

[0239] Once verification is complete, the server sends a completion notification to the user. The user is informed through the notification system that the application has been successfully completed.

[0240] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0241] This invention is a system that improves the efficiency and accuracy of travel expense claims, as well as analyzes user emotions to enhance the smoothness of the process. This system works in conjunction with the user, terminal, and server to efficiently process travel expense-related information and emotional data.

[0242] Terminal operation

[0243] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[0244] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[0245] Server operation

[0246] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[0247] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[0248] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it calculates that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[0249] The server runs an emotion engine to recognize emotions from user behavior and input. The emotion engine has the function of analyzing the emotions in the text entered by the user and the function of analyzing the user's facial expressions from uploaded images to recognize emotions.

[0250] Notification to the user

[0251] The matching and calculation results are sent to the user via the server's notification system. The user reviews the received notification message (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"). Based on the perceived emotions, the notification content is appropriately adjusted.

[0252] The user reviews the notification and, if the information is correct, presses the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. At the same time, it sends a processing completion notification to the user. If the recognized emotions require correction, the server can provide the user with further confirmation and support.

[0253] Specific example

[0254] 1. Example of user input

[0255] Destination: Tokyo

[0256] Date and time: October 5, 2023 10:00

[0257] Upload a photo of your travel expense receipt.

[0258] 2. Server Analysis and Matching Examples

[0259] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0260] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0261] 3. Calculation results

[0262] Transportation expenses: 500 yen

[0263] 4. Emotion recognition example

[0264] Text sentiment analysis: "The analysis determined that the user is feeling anxious."

[0265] Image analysis: The user's facial expression indicates fatigue.

[0266] 5. Example of a notification to the user

[0267] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0268] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[0269] Thus, the system of the present invention can streamline the travel expense application process and improve the user experience through emotion recognition.

[0270] The following describes the processing flow.

[0271] Step 1:

[0272] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0273] Step 2:

[0274] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[0275] Step 3:

[0276] The terminal temporarily stores text messages and image files received from the user in a storage device.

[0277] Step 4:

[0278] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[0279] Step 5:

[0280] The server analyzes the received text data and extracts destination and date / time information.

[0281] Step 6:

[0282] The server analyzes the received image file using optical character recognition (OCR) means and extracts text data from the image.

[0283] Step 7:

[0284] The server compares and collates the extracted text data and the text data input by the user using collation means.

[0285] Step 8:

[0286] The server calculates the transportation expenses based on the collation result using calculation means. For example, it calculates that the transportation expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[0287] Step 9:

[0288] The server executes an emotion engine for recognizing the user's emotion from the text input by the user and the uploaded image.

[0289] Step 10:

[0290] The server performs text emotion analysis using the emotion engine. For example, it is determined that the user's text is "nervous".

[0291] Step 11:

[0292] The server performs image analysis using the emotion engine and recognizes the emotion from the user's expression. For example, it is determined that the user's expression is "tired".

[0293] Step 12:

[0294] The server sends the collation result and the calculation result to the user using notification means. The notification content is adjusted based on the analyzed emotion. For example, it becomes "a kind tone prompting quick confirmation".

[0295] Step 13:

[0296] The user checks the received notification message (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00, Transportation Expenses: 500 yen").

[0297] Step 14:

[0298] The user can check the content and send a correction message if necessary.

[0299] Step 15:

[0300] The user presses the "Application Confirmation" button.

[0301] Step 16:

[0302] The server receives the application confirmation instruction and saves the application data in the database.

[0303] Step 17:

[0304] The server sends a processing completion notification to the user. If the recognized emotion requires correction, further confirmation and support are provided to the user.

[0305] With this processing step, the user can efficiently apply for transportation expenses, and the server automatically performs data analysis, verification, calculation, and emotion recognition, reducing the workload of both parties and improving the accuracy of the application.

[0306] <​​​​​​​​Traditional travel expense claim systems suffered from problems such as user input errors, cumbersome procedures, and user stress. Furthermore, requiring users to input accurate information often required significant time and effort. This reduced the efficiency of the application process and compromised the user experience. Additionally, existing systems lacked emotion recognition capabilities, failing to adequately address user stress and anxiety. To address these issues, a system was needed that would automate the travel expense claim process and reduce user stress.

[0309] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0310] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating travel expenses, notification means for notifying the user of the matching results and calculation results, emotion recognition means for analyzing emotions from the user's input data and images, and means for adjusting the notification content based on the recognized emotions. This enables the automation of the travel expense application process and an improvement in the user experience.

[0311] "Communication method" refers to an interface for users to input destination and date / time information.

[0312] The "upload method" is a function that allows users to send images of receipts to the system.

[0313] "Storage means" refers to data storage that temporarily stores received text messages and image files.

[0314] "Transmission means" refers to the function for sending data stored in the storage means to the server.

[0315] "Optical character recognition means" refers to a technology for extracting character information from a received image file.

[0316] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[0317] The "calculation means" refers to a processing function for calculating transportation expenses based on the verified information.

[0318] "Notification means" refers to a messaging function for communicating calculation results and verification results to the user.

[0319] "Emotion recognition means" refers to technology for analyzing and recognizing emotions from user input data and images.

[0320] "Means for adjusting notification content" refers to a function that appropriately modifies the content of notifications sent to the user based on recognized emotions.

[0321] This invention is a system designed to improve the efficiency and accuracy of travel expense claims, and it operates in cooperation with the user, terminal, and server. The detailed configuration and operation of this system will be described below.

[0322] System Configuration

[0323] The system includes the following main components:

[0324] 1. Means of communication

[0325] 2. Upload method

[0326] 3. Preservation means

[0327] 4. Transmission method

[0328] 5. Optical character recognition means (OCR means)

[0329] 6. Verification means

[0330] 7. Means of calculation

[0331] 8. Means of notification

[0332] 9. Emotion recognition means

[0333] 10. Means for adjusting notification content

[0334] Specific actions

[0335] 1. User actions

[0336] Users use their devices to enter destination and date / time information for travel expense claims. For example, a user might use their smartphone to enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Alternatively, the user might use their smartphone camera to take a picture of their travel expense receipt and upload the image via a chat or messaging app.

[0337] 2. Data processing on the terminal

[0338] The terminal temporarily stores text data of destination and date / time received from the user, as well as uploaded receipt images. This data is then transmitted to the server via the terminal's communication method. Wi-Fi or cellular networks are used for this communication.

[0339] 3. Receiving and analyzing data on the server

[0340] The server receives data sent from the terminal. The text analysis engine within the server first extracts destination and date / time information from the received text data. Next, it uses an OCR (Optical Character Recognition) engine to extract text information from the receipt image. For example, it obtains information such as "Tokyo Station to Shinagawa Station" and "500 yen" from the receipt image.

[0341] 4. Matching and Calculation

[0342] The server's matching mechanism compares the text data of the extracted image with the text data entered by the user to confirm a match. After the matching mechanism confirms a match, the calculation mechanism calculates the transportation cost. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0343] 5. Emotion recognition

[0344] The server's emotion recognition system analyzes the user's emotions from user input data and uploaded images. Specifically, a text analysis engine reads emotions from the strings entered by the user, and an image analysis engine recognizes emotions based on the user's facial expressions. For example, the system might determine from the text analysis results that "the user is nervous" and recognize from the image analysis results that "the user has a tired expression."

[0345] 6. Notification and adjustment of results

[0346] The server generates a notification message for the user based on the matching and calculation results. For example, it might create a message such as "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen." Furthermore, it adjusts the tone of the notification based on the sentiment recognition results. The result notification is sent from the server to the device and displayed in the user's chat or messaging app.

[0347] Specific example

[0348] 1. User input

[0349] Destination: Tokyo

[0350] Date and time: October 5, 2023 10:00

[0351] Upload a photo of your travel expense receipt.

[0352] 2. Server analysis and matching

[0353] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0354] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0355] 3. Calculation results

[0356] Transportation expenses: 500 yen

[0357] 4. Emotion recognition

[0358] Text sentiment analysis: "The user is feeling anxious."

[0359] Image analysis: The user's facial expression indicates fatigue.

[0360] 5. Notification to the user

[0361] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0362] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[0363] Examples of prompts for generative AI models

[0364] 1. Example of a travel expense claim: "The destination is Tokyo, and the date and time is October 5, 2023, at 10:00 AM. Please upload your travel expense receipt."

[0365] 2. Specific example of emotion recognition: "Analyze the user's emotions from their input and facial expressions to determine if there are signs of tension or fatigue."

[0366] The above describes the embodiment for carrying out the invention. This system enables the automation of the travel expense application process and improves the user experience.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Program processing flow

[0369] Step 1: User data entry

[0370] Operation: The user uses a terminal to enter destination and date / time information for the travel expense claim. For example, enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0371] Input: Destination ("Tokyo"), Date and Time ("October 5, 2023, 10:00")

[0372] Output: The input data is saved to the terminal.

[0373] Step 2: Upload the receipt image

[0374] Operation: The user takes a picture of the receipt with their device's camera and uploads it via a chat or messaging app.

[0375] Input: Image of the receipt that was photographed.

[0376] Output: The uploaded image is saved to the device.

[0377] Step 3: Save data temporarily

[0378] Operation: The terminal temporarily saves the entered text data (destination and date / time) and uploaded image files.

[0379] Input: Text data of destination and date / time, image of receipt

[0380] Output: Temporarily saved text data and image files are stored on the device.

[0381] Step 4: Send data to the server

[0382] Operation: The terminal sends temporarily stored data to the server via a communication method. Wi-Fi or cellular networks are used.

[0383] Input: Temporarily saved text data and image files

[0384] Output: Data sent to the server

[0385] Step 5: Receiving data on the server

[0386] Operation: The server receives text data and image files sent from the terminal.

[0387] Input: Data sent from the device

[0388] Output: Received data is stored on the server.

[0389] Step 6: Analyzing Text Data

[0390] Operation: The server's text analysis engine extracts destination and date / time information from the received text data.

[0391] Input: Received text data

[0392] Data processing: Text analysis engine analyzes destination and date / time information.

[0393] Output: Analyzed destination and date / time (e.g., "Tokyo", "October 5, 2023, 10:00")

[0394] Step 7: Optical Character Recognition (OCR) of Images

[0395] Operation: The server uses an OCR engine to extract text data from the receipt image.

[0396] Input: Received image file

[0397] Data processing: Extraction of text data using an OCR engine (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[0398] Output: Extracted text data (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[0399] Step 8: Data Verification

[0400] Operation: The server's matching mechanism compares the extracted character data with the text data entered by the user.

[0401] Input: User-inputted text data, OCR-extracted data

[0402] Data calculation: Perform data consistency checks.

[0403] Output: Matching result (e.g., Match, Mismatch)

[0404] Step 9: Calculating transportation expenses

[0405] Operation: The server's calculation mechanism calculates travel expenses based on the verified information.

[0406] Input: Matched data (e.g., destination and route)

[0407] Data processing: Calculation of transportation expenses (Example: The transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen)

[0408] Output: Calculated transportation cost (e.g., 500 yen)

[0409] Step 10: Emotion Recognition

[0410] Operation: The server's emotion recognition system analyzes emotions from the user's input data and images.

[0411] Input: User text input, uploaded image

[0412] Data processing: Emotion recognition using text analysis engine and image analysis engine.

[0413] Output: Recognized emotion (e.g., feeling nervous, feeling tired)

[0414] Step 11: Generate and adjust notification content

[0415] Operation: The server generates a notification message adjusted based on the emotion recognition result, using the matching and calculation results.

[0416] Input: Matching result, calculation result, recognized emotion

[0417] Data processing: Generating notification messages and adjusting the tone (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Gentle tone to encourage quick confirmation.")

[0418] Output: Generated notification message

[0419] Step 12: Sending notifications to users

[0420] Operation: The server sends the generated notification message to the terminal.

[0421] Input: Generated notification message

[0422] Output: A notification message is delivered to the device.

[0423] Step 13: User confirmation and confirmation

[0424] Operation: The user checks the notification message using their device, and if the content is correct, press the "Confirm Application" button.

[0425] Input: Notified message

[0426] Output: The instruction "Application Confirmed" is sent from the terminal to the server.

[0427] Step 14: Server saves data and sends completion notification.

[0428] Operation: The server receives the "Application Confirmation" instruction and saves the application data to the database. It also sends a processing completion notification to the user.

[0429] Input: "Application Confirmed" Instruction

[0430] Data processing: Saving to a database

[0431] Output: A processing completion notification is sent to the device.

[0432] The above is a detailed explanation of the processing steps of this system's program.

[0433] (Application Example 2)

[0434] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0435] Conventional travel expense claim systems are inefficient due to cumbersome manual data entry and verification processes. Furthermore, they often lack consideration for users' feelings and circumstances, making them highly likely to cause stress and dissatisfaction. Similar problems are likely to occur when using autonomous vehicles, highlighting the need for a smooth travel expense claim and user experience.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, and storage means for temporarily storing received text messages and image files. This makes it possible to improve the efficiency and accuracy of travel expense applications and to provide appropriate support according to the user's emotional state.

[0437] "Communication means" refers to the means by which the user inputs destination and date / time information.

[0438] "Upload method" refers to the means by which users can upload images of receipts.

[0439] "Storage means" refers to means for temporarily storing received text messages and image files.

[0440] "Transmission means" refers to the means for sending received text messages and image files to the server.

[0441] "Optical character recognition means" refers to a means for analyzing a received image file and extracting text data.

[0442] "Verification means" refers to means for comparing extracted text data with text data entered by the user.

[0443] "Calculation method" refers to the means used to calculate transportation expenses.

[0444] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[0445] "Emotional analysis tools" are means of analyzing a user's emotional state and providing appropriate support.

[0446] This invention aims to improve the user experience by streamlining the travel expense claim process in autonomous vehicles and analyzing the user's emotional state. The embodiments for carrying out the invention are described below.

[0447] System Overview

[0448] This system uses a terminal installed inside an autonomous vehicle to allow the user to input destination and date / time information and upload an image of their receipt. The terminal temporarily stores the received data and sends it to a server. The server uses an optical character recognition (OCR) engine to extract text data from the image and compares it with the user's input data. It also calculates the travel expenses and notifies the user. Furthermore, an emotion analysis engine analyzes the user's emotions and provides appropriate support.

[0449] Hardware and software to be used

[0450] Hardware:

[0451] Main computer of an autonomous vehicle

[0452] In-car camera (captures the user's facial expressions)

[0453] In-car microphone (collection of audio data)

[0454] Touchscreen display (user interface)

[0455] GPS module (for recording location information)

[0456] software:

[0457] Vehicle operation system (recording of travel routes and times)

[0458] External library as an OCR (Optical Character Recognition) engine

[0459] Emotion analysis engine (analysis of text and facial expressions)

[0460] Notification system

[0461] Processing flow

[0462] The user enters destination and date / time information using a touchscreen display and uploads an image of the receipt. The system temporarily stores the entered text data and image file and sends it to the server. The server uses an OCR engine to extract text data from the image and compares it with the user's input data. It then calculates the travel expenses and notifies the user of the results through a notification system. An emotion analysis engine recognizes the user's emotions from their text and facial expressions and provides appropriate support.

[0463] Examples of specific cases and prompt statements

[0464] As a concrete example, the following user operations are anticipated.

[0465] 1. Example of user input

[0466] Departure point: Tokyo Station

[0467] Destination: Shinagawa Station

[0468] Date and time: October 5, 2023 10:00

[0469] Upload a photo of the receipt.

