System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting question data related with accounting from a user, a means for analyzing the inputted question data, a means for retrieving a database based on the analyzed data, a means for generating an answer based on information from the database, and a means for transmitting the generated answer to a user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Accounting procedures and processes are extremely complex and often difficult for the average user to understand. Furthermore, traditional methods of consulting with an accountant are expensive and the going rate is unclear, so there is a need for an easily accessible means of consultation. Furthermore, issues such as incompatibility with accountants and biased information exist, making it difficult to provide fast and accurate information. To solve these problems, a system is needed that allows users to easily ask accounting questions and receive appropriate answers and reminders. [Means for solving the problem]

[0005] This invention is a system that includes a means for inputting accounting-related question data from users, a means for analyzing the input question data, a means for searching a database based on the analyzed data, a means for generating answers based on information from the database, and a means for sending the generated answers to a user terminal. Furthermore, by including a means for inputting information data from accountants, a means for adding and updating the input information data to the database, a means for training an AI engine based on the information added to the database, and a means for rewarding accountants who provide information, users can easily consult with accountants, and accountants can receive rewards for providing the latest information. Furthermore, by including a means for setting and sending reminders for accounting procedures to users, oversight of procedures can be prevented. This eliminates accounting concerns and provides a system that is beneficial to both users and accountants.

[0006] "User" refers to a user who asks questions or makes inquiries about accounting.

[0007] An "accountant" refers to a professional who has professional knowledge and provides and updates accounting information.

[0008] "Question data" refers to the digital data entered by users regarding their accounting questions or inquiries.

[0009] "Means" refers to methods or technical devices for achieving a particular purpose.

[0010] "Analysis" refers to the act of understanding and structuring input data using natural language processing and other technologies.

[0011] A "database" refers to a collection of digital information that stores accounting information and past cases and can be searched and referenced.

[0012] "Search" refers to the act of locating information in a database based on specific keywords or criteria.

[0013] "Answer" refers to the appropriate information or solution provided to a user's question.

[0014] "Remind" refers to the act of notifying a user in advance of a specific period or procedure.

[0015] "Information data" refers to digital data on the latest accounting information and legal changes provided by accountants.

[0016] "Update" refers to the act of adding new information to existing data and keeping the content up to date.

[0017] "AI engine" refers to a system or algorithm based on artificial intelligence, and its analytical and answer-generating functions.

[0018] "Remuneration" refers to the compensation or points given to accountants when they provide information or perform update activities.

[0019] The above are definitions of important words related to the present invention. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] A specific embodiment of the present invention will be described below: This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders.

[0042] System Configuration

[0043] 1. User Device

[0044] This is a device used by users to input questions and inquiries about accounting, and also has the function of displaying answers and reminders sent from the server.

[0045] 2. Server

[0046] It is a central system running an AI engine and database that analyzes the query data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants.

[0047] 3. Accountant's Terminal

[0048] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[0049] System Operation

[0050] Flow of processing user questions

[0051] 1. Input

[0052] A user uses a terminal to enter an accounting question, for example, "How do I process expenses?"

[0053] 2. Send

[0054] The user device sends the question data to the server in a format such as JSON.

[0055] 3. Analysis

[0056] The server analyzes the received question data. For example, natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0057] 4. Search

[0058] The server searches for relevant FAQs and case studies from an internal database, which contains a wealth of accounting-related information.

[0059] 5. Answer generation

[0060] The server generates the best answer for the user based on the search results, and the answer is provided in an easy-to-understand format using natural language generation (NLG).

[0061] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0062] 6. Transmission and Display

[0063] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0064] Flow of information updates from accountants

[0065] 1. Enter information

[0066] Accountants input and submit information about new accounting information and legal changes from their terminals. For example, they input "Important changes regarding the 2023 tax reform."

[0067] 2. Send

[0068] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0069] 3. Data Update

[0070] The server adds and updates the received information data to its internal database, so that the database always contains the latest information.

[0071] 4. Learning

[0072] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0073] 5. Reward Sending

[0074] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0075] This system allows users to quickly resolve their accounting concerns, while accountants can provide the latest information and receive compensation for it. This creates a win-win situation for both parties. In addition, by reminding users of important procedures, it prevents oversights and provides an environment where users can carry out accounting work with peace of mind.

[0076] The processing flow will be explained below.

[0077] Question processing flow

[0078] Step 1: User enters question

[0079] The user enters a billing question into their device.

[0080] For example: "How do you handle expenses?"

[0081] Step 2: Submit your question data

[0082] The user terminal transmits the input question data to the server.

[0083] The data is sent to the server using a format such as JSON.

[0084] Step 3: Data analysis

[0085] The server analyzes the received query data.

[0086] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0087] Step 4: Database Search

[0088] The server searches its internal database based on the analysis results.

[0089] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[0090] Step 5: Answer Generation

[0091] The server generates the best answer based on the information obtained from the database.

[0092] Use natural language generation (NLG) to create answers in a user-friendly format.

[0093] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0094] Step 6: Submit your response

[0095] The server transmits the generated answer to the user terminal.

[0096] The response is sent to the user's device using JSON format or similar.

[0097] Step 7: View your answers

[0098] The user terminal displays the received answer to the user.

[0099] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[0100] Information update flow

[0101] Step 1: Enter your information

[0102] Accountants enter new accounting information and legal changes on their own devices.

[0103] Example: Enter "Important changes to the 2023 tax reform."

[0104] Step 2: Send information data

[0105] The accountant terminal transmits the input information data to the server.

[0106] The data is sent to the server using a format such as JSON.

[0107] Step 3: Save and update information

[0108] The server stores and updates the information received from the accountant in a database.

[0109] Verify the accuracy of the information and update the relevant entries in our database.

[0110] Step 4: Training the AI ​​engine

[0111] The server updates and trains the AI ​​engine based on new information.

[0112] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[0113] Step 5: Offer Rewards

[0114] The server rewards the accountant who adds the information.

[0115] Rewards are awarded as points within the system and reflected in the accountant's account.

[0116] Example 1

[0117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] Previously, when users had questions about accounting, it was difficult to obtain the appropriate information quickly and accurately. Furthermore, when accountants shared the latest information, the information was not properly reflected, resulting in a decline in the quality of service to users. This created a need for an efficient and reliable system for both users and accountants.

[0119] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0120] In this invention, the server includes means for inputting transaction-related question data from users, means for analyzing the question data using natural language processing technology, means for searching a database based on the analyzed data, means for generating answers using natural language generation technology, and means for sending the generated answers to the user terminal, allowing users to quickly and accurately obtain transaction-related information.

[0121] A "user terminal" is an electronic device used by a user that has the function of inputting questions or inquiries about accounting and displaying responses or reminders from the server.

[0122] The "server" is the central system where the AI ​​engine and database run, and is the device that analyzes question data received from users, searches the database, generates answers, and updates information provided by accountants.

[0123] An "accountant terminal" is an electronic device used by accountants that has the function of inputting and transmitting data related to new accounting information and legal amendments, and receiving remuneration information from a server.

[0124] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to extract the context and keywords of user inquiries.

[0125] "Natural language generation technology" is a technology in which a computer generates answers in natural, human-like language based on analyzed data.

[0126] A "database" is a system for searching, storing, and managing information, and it stores a wealth of accounting-related information.

[0127] The "search means" is a function that allows the server to search for related information from its internal database, and is a means for retrieving necessary information based on received query data.

[0128] The "answer generation means" is a function for creating an answer in a form that is easy for the user to understand, based on information obtained from the database.

[0129] "Transmission means" is a function for sending the generated answer to the user terminal, and refers to the technology for transmitting information from the server to the user terminal.

[0130] An "AI engine" is an engine that uses machine learning models to analyze and learn from data and use it for future searches and answer generation.

[0131] The "rewarding means" is a function for awarding rewards such as points that can be used within the system to accountants who provide information.

[0132] "Reminder means" is a function that notifies and reminds users of important accounting procedures.

[0133] This invention is a system that allows users to easily input questions about accounting and receive appropriate answers and procedural reminders. A specific embodiment of this system is described below.

[0134] System Configuration

[0135] 1. User Device

[0136] A device used by a user. Examples include smartphones and PCs. Through these devices, users can input questions and inquiries about accounting and view answers and reminders sent from the server.

[0137] 2. Server

[0138] This is the central system where the AI ​​engine and database run. It is responsible for analyzing the query data received from users, searching the appropriate database, generating answers, and updating the information provided by accountants. Specifically, it uses the following technologies:

[0139] Natural Language Processing (NLP) libraries: Python's spaCy and NLTK

[0140] Database management systems: MySQL, PostgreSQL, MongoDB, Elasticsearch

[0141] Natural Language Generation (NLG) engines: Generative AI models such as GPT-3

[0142] Machine learning frameworks: TensorFlow and PyTorch

[0143] 3. Accountant's Terminal

[0144] A device where accountants input and transmit data on new accounting information and legal changes. It also has the function of receiving compensation information from a server. Examples include personal computers and specialized tablet terminals.

[0145] System Operation

[0146] Handling user questions

[0147] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." The user's device then sends the question data in JSON format to the server.

[0148] The server analyzes the received question data using natural language processing technology to extract important keywords such as "expenses" and "processing methods." The server then searches its internal database for relevant FAQs and case studies. Based on the search results, the server uses a generative AI model to generate the optimal answer.

[0149] The generated answer is sent to the user's terminal, where the user can check the answer.

[0150] A user asks: "How do I process expenses?"

[0151] The system answers: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0152] Update from your accountant

[0153] Accountants input new accounting information and information about legal amendments into their terminals and send it to the server. The server receives the information data and adds or updates it to the database.

[0154] The server's AI engine learns from new information and uses it for future searches and answer generation. Accountants who provide information are rewarded with points that can be used within the system. These points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0155] Reminder function

[0156] The system also has a function that reminds users of important accounting procedures. Reminder notifications are sent to users' devices, preventing them from missing procedures and providing an environment where they can carry out accounting work with peace of mind.

[0157] As described above, this system provides highly convenient functions for both users and accountants, enabling the rapid acquisition and provision of accurate accounting information.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] The user types an accounting question into the terminal. The user uses a smartphone or computer to type in a specific question. For example, they might type, "Please tell me how to process expenses." This input is captured by the terminal and prepared for transmission to the server in the next step.

[0161] Step 2:

[0162] The device sends the user's question data to the server. The user's device converts the entered question data into JSON format and sends it to the server via an HTTP POST request. This data includes the user's question text.

[0163] Step 3:

[0164] The server analyzes the received question data using natural language processing technology. The server uses Python libraries such as spaCy and NLTK to extract important keywords from the question. For example, keywords such as "expenses" and "processing method" are extracted. The input is the question text, and the output is a list of keywords.

[0165] Step 4:

[0166] The server searches the database using the extracted keywords. The server uses a database management system such as MySQL or PostgreSQL to search for accounting information related to the keywords. For example, FAQs and case studies related to "expenses" are obtained as search results. The input is a list of keywords, and the output is a list of search results.

[0167] Step 5:

[0168] The server generates an answer based on the search results. The server uses a generative AI model (e.g., GPT-3) to create an answer in natural language that is easy for humans to understand. For example, an answer such as "When it comes to expense processing methods, it is important to organize receipts, create expense reports, and categorize expenses" is generated. The input is a list of search results, and the output is the generated answer.

[0169] Step 6:

[0170] The server sends the generated answer to the user terminal. The server converts the generated answer into JSON format and sends it to the user terminal as an HTTP response. The input is the generated answer, and the output is the data sent to the user terminal.