[0470] 2. Server Analysis and Matching Examples

[0471] Text analysis results: Destination = Tokyo Station, Date and Time = October 5, 2023, 10:00

[0472] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0473] 3. Calculation results

[0474] Transportation expenses: 500 yen

[0475] 4. Emotion recognition example

[0476] Text sentiment analysis: "The analysis determined that the user is tired."

[0477] Image analysis: The user's facial expression indicates fatigue.

[0478] 5. Example of a notification to the user

[0479] Confirmation message: "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0480] Based on the recognized emotion, the notification content will be adjusted to "Thank you for your hard work. Your travel expense application has been completed."

[0481] Examples of prompt statements include the following:

[0482] The text "This travel expense application is very stressful" and a photo of a tired-looking person are input into the analysis engine to generate an appropriate support message.

[0483] This system streamlines the travel expense application process and improves the user experience by providing support tailored to the user's emotional state.

[0484] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0485] Step 1:

[0486] The user enters destination and date / time information.

[0487] Input: The user enters destination and date / time information using the touchscreen display.

[0488] Output: Destination and date / time information is temporarily saved on the terminal.

[0489] Specific operation: The user inputs information such as "Tokyo Station" and "October 5, 2023, 10:00" using the on-screen keyboard or voice input.

[0490] Step 2:

[0491] The user uploads an image of the receipt.

[0492] Input: The user takes a picture of the receipt using the touchscreen display or camera and uploads it.

[0493] Output: The image file uploaded to the device is temporarily saved.

[0494] Specific operation: The user takes a picture of the receipt with the camera and presses the upload button to send the image file to the system.

[0495] Step 3:

[0496] The device sends received text messages and image files to the server.

[0497] Input: Destination, date and time information, and receipt image file saved on the device.

[0498] Output: A text message and an image file are sent to the server.

[0499] Specific operation: The device bundles the data it has stored into packets and sends them to the server over the network.

[0500] Step 4:

[0501] The server analyzes the text data and image files it receives.

[0502] Input: Destination, date and time information, and receipt image file sent to the server.

[0503] Output: Text data such as destination, date and time, and fare.

[0504] Specific operation: The server inputs an image file into the OCR engine and extracts text data from the image. For example, it extracts data such as "Tokyo Station to Shinagawa Station" and "500 yen" from a receipt image.

[0505] Step 5:

[0506] The server compares the extracted text data with the user's input data.

[0507] Input: Text data analyzed by the server and destination and date / time information entered by the user.

[0508] Output: Matching result.

[0509] Specific operation: The server compares the extracted text data with the user's input data to check for a match. For example, it matches "Destination: Tokyo Station" with "OCR result: Tokyo Station".

[0510] Step 6:

[0511] The server calculates the travel expenses.

[0512] Input: Matched destination and date / time information.

[0513] Output: Calculated transportation costs.

[0514] Specific operation: The server calculates the transportation cost from the departure point to the destination based on the matching results. For example, it might calculate "500 yen from Tokyo Station to Shinagawa Station".

[0515] Step 7:

[0516] The server analyzes the user's emotional state.

[0517] Input: User's text messages and image files.

[0518] Output: Analyzed emotion information.

[0519] Specific operation: The server uses an emotion analysis engine to analyze the user's emotions from the text and facial expressions they input. For example, it might determine from the text that "the user is feeling stressed" and recognize from the image that "the face looks tired."

[0520] Step 8:

[0521] The server notifies the user of the matching results, calculation results, and sentiment information.

[0522] Input: Calculated travel expenses, user's emotional state.

[0523] Output: Notification message to the user.

[0524] Specific operation: The server adjusts the notification content based on the analyzed sentiment and sends a message to the user. For example, it might notify the user, "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Thank you for your hard work. Your transportation expense claim has been completed."

[0525] In this way, the system operates smoothly through each processing step, resulting in more efficient travel expense claims and an improved user experience.

[0526] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0527] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0528] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0529] [Second Embodiment]

[0530] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0531] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0532] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0533] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0534] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0535] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0536] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0537] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0538] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0539] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0540] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0541] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0542] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[0543] Terminal operation

[0544] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[0545] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[0546] Server operation

[0547] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[0548] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[0549] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it can calculate that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[0550] The matching and calculation results are sent to the user via the server's notification system. The user receives the notification and reviews its contents. They can also make corrections as needed.

[0551] Specific example

[0552] 1. Example of user input

[0553] Destination: Tokyo

[0554] Date and time: October 5, 2023 10:00

[0555] Upload a photo of your travel expense receipt.

[0556] 2. Server Analysis and Matching Examples

[0557] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0558] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0559] 3. Calculation results

[0560] Transportation expenses: 500 yen

[0561] 4. Example of a notification to the user

[0562] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0563] User verification and application confirmation

[0564] Once the user receives a confirmation message and verifies its contents are correct, they press the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. Simultaneously, it sends a processing completion notification to the user. This entire process ensures that travel expense applications are completed efficiently and accurately.

[0565] This system configuration allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0569] Step 2:

[0570] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[0571] Step 3:

[0572] The terminal temporarily stores text messages and image files received from the user in a storage device.

[0573] Step 4:

[0574] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[0575] Step 5:

[0576] The server analyzes the received text data and extracts destination and date / time information.

[0577] Step 6:

[0578] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[0579] Step 7:

[0580] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[0581] Step 8:

[0582] The server calculates the transportation cost using a calculation tool based on the results of the matching tool. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0583] Step 9:

[0584] The server sends the matching results and calculation results to the user via a notification system.

[0585] Step 10:

[0586] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[0587] Step 11:

[0588] Users can review the content and send correction messages if necessary.

[0589] Step 12:

[0590] The user presses the "Confirm Application" button.

[0591] Step 13:

[0592] The server receives the instruction to confirm the application and saves the application data to the database.

[0593] Step 14:

[0594] The server sends a processing completion notification to the user.

[0595] This processing step allows users to efficiently submit travel expense claims, and the server automatically performs data analysis, matching, and calculations, improving the accuracy of the claims.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0598] The current travel expense application system has problems such as the large amount of information that users have to manually enter, and the inefficiency of processing travel expense applications due to input errors and inaccuracies. In addition, manual data verification and calculation work is required, which is time-consuming and resource-intensive.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes a text analysis means for analyzing received text data and extracting destination and date / time information, an optical character recognition means for analyzing received image files and extracting text data, and a matching means for comparing the extracted text data with text data entered by the user. This makes it possible to automatically compare the information entered by the user with the information on the receipt, and to quickly and accurately calculate and notify the travel expenses.

[0601] "Communication means" refers to a device used by users to input destination and date / time information, and which communicates via a messaging interface.

[0602] An "uploading device" is a device that provides the functionality for a user to capture an image of a receipt and send it to a server.

[0603] A "storage device" is a storage device for temporarily saving received text messages and image files.

[0604] "Transmission means" refers to a communication device for sending text messages and image files stored in the storage means to a server.

[0605] "Text analysis means" refers to software or hardware used to analyze text data received by a server and extract destination and date / time information.

[0606] "Optical character recognition means" refers to a technology for extracting text data from image files received by a server, and can utilize external libraries.

[0607] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[0608] "Calculation means" refers to software or hardware that the server uses to calculate travel expenses based on the verified information.

[0609] "Notification means" refers to a device or system that has the function of notifying the user of the matching results and calculation results.

[0610] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[0611] Device operation:

[0612] The user enters destination and date / time information using the communication means on their device. For example, the user can enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload means on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application. The device temporarily stores the received text message and image file in its storage means. This data is then sent to the server via the transmission means.

[0613] Server operation:

[0614] The server receives text messages and image files sent from the terminal. The specific operating procedure of the server is as follows:

[0615] 1. Data reception:

[0616] The server receives text messages and image files sent from the terminal.

[0617] 2. Text data analysis:

[0618] The server parses the received text data and extracts destination and date / time information. This process can utilize parsing tools such as Python's Natural Language Toolkit (NLTK).

[0619] 3. OCR analysis:

[0620] The server extracts text data from the received image file using optical character recognition (OCR) technology. Specifically, it can use OCR services such as the Google Cloud Vision API.

[0621] 4. Data matching:

[0622] The server compares and matches the data extracted by OCR analysis with the text data entered by the user. This matching is performed using an SQL database and database queries.

[0623] 5. Transportation cost calculation:

[0624] The server calculates travel expenses based on the verified information. For example, it calculates 500 yen for travel between "Tokyo Station and Shinagawa Station". This calculation uses Python's pandas library and built-in calculation functions.

[0625] 6. Notification to users:

[0626] The server notifies the user of the calculation results and the comparison results. Notification methods include email (e.g., email services such as SendGrid) and push notifications (Firebase Cloud Messaging).

[0627] Specific example:

[0628] 1. Example of user input:

[0629] Destination: Tokyo

[0630] Date and time: October 5, 2023 10:00

[0631] Upload a photo of your travel expense receipt.

[0632] 2. Server analysis and matching examples:

[0633] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0634] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0635] 3. Calculation results:

[0636] Transportation expenses: 500 yen

[0637] 4. Example of a notification to the user:

[0638] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0639] Examples of input prompts for a generative AI model:

[0640] "To claim your travel expenses, please enter the following information: destination, date and time, and upload an image of your travel expense receipt."

[0641] In this way, the entire system functions in a way that allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] Enter destination and date / time

[0645] The user uses their device's communication method to enter destination and date / time information. For example, the user might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00 AM".

[0646] Input: The user enters the destination and date / time into a form on the device.

[0647] Output: The destination and date / time information are stored on the terminal.

[0648] Specific action: Enter text into the terminal's input field and press the send button.

[0649] Step 2:

[0650] Upload receipt image

[0651] The user uses their device's camera function to capture an image of their travel expense receipt and uploads it to a messaging application.

[0652] Input: The user takes a picture of their travel expense receipt and presses the upload button.

[0653] Output: The receipt image is saved to the device and uploaded.

[0654] Specific steps: Launch the camera app on your device, take a picture of the receipt, and then upload it.

[0655] Step 3:

[0656] Data is temporarily stored.

[0657] The terminal temporarily stores text data such as the destination and date / time entered by the user, as well as uploaded image files.

[0658] Input: Text data of destination and date / time, and an image file of the receipt.

[0659] Output: Text data and image files are saved to the device's storage.

[0660] Specific action: Saves data to the device's cache or temporary folder.

[0661] Step 4:

[0662] Sending data

[0663] The device sends the saved text data and image files to the server.

[0664] Input: Text data and image files.

[0665] Output: Data is sent to the server.

[0666] Specific operation: Send data to the server using the HTTPS protocol.

[0667] Step 5:

[0668] Data reception

[0669] The server receives text messages and image files sent from the terminal.

[0670] Input: Text data and image files.

[0671] Output: The received data is stored on the server.

[0672] Specific operation: Receive data and save it to local storage.

[0673] Step 6:

[0674] Text data analysis

[0675] The server analyzes the received text data and extracts destination and date / time information. Tools such as the Natural Language Toolkit (NLTK) are used for the analysis.

[0676] Input: Received text data.

[0677] Output: Destination and date / time are extracted.

[0678] Specific operation: The text analysis engine analyzes the data and extracts the necessary information.

[0679] Step 7:

[0680] OCR analysis

[0681] The server extracts text data from received image files using optical character recognition (OCR). It utilizes APIs such as the Google Cloud Vision API.

[0682] Input: Received receipt image file.

[0683] Output: Text data extracted from the image.

[0684] Specific operation: The OCR engine analyzes image data and extracts text data.

[0685] Step 8:

[0686] Data matching

[0687] The server compares and matches the data extracted by OCR analysis with the text data entered by the user, using database queries.

[0688] Input: Text data entered by the user and text data extracted by OCR.

[0689] Output: Matching results (data match / mismatch information).

[0690] Specific operation: Perform collation and verification using SQL queries.

[0691] Step 9:

[0692] Transportation expense calculation

[0693] The server calculates travel expenses based on the verified information, using Python's pandas library and built-in calculation functions.

[0694] Input: Matched, accurate destination information and transportation route.

[0695] Output: Calculated transportation costs.

[0696] Specific operation: Calculates transportation expenses based on the calculation logic.

[0697] Step 10:

[0698] Notification to the user

[0699] The server notifies the user of the calculation results and the comparison results. This is done using email services such as SendGrid or push notifications (Firebase Cloud Messaging).

[0700] Input: Calculation results and comparison results.

[0701] Output: Notification message to the user.

[0702] Specific action: Send a message to the user using the notification system.

[0703] (Application Example 1)

[0704] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0705] The application process for transportation costs at logistics centers involves manual data entry and receipt organization, which is time-consuming and prone to errors. Furthermore, many of the calculation and application processes for transportation costs are analog, making them inefficient. Therefore, there is a need for increased efficiency while simultaneously improving accuracy.

[0706] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0707] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, transmission means for sending received text messages and image files to the server, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating transportation costs, notification means for notifying the user of the matching results and calculation results, storage means for saving transportation cost data to a database, and confirmation means for finalizing the transportation cost application. This makes it possible to improve the efficiency and accuracy of the transportation cost application process.

[0708] "Means of communication" refers to means by which users input destination and date / time information, and in particular includes chat interfaces.

[0709] "Upload method" refers to the means by which users upload images of receipts to the system.

[0710] "Storage means" refers to means for temporarily saving received text messages and image files.

[0711] "Transmission means" refers to the means for sending received text messages and image files to the server.

[0712] "Optical character recognition means" refers to means that use optical character recognition technology to extract text data from an image file.

[0713] "The matching means" refers to the means of comparing the extracted text data with the text data entered by the user.

[0714] "Calculation means" refers to the means used to calculate transportation costs.

[0715] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[0716] "Means of storage for saving to a database" refers to means of saving transportation cost data to a database.

[0717] "Confirmation method" refers to the means by which users can finalize their shipping cost claims.

[0718] This invention is a system aimed at improving the efficiency and accuracy of the application process for transportation costs at a logistics center. The system includes communication means, upload means, storage means, transmission means, optical character recognition means, matching means, calculation means, notification means, storage means for saving to a database, and verification means.

[0719] To use the system, users first enter destination and date / time information using their smartphones and upload images of their shipping expense receipts. A chat interface is used for communication, allowing users to intuitively input data.

[0720] Uploaded images are temporarily stored on the device using a storage method and then sent to the server via a transmission method. The server receives the image file and extracts text data from the image using optical character recognition (Tesseract OCR). This optical character recognition method is implemented using a Python library and recognizes the characters after converting the image to grayscale.

[0721] The extracted text data is compared with the destination and date / time information entered by the user using a matching mechanism. This matching is performed using a text analysis mechanism to confirm that the user's input data matches the OCR data. Once the matching is complete, the transportation cost is calculated using a calculation mechanism. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0722] The calculation and verification results are notified to the user using a notification system. The user receives the notification and is asked to confirm it. The user confirms the contents through the confirmation system and, if correct, confirms the application. Once this confirmation process is complete, the shipping cost data is stored in the database using a storage system.

[0723] As a concrete example, consider the following prompt:

[0724] "The destination is Tokyo. I have uploaded an image of the shipping receipt taken on October 5, 2023 at 10:00 AM. Please automatically calculate the shipping cost."

[0725] This series of processes effectively automates the application process for transportation costs, reducing the burden on users and improving the accuracy of application data.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The user opens the chat interface using their smartphone and enters destination and date / time information. The entered information is saved to the device via communication. The data saved as input is "Destination: Tokyo, Date / Time: October 5, 2023, 10:00".

[0729] Step 2:

[0730] The user uses the upload method to take a picture of the shipping receipt with their smartphone and upload it to the chat interface. The image file is saved on the device. The uploaded image file is temporarily stored on the device using a temporary storage method.