[0171] Step 7:

[0172] The user's device displays the received answer on the screen. The user's device parses the received JSON data and converts it into a format for display. The user can check the answer generated on the device. The input is the answer data from the server, and the output is the answer displayed on the screen.

[0173] This system allows users to quickly and accurately obtain accounting information, and also receives input and updates from accountants, ensuring that the latest information is always provided.

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] Conventional accounting question-answering systems required a lot of effort for users to ask questions about accounting, and there were problems with the accuracy and timing of answers. In particular, they lacked a function to provide regular reminders to prevent forgetting accounting procedures, making them inconvenient for users. Furthermore, there was no system for providing real-time accounting consultations, which placed a heavy burden on users. Furthermore, the compensation system for accountants was inadequate, resulting in issues with delayed information updates.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes means for inputting accounting question data from users, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating answers based on information from the database, means for transmitting the generated answers to the user terminal, means for reminding users of accounting information and procedures, and means for using a virtual currency payment platform that enables users to receive accounting consultations in real time. This allows users to quickly and efficiently obtain answers to their accounting questions and ensure that important accounting procedures are not forgotten. Furthermore, the system automates the allocation of reward points to accountants, and it is expected that the latest information will always be reflected in the database.

[0179] The "means for inputting accounting inquiry data from users" is an interface that allows users to electronically input questions or inquiries about accounting.

[0180] The "means for analyzing input question data" refers to a means for using natural language processing techniques to understand the input question and extract relevant information.

[0181] The "means for searching a database based on the analyzed data" refers to a means for searching an appropriate database based on the analyzed question to obtain related information.

[0182] "Means for generating answers based on information from a database" refers to means for generating specific answers for users using information obtained through a search.

[0183] The "means for transmitting the generated answer to the user terminal" refers to a communication means for transmitting the generated answer to the user terminal and displaying it.

[0184] "Means of reminding users about accounting information and procedures" refers to means of informing users of important accounting procedures and deadlines so that they do not forget to carry them out.

[0185] "Means of using a virtual currency payment platform that enables users to receive accounting consultations in real time" refers to means of linking with an electronic payment system that enables users to receive accounting consultations in real time.

[0186] The "means for inputting information data from accountants" is an interface that accountants use to input new accounting-related information and legal amendment information into the system.

[0187] "Means for adding and updating input information data to a database" refers to a means for saving input accounting information in a database and updating existing information to the latest version.

[0188] "Means for training AI based on information added to the database" refers to a means for training an AI model using the latest accounting information added to the database to improve the accuracy of answers.

[0189] The "means for providing rewards to accountants who provide information" refers to a means for providing rewards such as points to accountants who provide information to the system.

[0190] A specific embodiment of the present invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and reminders. A distinctive feature of this invention is that it incorporates the use of a virtual currency payment platform.

[0191] System configuration

[0192] 1. User Device

[0193] A user terminal refers to a device such as a smartphone or tablet. Through this terminal, the user inputs accounting-related question data. For example, they can type "Please tell me how to process expenses" into a text box. The input information is sent to the server in JSON format or similar.

[0194] 2. Server

[0195] The server is a central system where the AI ​​engine and database run. It analyzes the question data sent by the user and searches for relevant database information. Based on the search results, a generative AI model (e.g., OpenAI's GPT-3) is used to generate an answer. The generated answer is sent to the user's device in an appropriate format. The server also manages accounting procedure reminders and periodically sends notifications to the user.

[0196] 3. Accountant's Terminal

[0197] The accountant terminal is a device that accountants use to input and send new accounting information and legal amendments. The information input by accountants through the terminal is sent to the server, where it is added to and updated in the database. The server uses this information to train the AI ​​engine, which is then used for subsequent searches and answer generation. Accountants are also awarded reward points, which can be viewed through the accountant terminal.

[0198] Processing flow

[0199] Handling user questions

[0200] Users input and send accounting-related questions from their device. The server analyzes the received question data and performs natural language processing using a generative AI model to extract keywords and themes and search the appropriate database. Based on the search results, the server generates an answer and sends it to the user's device.

[0201] Update from your accountant

[0202] Accountants input new accounting information and information about legal changes into their terminals and send it to the server. The server adds and updates the received data to the database, and the AI ​​engine learns the new information. This improves the accuracy of answers and provides more useful information to users.

[0203] Reminder function

[0204] The server has the ability to remind users of important accounting procedures, automatically sending notifications when certain deadlines are approaching, urging users not to forget to complete the procedures.

[0205] For example, a user may enter the question "How do you handle expenses?" This question is sent to the server through an API, and the AI ​​model generates an answer using the following prompt: "User asks: 'How do you handle expenses?' Please generate an answer."

[0206] As described above, this invention is a system that allows users to quickly and efficiently consult with their accountants and receive appropriate answers and reminders. This significantly improves user convenience and also provides a mechanism for providing appropriate compensation to accountants.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] The user's terminal inputs accounting question data from the user. For example, the user enters a question such as "Please tell me how to process expenses" into a text box, and this data is sent to the server in JSON format.

[0210] Step 2:

[0211] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods" from the question, thereby clarifying the theme and focus of the question.

[0212] Step 3:

[0213] The server searches the database based on the analyzed data. Based on the extracted keywords, the server searches its internal accounting database to retrieve relevant FAQs and past cases. At this stage, the most appropriate information is collected to answer the user's question.

[0214] Step 4:

[0215] The server generates answers based on information from the database. The server uses a generative AI model (such as OpenAI's GPT-3) to perform natural language generation (NLG) based on the collected data and creates answers in a format that is easy for the user to understand.

[0216] Step 5:

[0217] The server sends the generated answer to the user's device in JSON format, allowing the user to view the answer to their question on their device.

[0218] Step 6:

[0219] The server will remind users of accounting information and procedures. Based on the user's registered information, the server will automatically generate reminder notifications when important accounting procedures or deadlines are approaching and send them to the user's device. These notifications will help users remember to complete the procedures before the deadline.

[0220] Step 7:

[0221] The accountant's terminal inputs and sends new accounting information and legal amendment information. The accountant inputs new information from his / her terminal and sends it to the server in JSON format.

[0222] Step 8:

[0223] The server adds and updates the entered information data to the database. The server saves the new information received from the accountant in the database and updates the existing information.

[0224] Step 9:

[0225] The server trains the AI ​​engine based on the information added to the database, and uses the new information to train the generative AI model, improving the accuracy of future question answers.

[0226] Step 10:

[0227] The server rewards the accountant who provided the information. The server calculates reward points based on the database and awards points to the accountant according to the information provided. The accountant can check their reward through the terminal.

[0228] This process step allows users to get quick and accurate accounting answers, ensures important procedures are not forgotten, and ensures that accountants are compensated accordingly, ensuring that information is not overdue.

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. Furthermore, it has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[0231] System Configuration

[0232] 1. User Device

[0233] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, tone of voice, etc.).

[0234] 2. Server

[0235] It is a central system running an AI engine, an emotion engine, and a database, which analyzes the question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes the user's emotions and adjusts the behavior of the entire system accordingly.

[0236] 3. Accountant's Terminal

[0237] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[0238] System Operation

[0239] Flow of processing user questions

[0240] 1. Input

[0241] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[0242] 2. Send

[0243] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[0244] 3. Analysis

[0245] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[0246] 4. Search

[0247] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[0248] 5. Answer generation

[0249] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[0250] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0251] 6. Transmission and Display

[0252] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0253] Information update flow

[0254] 1. Enter information

[0255] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[0256] 2. Send

[0257] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0258] 3. Data Update

[0259] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[0260] 4. Learning

[0261] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0262] 5. Providing rewards

[0263] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0264] Emotion Recognition Flow

[0265] 1. Entering emotion data

[0266] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0267] 2. Emotion analysis

[0268] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[0269] 3. Adjusting responses and reminders

[0270] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[0271] In this way, the system not only helps users solve their accounting problems quickly, but also provides more personalized responses through emotion recognition, improving user satisfaction and encouraging accountants to provide up-to-date information and get paid for it.

[0272] The processing flow will be explained below.

[0273] Question processing flow using emotion engine

[0274] Step 1: User enters question

[0275] The user enters a billing question into their device.

[0276] For example: "How do you handle expenses?"

[0277] Step 2: Submit your question data

[0278] The user terminal transmits the input question data to the server.

[0279] The data is sent to the server using a format such as JSON.

[0280] Step 3: Sending emotion data

[0281] The user terminal transmits emotion recognition data such as voice input and facial expression data to the server.

[0282] Emotion data is also sent to the server in JSON format.

[0283] Step 4: Analyze the Question Data

[0284] The server analyzes the received query data.

[0285] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0286] Step 5: Analyze the sentiment data

[0287] The server analyzes the received emotion data using an emotion engine.

[0288] It uses voice tone analysis and facial expression analysis to identify the user's emotional state (e.g., joy, stress, tension, etc.).

[0289] Step 6: Database Search

[0290] The server searches its internal database based on the analysis results and emotional state.

[0291] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[0292] Step 7: Answer Generation

[0293] The server generates the best answer based on the information obtained from the database.

[0294] Use natural language generation (NLG) to create answers in a user-friendly format.

[0295] The tone and content of the response is adjusted depending on the user's emotional state.

[0296] For example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0297] Step 8: Submit your response

[0298] The server transmits the generated answer to the user terminal.

[0299] The response is sent to the user's device using JSON format or similar.

[0300] Step 9: View your answers

[0301] The user terminal displays the received answer to the user.

[0302] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[0303] Information update flow

[0304] Step 1: Enter your information

[0305] Accountants input new accounting information and information about legal changes into their terminals.

[0306] Example: Enter "Important changes to the 2023 tax reform."

[0307] Step 2: Send information data

[0308] The accountant terminal transmits the input information data to the server.

[0309] The data is sent to the server using a format such as JSON.

[0310] Step 3: Save and update information

[0311] The server stores and updates the information received from the accountant in a database.

[0312] Verify the accuracy of the information and update the relevant entries in our database.

[0313] Step 4: Training the AI ​​engine

[0314] The server updates and trains the AI ​​engine based on new information.

[0315] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[0316] Step 5: Offer Rewards

[0317] The server rewards the accountant who adds the information.

[0318] Rewards are awarded as points within the system and reflected in the accountant's account.

[0319] Adjusting emotion recognition

[0320] Step 1: Input emotion data

[0321] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0322] Step 2: Sentiment Analysis

[0323] The server's emotion engine analyzes the received emotion data and identifies the user's emotional state.

[0324] For example, if a user is feeling stressed, their emotional state is identified.

[0325] Step 3: Adjusting responses and reminders

[0326] The server adjusts the tone and content of the responses and reminders it generates based on the results of sentiment analysis.

[0327] For example, users who are feeling stressed will receive answers and reminders in a more considerate tone.

[0328] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[0329] Example 2

[0330] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0331] Conventional accounting consultation systems have the problem that they provide uniform answers to users' accounting questions and are unable to provide personalized responses based on the user's emotional state. Additionally, the latest accounting information and legal amendments provided by accountants are not updated immediately, making it difficult for users to obtain the latest and accurate information.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0333] In this invention, the server includes a means for inputting accounting question data and emotion recognition data from a user, a means for analyzing the input data and extracting important keywords and the user's emotional state, a means for searching a database based on the analyzed data and emotional state, a means for generating an answer based on the user's emotional state based on information obtained from the database, and a means for transmitting the generated answer to a user terminal and displaying it. This allows users to receive prompt and appropriate answers to their accounting questions and also enables personalized responses based on the user's emotional state. In addition, the latest accounting information and legal amendments provided by accountants are quickly updated in the system, ensuring that the latest and most accurate information is always provided.

[0334] "Accounting question data from users" refers to specific accounting-related questions that users input into the system.

[0335] "Emotion recognition data" refers to data that represents a user's emotional state, obtained from the user's voice, facial expressions, etc.

[0336] "Means for analyzing" refers to software and hardware functions for identifying important keywords and the user's emotional state based on input data.

[0337] "Means for searching the database" refers to the algorithms and associated hardware for searching the internal database for appropriate information based on the analyzed data.

[0338] "Answer generation means" refers to software and hardware capabilities for generating answers to user questions based on information retrieved from a database and using natural language generation techniques.

[0339] "User Device" means the device (e.g., smartphone, tablet, PC) used by a User to enter accounting questions and receive answers.

[0340] "Information data from accountants" refers to the latest accounting information and data on legal changes provided to the system by accountants.

[0341] "Means for adding and updating the database" refers to the software and hardware functions for storing information data entered by accountants in the internal database and updating existing data.

[0342] "Means for training the AI ​​engine" refers to the algorithms and related hardware that allow the AI ​​to learn from the information added to the database and use it in future question-answering processes.

[0343] "Means for awarding rewards" refers to the software and hardware functions for adding reward points within the system to accountants who provide information, and for managing and displaying these points.

[0344] "Means for setting and sending reminders" refers to software and hardware functions that notify users of important accounting deadlines and send such reminder information to the user's device.

[0345] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. It also has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[0346] System Configuration

[0347] The system consists of the following main components:

[0348] User Device

[0349] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, voice tone, etc.). Specifically, smartphones, tablets, PCs, etc. are used as user devices. An accounting consultation application and emotion recognition software such as Emotion SDK are installed on the user device.

[0350] server

[0351] This is a central system running an AI engine, an emotion engine, and a database. It analyzes question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes user emotions and adjusts the behavior of the entire system accordingly. The server runs on a virtual machine in the cloud (e.g., AWS EC2). The software used includes a natural language processing engine (e.g., GPT-4), an emotion recognition engine (e.g., EmotionAPI), and a database management system (e.g., MySQL).

[0352] Accountant's Terminal

[0353] This is a device used by accountants to input and send data on new accounting information and legal amendments. It also receives compensation information from the server. Accountant terminals are PCs or tablets with a dedicated accounting information input application installed.

[0354] System Operation

[0355] Handling user questions

[0356] 1. Input

[0357] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[0358] 2. Send

[0359] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[0360] 3. Analysis

[0361] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[0362] 4. Search

[0363] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[0364] 5. Answer generation

[0365] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[0366] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0367] 6. Transmission and Display

[0368] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0369] Information update flow

[0370] 1. Enter information

[0371] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[0372] 2. Send

[0373] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0374] 3. Data Update

[0375] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[0376] 4. Learning

[0377] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0378] 5. Providing rewards

[0379] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0380] Emotion Recognition Flow

[0381] 1. Entering emotion data

[0382] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0383] 2. Emotion analysis

[0384] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[0385] 3. Adjusting responses and reminders

[0386] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[0387] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] The flow of this system's program processing

[0390] Step 1: User Input

[0391] Users use the device to input their billing questions, and data such as voice and facial expressions are automatically collected for emotion recognition.

[0392] Input: The user types in a question by voice, such as "Please tell me how to process expenses." The user's facial expression is captured by the camera.

[0393] Output: Question text data and emotion recognition data (voice, facial expression)

[0394] Step 2: Sending data

[0395] The device sends the collected question data and emotion recognition data to the server in a specified format (e.g., JSON).

[0396] Input: Question data and emotion recognition data

[0397] Output: Question data and emotion recognition data in JSON format

[0398] Step 3: Analyze the data

[0399] The server analyzes the received data. A natural language processing engine is used to extract important keywords (e.g., "expenses" and "processing methods") from the question text. An emotion engine analyzes the emotion data to identify the user's emotional state.

[0400] Input: JSON-formatted question data and emotion recognition data

[0401] Output: Extracted keywords and emotional state data

[0402] Step 4: Search the database

[0403] The server searches its internal database based on the analysis results, extracting relevant FAQs, past cases, and legal and regulatory information.

[0404] Input: Extracted keywords and emotional state data

[0405] Output: Dataset of search results (FAQs, past cases, legal and regulatory information)

[0406] Step 5: Generate an answer

[0407] The server uses a natural language generation engine to generate answers based on a dataset of search results, adjusting the tone and content of the answers depending on the user's emotional state.

[0408] Input: Search result dataset, emotional state data

[0409] Output: Generated answer text

[0410] Step 6: Submit and view your responses

[0411] The server generates a response and sends it to the user's device, where the user can view it. The response is displayed in a user-friendly format.

[0412] Input: Generated answer text

[0413] Output: The answer displayed on the user's terminal

[0414] Step 7: Accountant updates information

[0415] Accountants use accountant terminals to input and submit data on new accounting information and legal changes.

[0416] Input: New accounting information and legal change data

[0417] Output: Data update request

[0418] Step 8: Update the internal database

[0419] The server saves and updates the information data received from the accountant in its internal database, ensuring that the information in the database is up to date.

[0420] Input: Data update request

[0421] Output: Updated database

[0422] Step 9: Learning new information

[0423] The server's AI engine learns from the new information and uses it in future question-answering processes.

[0424] Input: Updated database

[0425] Output: Trained AI model

[0426] Step 10: Rewarding

[0427] The server gives reward points to the accountant who provided the information, and the point information is displayed on the accountant's terminal.

[0428] Input: Information provision history

[0429] Output: Reward points awarded

[0430] The above is the specific processing flow of this system. Based on this flow, users can get quick and appropriate answers regarding accounting matters, and accountants can receive the latest information and receive compensation.

[0431] (Application example 2)

[0432] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0433] There is a demand for providing an environment in which users can smoothly answer questions and complete procedures related to checkouts at physical stores. It is also necessary to improve the quality of service by responding flexibly to users' emotions. Conventional systems have responded uniformly without considering users' emotions to meet these needs, which has prevented them from fully achieving user satisfaction.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0435] In this invention, the server includes means for inputting transaction-related question data from a user, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating an answer based on information from the database, means for sending the generated answer to the user terminal, means for recognizing the user's emotion, and means for adjusting the content and tone of the answer based on the recognized emotion. This allows for the provision of an optimal answer based on the user's emotion, enabling smooth and personalized transaction procedures in physical stores.

[0436] "User" means any person or organization that uses this system to ask or seek advice about accounting matters.

[0437] "Question data" refers to data including questions and inquiries about accounting that users enter into the system.

[0438] "Means of analysis" refers to the means for understanding the question data entered by the user and extracting important keywords and information.

[0439] "Database" means a storage device containing accounting information, FAQs, and related legal and regulatory information.

[0440] "Answer generation means" refers to a means for creating an appropriate answer to a user's question based on information obtained from a database.

[0441] "User terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0442] "Means for recognizing emotions" refers to means for identifying a user's emotional state through data such as facial expressions and tone of voice.

[0443] "Means for adjusting the content and tone of responses" refers to means for appropriately changing the content and tone of responses depending on the user's emotional state.

[0444] "Accountants" are professionals who provide specialized accounting information to this system.

[0445] "Information data" refers to data provided by accountants, including information on new accounting information and legal changes.

[0446] An "AI engine" is an artificial intelligence engine that learns from data and generates optimal answers.

[0447] "Means for providing rewards" means means for providing rewards to accountants who provide information to the system.

[0448] "Means for setting and sending reminders" refers to a means for notifying users of deadlines for accounting procedures and important matters that they tend to forget.

[0449] MODE FOR CARRYING OUT THE INVENTION

[0450] The system for realizing this invention mainly includes three main components: a user terminal, a server, and an accountant terminal. Each component functions as follows:

[0451] User Device

[0452] User devices refer to devices such as smartphones, tablets, and PCs. User devices have the following functions:

[0453] 1. Input of question data: Users can input their accounting questions or inquiries.

[0454] 2. Emotion data capture: Emotion recognition data is acquired through the user's facial expressions and voice tone and sent to the server.

[0455] 3. View Answers: View answers and reminders sent from the server.

[0456] server

[0457] The server has the following functions:

[0458] 1. Data analysis: Question data sent by users is analyzed using a natural language processing (NLP) engine to extract important keywords.

[0459] 2. Database search: Based on the analysis results, the internal database is searched to obtain related information.

[0460] 3. Answer generation: Answers are generated based on information retrieved from the database, using a natural language generation (NLG) engine.

[0461] 4. Emotion Recognition: Use an emotion recognition engine to analyze the user's emotional state and adjust the content and tone of the response.

[0462] 5. Accounting information update: Add / update new information provided by accountants to the database.

[0463] 6. AI engine learning: The AI ​​engine learns from the newly added information and uses it for subsequent searches and answer generation.

[0464] Accountant's Terminal

[0465] The accountant terminal is a device that allows accountants to input and submit the latest accounting information and receive compensation.

[0466] 1. Information data entry: Accountants enter information about new accounting information and legal changes.

[0467] 2. Sending information data: Send the entered information data to the server.

[0468] 3. Receiving rewards: Receive reward points in exchange for providing information.

[0469] Example of overall system operation

[0470] As a specific example of the operation of this system, consider the following scenario.

[0471] Scenario 1: User enters accounting question

[0472] 1. A user uses their smartphone to type, "How do I process expenses?"

[0473] 2. At the same time, the user's facial expression data and voice tone are automatically captured and sent to the server.

[0474] 3. The server analyzes the question data and extracts important keywords such as "expenses" and "processing methods."

[0475] 4. The server uses an emotion recognition engine to identify when the user is in a positive emotional state.

[0476] 5. The server searches the database and retrieves the relevant information.

[0477] 6. The server uses its NLG engine to generate a positive response such as, "The key to managing expenses is organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[0478] 7. The server sends the generated answer to the user's terminal, and the user checks the answer on the terminal.

[0479] Sample prompt sentence

[0480] Below are some example prompts to aid in the analysis of the emotion recognition engine and the generation of answers by the NLG engine:

[0481] "Generate instructions for credit card payment methods for when users are nervous."

[0482] "Write a description of how to process expenses for users in a positive emotional state."

[0483] These prompts are then used by a generative AI model to generate answers tailored to specific situations.

[0484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0485] Step 1:

[0486] A user uses a user device such as a smartphone or tablet to input a question about accounting, for example, "Please tell me how to process expenses." At the same time, the user device uses a camera and microphone to capture facial expression data and voice tone, and obtains this data.

[0487] Input: accounting question data, facial expression data, voice tone

[0488] Output: Question data and emotion recognition data

[0489] Step 2:

[0490] The user device sends the acquired question data and emotion recognition data to the server in a standard format such as JSON.

[0491] Input: Question data, emotion recognition data

[0492] Output: JSON formatted data sent

[0493] Step 3:

[0494] The server receives the question data and analyzes it using a natural language processing (NLP) engine. This extracts important keywords and phrases. For example, the keywords "expenses" and "processing method" are extracted.

[0495] Input: Question data

[0496] Output: Extracted keywords

[0497] Step 4:

[0498] The server analyzes the received emotion data using an emotion recognition engine, which identifies the user's emotional state (e.g., positive, negative, nervous, etc.).

[0499] Input: Emotion recognition data

[0500] Output: Identified emotional state

[0501] Step 5:

[0502] The server searches a database based on the extracted keywords and the identified emotional state. The database includes accounting-related FAQs, past cases, and relevant legal information. For example, it searches for information related to "expenses" and "processing methods."