[0731] Step 3:

[0732] The terminal sends the saved input data and image files to the server using a transmission device. The transmitted data includes destination and date / time information, as well as an image file of the receipt. The server receives the transmitted data and temporarily stores it using a storage device.

[0733] Step 4:

[0734] The server uses optical character recognition (OCR) to extract text data from the received image file. Specifically, it uses Tesseract OCR to convert the image to grayscale and then reads the text from it. The extracted text data might be something like "Tokyo Station to Shinagawa Station" and "500 yen".

[0735] Step 5:

[0736] The server uses a matching mechanism to compare the extracted text data with the destination and date / time information entered by the user. Text analysis is performed to confirm that the input data and OCR data match. If the matching is successful, the transportation cost from "Tokyo Station to Shinagawa Station" is calculated.

[0737] Step 6:

[0738] The server uses a calculation tool to calculate the shipping cost based on the verified information. It verifies that the calculated shipping cost is 500 yen. The calculation result is notified to the user via a notification tool.

[0739] Step 7:

[0740] The user reviews the calculated shipping cost received as a notification. The notification includes details of the destination, date and time, and shipping costs. The user uses the verification method to confirm the details and finalize the application.

[0741] Step 8:

[0742] The server receives confirmation instructions from the user and stores the shipping cost data in the database using a storage method. This completes the shipping cost application and saves it to the database.

[0743] Step 9:

[0744] Once verification is complete, the server sends a completion notification to the user. The user is informed through the notification system that the application has been successfully completed.

[0745] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0746] This invention is a system that improves the efficiency and accuracy of travel expense claims, as well as analyzes user emotions to enhance the smoothness of the process. This system works in conjunction with the user, terminal, and server to efficiently process travel expense-related information and emotional data.

[0747] Terminal operation

[0748] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[0749] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[0750] Server operation

[0751] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[0752] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[0753] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it calculates that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[0754] The server runs an emotion engine to recognize emotions from user behavior and input. The emotion engine has the function of analyzing the emotions in the text entered by the user and the function of analyzing the user's facial expressions from uploaded images to recognize emotions.

[0755] Notification to the user

[0756] The matching and calculation results are sent to the user via the server's notification system. The user reviews the received notification message (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"). Based on the perceived emotions, the notification content is appropriately adjusted.

[0757] The user reviews the notification and, if the information is correct, presses the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. At the same time, it sends a processing completion notification to the user. If the recognized emotions require correction, the server can provide the user with further confirmation and support.

[0758] Specific example

[0759] 1. Example of user input

[0760] Destination: Tokyo

[0761] Date and time: October 5, 2023 10:00

[0762] Upload a photo of your travel expense receipt.

[0763] 2. Server Analysis and Matching Examples

[0764] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0765] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0766] 3. Calculation results

[0767] Transportation expenses: 500 yen

[0768] 4. Emotion recognition example

[0769] Text sentiment analysis: "The analysis determined that the user is feeling anxious."

[0770] Image analysis: The user's facial expression indicates fatigue.

[0771] 5. Example of a notification to the user

[0772] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0773] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[0774] Thus, the system of the present invention can streamline the travel expense application process and improve the user experience through emotion recognition.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0778] Step 2:

[0779] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[0780] Step 3:

[0781] The terminal temporarily stores text messages and image files received from the user in a storage device.

[0782] Step 4:

[0783] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[0784] Step 5:

[0785] The server analyzes the received text data and extracts destination and date / time information.

[0786] Step 6:

[0787] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[0788] Step 7:

[0789] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[0790] Step 8:

[0791] The server calculates the transportation cost using a calculation method based on the matching results. For example, it might calculate that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0792] Step 9:

[0793] The server runs an emotion engine to recognize the user's emotions from the text entered by the user and the images uploaded.

[0794] Step 10:

[0795] The server uses an emotion engine to perform text sentiment analysis. For example, the user's text might be judged as "stressed."

[0796] Step 11:

[0797] The server uses an emotion engine to analyze images and recognize emotions from the user's facial expressions. For example, the server might determine that the user's facial expression indicates "tiredness."

[0798] Step 12:

[0799] The server sends the matching and calculation results to the user via a notification system. The notification content is adjusted based on the analyzed emotions. For example, it might be a "gentle tone that encourages quick confirmation."

[0800] Step 13:

[0801] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[0802] Step 14:

[0803] Users can review the content and send correction messages if necessary.

[0804] Step 15:

[0805] The user presses the "Confirm Application" button.

[0806] Step 16:

[0807] The server receives the instruction to confirm the application and saves the application data to the database.

[0808] Step 17:

[0809] The server sends a processing completion notification to the user. If the recognized emotions require correction, the server provides the user with further confirmation and support.

[0810] This processing step allows users to efficiently submit travel expense claims, while the server automatically performs data analysis, matching, calculation, and sentiment recognition, reducing the workload for both parties and improving the accuracy of the claims.

[0811] (Example 2)

[0812] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0813] Traditional travel expense claim systems suffered from problems such as user input errors, cumbersome procedures, and user stress. Furthermore, requiring users to input accurate information often required significant time and effort. This reduced the efficiency of the application process and compromised the user experience. Additionally, existing systems lacked emotion recognition capabilities, failing to adequately address user stress and anxiety. To address these issues, a system was needed that would automate the travel expense claim process and reduce user stress.

[0814] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0815] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating travel expenses, notification means for notifying the user of the matching results and calculation results, emotion recognition means for analyzing emotions from the user's input data and images, and means for adjusting the notification content based on the recognized emotions. This enables the automation of the travel expense application process and an improvement in the user experience.

[0816] "Communication method" refers to an interface for users to input destination and date / time information.

[0817] The "upload method" is a function that allows users to send images of receipts to the system.

[0818] "Storage means" refers to data storage that temporarily stores received text messages and image files.

[0819] "Transmission means" refers to the function for sending data stored in the storage means to the server.

[0820] "Optical character recognition means" refers to a technology for extracting character information from a received image file.

[0821] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[0822] The "calculation means" refers to a processing function for calculating transportation expenses based on the verified information.

[0823] "Notification means" refers to a messaging function for communicating calculation results and verification results to the user.

[0824] "Emotion recognition means" refers to technology for analyzing and recognizing emotions from user input data and images.

[0825] "Means for adjusting notification content" refers to a function that appropriately modifies the content of notifications sent to the user based on recognized emotions.

[0826] This invention is a system designed to improve the efficiency and accuracy of travel expense claims, and it operates in cooperation with the user, terminal, and server. The detailed configuration and operation of this system will be described below.

[0827] System Configuration

[0828] The system includes the following main components:

[0829] 1. Means of communication

[0830] 2. Upload method

[0831] 3. Preservation means

[0832] 4. Transmission method

[0833] 5. Optical character recognition means (OCR means)

[0834] 6. Verification means

[0835] 7. Means of calculation

[0836] 8. Means of notification

[0837] 9. Emotion recognition means

[0838] 10. Means for adjusting notification content

[0839] Specific actions

[0840] 1. User actions

[0841] Users use their devices to enter destination and date / time information for travel expense claims. For example, a user might use their smartphone to enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Alternatively, the user might use their smartphone camera to take a picture of their travel expense receipt and upload the image via a chat or messaging app.

[0842] 2. Data processing on the terminal

[0843] The terminal temporarily stores text data of destination and date / time received from the user, as well as uploaded receipt images. This data is then transmitted to the server via the terminal's communication method. Wi-Fi or cellular networks are used for this communication.

[0844] 3. Receiving and analyzing data on the server

[0845] The server receives data sent from the terminal. The text analysis engine within the server first extracts destination and date / time information from the received text data. Next, it uses an OCR (Optical Character Recognition) engine to extract text information from the receipt image. For example, it obtains information such as "Tokyo Station to Shinagawa Station" and "500 yen" from the receipt image.

[0846] 4. Matching and Calculation

[0847] The server's matching mechanism compares the text data of the extracted image with the text data entered by the user to confirm a match. After the matching mechanism confirms a match, the calculation mechanism calculates the transportation cost. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[0848] 5. Emotion recognition

[0849] The server's emotion recognition system analyzes the user's emotions from user input data and uploaded images. Specifically, a text analysis engine reads emotions from the strings entered by the user, and an image analysis engine recognizes emotions based on the user's facial expressions. For example, the system might determine from the text analysis results that "the user is nervous" and recognize from the image analysis results that "the user has a tired expression."

[0850] 6. Notification and adjustment of results

[0851] The server generates a notification message for the user based on the matching and calculation results. For example, it might create a message such as "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen." Furthermore, it adjusts the tone of the notification based on the sentiment recognition results. The result notification is sent from the server to the device and displayed in the user's chat or messaging app.

[0852] Specific example

[0853] 1. User input

[0854] Destination: Tokyo

[0855] Date and time: October 5, 2023 10:00

[0856] Upload a photo of your travel expense receipt.

[0857] 2. Server analysis and matching

[0858] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[0859] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0860] 3. Calculation results

[0861] Transportation expenses: 500 yen

[0862] 4. Emotion recognition

[0863] Text sentiment analysis: "The user is feeling anxious."

[0864] Image analysis: The user's facial expression indicates fatigue.

[0865] 5. Notification to the user

[0866] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0867] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[0868] Examples of prompts for generative AI models

[0869] 1. Example of a travel expense claim: "The destination is Tokyo, and the date and time is October 5, 2023, at 10:00 AM. Please upload your travel expense receipt."

[0870] 2. Specific example of emotion recognition: "Analyze the user's emotions from their input and facial expressions to determine if there are signs of tension or fatigue."

[0871] The above describes the embodiment for carrying out the invention. This system enables the automation of the travel expense application process and improves the user experience.

[0872] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0873] Program processing flow

[0874] Step 1: User data entry

[0875] Operation: The user uses a terminal to enter destination and date / time information for the travel expense claim. For example, enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[0876] Input: Destination ("Tokyo"), Date and Time ("October 5, 2023, 10:00")

[0877] Output: The input data is saved to the terminal.

[0878] Step 2: Upload the receipt image

[0879] Operation: The user takes a picture of the receipt with their device's camera and uploads it via a chat or messaging app.

[0880] Input: Image of the receipt that was photographed.

[0881] Output: The uploaded image is saved to the device.

[0882] Step 3: Save data temporarily

[0883] Operation: The terminal temporarily saves the entered text data (destination and date / time) and uploaded image files.

[0884] Input: Text data of destination and date / time, image of receipt

[0885] Output: Temporarily saved text data and image files are stored on the device.

[0886] Step 4: Send data to the server

[0887] Operation: The terminal sends temporarily stored data to the server via a communication method. Wi-Fi or cellular networks are used.

[0888] Input: Temporarily saved text data and image files

[0889] Output: Data sent to the server

[0890] Step 5: Receiving data on the server

[0891] Operation: The server receives text data and image files sent from the terminal.

[0892] Input: Data sent from the device

[0893] Output: Received data is stored on the server.

[0894] Step 6: Analyzing Text Data

[0895] Operation: The server's text analysis engine extracts destination and date / time information from the received text data.

[0896] Input: Received text data

[0897] Data processing: Text analysis engine analyzes destination and date / time information.

[0898] Output: Analyzed destination and date / time (e.g., "Tokyo", "October 5, 2023, 10:00")

[0899] Step 7: Optical Character Recognition (OCR) of Images

[0900] Operation: The server uses an OCR engine to extract text data from the receipt image.

[0901] Input: Received image file

[0902] Data processing: Extraction of text data using an OCR engine (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[0903] Output: Extracted text data (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[0904] Step 8: Data Verification

[0905] Operation: The server's matching mechanism compares the extracted character data with the text data entered by the user.

[0906] Input: User-inputted text data, OCR-extracted data

[0907] Data calculation: Perform data consistency checks.

[0908] Output: Matching result (e.g., Match, Mismatch)

[0909] Step 9: Calculating transportation expenses

[0910] Operation: The server's calculation mechanism calculates travel expenses based on the verified information.

[0911] Input: Matched data (e.g., destination and route)

[0912] Data processing: Calculation of transportation expenses (Example: The transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen)

[0913] Output: Calculated transportation cost (e.g., 500 yen)

[0914] Step 10: Emotion Recognition

[0915] Operation: The server's emotion recognition system analyzes emotions from the user's input data and images.

[0916] Input: User text input, uploaded image

[0917] Data processing: Emotion recognition using text analysis engine and image analysis engine.

[0918] Output: Recognized emotion (e.g., feeling nervous, feeling tired)

[0919] Step 11: Generate and adjust notification content

[0920] Operation: The server generates a notification message adjusted based on the emotion recognition result, using the matching and calculation results.

[0921] Input: Matching result, calculation result, recognized emotion

[0922] Data processing: Generating notification messages and adjusting the tone (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Gentle tone to encourage quick confirmation.")

[0923] Output: Generated notification message

[0924] Step 12: Sending notifications to users

[0925] Operation: The server sends the generated notification message to the terminal.

[0926] Input: Generated notification message

[0927] Output: A notification message is delivered to the device.

[0928] Step 13: User confirmation and confirmation

[0929] Operation: The user checks the notification message using their device, and if the content is correct, press the "Confirm Application" button.

[0930] Input: Notified message

[0931] Output: The instruction "Application Confirmed" is sent from the terminal to the server.

[0932] Step 14: Server saves data and sends completion notification.

[0933] Operation: The server receives the "Application Confirmation" instruction and saves the application data to the database. It also sends a processing completion notification to the user.

[0934] Input: "Application Confirmed" Instruction

[0935] Data processing: Saving to a database

[0936] Output: A processing completion notification is sent to the device.

[0937] The above is a detailed explanation of the processing steps of this system's program.

[0938] (Application Example 2)

[0939] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0940] Conventional travel expense claim systems are inefficient due to cumbersome manual data entry and verification processes. Furthermore, they often lack consideration for users' feelings and circumstances, making them highly likely to cause stress and dissatisfaction. Similar problems are likely to occur when using autonomous vehicles, highlighting the need for a smooth travel expense claim and user experience.

[0941] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, and storage means for temporarily storing received text messages and image files. This makes it possible to improve the efficiency and accuracy of travel expense applications and to provide appropriate support according to the user's emotional state.

[0942] "Communication means" refers to the means by which the user inputs destination and date / time information.

[0943] "Upload method" refers to the means by which users can upload images of receipts.

[0944] "Storage means" refers to means for temporarily storing received text messages and image files.

[0945] "Transmission means" refers to the means for sending received text messages and image files to the server.

[0946] "Optical character recognition means" refers to a means for analyzing a received image file and extracting text data.

[0947] "Verification means" refers to means for comparing extracted text data with text data entered by the user.

[0948] "Calculation method" refers to the means used to calculate transportation expenses.

[0949] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[0950] "Emotional analysis tools" are means of analyzing a user's emotional state and providing appropriate support.

[0951] This invention aims to improve the user experience by streamlining the travel expense claim process in autonomous vehicles and analyzing the user's emotional state. The embodiments for carrying out the invention are described below.

[0952] System Overview

[0953] This system uses a terminal installed inside an autonomous vehicle to allow the user to input destination and date / time information and upload an image of their receipt. The terminal temporarily stores the received data and sends it to a server. The server uses an optical character recognition (OCR) engine to extract text data from the image and compares it with the user's input data. It also calculates the travel expenses and notifies the user. Furthermore, an emotion analysis engine analyzes the user's emotions and provides appropriate support.