[0503] Input: extracted keywords, identified emotional states

[0504] Output: Retrieved information

[0505] Step 6:

[0506] The server uses a natural language generation (NLG) engine to generate answers based on information obtained from the database. It adjusts the tone and content of the answer depending on the user's emotional state. For example, in a positive emotional state, it generates a positive answer such as, "Important expense management methods include organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[0507] Input: Retrieved information, identified emotional state

[0508] Output: The generated answer

[0509] Step 7:

[0510] The server sends the generated answer to the user terminal, which receives the answer and displays it on the screen, allowing the user to check and understand the provided answer.

[0511] Input: Generated Answer

[0512] Output: Answer sent to user terminal

[0513] Step 8:

[0514] The accountant's terminal inputs new accounting information and information on legal amendments and transmits it to the server. For example, the accountant inputs "the latest tax reform points."

[0515] Input: New Accounting Information

[0516] Output: Transmitted information data

[0517] Step 9:

[0518] The server saves and updates the received information data in a database, so that the database always contains the latest information.

[0519] Input: Information data

[0520] Output: Updated database

[0521] Step 10:

[0522] The server rewards the accountant who provided the information as points in the system, which are displayed on the accountant's terminal.

[0523] Input: Record of information provided

[0524] Output: Reward as points

[0525] In this way, the system provides optimal answers based on the user's emotions and efficiently updates information from accountants.

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

[0527] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

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

[0529] [Second embodiment]

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

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

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

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

[0534] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

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

[0536] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0537] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0540] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0542] A specific embodiment of the present invention will be described below: This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders.

[0543] System Configuration

[0544] 1. User Device

[0545] This is a device used by users to input questions and inquiries about accounting, and also has the function of displaying answers and reminders sent from the server.

[0546] 2. Server

[0547] It is a central system running an AI engine and database that analyzes the query data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants.

[0548] 3. Accountant's Terminal

[0549] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[0550] System Operation

[0551] Flow of processing user questions

[0552] 1. Input

[0553] A user uses a terminal to enter an accounting question, for example, "How do I process expenses?"

[0554] 2. Send

[0555] The user device sends the question data to the server in a format such as JSON.

[0556] 3. Analysis

[0557] The server analyzes the received question data. For example, natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0558] 4. Search

[0559] The server searches for relevant FAQs and case studies from an internal database, which contains a wealth of accounting-related information.

[0560] 5. Answer generation

[0561] The server generates the best answer for the user based on the search results, and the answer is provided in an easy-to-understand format using natural language generation (NLG).

[0562] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0563] 6. Transmission and Display

[0564] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0565] Flow of information updates from accountants

[0566] 1. Enter information

[0567] Accountants input and submit information about new accounting information and legal changes from their terminals. For example, they input "Important changes regarding the 2023 tax reform."

[0568] 2. Send

[0569] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0570] 3. Data Update

[0571] The server adds and updates the received information data to its internal database, so that the database always contains the latest information.

[0572] 4. Learning

[0573] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0574] 5. Reward Sending

[0575] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0576] This system allows users to quickly resolve their accounting concerns, while accountants can provide the latest information and receive compensation for it. This creates a win-win situation for both parties. In addition, by reminding users of important procedures, it prevents oversights and provides an environment where users can carry out accounting work with peace of mind.

[0577] The processing flow will be explained below.

[0578] Question processing flow

[0579] Step 1: User enters question

[0580] The user enters a billing question into their device.

[0581] For example: "How do you handle expenses?"

[0582] Step 2: Submit your question data

[0583] The user terminal transmits the input question data to the server.

[0584] The data is sent to the server using a format such as JSON.

[0585] Step 3: Data analysis

[0586] The server analyzes the received query data.

[0587] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0588] Step 4: Database Search

[0589] The server searches its internal database based on the analysis results.

[0590] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[0591] Step 5: Answer Generation

[0592] The server generates the best answer based on the information obtained from the database.

[0593] Use natural language generation (NLG) to create answers in a user-friendly format.

[0594] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0595] Step 6: Submit your response

[0596] The server transmits the generated answer to the user terminal.

[0597] The response is sent to the user's device using JSON format or similar.

[0598] Step 7: View your answers

[0599] The user terminal displays the received answer to the user.

[0600] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[0601] Information update flow

[0602] Step 1: Enter your information

[0603] Accountants enter new accounting information and legal changes on their own devices.

[0604] Example: Enter "Important changes to the 2023 tax reform."

[0605] Step 2: Send information data

[0606] The accountant terminal transmits the input information data to the server.

[0607] The data is sent to the server using a format such as JSON.

[0608] Step 3: Save and update information

[0609] The server stores and updates the information received from the accountant in a database.

[0610] Verify the accuracy of the information and update the relevant entries in our database.

[0611] Step 4: Training the AI ​​engine

[0612] The server updates and trains the AI ​​engine based on new information.

[0613] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[0614] Step 5: Offer Rewards

[0615] The server rewards the accountant who adds the information.

[0616] Rewards are awarded as points within the system and reflected in the accountant's account.

[0617] Example 1

[0618] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] Previously, when users had questions about accounting, it was difficult to obtain the appropriate information quickly and accurately. Furthermore, when accountants shared the latest information, the information was not properly reflected, resulting in a decline in the quality of service to users. This created a need for an efficient and reliable system for both users and accountants.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0621] In this invention, the server includes means for inputting transaction-related question data from users, means for analyzing the question data using natural language processing technology, means for searching a database based on the analyzed data, means for generating answers using natural language generation technology, and means for sending the generated answers to the user terminal, allowing users to quickly and accurately obtain transaction-related information.

[0622] A "user terminal" is an electronic device used by a user that has the function of inputting questions or inquiries about accounting and displaying responses or reminders from the server.

[0623] The "server" is the central system where the AI ​​engine and database run, and is the device that analyzes question data received from users, searches the database, generates answers, and updates information provided by accountants.

[0624] An "accountant terminal" is an electronic device used by accountants that has the function of inputting and transmitting data related to new accounting information and legal amendments, and receiving remuneration information from a server.

[0625] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to extract the context and keywords of user inquiries.

[0626] "Natural language generation technology" is a technology in which a computer generates answers in natural, human-like language based on analyzed data.

[0627] A "database" is a system for searching, storing, and managing information, and it stores a wealth of accounting-related information.

[0628] The "search means" is a function that allows the server to search for related information from its internal database, and is a means for retrieving necessary information based on received query data.

[0629] The "answer generation means" is a function for creating an answer in a form that is easy for the user to understand, based on information obtained from the database.

[0630] "Transmission means" is a function for sending the generated answer to the user terminal, and refers to the technology for transmitting information from the server to the user terminal.

[0631] An "AI engine" is an engine that uses machine learning models to analyze and learn from data and use it for future searches and answer generation.

[0632] The "rewarding means" is a function for awarding rewards such as points that can be used within the system to accountants who provide information.

[0633] "Reminder means" is a function that notifies and reminds users of important accounting procedures.

[0634] This invention is a system that allows users to easily input questions about accounting and receive appropriate answers and procedural reminders. A specific embodiment of this system is described below.

[0635] System Configuration

[0636] 1. User Device

[0637] A device used by a user. Examples include smartphones and PCs. Through these devices, users can input questions and inquiries about accounting and view answers and reminders sent from the server.

[0638] 2. Server

[0639] This is the central system where the AI ​​engine and database run. It is responsible for analyzing the query data received from users, searching the appropriate database, generating answers, and updating the information provided by accountants. Specifically, it uses the following technologies:

[0640] Natural Language Processing (NLP) libraries: Python's spaCy and NLTK

[0641] Database management systems: MySQL, PostgreSQL, MongoDB, Elasticsearch

[0642] Natural Language Generation (NLG) engines: Generative AI models such as GPT-3

[0643] Machine learning frameworks: TensorFlow and PyTorch

[0644] 3. Accountant's Terminal

[0645] A device where accountants input and transmit data on new accounting information and legal changes. It also has the function of receiving compensation information from a server. Examples include personal computers and specialized tablet terminals.

[0646] System Operation

[0647] Handling user questions

[0648] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." The user's device then sends the question data in JSON format to the server.

[0649] The server analyzes the received question data using natural language processing technology to extract important keywords such as "expenses" and "processing methods." The server then searches its internal database for relevant FAQs and case studies. Based on the search results, the server uses a generative AI model to generate the optimal answer.

[0650] The generated answer is sent to the user's terminal, where the user can check the answer.

[0651] A user asks: "How do I process expenses?"

[0652] The system answers: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[0653] Update from your accountant

[0654] Accountants input new accounting information and information about legal amendments into their terminals and send it to the server. The server receives the information data and adds or updates it to the database.

[0655] The server's AI engine learns from new information and uses it for future searches and answer generation. Accountants who provide information are rewarded with points that can be used within the system. These points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0656] Reminder function

[0657] The system also has a function that reminds users of important accounting procedures. Reminder notifications are sent to users' devices, preventing them from missing procedures and providing an environment where they can carry out accounting work with peace of mind.

[0658] As described above, this system provides highly convenient functions for both users and accountants, enabling the rapid acquisition and provision of accurate accounting information.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] The user types an accounting question into the terminal. The user uses a smartphone or computer to type in a specific question. For example, they might type, "Please tell me how to process expenses." This input is captured by the terminal and prepared for transmission to the server in the next step.

[0662] Step 2:

[0663] The device sends the user's question data to the server. The user's device converts the entered question data into JSON format and sends it to the server via an HTTP POST request. This data includes the user's question text.

[0664] Step 3:

[0665] The server analyzes the received question data using natural language processing technology. The server uses Python libraries such as spaCy and NLTK to extract important keywords from the question. For example, keywords such as "expenses" and "processing method" are extracted. The input is the question text, and the output is a list of keywords.

[0666] Step 4:

[0667] The server searches the database using the extracted keywords. The server uses a database management system such as MySQL or PostgreSQL to search for accounting information related to the keywords. For example, FAQs and case studies related to "expenses" are obtained as search results. The input is a list of keywords, and the output is a list of search results.

[0668] Step 5:

[0669] The server generates an answer based on the search results. The server uses a generative AI model (e.g., GPT-3) to create an answer in natural language that is easy for humans to understand. For example, an answer such as "When it comes to expense processing methods, it is important to organize receipts, create expense reports, and categorize expenses" is generated. The input is a list of search results, and the output is the generated answer.

[0670] Step 6:

[0671] The server sends the generated answer to the user terminal. The server converts the generated answer into JSON format and sends it to the user terminal as an HTTP response. The input is the generated answer, and the output is the data sent to the user terminal.

[0672] Step 7:

[0673] The user's device displays the received answer on the screen. The user's device parses the received JSON data and converts it into a format for display. The user can check the answer generated on the device. The input is the answer data from the server, and the output is the answer displayed on the screen.

[0674] This system allows users to quickly and accurately obtain accounting information, and also receives input and updates from accountants, ensuring that the latest information is always provided.

[0675] (Application example 1)

[0676] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0677] Conventional accounting question-answering systems required a lot of effort for users to ask questions about accounting, and there were problems with the accuracy and timing of answers. In particular, they lacked a function to provide regular reminders to prevent forgetting accounting procedures, making them inconvenient for users. Furthermore, there was no system for providing real-time accounting consultations, which placed a heavy burden on users. Furthermore, the compensation system for accountants was inadequate, resulting in issues with delayed information updates.

[0678] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0679] In this invention, the server includes means for inputting accounting question data from users, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating answers based on information from the database, means for transmitting the generated answers to the user terminal, means for reminding users of accounting information and procedures, and means for using a virtual currency payment platform that enables users to receive accounting consultations in real time. This allows users to quickly and efficiently obtain answers to their accounting questions and ensure that important accounting procedures are not forgotten. Furthermore, the system automates the allocation of reward points to accountants, and it is expected that the latest information will always be reflected in the database.

[0680] The "means for inputting accounting inquiry data from users" is an interface that allows users to electronically input questions or inquiries about accounting.

[0681] The "means for analyzing input question data" refers to a means for using natural language processing techniques to understand the input question and extract relevant information.

[0682] The "means for searching a database based on the analyzed data" refers to a means for searching an appropriate database based on the analyzed question to obtain related information.

[0683] "Means for generating answers based on information from a database" refers to means for generating specific answers for users using information obtained through a search.

[0684] The "means for transmitting the generated answer to the user terminal" refers to a communication means for transmitting the generated answer to the user terminal and displaying it.

[0685] "Means of reminding users about accounting information and procedures" refers to means of informing users of important accounting procedures and deadlines so that they do not forget to carry them out.

[0686] "Means of using a virtual currency payment platform that enables users to receive accounting consultations in real time" refers to means of linking with an electronic payment system that enables users to receive accounting consultations in real time.

[0687] The "means for inputting information data from accountants" is an interface that accountants use to input new accounting-related information and legal amendment information into the system.

[0688] "Means for adding and updating input information data to a database" refers to a means for saving input accounting information in a database and updating existing information to the latest version.

[0689] "Means for training AI based on information added to the database" refers to a means for training an AI model using the latest accounting information added to the database to improve the accuracy of answers.

[0690] The "means for providing rewards to accountants who provide information" refers to a means for providing rewards such as points to accountants who provide information to the system.

[0691] A specific embodiment of the present invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and reminders. A distinctive feature of this invention is that it incorporates the use of a virtual currency payment platform.

[0692] System configuration

[0693] 1. User Device

[0694] A user terminal refers to a device such as a smartphone or tablet. Through this terminal, the user inputs accounting-related question data. For example, they can type "Please tell me how to process expenses" into a text box. The input information is sent to the server in JSON format or similar.

[0695] 2. Server

[0696] The server is a central system where the AI ​​engine and database run. It analyzes the question data sent by the user and searches for relevant database information. Based on the search results, a generative AI model (e.g., OpenAI's GPT-3) is used to generate an answer. The generated answer is sent to the user's device in an appropriate format. The server also manages accounting procedure reminders and periodically sends notifications to the user.

[0697] 3. Accountant's Terminal

[0698] The accountant terminal is a device that accountants use to input and send new accounting information and legal amendments. The information input by accountants through the terminal is sent to the server, where it is added to and updated in the database. The server uses this information to train the AI ​​engine, which is then used for subsequent searches and answer generation. Accountants are also awarded reward points, which can be viewed through the accountant terminal.

[0699] Processing flow

[0700] Handling user questions

[0701] Users input and send accounting-related questions from their device. The server analyzes the received question data and performs natural language processing using a generative AI model to extract keywords and themes and search the appropriate database. Based on the search results, the server generates an answer and sends it to the user's device.

[0702] Update from your accountant

[0703] Accountants input new accounting information and information about legal changes into their terminals and send it to the server. The server adds and updates the received data to the database, and the AI ​​engine learns the new information. This improves the accuracy of answers and provides more useful information to users.

[0704] Reminder function

[0705] The server has the ability to remind users of important accounting procedures, automatically sending notifications when certain deadlines are approaching, urging users not to forget to complete the procedures.

[0706] For example, a user may enter the question "How do you handle expenses?" This question is sent to the server through an API, and the AI ​​model generates an answer using the following prompt: "User asks: 'How do you handle expenses?' Please generate an answer."

[0707] As described above, this invention is a system that allows users to quickly and efficiently consult with their accountants and receive appropriate answers and reminders. This significantly improves user convenience and also provides a mechanism for providing appropriate compensation to accountants.

[0708] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0709] Step 1:

[0710] The user's terminal inputs accounting question data from the user. For example, the user enters a question such as "Please tell me how to process expenses" into a text box, and this data is sent to the server in JSON format.

[0711] Step 2:

[0712] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods" from the question, thereby clarifying the theme and focus of the question.

[0713] Step 3:

[0714] The server searches the database based on the analyzed data. Based on the extracted keywords, the server searches its internal accounting database to retrieve relevant FAQs and past cases. At this stage, the most appropriate information is collected to answer the user's question.

[0715] Step 4:

[0716] The server generates answers based on information from the database. The server uses a generative AI model (such as OpenAI's GPT-3) to perform natural language generation (NLG) based on the collected data and creates answers in a format that is easy for the user to understand.

[0717] Step 5:

[0718] The server sends the generated answer to the user's device in JSON format, allowing the user to view the answer to their question on their device.

[0719] Step 6:

[0720] The server will remind users of accounting information and procedures. Based on the user's registered information, the server will automatically generate reminder notifications when important accounting procedures or deadlines are approaching and send them to the user's device. These notifications will help users remember to complete the procedures before the deadline.

[0721] Step 7:

[0722] The accountant's terminal inputs and sends new accounting information and legal amendment information. The accountant inputs new information from his / her terminal and sends it to the server in JSON format.

[0723] Step 8:

[0724] The server adds and updates the entered information data to the database. The server saves the new information received from the accountant in the database and updates the existing information.

[0725] Step 9:

[0726] The server trains the AI ​​engine based on the information added to the database, and uses the new information to train the generative AI model, improving the accuracy of future question answers.

[0727] Step 10:

[0728] The server rewards the accountant who provided the information. The server calculates reward points based on the database and awards points to the accountant according to the information provided. The accountant can check their reward through the terminal.

[0729] This process step allows users to get quick and accurate accounting answers, ensures important procedures are not forgotten, and ensures that accountants are compensated accordingly, ensuring that information is not overdue.

[0730] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0731] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. Furthermore, it has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[0732] System Configuration

[0733] 1. User Device

[0734] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, tone of voice, etc.).

[0735] 2. Server

[0736] It is a central system running an AI engine, an emotion engine, and a database, which analyzes the question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes the user's emotions and adjusts the behavior of the entire system accordingly.

[0737] 3. Accountant's Terminal

[0738] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[0739] System Operation

[0740] Flow of processing user questions

[0741] 1. Input

[0742] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[0743] 2. Send

[0744] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[0745] 3. Analysis

[0746] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[0747] 4. Search

[0748] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[0749] 5. Answer generation

[0750] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[0751] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0752] 6. Transmission and Display

[0753] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0754] Information update flow

[0755] 1. Enter information

[0756] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[0757] 2. Send

[0758] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0759] 3. Data Update

[0760] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[0761] 4. Learning

[0762] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0763] 5. Providing rewards

[0764] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0765] Emotion Recognition Flow

[0766] 1. Entering emotion data

[0767] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0768] 2. Emotion analysis

[0769] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[0770] 3. Adjusting responses and reminders

[0771] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[0772] In this way, the system not only helps users solve their accounting problems quickly, but also provides more personalized responses through emotion recognition, improving user satisfaction and encouraging accountants to provide up-to-date information and get paid for it.

[0773] The processing flow will be explained below.

[0774] Question processing flow using emotion engine

[0775] Step 1: User enters question

[0776] The user enters a billing question into their device.

[0777] For example: "How do you handle expenses?"

[0778] Step 2: Submit your question data

[0779] The user terminal transmits the input question data to the server.

[0780] The data is sent to the server using a format such as JSON.

[0781] Step 3: Sending emotion data

[0782] The user terminal transmits emotion recognition data such as voice input and facial expression data to the server.

[0783] Emotion data is also sent to the server in JSON format.

[0784] Step 4: Analyze the Question Data

[0785] The server analyzes the received query data.

[0786] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[0787] Step 5: Analyze the sentiment data

[0788] The server analyzes the received emotion data using an emotion engine.

[0789] It uses voice tone analysis and facial expression analysis to identify the user's emotional state (e.g., joy, stress, tension, etc.).

[0790] Step 6: Database Search

[0791] The server searches its internal database based on the analysis results and emotional state.

[0792] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[0793] Step 7: Answer Generation

[0794] The server generates the best answer based on the information obtained from the database.

[0795] Use natural language generation (NLG) to create answers in a user-friendly format.

[0796] The tone and content of the response is adjusted depending on the user's emotional state.

[0797] For example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0798] Step 8: Submit your response

[0799] The server transmits the generated answer to the user terminal.

[0800] The response is sent to the user's device using JSON format or similar.

[0801] Step 9: View your answers

[0802] The user terminal displays the received answer to the user.

[0803] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[0804] Information update flow

[0805] Step 1: Enter your information

[0806] Accountants input new accounting information and information about legal changes into their terminals.

[0807] Example: Enter "Important changes to the 2023 tax reform."

[0808] Step 2: Send information data

[0809] The accountant terminal transmits the input information data to the server.

[0810] The data is sent to the server using a format such as JSON.

[0811] Step 3: Save and update information

[0812] The server stores and updates the information received from the accountant in a database.

[0813] Verify the accuracy of the information and update the relevant entries in our database.

[0814] Step 4: Training the AI ​​engine

[0815] The server updates and trains the AI ​​engine based on new information.

[0816] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[0817] Step 5: Offer Rewards

[0818] The server rewards the accountant who adds the information.

[0819] Rewards are awarded as points within the system and reflected in the accountant's account.

[0820] Adjusting emotion recognition

[0821] Step 1: Input emotion data

[0822] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0823] Step 2: Sentiment Analysis

[0824] The server's emotion engine analyzes the received emotion data and identifies the user's emotional state.

[0825] For example, if a user is feeling stressed, their emotional state is identified.

[0826] Step 3: Adjusting responses and reminders

[0827] The server adjusts the tone and content of the responses and reminders it generates based on the results of sentiment analysis.

[0828] For example, users who are feeling stressed will receive answers and reminders in a more considerate tone.

[0829] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[0830] Example 2

[0831] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0832] Conventional accounting consultation systems have the problem that they provide uniform answers to users' accounting questions and are unable to provide personalized responses based on the user's emotional state. Additionally, the latest accounting information and legal amendments provided by accountants are not updated immediately, making it difficult for users to obtain the latest and accurate information.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0834] In this invention, the server includes a means for inputting accounting question data and emotion recognition data from a user, a means for analyzing the input data and extracting important keywords and the user's emotional state, a means for searching a database based on the analyzed data and emotional state, a means for generating an answer based on the user's emotional state based on information obtained from the database, and a means for transmitting the generated answer to a user terminal and displaying it. This allows users to receive prompt and appropriate answers to their accounting questions and also enables personalized responses based on the user's emotional state. In addition, the latest accounting information and legal amendments provided by accountants are quickly updated in the system, ensuring that the latest and most accurate information is always provided.

[0835] "Accounting question data from users" refers to specific accounting-related questions that users input into the system.

[0836] "Emotion recognition data" refers to data that represents a user's emotional state, obtained from the user's voice, facial expressions, etc.

[0837] "Means for analyzing" refers to software and hardware functions for identifying important keywords and the user's emotional state based on input data.

[0838] "Means for searching the database" refers to the algorithms and associated hardware for searching the internal database for appropriate information based on the analyzed data.

[0839] "Answer generation means" refers to software and hardware capabilities for generating answers to user questions based on information retrieved from a database and using natural language generation techniques.

[0840] "User Device" means the device (e.g., smartphone, tablet, PC) used by a User to enter accounting questions and receive answers.

[0841] "Information data from accountants" refers to the latest accounting information and data on legal changes provided to the system by accountants.

[0842] "Means for adding and updating the database" refers to the software and hardware functions for storing information data entered by accountants in the internal database and updating existing data.