[0954] Hardware and software to be used

[0955] Hardware:

[0956] Main computer of an autonomous vehicle

[0957] In-car camera (captures the user's facial expressions)

[0958] In-car microphone (collection of audio data)

[0959] Touchscreen display (user interface)

[0960] GPS module (for recording location information)

[0961] software:

[0962] Vehicle operation system (recording of travel routes and times)

[0963] External library as an OCR (Optical Character Recognition) engine

[0964] Emotion analysis engine (analysis of text and facial expressions)

[0965] Notification system

[0966] Processing flow

[0967] The user enters destination and date / time information using a touchscreen display and uploads an image of the receipt. The system temporarily stores the entered text data and image file and sends it to the server. The server uses an OCR engine to extract text data from the image and compares it with the user's input data. It then calculates the travel expenses and notifies the user of the results through a notification system. An emotion analysis engine recognizes the user's emotions from their text and facial expressions and provides appropriate support.

[0968] Examples of specific cases and prompt statements

[0969] As a concrete example, the following user operations are anticipated.

[0970] 1. Example of user input

[0971] Departure point: Tokyo Station

[0972] Destination: Shinagawa Station

[0973] Date and time: October 5, 2023 10:00

[0974] Upload a photo of the receipt.

[0975] 2. Server Analysis and Matching Examples

[0976] Text analysis results: Destination = Tokyo Station, Date and Time = October 5, 2023, 10:00

[0977] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[0978] 3. Calculation results

[0979] Transportation expenses: 500 yen

[0980] 4. Emotion recognition example

[0981] Text sentiment analysis: "The analysis determined that the user is tired."

[0982] Image analysis: The user's facial expression indicates fatigue.

[0983] 5. Example of a notification to the user

[0984] Confirmation message: "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[0985] Based on the recognized emotion, the notification content will be adjusted to "Thank you for your hard work. Your travel expense application has been completed."

[0986] Examples of prompt statements include the following:

[0987] The text "This travel expense application is very stressful" and a photo of a tired-looking person are input into the analysis engine to generate an appropriate support message.

[0988] This system streamlines the travel expense application process and improves the user experience by providing support tailored to the user's emotional state.

[0989] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0990] Step 1:

[0991] The user enters destination and date / time information.

[0992] Input: The user enters destination and date / time information using the touchscreen display.

[0993] Output: Destination and date / time information is temporarily saved on the terminal.

[0994] Specific operation: The user inputs information such as "Tokyo Station" and "October 5, 2023, 10:00" using the on-screen keyboard or voice input.

[0995] Step 2:

[0996] The user uploads an image of the receipt.

[0997] Input: The user takes a picture of the receipt using the touchscreen display or camera and uploads it.

[0998] Output: The image file uploaded to the device is temporarily saved.

[0999] Specific operation: The user takes a picture of the receipt with the camera and presses the upload button to send the image file to the system.

[1000] Step 3:

[1001] The device sends received text messages and image files to the server.

[1002] Input: Destination, date and time information, and receipt image file saved on the device.

[1003] Output: A text message and an image file are sent to the server.

[1004] Specific operation: The device bundles the data it has stored into packets and sends them to the server over the network.

[1005] Step 4:

[1006] The server analyzes the text data and image files it receives.

[1007] Input: Destination, date and time information, and receipt image file sent to the server.

[1008] Output: Text data such as destination, date and time, and fare.

[1009] Specific operation: The server inputs an image file into the OCR engine and extracts text data from the image. For example, it extracts data such as "Tokyo Station to Shinagawa Station" and "500 yen" from a receipt image.

[1010] Step 5:

[1011] The server compares the extracted text data with the user's input data.

[1012] Input: Text data analyzed by the server and destination and date / time information entered by the user.

[1013] Output: Matching result.

[1014] Specific operation: The server compares the extracted text data with the user's input data to check for a match. For example, it matches "Destination: Tokyo Station" with "OCR result: Tokyo Station".

[1015] Step 6:

[1016] The server calculates the travel expenses.

[1017] Input: Matched destination and date / time information.

[1018] Output: Calculated transportation costs.

[1019] Specific operation: The server calculates the transportation cost from the departure point to the destination based on the matching results. For example, it might calculate "500 yen from Tokyo Station to Shinagawa Station".

[1020] Step 7:

[1021] The server analyzes the user's emotional state.

[1022] Input: User's text messages and image files.

[1023] Output: Analyzed emotion information.

[1024] Specific operation: The server uses an emotion analysis engine to analyze the user's emotions from the text and facial expressions they input. For example, it might determine from the text that "the user is feeling stressed" and recognize from the image that "the face looks tired."

[1025] Step 8:

[1026] The server notifies the user of the matching results, calculation results, and sentiment information.

[1027] Input: Calculated travel expenses, user's emotional state.

[1028] Output: Notification message to the user.

[1029] Specific operation: The server adjusts the notification content based on the analyzed sentiment and sends a message to the user. For example, it might notify the user, "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Thank you for your hard work. Your transportation expense claim has been completed."

[1030] In this way, the system operates smoothly through each processing step, resulting in more efficient travel expense claims and an improved user experience.

[1031] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1032] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1033] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1034] [Third Embodiment]

[1035] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1036] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1037] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1038] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1039] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1040] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1041] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1042] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1043] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1044] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1045] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1046] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1047] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[1048] Terminal operation

[1049] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[1050] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[1051] Server operation

[1052] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[1053] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[1054] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it can calculate that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[1055] The matching and calculation results are sent to the user via the server's notification system. The user receives the notification and reviews its contents. They can also make corrections as needed.

[1056] Specific example

[1057] 1. Example of user input

[1058] Destination: Tokyo

[1059] Date and time: October 5, 2023 10:00

[1060] Upload a photo of your travel expense receipt.

[1061] 2. Server Analysis and Matching Examples

[1062] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1063] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1064] 3. Calculation results

[1065] Transportation expenses: 500 yen

[1066] 4. Example of a notification to the user

[1067] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1068] User verification and application confirmation

[1069] Once the user receives a confirmation message and verifies its contents are correct, they press the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. Simultaneously, it sends a processing completion notification to the user. This entire process ensures that travel expense applications are completed efficiently and accurately.

[1070] This system configuration allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[1071] The following describes the processing flow.

[1072] Step 1:

[1073] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1074] Step 2:

[1075] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[1076] Step 3:

[1077] The terminal temporarily stores text messages and image files received from the user in a storage device.

[1078] Step 4:

[1079] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[1080] Step 5:

[1081] The server analyzes the received text data and extracts destination and date / time information.

[1082] Step 6:

[1083] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[1084] Step 7:

[1085] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[1086] Step 8:

[1087] The server calculates the transportation cost using a calculation tool based on the results of the matching tool. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1088] Step 9:

[1089] The server sends the matching results and calculation results to the user via a notification system.

[1090] Step 10:

[1091] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[1092] Step 11:

[1093] Users can review the content and send correction messages if necessary.

[1094] Step 12:

[1095] The user presses the "Confirm Application" button.

[1096] Step 13:

[1097] The server receives the instruction to confirm the application and saves the application data to the database.

[1098] Step 14:

[1099] The server sends a processing completion notification to the user.

[1100] This processing step allows users to efficiently submit travel expense claims, and the server automatically performs data analysis, matching, and calculations, improving the accuracy of the claims.

[1101] (Example 1)

[1102] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1103] The current travel expense application system has problems such as the large amount of information that users have to manually enter, and the inefficiency of processing travel expense applications due to input errors and inaccuracies. In addition, manual data verification and calculation work is required, which is time-consuming and resource-intensive.

[1104] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1105] In this invention, the server includes a text analysis means for analyzing received text data and extracting destination and date / time information, an optical character recognition means for analyzing received image files and extracting text data, and a matching means for comparing the extracted text data with text data entered by the user. This makes it possible to automatically compare the information entered by the user with the information on the receipt, and to quickly and accurately calculate and notify the travel expenses.

[1106] "Communication means" refers to a device used by users to input destination and date / time information, and which communicates via a messaging interface.

[1107] An "uploading device" is a device that provides the functionality for a user to capture an image of a receipt and send it to a server.

[1108] A "storage device" is a storage device for temporarily saving received text messages and image files.

[1109] "Transmission means" refers to a communication device for sending text messages and image files stored in the storage means to a server.

[1110] "Text analysis means" refers to software or hardware used to analyze text data received by a server and extract destination and date / time information.

[1111] "Optical character recognition means" refers to a technology for extracting text data from image files received by a server, and can utilize external libraries.

[1112] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[1113] "Calculation means" refers to software or hardware that the server uses to calculate travel expenses based on the verified information.

[1114] "Notification means" refers to a device or system that has the function of notifying the user of the matching results and calculation results.

[1115] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[1116] Device operation:

[1117] The user enters destination and date / time information using the communication means on their device. For example, the user can enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload means on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application. The device temporarily stores the received text message and image file in its storage means. This data is then sent to the server via the transmission means.

[1118] Server operation:

[1119] The server receives text messages and image files sent from the terminal. The specific operating procedure of the server is as follows:

[1120] 1. Data reception:

[1121] The server receives text messages and image files sent from the terminal.

[1122] 2. Text data analysis:

[1123] The server parses the received text data and extracts destination and date / time information. This process can utilize parsing tools such as Python's Natural Language Toolkit (NLTK).

[1124] 3. OCR analysis:

[1125] The server extracts text data from the received image file using optical character recognition (OCR) technology. Specifically, it can use OCR services such as the Google Cloud Vision API.

[1126] 4. Data matching:

[1127] The server compares and matches the data extracted by OCR analysis with the text data entered by the user. This matching is performed using an SQL database and database queries.

[1128] 5. Transportation cost calculation:

[1129] The server calculates travel expenses based on the verified information. For example, it calculates 500 yen for travel between "Tokyo Station and Shinagawa Station". This calculation uses Python's pandas library and built-in calculation functions.

[1130] 6. Notification to users:

[1131] The server notifies the user of the calculation results and the comparison results. Notification methods include email (e.g., email services such as SendGrid) and push notifications (Firebase Cloud Messaging).

[1132] Specific example:

[1133] 1. Example of user input:

[1134] Destination: Tokyo

[1135] Date and time: October 5, 2023 10:00

[1136] Upload a photo of your travel expense receipt.

[1137] 2. Server analysis and matching examples:

[1138] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1139] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1140] 3. Calculation results:

[1141] Transportation expenses: 500 yen

[1142] 4. Example of a notification to the user:

[1143] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1144] Examples of input prompts for a generative AI model:

[1145] "To claim your travel expenses, please enter the following information: destination, date and time, and upload an image of your travel expense receipt."

[1146] In this way, the entire system functions in a way that allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[1147] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1148] Step 1:

[1149] Enter destination and date / time

[1150] The user uses their device's communication method to enter destination and date / time information. For example, the user might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00 AM".

[1151] Input: The user enters the destination and date / time into a form on the device.

[1152] Output: The destination and date / time information are stored on the terminal.

[1153] Specific action: Enter text into the terminal's input field and press the send button.

[1154] Step 2:

[1155] Upload receipt image

[1156] The user uses their device's camera function to capture an image of their travel expense receipt and uploads it to a messaging application.

[1157] Input: The user takes a picture of their travel expense receipt and presses the upload button.

[1158] Output: The receipt image is saved to the device and uploaded.

[1159] Specific steps: Launch the camera app on your device, take a picture of the receipt, and then upload it.

[1160] Step 3:

[1161] Data is temporarily stored.

[1162] The terminal temporarily stores text data such as the destination and date / time entered by the user, as well as uploaded image files.

[1163] Input: Text data of destination and date / time, and an image file of the receipt.

[1164] Output: Text data and image files are saved to the device's storage.

[1165] Specific action: Saves data to the device's cache or temporary folder.

[1166] Step 4:

[1167] Sending data

[1168] The device sends the saved text data and image files to the server.

[1169] Input: Text data and image files.

[1170] Output: Data is sent to the server.

[1171] Specific operation: Send data to the server using the HTTPS protocol.

[1172] Step 5:

[1173] Data reception

[1174] The server receives text messages and image files sent from the terminal.

[1175] Input: Text data and image files.

[1176] Output: The received data is stored on the server.

[1177] Specific operation: Receive data and save it to local storage.

[1178] Step 6:

[1179] Text data analysis

[1180] The server analyzes the received text data and extracts destination and date / time information. Tools such as the Natural Language Toolkit (NLTK) are used for the analysis.

[1181] Input: Received text data.

[1182] Output: Destination and date / time are extracted.

[1183] Specific operation: The text analysis engine analyzes the data and extracts the necessary information.

[1184] Step 7:

[1185] OCR analysis

[1186] The server extracts text data from received image files using optical character recognition (OCR). It utilizes APIs such as the Google Cloud Vision API.

[1187] Input: Received receipt image file.

[1188] Output: Text data extracted from the image.

[1189] Specific operation: The OCR engine analyzes image data and extracts text data.

[1190] Step 8:

[1191] Data matching

[1192] The server compares and matches the data extracted by OCR analysis with the text data entered by the user, using database queries.

[1193] Input: Text data entered by the user and text data extracted by OCR.

[1194] Output: Matching results (data match / mismatch information).

[1195] Specific operation: Perform collation and verification using SQL queries.

[1196] Step 9:

[1197] Transportation expense calculation

[1198] The server calculates travel expenses based on the verified information, using Python's pandas library and built-in calculation functions.

[1199] Input: Matched, accurate destination information and transportation route.

[1200] Output: Calculated transportation costs.

[1201] Specific operation: Calculates transportation expenses based on the calculation logic.

[1202] Step 10:

[1203] Notification to the user

[1204] The server notifies the user of the calculation results and the comparison results. This is done using email services such as SendGrid or push notifications (Firebase Cloud Messaging).

[1205] Input: Calculation results and comparison results.

[1206] Output: Notification message to the user.

[1207] Specific action: Send a message to the user using the notification system.

[1208] (Application Example 1)

[1209] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1210] The application process for transportation costs at logistics centers involves manual data entry and receipt organization, which is time-consuming and prone to errors. Furthermore, many of the calculation and application processes for transportation costs are analog, making them inefficient. Therefore, there is a need for increased efficiency while simultaneously improving accuracy.

[1211] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1212] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, transmission means for sending received text messages and image files to the server, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating transportation costs, notification means for notifying the user of the matching results and calculation results, storage means for saving transportation cost data to a database, and confirmation means for finalizing the transportation cost application. This makes it possible to improve the efficiency and accuracy of the transportation cost application process.

[1213] "Means of communication" refers to means by which users input destination and date / time information, and in particular includes chat interfaces.

[1214] "Upload method" refers to the means by which users upload images of receipts to the system.

[1215] "Storage means" refers to means for temporarily saving received text messages and image files.

[1216] "Transmission means" refers to the means for sending received text messages and image files to the server.

[1217] "Optical character recognition means" refers to means that use optical character recognition technology to extract text data from an image file.

[1218] "The matching means" refers to the means of comparing the extracted text data with the text data entered by the user.

[1219] "Calculation means" refers to the means used to calculate transportation costs.

[1220] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[1221] "Means of storage for saving to a database" refers to means of saving transportation cost data to a database.

[1222] "Confirmation method" refers to the means by which users can finalize their shipping cost claims.

[1223] This invention is a system aimed at improving the efficiency and accuracy of the application process for transportation costs at a logistics center. The system includes communication means, upload means, storage means, transmission means, optical character recognition means, matching means, calculation means, notification means, storage means for saving to a database, and verification means.

[1224] To use the system, users first enter destination and date / time information using their smartphones and upload images of their shipping expense receipts. A chat interface is used for communication, allowing users to intuitively input data.