[0843] "Means for training the AI ​​engine" refers to the algorithms and related hardware that allow the AI ​​to learn from the information added to the database and use it in future question-answering processes.

[0844] "Means for awarding rewards" refers to the software and hardware functions for adding reward points within the system to accountants who provide information, and for managing and displaying these points.

[0845] "Means for setting and sending reminders" refers to software and hardware functions that notify users of important accounting deadlines and send such reminder information to the user's device.

[0846] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. It also has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[0847] System Configuration

[0848] The system consists of the following main components:

[0849] User Device

[0850] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, voice tone, etc.). Specifically, smartphones, tablets, PCs, etc. are used as user devices. An accounting consultation application and emotion recognition software such as Emotion SDK are installed on the user device.

[0851] server

[0852] This is a central system running an AI engine, an emotion engine, and a database. It analyzes question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes user emotions and adjusts the behavior of the entire system accordingly. The server runs on a virtual machine in the cloud (e.g., AWS EC2). The software used includes a natural language processing engine (e.g., GPT-4), an emotion recognition engine (e.g., EmotionAPI), and a database management system (e.g., MySQL).

[0853] Accountant's Terminal

[0854] This is a device used by accountants to input and send data on new accounting information and legal amendments. It also receives compensation information from the server. Accountant terminals are PCs or tablets with a dedicated accounting information input application installed.

[0855] System Operation

[0856] Handling user questions

[0857] 1. Input

[0858] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[0859] 2. Send

[0860] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[0861] 3. Analysis

[0862] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[0863] 4. Search

[0864] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[0865] 5. Answer generation

[0866] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[0867] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[0868] 6. Transmission and Display

[0869] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[0870] Information update flow

[0871] 1. Enter information

[0872] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[0873] 2. Send

[0874] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[0875] 3. Data Update

[0876] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[0877] 4. Learning

[0878] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[0879] 5. Providing rewards

[0880] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[0881] Emotion Recognition Flow

[0882] 1. Entering emotion data

[0883] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[0884] 2. Emotion analysis

[0885] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[0886] 3. Adjusting responses and reminders

[0887] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[0888] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[0889] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0890] The flow of this system's program processing

[0891] Step 1: User Input

[0892] Users use the device to input their billing questions, and data such as voice and facial expressions are automatically collected for emotion recognition.

[0893] Input: The user types in a question by voice, such as "Please tell me how to process expenses." The user's facial expression is captured by the camera.

[0894] Output: Question text data and emotion recognition data (voice, facial expression)

[0895] Step 2: Sending data

[0896] The device sends the collected question data and emotion recognition data to the server in a specified format (e.g., JSON).

[0897] Input: Question data and emotion recognition data

[0898] Output: Question data and emotion recognition data in JSON format

[0899] Step 3: Analyze the data

[0900] The server analyzes the received data. A natural language processing engine is used to extract important keywords (e.g., "expenses" and "processing methods") from the question text. An emotion engine analyzes the emotion data to identify the user's emotional state.

[0901] Input: JSON-formatted question data and emotion recognition data

[0902] Output: Extracted keywords and emotional state data

[0903] Step 4: Search the database

[0904] The server searches its internal database based on the analysis results, extracting relevant FAQs, past cases, and legal and regulatory information.

[0905] Input: Extracted keywords and emotional state data

[0906] Output: Dataset of search results (FAQs, past cases, legal and regulatory information)

[0907] Step 5: Generate an answer

[0908] The server uses a natural language generation engine to generate answers based on a dataset of search results, adjusting the tone and content of the answers depending on the user's emotional state.

[0909] Input: Search result dataset, emotional state data

[0910] Output: Generated answer text

[0911] Step 6: Submit and view your responses

[0912] The server generates a response and sends it to the user's device, where the user can view it. The response is displayed in a user-friendly format.

[0913] Input: Generated answer text

[0914] Output: The answer displayed on the user's terminal

[0915] Step 7: Accountant updates information

[0916] Accountants use accountant terminals to input and submit data on new accounting information and legal changes.

[0917] Input: New accounting information and legal change data

[0918] Output: Data update request

[0919] Step 8: Update the internal database

[0920] The server saves and updates the information data received from the accountant in its internal database, ensuring that the information in the database is up to date.

[0921] Input: Data update request

[0922] Output: Updated database

[0923] Step 9: Learning new information

[0924] The server's AI engine learns from the new information and uses it in future question-answering processes.

[0925] Input: Updated database

[0926] Output: Trained AI model

[0927] Step 10: Rewarding

[0928] The server gives reward points to the accountant who provided the information, and the point information is displayed on the accountant's terminal.

[0929] Input: Information provision history

[0930] Output: Reward points awarded

[0931] The above is the specific processing flow of this system. Based on this flow, users can get quick and appropriate answers regarding accounting matters, and accountants can receive the latest information and receive compensation.

[0932] (Application example 2)

[0933] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0934] There is a demand for providing an environment in which users can smoothly answer questions and complete procedures related to checkouts at physical stores. It is also necessary to improve the quality of service by responding flexibly to users' emotions. Conventional systems have responded uniformly without considering users' emotions to meet these needs, which has prevented them from fully achieving user satisfaction.

[0935] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0936] In this invention, the server includes means for inputting transaction-related question data from a user, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating an answer based on information from the database, means for sending the generated answer to the user terminal, means for recognizing the user's emotion, and means for adjusting the content and tone of the answer based on the recognized emotion. This allows for the provision of an optimal answer based on the user's emotion, enabling smooth and personalized transaction procedures in physical stores.

[0937] "User" means any person or organization that uses this system to ask or seek advice about accounting matters.

[0938] "Question data" refers to data including questions and inquiries about accounting that users enter into the system.

[0939] "Means of analysis" refers to the means for understanding the question data entered by the user and extracting important keywords and information.

[0940] "Database" means a storage device containing accounting information, FAQs, and related legal and regulatory information.

[0941] "Answer generation means" refers to a means for creating an appropriate answer to a user's question based on information obtained from a database.

[0942] "User terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0943] "Means for recognizing emotions" refers to means for identifying a user's emotional state through data such as facial expressions and tone of voice.

[0944] "Means for adjusting the content and tone of responses" refers to means for appropriately changing the content and tone of responses depending on the user's emotional state.

[0945] "Accountants" are professionals who provide specialized accounting information to this system.

[0946] "Information data" refers to data provided by accountants, including information on new accounting information and legal changes.

[0947] An "AI engine" is an artificial intelligence engine that learns from data and generates optimal answers.

[0948] "Means for providing rewards" means means for providing rewards to accountants who provide information to the system.

[0949] "Means for setting and sending reminders" refers to a means for notifying users of deadlines for accounting procedures and important matters that they tend to forget.

[0950] MODE FOR CARRYING OUT THE INVENTION

[0951] The system for realizing this invention mainly includes three main components: a user terminal, a server, and an accountant terminal. Each component functions as follows:

[0952] User Device

[0953] User devices refer to devices such as smartphones, tablets, and PCs. User devices have the following functions:

[0954] 1. Input of question data: Users can input their accounting questions or inquiries.

[0955] 2. Emotion data capture: Emotion recognition data is acquired through the user's facial expressions and voice tone and sent to the server.

[0956] 3. View Answers: View answers and reminders sent from the server.

[0957] server

[0958] The server has the following functions:

[0959] 1. Data analysis: Question data sent by users is analyzed using a natural language processing (NLP) engine to extract important keywords.

[0960] 2. Database search: Based on the analysis results, the internal database is searched to obtain related information.

[0961] 3. Answer generation: Answers are generated based on information retrieved from the database, using a natural language generation (NLG) engine.

[0962] 4. Emotion Recognition: Use an emotion recognition engine to analyze the user's emotional state and adjust the content and tone of the response.

[0963] 5. Accounting information update: Add / update new information provided by accountants to the database.

[0964] 6. AI engine learning: The AI ​​engine learns from the newly added information and uses it for subsequent searches and answer generation.

[0965] Accountant's Terminal

[0966] The accountant terminal is a device that allows accountants to input and submit the latest accounting information and receive compensation.

[0967] 1. Information data entry: Accountants enter information about new accounting information and legal changes.

[0968] 2. Sending information data: Send the entered information data to the server.

[0969] 3. Receiving rewards: Receive reward points in exchange for providing information.

[0970] Example of overall system operation

[0971] As a specific example of the operation of this system, consider the following scenario.

[0972] Scenario 1: User enters accounting question

[0973] 1. A user uses their smartphone to type, "How do I process expenses?"

[0974] 2. At the same time, the user's facial expression data and voice tone are automatically captured and sent to the server.

[0975] 3. The server analyzes the question data and extracts important keywords such as "expenses" and "processing methods."

[0976] 4. The server uses an emotion recognition engine to identify when the user is in a positive emotional state.

[0977] 5. The server searches the database and retrieves the relevant information.

[0978] 6. The server uses its NLG engine to generate a positive response such as, "The key to managing expenses is organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[0979] 7. The server sends the generated answer to the user's terminal, and the user checks the answer on the terminal.

[0980] Sample prompt sentence

[0981] Below are some example prompts to aid in the analysis of the emotion recognition engine and the generation of answers by the NLG engine:

[0982] "Generate instructions for credit card payment methods for when users are nervous."

[0983] "Write a description of how to process expenses for users in a positive emotional state."

[0984] These prompts are then used by a generative AI model to generate answers tailored to specific situations.

[0985] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0986] Step 1:

[0987] A user uses a user device such as a smartphone or tablet to input a question about accounting, for example, "Please tell me how to process expenses." At the same time, the user device uses a camera and microphone to capture facial expression data and voice tone, and obtains this data.

[0988] Input: accounting question data, facial expression data, voice tone

[0989] Output: Question data and emotion recognition data

[0990] Step 2:

[0991] The user device sends the acquired question data and emotion recognition data to the server in a standard format such as JSON.

[0992] Input: Question data, emotion recognition data

[0993] Output: JSON formatted data sent

[0994] Step 3:

[0995] The server receives the question data and analyzes it using a natural language processing (NLP) engine. This extracts important keywords and phrases. For example, the keywords "expenses" and "processing method" are extracted.

[0996] Input: Question data

[0997] Output: Extracted keywords

[0998] Step 4:

[0999] The server analyzes the received emotion data using an emotion recognition engine, which identifies the user's emotional state (e.g., positive, negative, nervous, etc.).

[1000] Input: Emotion recognition data

[1001] Output: Identified emotional state

[1002] Step 5:

[1003] The server searches a database based on the extracted keywords and the identified emotional state. The database includes accounting-related FAQs, past cases, and relevant legal information. For example, it searches for information related to "expenses" and "processing methods."

[1004] Input: extracted keywords, identified emotional states

[1005] Output: Retrieved information

[1006] Step 6:

[1007] The server uses a natural language generation (NLG) engine to generate answers based on information obtained from the database. It adjusts the tone and content of the answer depending on the user's emotional state. For example, in a positive emotional state, it generates a positive answer such as, "Important expense management methods include organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[1008] Input: Retrieved information, identified emotional state

[1009] Output: The generated answer

[1010] Step 7:

[1011] The server sends the generated answer to the user terminal, which receives the answer and displays it on the screen, allowing the user to check and understand the provided answer.

[1012] Input: Generated Answer

[1013] Output: Answer sent to user terminal

[1014] Step 8:

[1015] The accountant's terminal inputs new accounting information and information on legal amendments and transmits it to the server. For example, the accountant inputs "the latest tax reform points."

[1016] Input: New Accounting Information

[1017] Output: Transmitted information data

[1018] Step 9:

[1019] The server saves and updates the received information data in a database, so that the database always contains the latest information.

[1020] Input: Information data

[1021] Output: Updated database

[1022] Step 10:

[1023] The server rewards the accountant who provided the information as points in the system, which are displayed on the accountant's terminal.

[1024] Input: Record of information provided

[1025] Output: Reward as points

[1026] In this way, the system provides optimal answers based on the user's emotions and efficiently updates information from accountants.

[1027] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1028] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1029] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1030] [Third embodiment]

[1031] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1032] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1034] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1035] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1036] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1038] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1039] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1041] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1042] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1043] A specific embodiment of the present invention will be described below: This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders.

[1044] System Configuration

[1045] 1. User Device

[1046] This is a device used by users to input questions and inquiries about accounting, and also has the function of displaying answers and reminders sent from the server.

[1047] 2. Server

[1048] It is a central system running an AI engine and database that analyzes the query data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants.

[1049] 3. Accountant's Terminal

[1050] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[1051] System Operation

[1052] Flow of processing user questions

[1053] 1. Input

[1054] A user uses a terminal to enter an accounting question, for example, "How do I process expenses?"

[1055] 2. Send

[1056] The user device sends the question data to the server in a format such as JSON.

[1057] 3. Analysis

[1058] The server analyzes the received question data. For example, natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1059] 4. Search

[1060] The server searches for relevant FAQs and case studies from an internal database, which contains a wealth of accounting-related information.

[1061] 5. Answer generation

[1062] The server generates the best answer for the user based on the search results, and the answer is provided in an easy-to-understand format using natural language generation (NLG).

[1063] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1064] 6. Transmission and Display

[1065] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1066] Flow of information updates from accountants

[1067] 1. Enter information

[1068] Accountants input and submit information about new accounting information and legal changes from their terminals. For example, they input "Important changes regarding the 2023 tax reform."

[1069] 2. Send

[1070] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1071] 3. Data Update

[1072] The server adds and updates the received information data to its internal database, so that the database always contains the latest information.

[1073] 4. Learning

[1074] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1075] 5. Reward Sending

[1076] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1077] This system allows users to quickly resolve their accounting concerns, while accountants can provide the latest information and receive compensation for it. This creates a win-win situation for both parties. In addition, by reminding users of important procedures, it prevents oversights and provides an environment where users can carry out accounting work with peace of mind.

[1078] The processing flow will be explained below.

[1079] Question processing flow

[1080] Step 1: User enters question

[1081] The user enters a billing question into their device.

[1082] For example: "How do you handle expenses?"

[1083] Step 2: Submit your question data

[1084] The user terminal transmits the input question data to the server.

[1085] The data is sent to the server using a format such as JSON.

[1086] Step 3: Data analysis

[1087] The server analyzes the received query data.

[1088] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1089] Step 4: Database Search

[1090] The server searches its internal database based on the analysis results.

[1091] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[1092] Step 5: Answer Generation

[1093] The server generates the best answer based on the information obtained from the database.

[1094] Use natural language generation (NLG) to create answers in a user-friendly format.

[1095] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1096] Step 6: Submit your response

[1097] The server transmits the generated answer to the user terminal.

[1098] The response is sent to the user's device using JSON format or similar.

[1099] Step 7: View your answers

[1100] The user terminal displays the received answer to the user.

[1101] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[1102] Information update flow

[1103] Step 1: Enter your information

[1104] Accountants enter new accounting information and legal changes on their own devices.

[1105] Example: Enter "Important changes to the 2023 tax reform."

[1106] Step 2: Send information data

[1107] The accountant terminal transmits the input information data to the server.

[1108] The data is sent to the server using a format such as JSON.

[1109] Step 3: Save and update information

[1110] The server stores and updates the information received from the accountant in a database.

[1111] Verify the accuracy of the information and update the relevant entries in our database.

[1112] Step 4: Training the AI ​​engine

[1113] The server updates and trains the AI ​​engine based on new information.

[1114] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[1115] Step 5: Offer Rewards

[1116] The server rewards the accountant who adds the information.

[1117] Rewards are awarded as points within the system and reflected in the accountant's account.

[1118] Example 1

[1119] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] Previously, when users had questions about accounting, it was difficult to obtain the appropriate information quickly and accurately. Furthermore, when accountants shared the latest information, the information was not properly reflected, resulting in a decline in the quality of service to users. This created a need for an efficient and reliable system for both users and accountants.

[1121] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1122] In this invention, the server includes means for inputting transaction-related question data from users, means for analyzing the question data using natural language processing technology, means for searching a database based on the analyzed data, means for generating answers using natural language generation technology, and means for sending the generated answers to the user terminal, allowing users to quickly and accurately obtain transaction-related information.

[1123] A "user terminal" is an electronic device used by a user that has the function of inputting questions or inquiries about accounting and displaying responses or reminders from the server.

[1124] The "server" is the central system where the AI ​​engine and database run, and is the device that analyzes question data received from users, searches the database, generates answers, and updates information provided by accountants.

[1125] An "accountant terminal" is an electronic device used by accountants that has the function of inputting and transmitting data related to new accounting information and legal amendments, and receiving remuneration information from a server.

[1126] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to extract the context and keywords of user inquiries.

[1127] "Natural language generation technology" is a technology in which a computer generates answers in natural, human-like language based on analyzed data.

[1128] A "database" is a system for searching, storing, and managing information, and it stores a wealth of accounting-related information.

[1129] The "search means" is a function that allows the server to search for related information from its internal database, and is a means for retrieving necessary information based on received query data.

[1130] The "answer generation means" is a function for creating an answer in a form that is easy for the user to understand, based on information obtained from the database.

[1131] "Transmission means" is a function for sending the generated answer to the user terminal, and refers to the technology for transmitting information from the server to the user terminal.

[1132] An "AI engine" is an engine that uses machine learning models to analyze and learn from data and use it for future searches and answer generation.

[1133] The "rewarding means" is a function for awarding rewards such as points that can be used within the system to accountants who provide information.

[1134] "Reminder means" is a function that notifies and reminds users of important accounting procedures.

[1135] This invention is a system that allows users to easily input questions about accounting and receive appropriate answers and procedural reminders. A specific embodiment of this system is described below.

[1136] System Configuration

[1137] 1. User Device

[1138] A device used by a user. Examples include smartphones and PCs. Through these devices, users can input questions and inquiries about accounting and view answers and reminders sent from the server.

[1139] 2. Server

[1140] This is the central system where the AI ​​engine and database run. It is responsible for analyzing the query data received from users, searching the appropriate database, generating answers, and updating the information provided by accountants. Specifically, it uses the following technologies:

[1141] Natural Language Processing (NLP) libraries: Python's spaCy and NLTK

[1142] Database management systems: MySQL, PostgreSQL, MongoDB, Elasticsearch

[1143] Natural Language Generation (NLG) engines: Generative AI models such as GPT-3

[1144] Machine learning frameworks: TensorFlow and PyTorch

[1145] 3. Accountant's Terminal

[1146] A device where accountants input and transmit data on new accounting information and legal changes. It also has the function of receiving compensation information from a server. Examples include personal computers and specialized tablet terminals.

[1147] System Operation

[1148] Handling user questions

[1149] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." The user's device then sends the question data in JSON format to the server.

[1150] The server analyzes the received question data using natural language processing technology to extract important keywords such as "expenses" and "processing methods." The server then searches its internal database for relevant FAQs and case studies. Based on the search results, the server uses a generative AI model to generate the optimal answer.

[1151] The generated answer is sent to the user's terminal, where the user can check the answer.

[1152] A user asks: "How do I process expenses?"

[1153] The system answers: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1154] Update from your accountant

[1155] Accountants input new accounting information and information about legal amendments into their terminals and send it to the server. The server receives the information data and adds or updates it to the database.

[1156] The server's AI engine learns from new information and uses it for future searches and answer generation. Accountants who provide information are rewarded with points that can be used within the system. These points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1157] Reminder function

[1158] The system also has a function that reminds users of important accounting procedures. Reminder notifications are sent to users' devices, preventing them from missing procedures and providing an environment where they can carry out accounting work with peace of mind.

[1159] As described above, this system provides highly convenient functions for both users and accountants, enabling the rapid acquisition and provision of accurate accounting information.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user types an accounting question into the terminal. The user uses a smartphone or computer to type in a specific question. For example, they might type, "Please tell me how to process expenses." This input is captured by the terminal and prepared for transmission to the server in the next step.

[1163] Step 2:

[1164] The device sends the user's question data to the server. The user's device converts the entered question data into JSON format and sends it to the server via an HTTP POST request. This data includes the user's question text.

[1165] Step 3:

[1166] The server analyzes the received question data using natural language processing technology. The server uses Python libraries such as spaCy and NLTK to extract important keywords from the question. For example, keywords such as "expenses" and "processing method" are extracted. The input is the question text, and the output is a list of keywords.

[1167] Step 4:

[1168] The server searches the database using the extracted keywords. The server uses a database management system such as MySQL or PostgreSQL to search for accounting information related to the keywords. For example, FAQs and case studies related to "expenses" are obtained as search results. The input is a list of keywords, and the output is a list of search results.

[1169] Step 5:

[1170] The server generates an answer based on the search results. The server uses a generative AI model (e.g., GPT-3) to create an answer in natural language that is easy for humans to understand. For example, an answer such as "When it comes to expense processing methods, it is important to organize receipts, create expense reports, and categorize expenses" is generated. The input is a list of search results, and the output is the generated answer.

[1171] Step 6:

[1172] The server sends the generated answer to the user terminal. The server converts the generated answer into JSON format and sends it to the user terminal as an HTTP response. The input is the generated answer, and the output is the data sent to the user terminal.

[1173] Step 7:

[1174] The user's device displays the received answer on the screen. The user's device parses the received JSON data and converts it into a format for display. The user can check the answer generated on the device. The input is the answer data from the server, and the output is the answer displayed on the screen.

[1175] This system allows users to quickly and accurately obtain accounting information, and also receives input and updates from accountants, ensuring that the latest information is always provided.

[1176] (Application example 1)

[1177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1178] Conventional accounting question-answering systems required a lot of effort for users to ask questions about accounting, and there were problems with the accuracy and timing of answers. In particular, they lacked a function to provide regular reminders to prevent forgetting accounting procedures, making them inconvenient for users. Furthermore, there was no system for providing real-time accounting consultations, which placed a heavy burden on users. Furthermore, the compensation system for accountants was inadequate, resulting in issues with delayed information updates.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1180] In this invention, the server includes means for inputting accounting question data from users, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating answers based on information from the database, means for transmitting the generated answers to the user terminal, means for reminding users of accounting information and procedures, and means for using a virtual currency payment platform that enables users to receive accounting consultations in real time. This allows users to quickly and efficiently obtain answers to their accounting questions and ensure that important accounting procedures are not forgotten. Furthermore, the system automates the allocation of reward points to accountants, and it is expected that the latest information will always be reflected in the database.

[1181] The "means for inputting accounting inquiry data from users" is an interface that allows users to electronically input questions or inquiries about accounting.

[1182] The "means for analyzing input question data" refers to a means for using natural language processing techniques to understand the input question and extract relevant information.

[1183] The "means for searching a database based on the analyzed data" refers to a means for searching an appropriate database based on the analyzed question to obtain related information.

[1184] "Means for generating answers based on information from a database" refers to means for generating specific answers for users using information obtained through a search.

[1185] The "means for transmitting the generated answer to the user terminal" refers to a communication means for transmitting the generated answer to the user terminal and displaying it.

[1186] "Means of reminding users about accounting information and procedures" refers to means of informing users of important accounting procedures and deadlines so that they do not forget to carry them out.