[1225] Uploaded images are temporarily stored on the device using a storage method and then sent to the server via a transmission method. The server receives the image file and extracts text data from the image using optical character recognition (Tesseract OCR). This optical character recognition method is implemented using a Python library and recognizes the characters after converting the image to grayscale.

[1226] The extracted text data is compared with the destination and date / time information entered by the user using a matching mechanism. This matching is performed using a text analysis mechanism to confirm that the user's input data matches the OCR data. Once the matching is complete, the transportation cost is calculated using a calculation mechanism. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1227] The calculation and verification results are notified to the user using a notification system. The user receives the notification and is asked to confirm it. The user confirms the contents through the confirmation system and, if correct, confirms the application. Once this confirmation process is complete, the shipping cost data is stored in the database using a storage system.

[1228] As a concrete example, consider the following prompt:

[1229] "The destination is Tokyo. I have uploaded an image of the shipping receipt taken on October 5, 2023 at 10:00 AM. Please automatically calculate the shipping cost."

[1230] This series of processes effectively automates the application process for transportation costs, reducing the burden on users and improving the accuracy of application data.

[1231] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1232] Step 1:

[1233] The user opens the chat interface using their smartphone and enters destination and date / time information. The entered information is saved to the device via communication. The data saved as input is "Destination: Tokyo, Date / Time: October 5, 2023, 10:00".

[1234] Step 2:

[1235] The user uses the upload method to take a picture of the shipping receipt with their smartphone and upload it to the chat interface. The image file is saved on the device. The uploaded image file is temporarily stored on the device using a temporary storage method.

[1236] Step 3:

[1237] The terminal sends the saved input data and image files to the server using a transmission device. The transmitted data includes destination and date / time information, as well as an image file of the receipt. The server receives the transmitted data and temporarily stores it using a storage device.

[1238] Step 4:

[1239] The server uses optical character recognition (OCR) to extract text data from the received image file. Specifically, it uses Tesseract OCR to convert the image to grayscale and then reads the text from it. The extracted text data might be something like "Tokyo Station to Shinagawa Station" and "500 yen".

[1240] Step 5:

[1241] The server uses a matching mechanism to compare the extracted text data with the destination and date / time information entered by the user. Text analysis is performed to confirm that the input data and OCR data match. If the matching is successful, the transportation cost from "Tokyo Station to Shinagawa Station" is calculated.

[1242] Step 6:

[1243] The server uses a calculation tool to calculate the shipping cost based on the verified information. It verifies that the calculated shipping cost is 500 yen. The calculation result is notified to the user via a notification tool.

[1244] Step 7:

[1245] The user reviews the calculated shipping cost received as a notification. The notification includes details of the destination, date and time, and shipping costs. The user uses the verification method to confirm the details and finalize the application.

[1246] Step 8:

[1247] The server receives confirmation instructions from the user and stores the shipping cost data in the database using a storage method. This completes the shipping cost application and saves it to the database.

[1248] Step 9:

[1249] Once verification is complete, the server sends a completion notification to the user. The user is informed through the notification system that the application has been successfully completed.

[1250] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1251] This invention is a system that improves the efficiency and accuracy of travel expense claims, as well as analyzes user emotions to enhance the smoothness of the process. This system works in conjunction with the user, terminal, and server to efficiently process travel expense-related information and emotional data.

[1252] Terminal operation

[1253] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[1254] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[1255] Server operation

[1256] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[1257] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[1258] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it calculates that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[1259] The server runs an emotion engine to recognize emotions from user behavior and input. The emotion engine has the function of analyzing the emotions in the text entered by the user and the function of analyzing the user's facial expressions from uploaded images to recognize emotions.

[1260] Notification to the user

[1261] The matching and calculation results are sent to the user via the server's notification system. The user reviews the received notification message (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"). Based on the perceived emotions, the notification content is appropriately adjusted.

[1262] The user reviews the notification and, if the information is correct, presses the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. At the same time, it sends a processing completion notification to the user. If the recognized emotions require correction, the server can provide the user with further confirmation and support.

[1263] Specific example

[1264] 1. Example of user input

[1265] Destination: Tokyo

[1266] Date and time: October 5, 2023 10:00

[1267] Upload a photo of your travel expense receipt.

[1268] 2. Server Analysis and Matching Examples

[1269] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1270] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1271] 3. Calculation results

[1272] Transportation expenses: 500 yen

[1273] 4. Emotion recognition example

[1274] Text sentiment analysis: "The analysis determined that the user is feeling anxious."

[1275] Image analysis: The user's facial expression indicates fatigue.

[1276] 5. Example of a notification to the user

[1277] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1278] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[1279] Thus, the system of the present invention can streamline the travel expense application process and improve the user experience through emotion recognition.

[1280] The following describes the processing flow.

[1281] Step 1:

[1282] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1283] Step 2:

[1284] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[1285] Step 3:

[1286] The terminal temporarily stores text messages and image files received from the user in a storage device.

[1287] Step 4:

[1288] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[1289] Step 5:

[1290] The server analyzes the received text data and extracts destination and date / time information.

[1291] Step 6:

[1292] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[1293] Step 7:

[1294] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[1295] Step 8:

[1296] The server calculates the transportation cost using a calculation method based on the matching results. For example, it might calculate that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1297] Step 9:

[1298] The server runs an emotion engine to recognize the user's emotions from the text entered by the user and the images uploaded.

[1299] Step 10:

[1300] The server uses an emotion engine to perform text sentiment analysis. For example, the user's text might be judged as "stressed."

[1301] Step 11:

[1302] The server uses an emotion engine to analyze images and recognize emotions from the user's facial expressions. For example, the server might determine that the user's facial expression indicates "tiredness."

[1303] Step 12:

[1304] The server sends the matching and calculation results to the user via a notification system. The notification content is adjusted based on the analyzed emotions. For example, it might be a "gentle tone that encourages quick confirmation."

[1305] Step 13:

[1306] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[1307] Step 14:

[1308] Users can review the content and send correction messages if necessary.

[1309] Step 15:

[1310] The user presses the "Confirm Application" button.

[1311] Step 16:

[1312] The server receives the instruction to confirm the application and saves the application data to the database.

[1313] Step 17:

[1314] The server sends a processing completion notification to the user. If the recognized emotions require correction, the server provides the user with further confirmation and support.

[1315] This processing step allows users to efficiently submit travel expense claims, while the server automatically performs data analysis, matching, calculation, and sentiment recognition, reducing the workload for both parties and improving the accuracy of the claims.

[1316] (Example 2)

[1317] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1318] Traditional travel expense claim systems suffered from problems such as user input errors, cumbersome procedures, and user stress. Furthermore, requiring users to input accurate information often required significant time and effort. This reduced the efficiency of the application process and compromised the user experience. Additionally, existing systems lacked emotion recognition capabilities, failing to adequately address user stress and anxiety. To address these issues, a system was needed that would automate the travel expense claim process and reduce user stress.

[1319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1320] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating travel expenses, notification means for notifying the user of the matching results and calculation results, emotion recognition means for analyzing emotions from the user's input data and images, and means for adjusting the notification content based on the recognized emotions. This enables the automation of the travel expense application process and an improvement in the user experience.

[1321] "Communication method" refers to an interface for users to input destination and date / time information.

[1322] The "upload method" is a function that allows users to send images of receipts to the system.

[1323] "Storage means" refers to data storage that temporarily stores received text messages and image files.

[1324] "Transmission means" refers to the function for sending data stored in the storage means to the server.

[1325] "Optical character recognition means" refers to a technology for extracting character information from a received image file.

[1326] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[1327] The "calculation means" refers to a processing function for calculating transportation expenses based on the verified information.

[1328] "Notification means" refers to a messaging function for communicating calculation results and verification results to the user.

[1329] "Emotion recognition means" refers to technology for analyzing and recognizing emotions from user input data and images.

[1330] "Means for adjusting notification content" refers to a function that appropriately modifies the content of notifications sent to the user based on recognized emotions.

[1331] This invention is a system designed to improve the efficiency and accuracy of travel expense claims, and it operates in cooperation with the user, terminal, and server. The detailed configuration and operation of this system will be described below.

[1332] System Configuration

[1333] The system includes the following main components:

[1334] 1. Means of communication

[1335] 2. Upload method

[1336] 3. Preservation means

[1337] 4. Transmission method

[1338] 5. Optical character recognition means (OCR means)

[1339] 6. Verification means

[1340] 7. Means of calculation

[1341] 8. Means of notification

[1342] 9. Emotion recognition means

[1343] 10. Means for adjusting notification content

[1344] Specific actions

[1345] 1. User actions

[1346] Users use their devices to enter destination and date / time information for travel expense claims. For example, a user might use their smartphone to enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Alternatively, the user might use their smartphone camera to take a picture of their travel expense receipt and upload the image via a chat or messaging app.

[1347] 2. Data processing on the terminal

[1348] The terminal temporarily stores text data of destination and date / time received from the user, as well as uploaded receipt images. This data is then transmitted to the server via the terminal's communication method. Wi-Fi or cellular networks are used for this communication.

[1349] 3. Receiving and analyzing data on the server

[1350] The server receives data sent from the terminal. The text analysis engine within the server first extracts destination and date / time information from the received text data. Next, it uses an OCR (Optical Character Recognition) engine to extract text information from the receipt image. For example, it obtains information such as "Tokyo Station to Shinagawa Station" and "500 yen" from the receipt image.

[1351] 4. Matching and Calculation

[1352] The server's matching mechanism compares the text data of the extracted image with the text data entered by the user to confirm a match. After the matching mechanism confirms a match, the calculation mechanism calculates the transportation cost. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1353] 5. Emotion recognition

[1354] The server's emotion recognition system analyzes the user's emotions from user input data and uploaded images. Specifically, a text analysis engine reads emotions from the strings entered by the user, and an image analysis engine recognizes emotions based on the user's facial expressions. For example, the system might determine from the text analysis results that "the user is nervous" and recognize from the image analysis results that "the user has a tired expression."

[1355] 6. Notification and adjustment of results

[1356] The server generates a notification message for the user based on the matching and calculation results. For example, it might create a message such as "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen." Furthermore, it adjusts the tone of the notification based on the sentiment recognition results. The result notification is sent from the server to the device and displayed in the user's chat or messaging app.

[1357] Specific example

[1358] 1. User input

[1359] Destination: Tokyo

[1360] Date and time: October 5, 2023 10:00

[1361] Upload a photo of your travel expense receipt.

[1362] 2. Server analysis and matching

[1363] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1364] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1365] 3. Calculation results

[1366] Transportation expenses: 500 yen

[1367] 4. Emotion recognition

[1368] Text sentiment analysis: "The user is feeling anxious."

[1369] Image analysis: The user's facial expression indicates fatigue.

[1370] 5. Notification to the user

[1371] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1372] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[1373] Examples of prompts for generative AI models

[1374] 1. Example of a travel expense claim: "The destination is Tokyo, and the date and time is October 5, 2023, at 10:00 AM. Please upload your travel expense receipt."

[1375] 2. Specific example of emotion recognition: "Analyze the user's emotions from their input and facial expressions to determine if there are signs of tension or fatigue."

[1376] The above describes the embodiment for carrying out the invention. This system enables the automation of the travel expense application process and improves the user experience.

[1377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1378] Program processing flow

[1379] Step 1: User data entry

[1380] Operation: The user uses a terminal to enter destination and date / time information for the travel expense claim. For example, enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1381] Input: Destination ("Tokyo"), Date and Time ("October 5, 2023, 10:00")

[1382] Output: The input data is saved to the terminal.

[1383] Step 2: Upload the receipt image

[1384] Operation: The user takes a picture of the receipt with their device's camera and uploads it via a chat or messaging app.

[1385] Input: Image of the receipt that was photographed.

[1386] Output: The uploaded image is saved to the device.

[1387] Step 3: Save data temporarily

[1388] Operation: The terminal temporarily saves the entered text data (destination and date / time) and uploaded image files.

[1389] Input: Text data of destination and date / time, image of receipt

[1390] Output: Temporarily saved text data and image files are stored on the device.

[1391] Step 4: Send data to the server

[1392] Operation: The terminal sends temporarily stored data to the server via a communication method. Wi-Fi or cellular networks are used.

[1393] Input: Temporarily saved text data and image files

[1394] Output: Data sent to the server

[1395] Step 5: Receiving data on the server

[1396] Operation: The server receives text data and image files sent from the terminal.

[1397] Input: Data sent from the device

[1398] Output: Received data is stored on the server.

[1399] Step 6: Analyzing Text Data

[1400] Operation: The server's text analysis engine extracts destination and date / time information from the received text data.

[1401] Input: Received text data

[1402] Data processing: Text analysis engine analyzes destination and date / time information.

[1403] Output: Analyzed destination and date / time (e.g., "Tokyo", "October 5, 2023, 10:00")

[1404] Step 7: Optical Character Recognition (OCR) of Images

[1405] Operation: The server uses an OCR engine to extract text data from the receipt image.

[1406] Input: Received image file

[1407] Data processing: Extraction of text data using an OCR engine (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[1408] Output: Extracted text data (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[1409] Step 8: Data Verification

[1410] Operation: The server's matching mechanism compares the extracted character data with the text data entered by the user.

[1411] Input: User-inputted text data, OCR-extracted data

[1412] Data calculation: Perform data consistency checks.

[1413] Output: Matching result (e.g., Match, Mismatch)

[1414] Step 9: Calculating transportation expenses

[1415] Operation: The server's calculation mechanism calculates travel expenses based on the verified information.

[1416] Input: Matched data (e.g., destination and route)

[1417] Data processing: Calculation of transportation expenses (Example: The transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen)

[1418] Output: Calculated transportation cost (e.g., 500 yen)

[1419] Step 10: Emotion Recognition

[1420] Operation: The server's emotion recognition system analyzes emotions from the user's input data and images.

[1421] Input: User text input, uploaded image

[1422] Data processing: Emotion recognition using text analysis engine and image analysis engine.

[1423] Output: Recognized emotion (e.g., feeling nervous, feeling tired)

[1424] Step 11: Generate and adjust notification content

[1425] Operation: The server generates a notification message adjusted based on the emotion recognition result, using the matching and calculation results.

[1426] Input: Matching result, calculation result, recognized emotion

[1427] Data processing: Generating notification messages and adjusting the tone (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Gentle tone to encourage quick confirmation.")

[1428] Output: Generated notification message

[1429] Step 12: Sending notifications to users

[1430] Operation: The server sends the generated notification message to the terminal.

[1431] Input: Generated notification message

[1432] Output: A notification message is delivered to the device.

[1433] Step 13: User confirmation and confirmation

[1434] Operation: The user checks the notification message using their device, and if the content is correct, press the "Confirm Application" button.

[1435] Input: Notified message

[1436] Output: The instruction "Application Confirmed" is sent from the terminal to the server.

[1437] Step 14: Server saves data and sends completion notification.

[1438] Operation: The server receives the "Application Confirmation" instruction and saves the application data to the database. It also sends a processing completion notification to the user.

[1439] Input: "Application Confirmed" Instruction

[1440] Data processing: Saving to a database

[1441] Output: A processing completion notification is sent to the device.

[1442] The above is a detailed explanation of the processing steps of this system's program.

[1443] (Application Example 2)

[1444] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1445] Conventional travel expense claim systems are inefficient due to cumbersome manual data entry and verification processes. Furthermore, they often lack consideration for users' feelings and circumstances, making them highly likely to cause stress and dissatisfaction. Similar problems are likely to occur when using autonomous vehicles, highlighting the need for a smooth travel expense claim and user experience.

[1446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, and storage means for temporarily storing received text messages and image files. This makes it possible to improve the efficiency and accuracy of travel expense applications and to provide appropriate support according to the user's emotional state.

[1447] "Communication means" refers to the means by which the user inputs destination and date / time information.

[1448] "Upload method" refers to the means by which users can upload images of receipts.

[1449] "Storage means" refers to means for temporarily storing received text messages and image files.

[1450] "Transmission means" refers to the means for sending received text messages and image files to the server.

[1451] "Optical character recognition means" refers to a means for analyzing a received image file and extracting text data.

[1452] "Verification means" refers to means for comparing extracted text data with text data entered by the user.

[1453] "Calculation method" refers to the means used to calculate transportation expenses.

[1454] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[1455] "Emotional analysis tools" are means of analyzing a user's emotional state and providing appropriate support.

[1456] This invention aims to improve the user experience by streamlining the travel expense claim process in autonomous vehicles and analyzing the user's emotional state. The embodiments for carrying out the invention are described below.

[1457] System Overview

[1458] This system uses a terminal installed inside an autonomous vehicle to allow the user to input destination and date / time information and upload an image of their receipt. The terminal temporarily stores the received data and sends it to a server. The server uses an optical character recognition (OCR) engine to extract text data from the image and compares it with the user's input data. It also calculates the travel expenses and notifies the user. Furthermore, an emotion analysis engine analyzes the user's emotions and provides appropriate support.

[1459] Hardware and software to be used

[1460] Hardware:

[1461] Main computer of an autonomous vehicle

[1462] In-car camera (captures the user's facial expressions)

[1463] In-car microphone (collection of audio data)

[1464] Touchscreen display (user interface)

[1465] GPS module (for recording location information)

[1466] software:

[1467] Vehicle operation system (recording of travel routes and times)

[1468] External library as an OCR (Optical Character Recognition) engine

[1469] Emotion analysis engine (analysis of text and facial expressions)

[1470] Notification system

[1471] Processing flow

[1472] The user enters destination and date / time information using a touchscreen display and uploads an image of the receipt. The system temporarily stores the entered text data and image file and sends it to the server. The server uses an OCR engine to extract text data from the image and compares it with the user's input data. It then calculates the travel expenses and notifies the user of the results through a notification system. An emotion analysis engine recognizes the user's emotions from their text and facial expressions and provides appropriate support.

[1473] Examples of specific cases and prompt statements

[1474] As a concrete example, the following user operations are anticipated.

[1475] 1. Example of user input

[1476] Departure point: Tokyo Station

[1477] Destination: Shinagawa Station

[1478] Date and time: October 5, 2023 10:00

[1479] Upload a photo of the receipt.

[1480] 2. Server Analysis and Matching Examples

[1481] Text analysis results: Destination = Tokyo Station, Date and Time = October 5, 2023, 10:00

[1482] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1483] 3. Calculation results

[1484] Transportation expenses: 500 yen

[1485] 4. Emotion recognition example

[1486] Text sentiment analysis: "The analysis determined that the user is tired."

[1487] Image analysis: The user's facial expression indicates fatigue.

[1488] 5. Example of a notification to the user

[1489] Confirmation message: "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1490] Based on the recognized emotion, the notification content will be adjusted to "Thank you for your hard work. Your travel expense application has been completed."

[1491] Examples of prompt statements include the following:

[1492] The text "This travel expense application is very stressful" and a photo of a tired-looking person are input into the analysis engine to generate an appropriate support message.

[1493] This system streamlines the travel expense application process and improves the user experience by providing support tailored to the user's emotional state.

[1494] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1495] Step 1:

[1496] The user enters destination and date / time information.

[1497] Input: The user enters destination and date / time information using the touchscreen display.

[1498] Output: Destination and date / time information is temporarily saved on the terminal.

[1499] Specific operation: The user inputs information such as "Tokyo Station" and "October 5, 2023, 10:00" using the on-screen keyboard or voice input.

[1500] Step 2:

[1501] The user uploads an image of the receipt.

[1502] Input: The user takes a picture of the receipt using the touchscreen display or camera and uploads it.

[1503] Output: The image file uploaded to the device is temporarily saved.

[1504] Specific operation: The user takes a picture of the receipt with the camera and presses the upload button to send the image file to the system.

[1505] Step 3:

[1506] The device sends received text messages and image files to the server.

[1507] Input: Destination, date and time information, and receipt image file saved on the device.

[1508] Output: A text message and an image file are sent to the server.

[1509] Specific operation: The device bundles the data it has stored into packets and sends them to the server over the network.

[1510] Step 4:

[1511] The server analyzes the text data and image files it receives.

[1512] Input: Destination, date and time information, and receipt image file sent to the server.

[1513] Output: Text data such as destination, date and time, and fare.

[1514] Specific operation: The server inputs an image file into the OCR engine and extracts text data from the image. For example, it extracts data such as "Tokyo Station to Shinagawa Station" and "500 yen" from a receipt image.

[1515] Step 5:

[1516] The server compares the extracted text data with the user's input data.

[1517] Input: Text data analyzed by the server and destination and date / time information entered by the user.

[1518] Output: Matching result.

[1519] Specific operation: The server compares the extracted text data with the user's input data to check for a match. For example, it matches "Destination: Tokyo Station" with "OCR result: Tokyo Station".

[1520] Step 6:

[1521] The server calculates the travel expenses.

[1522] Input: Matched destination and date / time information.

[1523] Output: Calculated transportation costs.

[1524] Specific operation: The server calculates the transportation cost from the departure point to the destination based on the matching results. For example, it might calculate "500 yen from Tokyo Station to Shinagawa Station".

[1525] Step 7:

[1526] The server analyzes the user's emotional state.

[1527] Input: User's text messages and image files.

[1528] Output: Analyzed emotion information.

[1529] Specific operation: The server uses an emotion analysis engine to analyze the user's emotions from the text and facial expressions they input. For example, it might determine from the text that "the user is feeling stressed" and recognize from the image that "the face looks tired."

[1530] Step 8:

[1531] The server notifies the user of the matching results, calculation results, and sentiment information.

[1532] Input: Calculated travel expenses, user's emotional state.

[1533] Output: Notification message to the user.

[1534] Specific operation: The server adjusts the notification content based on the analyzed sentiment and sends a message to the user. For example, it might notify the user, "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Thank you for your hard work. Your transportation expense claim has been completed."

[1535] In this way, the system operates smoothly through each processing step, resulting in more efficient travel expense claims and an improved user experience.

[1536] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1537] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1538] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1539] [Fourth Embodiment]

[1540] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1541] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1542] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1543] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1544] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1545] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1546] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1547] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1548] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1549] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1550] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1551] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1552] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1553] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[1554] Terminal operation

[1555] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[1556] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[1557] Server operation

[1558] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[1559] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[1560] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it can calculate that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[1561] The matching and calculation results are sent to the user via the server's notification system. The user receives the notification and reviews its contents. They can also make corrections as needed.

[1562] Specific example

[1563] 1. Example of user input

[1564] Destination: Tokyo

[1565] Date and time: October 5, 2023 10:00

[1566] Upload a photo of your travel expense receipt.

[1567] 2. Server Analysis and Matching Examples

[1568] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1569] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1570] 3. Calculation results

[1571] Transportation expenses: 500 yen

[1572] 4. Example of a notification to the user

[1573] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1574] User verification and application confirmation

[1575] Once the user receives a confirmation message and verifies its contents are correct, they press the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. Simultaneously, it sends a processing completion notification to the user. This entire process ensures that travel expense applications are completed efficiently and accurately.

[1576] This system configuration allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[1577] The following describes the processing flow.

[1578] Step 1:

[1579] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1580] Step 2:

[1581] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[1582] Step 3:

[1583] The terminal temporarily stores text messages and image files received from the user in a storage device.

[1584] Step 4:

[1585] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[1586] Step 5:

[1587] The server analyzes the received text data and extracts destination and date / time information.

[1588] Step 6:

[1589] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[1590] Step 7:

[1591] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[1592] Step 8:

[1593] The server calculates the transportation cost using a calculation tool based on the results of the matching tool. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1594] Step 9:

[1595] The server sends the matching results and calculation results to the user via a notification system.

[1596] Step 10:

[1597] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[1598] Step 11:

[1599] Users can review the content and send correction messages if necessary.

[1600] Step 12:

[1601] The user presses the "Confirm Application" button.

[1602] Step 13:

[1603] The server receives the instruction to confirm the application and saves the application data to the database.

[1604] Step 14:

[1605] The server sends a processing completion notification to the user.

[1606] This processing step allows users to efficiently submit travel expense claims, and the server automatically performs data analysis, matching, and calculations, improving the accuracy of the claims.

[1607] (Example 1)

[1608] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1609] The current travel expense application system has problems such as the large amount of information that users have to manually enter, and the inefficiency of processing travel expense applications due to input errors and inaccuracies. In addition, manual data verification and calculation work is required, which is time-consuming and resource-intensive.

[1610] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1611] In this invention, the server includes a text analysis means for analyzing received text data and extracting destination and date / time information, an optical character recognition means for analyzing received image files and extracting text data, and a matching means for comparing the extracted text data with text data entered by the user. This makes it possible to automatically compare the information entered by the user with the information on the receipt, and to quickly and accurately calculate and notify the travel expenses.

[1612] "Communication means" refers to a device used by users to input destination and date / time information, and which communicates via a messaging interface.

[1613] An "uploading device" is a device that provides the functionality for a user to capture an image of a receipt and send it to a server.

[1614] A "storage device" is a storage device for temporarily saving received text messages and image files.

[1615] "Transmission means" refers to a communication device for sending text messages and image files stored in the storage means to a server.

[1616] "Text analysis means" refers to software or hardware used to analyze text data received by a server and extract destination and date / time information.

[1617] "Optical character recognition means" refers to a technology for extracting text data from image files received by a server, and can utilize external libraries.

[1618] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[1619] "Calculation means" refers to software or hardware that the server uses to calculate travel expenses based on the verified information.

[1620] "Notification means" refers to a device or system that has the function of notifying the user of the matching results and calculation results.

[1621] This invention is a system designed to improve the efficiency and accuracy of travel expense claims. This system involves the coordinated operation of users, terminals, and a server to efficiently process travel expense-related information.

[1622] Device operation:

[1623] The user enters destination and date / time information using the communication means on their device. For example, the user can enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload means on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application. The device temporarily stores the received text message and image file in its storage means. This data is then sent to the server via the transmission means.

[1624] Server operation:

[1625] The server receives text messages and image files sent from the terminal. The specific operating procedure of the server is as follows:

[1626] 1. Data reception:

[1627] The server receives text messages and image files sent from the terminal.

[1628] 2. Text data analysis:

[1629] The server parses the received text data and extracts destination and date / time information. This process can utilize parsing tools such as Python's Natural Language Toolkit (NLTK).

[1630] 3. OCR analysis:

[1631] The server extracts text data from the received image file using optical character recognition (OCR) technology. Specifically, it can use OCR services such as the Google Cloud Vision API.

[1632] 4. Data matching:

[1633] The server compares and matches the data extracted by OCR analysis with the text data entered by the user. This matching is performed using an SQL database and database queries.

[1634] 5. Transportation cost calculation:

[1635] The server calculates travel expenses based on the verified information. For example, it calculates 500 yen for travel between "Tokyo Station and Shinagawa Station". This calculation uses Python's pandas library and built-in calculation functions.

[1636] 6. Notification to users:

[1637] The server notifies the user of the calculation results and the comparison results. Notification methods include email (e.g., email services such as SendGrid) and push notifications (Firebase Cloud Messaging).

[1638] Specific example:

[1639] 1. Example of user input:

[1640] Destination: Tokyo

[1641] Date and time: October 5, 2023 10:00

[1642] Upload a photo of your travel expense receipt.

[1643] 2. Server analysis and matching examples:

[1644] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1645] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1646] 3. Calculation results:

[1647] Transportation expenses: 500 yen

[1648] 4. Example of a notification to the user:

[1649] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1650] Examples of input prompts for a generative AI model:

[1651] "To claim your travel expenses, please enter the following information: destination, date and time, and upload an image of your travel expense receipt."

[1652] In this way, the entire system functions in a way that allows users to easily submit travel expense claims, and the automated analysis, matching, and calculation processes on the server improve the accuracy of the claims.

[1653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1654] Step 1:

[1655] Enter destination and date / time

[1656] The user uses their device's communication method to enter destination and date / time information. For example, the user might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00 AM".

[1657] Input: The user enters the destination and date / time into a form on the device.

[1658] Output: The destination and date / time information are stored on the terminal.

[1659] Specific action: Enter text into the terminal's input field and press the send button.

[1660] Step 2:

[1661] Upload receipt image

[1662] The user uses their device's camera function to capture an image of their travel expense receipt and uploads it to a messaging application.

[1663] Input: The user takes a picture of their travel expense receipt and presses the upload button.

[1664] Output: The receipt image is saved to the device and uploaded.

[1665] Specific steps: Launch the camera app on your device, take a picture of the receipt, and then upload it.

[1666] Step 3:

[1667] Data is temporarily stored.

[1668] The terminal temporarily stores text data such as the destination and date / time entered by the user, as well as uploaded image files.

[1669] Input: Text data of destination and date / time, and an image file of the receipt.

[1670] Output: Text data and image files are saved to the device's storage.

[1671] Specific action: Saves data to the device's cache or temporary folder.

[1672] Step 4:

[1673] Sending data

[1674] The device sends the saved text data and image files to the server.

[1675] Input: Text data and image files.

[1676] Output: Data is sent to the server.

[1677] Specific operation: Send data to the server using the HTTPS protocol.

[1678] Step 5:

[1679] Data reception

[1680] The server receives text messages and image files sent from the terminal.

[1681] Input: Text data and image files.

[1682] Output: The received data is stored on the server.

[1683] Specific operation: Receive data and save it to local storage.

[1684] Step 6:

[1685] Text data analysis

[1686] The server analyzes the received text data and extracts destination and date / time information. Tools such as the Natural Language Toolkit (NLTK) are used for the analysis.

[1687] Input: Received text data.

[1688] Output: Destination and date / time are extracted.

[1689] Specific operation: The text analysis engine analyzes the data and extracts the necessary information.

[1690] Step 7:

[1691] OCR analysis

[1692] The server extracts text data from received image files using optical character recognition (OCR). It utilizes APIs such as the Google Cloud Vision API.

[1693] Input: Received receipt image file.

[1694] Output: Text data extracted from the image.

[1695] Specific operation: The OCR engine analyzes image data and extracts text data.

[1696] Step 8:

[1697] Data matching

[1698] The server compares and matches the data extracted by OCR analysis with the text data entered by the user, using database queries.

[1699] Input: Text data entered by the user and text data extracted by OCR.

[1700] Output: Matching results (data match / mismatch information).

[1701] Specific operation: Perform collation and verification using SQL queries.

[1702] Step 9:

[1703] Transportation expense calculation

[1704] The server calculates travel expenses based on the verified information, using Python's pandas library and built-in calculation functions.

[1705] Input: Matched, accurate destination information and transportation route.

[1706] Output: Calculated transportation costs.

[1707] Specific operation: Calculates transportation expenses based on the calculation logic.

[1708] Step 10:

[1709] Notification to the user

[1710] The server notifies the user of the calculation results and the comparison results. This is done using email services such as SendGrid or push notifications (Firebase Cloud Messaging).

[1711] Input: Calculation results and comparison results.

[1712] Output: Notification message to the user.

[1713] Specific action: Send a message to the user using the notification system.

[1714] (Application Example 1)

[1715] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1716] The application process for transportation costs at logistics centers involves manual data entry and receipt organization, which is time-consuming and prone to errors. Furthermore, many of the calculation and application processes for transportation costs are analog, making them inefficient. Therefore, there is a need for increased efficiency while simultaneously improving accuracy.

[1717] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1718] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, transmission means for sending received text messages and image files to the server, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating transportation costs, notification means for notifying the user of the matching results and calculation results, storage means for saving transportation cost data to a database, and confirmation means for finalizing the transportation cost application. This makes it possible to improve the efficiency and accuracy of the transportation cost application process.

[1719] "Means of communication" refers to means by which users input destination and date / time information, and in particular includes chat interfaces.

[1720] "Upload method" refers to the means by which users upload images of receipts to the system.

[1721] "Storage means" refers to means for temporarily saving received text messages and image files.

[1722] "Transmission means" refers to the means for sending received text messages and image files to the server.

[1723] "Optical character recognition means" refers to means that use optical character recognition technology to extract text data from an image file.

[1724] "The matching means" refers to the means of comparing the extracted text data with the text data entered by the user.

[1725] "Calculation means" refers to the means used to calculate transportation costs.

[1726] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[1727] "Means of storage for saving to a database" refers to means of saving transportation cost data to a database.

[1728] "Confirmation method" refers to the means by which users can finalize their shipping cost claims.

[1729] This invention is a system aimed at improving the efficiency and accuracy of the application process for transportation costs at a logistics center. The system includes communication means, upload means, storage means, transmission means, optical character recognition means, matching means, calculation means, notification means, storage means for saving to a database, and verification means.

[1730] To use the system, users first enter destination and date / time information using their smartphones and upload images of their shipping expense receipts. A chat interface is used for communication, allowing users to intuitively input data.

[1731] Uploaded images are temporarily stored on the device using a storage method and then sent to the server via a transmission method. The server receives the image file and extracts text data from the image using optical character recognition (Tesseract OCR). This optical character recognition method is implemented using a Python library and recognizes the characters after converting the image to grayscale.

[1732] The extracted text data is compared with the destination and date / time information entered by the user using a matching mechanism. This matching is performed using a text analysis mechanism to confirm that the user's input data matches the OCR data. Once the matching is complete, the transportation cost is calculated using a calculation mechanism. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1733] The calculation and verification results are notified to the user using a notification system. The user receives the notification and is asked to confirm it. The user confirms the contents through the confirmation system and, if correct, confirms the application. Once this confirmation process is complete, the shipping cost data is stored in the database using a storage system.

[1734] As a concrete example, consider the following prompt:

[1735] "The destination is Tokyo. I have uploaded an image of the shipping receipt taken on October 5, 2023 at 10:00 AM. Please automatically calculate the shipping cost."

[1736] This series of processes effectively automates the application process for transportation costs, reducing the burden on users and improving the accuracy of application data.

[1737] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1738] Step 1:

[1739] The user opens the chat interface using their smartphone and enters destination and date / time information. The entered information is saved to the device via communication. The data saved as input is "Destination: Tokyo, Date / Time: October 5, 2023, 10:00".

[1740] Step 2:

[1741] The user uses the upload method to take a picture of the shipping receipt with their smartphone and upload it to the chat interface. The image file is saved on the device. The uploaded image file is temporarily stored on the device using a temporary storage method.

[1742] Step 3:

[1743] The terminal sends the saved input data and image files to the server using a transmission device. The transmitted data includes destination and date / time information, as well as an image file of the receipt. The server receives the transmitted data and temporarily stores it using a storage device.

[1744] Step 4:

[1745] The server uses optical character recognition (OCR) to extract text data from the received image file. Specifically, it uses Tesseract OCR to convert the image to grayscale and then reads the text from it. The extracted text data might be something like "Tokyo Station to Shinagawa Station" and "500 yen".

[1746] Step 5:

[1747] The server uses a matching mechanism to compare the extracted text data with the destination and date / time information entered by the user. Text analysis is performed to confirm that the input data and OCR data match. If the matching is successful, the transportation cost from "Tokyo Station to Shinagawa Station" is calculated.

[1748] Step 6:

[1749] The server uses a calculation tool to calculate the shipping cost based on the verified information. It verifies that the calculated shipping cost is 500 yen. The calculation result is notified to the user via a notification tool.

[1750] Step 7:

[1751] The user reviews the calculated shipping cost received as a notification. The notification includes details of the destination, date and time, and shipping costs. The user uses the verification method to confirm the details and finalize the application.

[1752] Step 8:

[1753] The server receives confirmation instructions from the user and stores the shipping cost data in the database using a storage method. This completes the shipping cost application and saves it to the database.

[1754] Step 9:

[1755] Once verification is complete, the server sends a completion notification to the user. The user is informed through the notification system that the application has been successfully completed.

[1756] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1757] This invention is a system that improves the efficiency and accuracy of travel expense claims, as well as analyzes user emotions to enhance the smoothness of the process. This system works in conjunction with the user, terminal, and server to efficiently process travel expense-related information and emotional data.

[1758] Terminal operation

[1759] The user enters destination and date / time information using the communication method on their device. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Next, the user uses the upload method on their device to capture an image of their travel expense receipt and uploads it to the chat screen or messaging application.

[1760] The terminal temporarily stores received text messages and image files in a storage device. These data are then sent to the server via a transmission device.

[1761] Server operation

[1762] The server receives text messages and image files sent from the terminal. First, the server analyzes the received text data and extracts destination and date / time information. Next, the server analyzes the image files and extracts text data from the images using optical character recognition (OCR) technology.

[1763] The extracted data is compared and verified against the text data entered by the user using the server's matching mechanism. This verification confirms that the data matches.

[1764] Next, the server uses a calculation tool to calculate the travel expenses based on the verified information. For example, it calculates that the travel expense from "Tokyo Station to Shinagawa Station" is 500 yen.

[1765] The server runs an emotion engine to recognize emotions from user behavior and input. The emotion engine has the function of analyzing the emotions in the text entered by the user and the function of analyzing the user's facial expressions from uploaded images to recognize emotions.

[1766] Notification to the user

[1767] The matching and calculation results are sent to the user via the server's notification system. The user reviews the received notification message (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"). Based on the perceived emotions, the notification content is appropriately adjusted.

[1768] The user reviews the notification and, if the information is correct, presses the "Confirm Application" button. The server receives this confirmation instruction and saves the application data to the database. At the same time, it sends a processing completion notification to the user. If the recognized emotions require correction, the server can provide the user with further confirmation and support.

[1769] Specific example

[1770] 1. Example of user input

[1771] Destination: Tokyo

[1772] Date and time: October 5, 2023 10:00

[1773] Upload a photo of your travel expense receipt.

[1774] 2. Server Analysis and Matching Examples

[1775] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1776] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1777] 3. Calculation results

[1778] Transportation expenses: 500 yen

[1779] 4. Emotion recognition example

[1780] Text sentiment analysis: "The analysis determined that the user is feeling anxious."

[1781] Image analysis: The user's facial expression indicates fatigue.

[1782] 5. Example of a notification to the user

[1783] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1784] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[1785] Thus, the system of the present invention can streamline the travel expense application process and improve the user experience through emotion recognition.

[1786] The following describes the processing flow.

[1787] Step 1:

[1788] The user enters destination and date / time information using a communication tool. For example, they might enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1789] Step 2:

[1790] The user takes a photo of their travel expense receipt and uploads it to the chat screen or messaging application.

[1791] Step 3:

[1792] The terminal temporarily stores text messages and image files received from the user in a storage device.

[1793] Step 4:

[1794] The terminal sends text messages and image files stored in the storage means to the server via the transmission means.

[1795] Step 5:

[1796] The server analyzes the received text data and extracts destination and date / time information.

[1797] Step 6:

[1798] The server analyzes the received image file using optical character recognition (OCR) and extracts text data from the image.

[1799] Step 7:

[1800] The server compares and verifies the extracted text data and the text data entered by the user using a matching mechanism.

[1801] Step 8:

[1802] The server calculates the transportation cost using a calculation method based on the matching results. For example, it might calculate that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1803] Step 9:

[1804] The server runs an emotion engine to recognize the user's emotions from the text entered by the user and the images uploaded.

[1805] Step 10:

[1806] The server uses an emotion engine to perform text sentiment analysis. For example, the user's text might be judged as "stressed."

[1807] Step 11:

[1808] The server uses an emotion engine to analyze images and recognize emotions from the user's facial expressions. For example, the server might determine that the user's facial expression indicates "tiredness."

[1809] Step 12:

[1810] The server sends the matching and calculation results to the user via a notification system. The notification content is adjusted based on the analyzed emotions. For example, it might be a "gentle tone that encourages quick confirmation."

[1811] Step 13:

[1812] Check the notification message received by the user (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen").

[1813] Step 14:

[1814] Users can review the content and send correction messages if necessary.

[1815] Step 15:

[1816] The user presses the "Confirm Application" button.

[1817] Step 16:

[1818] The server receives the instruction to confirm the application and saves the application data to the database.

[1819] Step 17:

[1820] The server sends a processing completion notification to the user. If the recognized emotions require correction, the server provides the user with further confirmation and support.

[1821] This processing step allows users to efficiently submit travel expense claims, while the server automatically performs data analysis, matching, calculation, and sentiment recognition, reducing the workload for both parties and improving the accuracy of the claims.

[1822] (Example 2)

[1823] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1824] Traditional travel expense claim systems suffered from problems such as user input errors, cumbersome procedures, and user stress. Furthermore, requiring users to input accurate information often required significant time and effort. This reduced the efficiency of the application process and compromised the user experience. Additionally, existing systems lacked emotion recognition capabilities, failing to adequately address user stress and anxiety. To address these issues, a system was needed that would automate the travel expense claim process and reduce user stress.

[1825] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1826] In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, storage means for temporarily storing received text messages and image files, optical character recognition means for analyzing received image files and extracting text data, matching means for comparing the extracted text data with the text data entered by the user, calculation means for calculating travel expenses, notification means for notifying the user of the matching results and calculation results, emotion recognition means for analyzing emotions from the user's input data and images, and means for adjusting the notification content based on the recognized emotions. This enables the automation of the travel expense application process and an improvement in the user experience.

[1827] "Communication method" refers to an interface for users to input destination and date / time information.

[1828] The "upload method" is a function that allows users to send images of receipts to the system.

[1829] "Storage means" refers to data storage that temporarily stores received text messages and image files.

[1830] "Transmission means" refers to the function for sending data stored in the storage means to the server.

[1831] "Optical character recognition means" refers to a technology for extracting character information from a received image file.

[1832] A "matching means" is a function that compares extracted text data with text data entered by the user to confirm a match.

[1833] The "calculation means" refers to a processing function for calculating transportation expenses based on the verified information.

[1834] "Notification means" refers to a messaging function for communicating calculation results and verification results to the user.

[1835] "Emotion recognition means" refers to technology for analyzing and recognizing emotions from user input data and images.

[1836] "Means for adjusting notification content" refers to a function that appropriately modifies the content of notifications sent to the user based on recognized emotions.

[1837] This invention is a system designed to improve the efficiency and accuracy of travel expense claims, and it operates in cooperation with the user, terminal, and server. The detailed configuration and operation of this system will be described below.

[1838] System Configuration

[1839] The system includes the following main components:

[1840] 1. Means of communication

[1841] 2. Upload method

[1842] 3. Preservation means

[1843] 4. Transmission method

[1844] 5. Optical character recognition means (OCR means)

[1845] 6. Verification means

[1846] 7. Means of calculation

[1847] 8. Means of notification

[1848] 9. Emotion recognition means

[1849] 10. Means for adjusting notification content

[1850] Specific actions

[1851] 1. User actions

[1852] Users use their devices to enter destination and date / time information for travel expense claims. For example, a user might use their smartphone to enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00". Alternatively, the user might use their smartphone camera to take a picture of their travel expense receipt and upload the image via a chat or messaging app.

[1853] 2. Data processing on the terminal

[1854] The terminal temporarily stores text data of destination and date / time received from the user, as well as uploaded receipt images. This data is then transmitted to the server via the terminal's communication method. Wi-Fi or cellular networks are used for this communication.

[1855] 3. Receiving and analyzing data on the server

[1856] The server receives data sent from the terminal. The text analysis engine within the server first extracts destination and date / time information from the received text data. Next, it uses an OCR (Optical Character Recognition) engine to extract text information from the receipt image. For example, it obtains information such as "Tokyo Station to Shinagawa Station" and "500 yen" from the receipt image.

[1857] 4. Matching and Calculation

[1858] The server's matching mechanism compares the text data of the extracted image with the text data entered by the user to confirm a match. After the matching mechanism confirms a match, the calculation mechanism calculates the transportation cost. For example, it calculates that the transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen.

[1859] 5. Emotion recognition

[1860] The server's emotion recognition system analyzes the user's emotions from user input data and uploaded images. Specifically, a text analysis engine reads emotions from the strings entered by the user, and an image analysis engine recognizes emotions based on the user's facial expressions. For example, the system might determine from the text analysis results that "the user is nervous" and recognize from the image analysis results that "the user has a tired expression."

[1861] 6. Notification and adjustment of results

[1862] The server generates a notification message for the user based on the matching and calculation results. For example, it might create a message such as "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen." Furthermore, it adjusts the tone of the notification based on the sentiment recognition results. The result notification is sent from the server to the device and displayed in the user's chat or messaging app.

[1863] Specific example

[1864] 1. User input

[1865] Destination: Tokyo

[1866] Date and time: October 5, 2023 10:00

[1867] Upload a photo of your travel expense receipt.

[1868] 2. Server analysis and matching

[1869] Text analysis results: Destination = Tokyo, Date and Time = October 5, 2023, 10:00

[1870] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1871] 3. Calculation results

[1872] Transportation expenses: 500 yen

[1873] 4. Emotion recognition

[1874] Text sentiment analysis: "The user is feeling anxious."

[1875] Image analysis: The user's facial expression indicates fatigue.

[1876] 5. Notification to the user

[1877] Confirmation message: "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1878] Based on the recognized emotions, the notification content is adjusted to a "gentle tone that encourages quick confirmation."

[1879] Examples of prompts for generative AI models

[1880] 1. Example of a travel expense claim: "The destination is Tokyo, and the date and time is October 5, 2023, at 10:00 AM. Please upload your travel expense receipt."

[1881] 2. Specific example of emotion recognition: "Analyze the user's emotions from their input and facial expressions to determine if there are signs of tension or fatigue."

[1882] The above describes the embodiment for carrying out the invention. This system enables the automation of the travel expense application process and improves the user experience.

[1883] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1884] Program processing flow

[1885] Step 1: User data entry

[1886] Operation: The user uses a terminal to enter destination and date / time information for the travel expense claim. For example, enter "Destination: Tokyo" and "Date / Time: October 5, 2023, 10:00".

[1887] Input: Destination ("Tokyo"), Date and Time ("October 5, 2023, 10:00")

[1888] Output: The input data is saved to the terminal.

[1889] Step 2: Upload the receipt image

[1890] Operation: The user takes a picture of the receipt with their device's camera and uploads it via a chat or messaging app.

[1891] Input: Image of the receipt that was photographed.

[1892] Output: The uploaded image is saved to the device.

[1893] Step 3: Save data temporarily

[1894] Operation: The terminal temporarily saves the entered text data (destination and date / time) and uploaded image files.

[1895] Input: Text data of destination and date / time, image of receipt

[1896] Output: Temporarily saved text data and image files are stored on the device.

[1897] Step 4: Send data to the server

[1898] Operation: The terminal sends temporarily stored data to the server via a communication method. Wi-Fi or cellular networks are used.

[1899] Input: Temporarily saved text data and image files

[1900] Output: Data sent to the server

[1901] Step 5: Receiving data on the server

[1902] Operation: The server receives text data and image files sent from the terminal.

[1903] Input: Data sent from the device

[1904] Output: Received data is stored on the server.

[1905] Step 6: Analyzing Text Data

[1906] Operation: The server's text analysis engine extracts destination and date / time information from the received text data.

[1907] Input: Received text data

[1908] Data processing: Text analysis engine analyzes destination and date / time information.

[1909] Output: Analyzed destination and date / time (e.g., "Tokyo", "October 5, 2023, 10:00")

[1910] Step 7: Optical Character Recognition (OCR) of Images

[1911] Operation: The server uses an OCR engine to extract text data from the receipt image.

[1912] Input: Received image file

[1913] Data processing: Extraction of text data using an OCR engine (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[1914] Output: Extracted text data (e.g., "Tokyo Station to Shinagawa Station", "500 yen")

[1915] Step 8: Data Verification

[1916] Operation: The server's matching mechanism compares the extracted character data with the text data entered by the user.

[1917] Input: User-inputted text data, OCR-extracted data

[1918] Data calculation: Perform data consistency checks.

[1919] Output: Matching result (e.g., Match, Mismatch)

[1920] Step 9: Calculating transportation expenses

[1921] Operation: The server's calculation mechanism calculates travel expenses based on the verified information.

[1922] Input: Matched data (e.g., destination and route)

[1923] Data processing: Calculation of transportation expenses (Example: The transportation cost from "Tokyo Station to Shinagawa Station" is 500 yen)

[1924] Output: Calculated transportation cost (e.g., 500 yen)

[1925] Step 10: Emotion Recognition

[1926] Operation: The server's emotion recognition system analyzes emotions from the user's input data and images.

[1927] Input: User text input, uploaded image

[1928] Data processing: Emotion recognition using text analysis engine and image analysis engine.

[1929] Output: Recognized emotion (e.g., feeling nervous, feeling tired)

[1930] Step 11: Generate and adjust notification content

[1931] Operation: The server generates a notification message adjusted based on the emotion recognition result, using the matching and calculation results.

[1932] Input: Matching result, calculation result, recognized emotion

[1933] Data processing: Generating notification messages and adjusting the tone (e.g., "Destination: Tokyo, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Gentle tone to encourage quick confirmation.")

[1934] Output: Generated notification message

[1935] Step 12: Sending notifications to users

[1936] Operation: The server sends the generated notification message to the terminal.

[1937] Input: Generated notification message

[1938] Output: A notification message is delivered to the device.

[1939] Step 13: User confirmation and confirmation

[1940] Operation: The user checks the notification message using their device, and if the content is correct, press the "Confirm Application" button.

[1941] Input: Notified message

[1942] Output: The instruction "Application Confirmed" is sent from the terminal to the server.

[1943] Step 14: Server saves data and sends completion notification.

[1944] Operation: The server receives the "Application Confirmation" instruction and saves the application data to the database. It also sends a processing completion notification to the user.

[1945] Input: "Application Confirmed" Instruction

[1946] Data processing: Saving to a database

[1947] Output: A processing completion notification is sent to the device.

[1948] The above is a detailed explanation of the processing steps of this system's program.

[1949] (Application Example 2)

[1950] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1951] Conventional travel expense claim systems are inefficient due to cumbersome manual data entry and verification processes. Furthermore, they often lack consideration for users' feelings and circumstances, making them highly likely to cause stress and dissatisfaction. Similar problems are likely to occur when using autonomous vehicles, highlighting the need for a smooth travel expense claim and user experience.

[1952] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes communication means for the user to input destination and date / time information, upload means for the user to upload an image of a receipt, and storage means for temporarily storing received text messages and image files. This makes it possible to improve the efficiency and accuracy of travel expense applications and to provide appropriate support according to the user's emotional state.

[1953] "Communication means" refers to the means by which the user inputs destination and date / time information.

[1954] "Upload method" refers to the means by which users can upload images of receipts.

[1955] "Storage means" refers to means for temporarily storing received text messages and image files.

[1956] "Transmission means" refers to the means for sending received text messages and image files to the server.

[1957] "Optical character recognition means" refers to a means for analyzing a received image file and extracting text data.

[1958] "Verification means" refers to means for comparing extracted text data with text data entered by the user.

[1959] "Calculation method" refers to the means used to calculate transportation expenses.

[1960] "Notification means" refers to the means of notifying the user of the matching results and calculation results.

[1961] "Emotional analysis tools" are means of analyzing a user's emotional state and providing appropriate support.

[1962] This invention aims to improve the user experience by streamlining the travel expense claim process in autonomous vehicles and analyzing the user's emotional state. The embodiments for carrying out the invention are described below.

[1963] System Overview

[1964] This system uses a terminal installed inside an autonomous vehicle to allow the user to input destination and date / time information and upload an image of their receipt. The terminal temporarily stores the received data and sends it to a server. The server uses an optical character recognition (OCR) engine to extract text data from the image and compares it with the user's input data. It also calculates the travel expenses and notifies the user. Furthermore, an emotion analysis engine analyzes the user's emotions and provides appropriate support.

[1965] Hardware and software to be used

[1966] Hardware:

[1967] Main computer of an autonomous vehicle

[1968] In-car camera (captures the user's facial expressions)

[1969] In-car microphone (collection of audio data)

[1970] Touchscreen display (user interface)

[1971] GPS module (for recording location information)

[1972] software:

[1973] Vehicle operation system (recording of travel routes and times)

[1974] External library as an OCR (Optical Character Recognition) engine

[1975] Emotion analysis engine (analysis of text and facial expressions)

[1976] Notification system

[1977] Processing flow

[1978] The user enters destination and date / time information using a touchscreen display and uploads an image of the receipt. The system temporarily stores the entered text data and image file and sends it to the server. The server uses an OCR engine to extract text data from the image and compares it with the user's input data. It then calculates the travel expenses and notifies the user of the results through a notification system. An emotion analysis engine recognizes the user's emotions from their text and facial expressions and provides appropriate support.

[1979] Examples of specific cases and prompt statements

[1980] As a concrete example, the following user operations are anticipated.

[1981] 1. Example of user input

[1982] Departure point: Tokyo Station

[1983] Destination: Shinagawa Station

[1984] Date and time: October 5, 2023 10:00

[1985] Upload a photo of the receipt.

[1986] 2. Server Analysis and Matching Examples

[1987] Text analysis results: Destination = Tokyo Station, Date and Time = October 5, 2023, 10:00

[1988] OCR analysis results: "Tokyo Station to Shinagawa Station" "500 yen"

[1989] 3. Calculation results

[1990] Transportation expenses: 500 yen

[1991] 4. Emotion recognition example

[1992] Text sentiment analysis: "The analysis determined that the user is tired."

[1993] Image analysis: The user's facial expression indicates fatigue.

[1994] 5. Example of a notification to the user

[1995] Confirmation message: "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen"

[1996] Based on the recognized emotion, the notification content will be adjusted to "Thank you for your hard work. Your travel expense application has been completed."

[1997] Examples of prompt statements include the following:

[1998] The text "This travel expense application is very stressful" and a photo of a tired-looking person are input into the analysis engine to generate an appropriate support message.

[1999] This system streamlines the travel expense application process and improves the user experience by providing support tailored to the user's emotional state.

[2000] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2001] Step 1:

[2002] The user enters destination and date / time information.

[2003] Input: The user enters destination and date / time information using the touchscreen display.

[2004] Output: Destination and date / time information is temporarily saved on the terminal.

[2005] Specific operation: The user inputs information such as "Tokyo Station" and "October 5, 2023, 10:00" using the on-screen keyboard or voice input.

[2006] Step 2:

[2007] The user uploads an image of the receipt.

[2008] Input: The user takes a picture of the receipt using the touchscreen display or camera and uploads it.

[2009] Output: The image file uploaded to the device is temporarily saved.

[2010] Specific operation: The user takes a picture of the receipt with the camera and presses the upload button to send the image file to the system.

[2011] Step 3:

[2012] The device sends received text messages and image files to the server.

[2013] Input: Destination, date and time information, and receipt image file saved on the device.

[2014] Output: A text message and an image file are sent to the server.

[2015] Specific operation: The device bundles the data it has stored into packets and sends them to the server over the network.

[2016] Step 4:

[2017] The server analyzes the text data and image files it receives.

[2018] Input: Destination, date and time information, and receipt image file sent to the server.

[2019] Output: Text data such as destination, date and time, and fare.

[2020] Specific operation: The server inputs an image file into the OCR engine and extracts text data from the image. For example, it extracts data such as "Tokyo Station to Shinagawa Station" and "500 yen" from a receipt image.

[2021] Step 5:

[2022] The server compares the extracted text data with the user's input data.

[2023] Input: Text data analyzed by the server and destination and date / time information entered by the user.

[2024] Output: Matching result.

[2025] Specific operation: The server compares the extracted text data with the user's input data to check for a match. For example, it matches "Destination: Tokyo Station" with "OCR result: Tokyo Station".

[2026] Step 6:

[2027] The server calculates the travel expenses.

[2028] Input: Matched destination and date / time information.

[2029] Output: Calculated transportation costs.

[2030] Specific operation: The server calculates the transportation cost from the departure point to the destination based on the matching results. For example, it might calculate "500 yen from Tokyo Station to Shinagawa Station".

[2031] Step 7:

[2032] The server analyzes the user's emotional state.

[2033] Input: User's text messages and image files.

[2034] Output: Analyzed emotion information.

[2035] Specific operation: The server uses an emotion analysis engine to analyze the user's emotions from the text and facial expressions they input. For example, it might determine from the text that "the user is feeling stressed" and recognize from the image that "the face looks tired."

[2036] Step 8:

[2037] The server notifies the user of the matching results, calculation results, and sentiment information.

[2038] Input: Calculated travel expenses, user's emotional state.

[2039] Output: Notification message to the user.

[2040] Specific operation: The server adjusts the notification content based on the analyzed sentiment and sends a message to the user. For example, it might notify the user, "Destination: Tokyo Station, Date and Time: October 5, 2023, 10:00 AM, Transportation Cost: 500 yen. Thank you for your hard work. Your transportation expense claim has been completed."

[2041] In this way, the system operates smoothly through each processing step, resulting in more efficient travel expense claims and an improved user experience.

[2042] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2043] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2044] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2045] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2046] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2047] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2048] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2049] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2050] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2051] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2052] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2053] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2054] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2055] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2056] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2057] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2058] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2059] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2060] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2061] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2062] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[2063] The following is further disclosed regarding the embodiments described above.

[2064] (Claim 1)

[2065] A means of communication for the user to input destination and date / time information,

[2066] A means for users to upload images of receipts,

[2067] A storage means for temporarily storing received text messages and image files,

[2068] A transmission means for sending received text messages and image files to a server,

[2069] An optical character recognition means for analyzing a received image file and extracting text data,

[2070] A matching means for comparing extracted text data with text data entered by the user,

[2071] A means of calculating transportation expenses,

[2072] A notification means for notifying the user of the matching results and calculation results,

[2073] A system that includes this.

[2074] (Claim 2)

[2075] The system according to claim 1, wherein communication is conducted via a chat interface.

[2076] (Claim 3)

[2077] The system according to claim 1, wherein the optical character recognition means extracts text from an image using an external library.

[2078] "Example 1"

[2079] (Claim 1)

[2080] A means of communication for the user to input destination and date / time information,

[2081] A means for users to upload images of receipts,

[2082] A storage means for temporarily storing received text messages and image files,

[2083] A transmission means for sending received text messages and image files to a server,

[2084] A text analysis means that analyzes received text data and extracts destination and date / time information,

[2085] An optical character recognition means for analyzing a received image file and extracting text data,

[2086] A matching means for comparing extracted text data with text data entered by the user,

[2087] A calculation method for calculating transportation expenses based on the verified information,

[2088] A notification means for notifying the user of the matching results and calculation results,

[2089] A system that includes this.

[2090] (Claim 2)

[2091] The system according to claim 1, wherein communication is conducted via a messaging interface.

[2092] (Claim 3)

[2093] The system according to claim 1, wherein the optical character recognition means extracts data from an image using an external library.

[2094] "Application Example 1"

[2095] (Claim 1)

[2096] A means of communication for the user to input destination and date / time information,

[2097] A means for users to upload images of receipts,

[2098] A storage means for temporarily storing received text messages and image files,

[2099] A transmission means for sending received text messages and image files to a server,

[2100] An optical character recognition means for analyzing a received image file and extracting text data,

[2101] A matching means for comparing extracted text data with text data entered by the user,

[2102] A means of calculating transportation costs,

[2103] A notification means for notifying the user of the matching results and calculation results,

[2104] A means of storing transportation cost data in a database,

[2105] A means of confirmation to finalize the shipping cost claim,

[2106] A system that includes this.

[2107] (Claim 2)

[2108] The system according to claim 1, wherein communication is conducted via a chat interface.

[2109] (Claim 3)

[2110] The system according to claim 1, wherein the optical character recognition means extracts text from an image using an external library.

[2111] "Example 2 of combining an emotion engine"

[2112] (Claim 1)

[2113] A means of communication for the user to input destination and date / time information,

[2114] A means for users to upload images of receipts,

[2115] A storage means for temporarily storing received text messages and image files,

[2116] A transmission means for sending received text messages and image files to a server,

[2117] An optical character recognition means for analyzing a received image file and extracting text data,

[2118] A matching means for comparing extracted text data with text data entered by the user,

[2119] A means of calculating transportation expenses,

[2120] A notification means for notifying the user of the matching results and calculation results,

[2121] An emotion recognition means that analyzes emotions from user input data and images,

[2122] A means of adjusting notification content based on recognized emotions,

[2123] A system that includes this.

[2124] (Claim 2)

[2125] The system according to claim 1, wherein communication is conducted via a chat interface.

[2126] (Claim 3)

[2127] The system according to claim 1, wherein the optical character recognition means extracts text from an image using an external library.

[2128] "Application example 2 when combining with an emotional engine"

[2129] (Claim 1)

[2130] A means of communication for the user to input destination and date / time information,

[2131] A means for users to upload images of receipts,

[2132] A storage means for temporarily storing received text messages and image files,

[2133] A transmission means for sending received text messages and image files to a server,

[2134] An optical character recognition means for analyzing a received image file and extracting text data,

[2135] A matching means for comparing extracted text data with text data entered by the user,

[2136] A means of calculating transportation expenses,

[2137] A notification means for notifying the user of the matching results and calculation results,

[2138] A means of analyzing the emotional state of a user and providing appropriate support,

[2139] A system that includes this.

[2140] (Claim 2)

[2141] The system according to claim 1, wherein the means of communication is performed via a chat interface.

[2142] (Claim 3)

[2143] The system according to claim 1, wherein the optical character recognition means extracts text from an image using an external library. [Explanation of symbols]

[2144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of communication for the user to input destination and date / time information, A means for users to upload images of receipts, A storage means for temporarily storing received text messages and image files, A transmission means for sending received text messages and image files to a server, An optical character recognition means for analyzing a received image file and extracting text data, A matching means for comparing extracted text data with text data entered by the user, A means of calculating transportation expenses, A notification means for notifying the user of the matching results and calculation results, A system that includes this.

2. The system according to claim 1, wherein communication is conducted via a chat interface.

3. The system according to claim 1, wherein the optical character recognition means extracts text from an image using an external library.

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