[1187] "Means of using a virtual currency payment platform that enables users to receive accounting consultations in real time" refers to means of linking with an electronic payment system that enables users to receive accounting consultations in real time.

[1188] The "means for inputting information data from accountants" is an interface that accountants use to input new accounting-related information and legal amendment information into the system.

[1189] "Means for adding and updating input information data to a database" refers to a means for saving input accounting information in a database and updating existing information to the latest version.

[1190] "Means for training AI based on information added to the database" refers to a means for training an AI model using the latest accounting information added to the database to improve the accuracy of answers.

[1191] The "means for providing rewards to accountants who provide information" refers to a means for providing rewards such as points to accountants who provide information to the system.

[1192] A specific embodiment of the present invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and reminders. A distinctive feature of this invention is that it incorporates the use of a virtual currency payment platform.

[1193] System configuration

[1194] 1. User Device

[1195] A user terminal refers to a device such as a smartphone or tablet. Through this terminal, the user inputs accounting-related question data. For example, they can type "Please tell me how to process expenses" into a text box. The input information is sent to the server in JSON format or similar.

[1196] 2. Server

[1197] The server is a central system where the AI ​​engine and database run. It analyzes the question data sent by the user and searches for relevant database information. Based on the search results, a generative AI model (e.g., OpenAI's GPT-3) is used to generate an answer. The generated answer is sent to the user's device in an appropriate format. The server also manages accounting procedure reminders and periodically sends notifications to the user.

[1198] 3. Accountant's Terminal

[1199] The accountant terminal is a device that accountants use to input and send new accounting information and legal amendments. The information input by accountants through the terminal is sent to the server, where it is added to and updated in the database. The server uses this information to train the AI ​​engine, which is then used for subsequent searches and answer generation. Accountants are also awarded reward points, which can be viewed through the accountant terminal.

[1200] Processing flow

[1201] Handling user questions

[1202] Users input and send accounting-related questions from their device. The server analyzes the received question data and performs natural language processing using a generative AI model to extract keywords and themes and search the appropriate database. Based on the search results, the server generates an answer and sends it to the user's device.

[1203] Update from your accountant

[1204] Accountants input new accounting information and information about legal changes into their terminals and send it to the server. The server adds and updates the received data to the database, and the AI ​​engine learns the new information. This improves the accuracy of answers and provides more useful information to users.

[1205] Reminder function

[1206] The server has the ability to remind users of important accounting procedures, automatically sending notifications when certain deadlines are approaching, urging users not to forget to complete the procedures.

[1207] For example, a user may enter the question "How do you handle expenses?" This question is sent to the server through an API, and the AI ​​model generates an answer using the following prompt: "User asks: 'How do you handle expenses?' Please generate an answer."

[1208] As described above, this invention is a system that allows users to quickly and efficiently consult with their accountants and receive appropriate answers and reminders. This significantly improves user convenience and also provides a mechanism for providing appropriate compensation to accountants.

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] The user's terminal inputs accounting question data from the user. For example, the user enters a question such as "Please tell me how to process expenses" into a text box, and this data is sent to the server in JSON format.

[1212] Step 2:

[1213] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods" from the question, thereby clarifying the theme and focus of the question.

[1214] Step 3:

[1215] The server searches the database based on the analyzed data. Based on the extracted keywords, the server searches its internal accounting database to retrieve relevant FAQs and past cases. At this stage, the most appropriate information is collected to answer the user's question.

[1216] Step 4:

[1217] The server generates answers based on information from the database. The server uses a generative AI model (such as OpenAI's GPT-3) to perform natural language generation (NLG) based on the collected data and creates answers in a format that is easy for the user to understand.

[1218] Step 5:

[1219] The server sends the generated answer to the user's device in JSON format, allowing the user to view the answer to their question on their device.

[1220] Step 6:

[1221] The server will remind users of accounting information and procedures. Based on the user's registered information, the server will automatically generate reminder notifications when important accounting procedures or deadlines are approaching and send them to the user's device. These notifications will help users remember to complete the procedures before the deadline.

[1222] Step 7:

[1223] The accountant's terminal inputs and sends new accounting information and legal amendment information. The accountant inputs new information from his / her terminal and sends it to the server in JSON format.

[1224] Step 8:

[1225] The server adds and updates the entered information data to the database. The server saves the new information received from the accountant in the database and updates the existing information.

[1226] Step 9:

[1227] The server trains the AI ​​engine based on the information added to the database, and uses the new information to train the generative AI model, improving the accuracy of future question answers.

[1228] Step 10:

[1229] The server rewards the accountant who provided the information. The server calculates reward points based on the database and awards points to the accountant according to the information provided. The accountant can check their reward through the terminal.

[1230] This process step allows users to get quick and accurate accounting answers, ensures important procedures are not forgotten, and ensures that accountants are compensated accordingly, ensuring that information is not overdue.

[1231] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1232] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. Furthermore, it has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[1233] System Configuration

[1234] 1. User Device

[1235] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, tone of voice, etc.).

[1236] 2. Server

[1237] It is a central system running an AI engine, an emotion engine, and a database, which analyzes the question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes the user's emotions and adjusts the behavior of the entire system accordingly.

[1238] 3. Accountant's Terminal

[1239] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[1240] System Operation

[1241] Flow of processing user questions

[1242] 1. Input

[1243] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[1244] 2. Send

[1245] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[1246] 3. Analysis

[1247] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[1248] 4. Search

[1249] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[1250] 5. Answer generation

[1251] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[1252] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1253] 6. Transmission and Display

[1254] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1255] Information update flow

[1256] 1. Enter information

[1257] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[1258] 2. Send

[1259] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1260] 3. Data Update

[1261] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[1262] 4. Learning

[1263] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1264] 5. Providing rewards

[1265] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1266] Emotion Recognition Flow

[1267] 1. Entering emotion data

[1268] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1269] 2. Emotion analysis

[1270] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[1271] 3. Adjusting responses and reminders

[1272] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[1273] In this way, the system not only helps users solve their accounting problems quickly, but also provides more personalized responses through emotion recognition, improving user satisfaction and encouraging accountants to provide up-to-date information and get paid for it.

[1274] The processing flow will be explained below.

[1275] Question processing flow using emotion engine

[1276] Step 1: User enters question

[1277] The user enters a billing question into their device.

[1278] For example: "How do you handle expenses?"

[1279] Step 2: Submit your question data

[1280] The user terminal transmits the input question data to the server.

[1281] The data is sent to the server using a format such as JSON.

[1282] Step 3: Sending emotion data

[1283] The user terminal transmits emotion recognition data such as voice input and facial expression data to the server.

[1284] Emotion data is also sent to the server in JSON format.

[1285] Step 4: Analyze the Question Data

[1286] The server analyzes the received query data.

[1287] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1288] Step 5: Analyze the sentiment data

[1289] The server analyzes the received emotion data using an emotion engine.

[1290] It uses voice tone analysis and facial expression analysis to identify the user's emotional state (e.g., joy, stress, tension, etc.).

[1291] Step 6: Database Search

[1292] The server searches its internal database based on the analysis results and emotional state.

[1293] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[1294] Step 7: Answer Generation

[1295] The server generates the best answer based on the information obtained from the database.

[1296] Use natural language generation (NLG) to create answers in a user-friendly format.

[1297] The tone and content of the response is adjusted depending on the user's emotional state.

[1298] For example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1299] Step 8: Submit your response

[1300] The server transmits the generated answer to the user terminal.

[1301] The response is sent to the user's device using JSON format or similar.

[1302] Step 9: View your answers

[1303] The user terminal displays the received answer to the user.

[1304] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[1305] Information update flow

[1306] Step 1: Enter your information

[1307] Accountants input new accounting information and information about legal changes into their terminals.

[1308] Example: Enter "Important changes to the 2023 tax reform."

[1309] Step 2: Send information data

[1310] The accountant terminal transmits the input information data to the server.

[1311] The data is sent to the server using a format such as JSON.

[1312] Step 3: Save and update information

[1313] The server stores and updates the information received from the accountant in a database.

[1314] Verify the accuracy of the information and update the relevant entries in our database.

[1315] Step 4: Training the AI ​​engine

[1316] The server updates and trains the AI ​​engine based on new information.

[1317] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[1318] Step 5: Offer Rewards

[1319] The server rewards the accountant who adds the information.

[1320] Rewards are awarded as points within the system and reflected in the accountant's account.

[1321] Adjusting emotion recognition

[1322] Step 1: Input emotion data

[1323] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1324] Step 2: Sentiment Analysis

[1325] The server's emotion engine analyzes the received emotion data and identifies the user's emotional state.

[1326] For example, if a user is feeling stressed, their emotional state is identified.

[1327] Step 3: Adjusting responses and reminders

[1328] The server adjusts the tone and content of the responses and reminders it generates based on the results of sentiment analysis.

[1329] For example, users who are feeling stressed will receive answers and reminders in a more considerate tone.

[1330] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[1331] Example 2

[1332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Conventional accounting consultation systems have the problem that they provide uniform answers to users' accounting questions and are unable to provide personalized responses based on the user's emotional state. Additionally, the latest accounting information and legal amendments provided by accountants are not updated immediately, making it difficult for users to obtain the latest and accurate information.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1335] In this invention, the server includes a means for inputting accounting question data and emotion recognition data from a user, a means for analyzing the input data and extracting important keywords and the user's emotional state, a means for searching a database based on the analyzed data and emotional state, a means for generating an answer based on the user's emotional state based on information obtained from the database, and a means for transmitting the generated answer to a user terminal and displaying it. This allows users to receive prompt and appropriate answers to their accounting questions and also enables personalized responses based on the user's emotional state. In addition, the latest accounting information and legal amendments provided by accountants are quickly updated in the system, ensuring that the latest and most accurate information is always provided.

[1336] "Accounting question data from users" refers to specific accounting-related questions that users input into the system.

[1337] "Emotion recognition data" refers to data that represents a user's emotional state, obtained from the user's voice, facial expressions, etc.

[1338] "Means for analyzing" refers to software and hardware functions for identifying important keywords and the user's emotional state based on input data.

[1339] "Means for searching the database" refers to the algorithms and associated hardware for searching the internal database for appropriate information based on the analyzed data.

[1340] "Answer generation means" refers to software and hardware capabilities for generating answers to user questions based on information retrieved from a database and using natural language generation techniques.

[1341] "User Device" means the device (e.g., smartphone, tablet, PC) used by a User to enter accounting questions and receive answers.

[1342] "Information data from accountants" refers to the latest accounting information and data on legal changes provided to the system by accountants.

[1343] "Means for adding and updating the database" refers to the software and hardware functions for storing information data entered by accountants in the internal database and updating existing data.

[1344] "Means for training the AI ​​engine" refers to the algorithms and related hardware that allow the AI ​​to learn from the information added to the database and use it in future question-answering processes.

[1345] "Means for awarding rewards" refers to the software and hardware functions for adding reward points within the system to accountants who provide information, and for managing and displaying these points.

[1346] "Means for setting and sending reminders" refers to software and hardware functions that notify users of important accounting deadlines and send such reminder information to the user's device.

[1347] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. It also has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[1348] System Configuration

[1349] The system consists of the following main components:

[1350] User Device

[1351] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, voice tone, etc.). Specifically, smartphones, tablets, PCs, etc. are used as user devices. An accounting consultation application and emotion recognition software such as Emotion SDK are installed on the user device.

[1352] server

[1353] This is a central system running an AI engine, an emotion engine, and a database. It analyzes question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes user emotions and adjusts the behavior of the entire system accordingly. The server runs on a virtual machine in the cloud (e.g., AWS EC2). The software used includes a natural language processing engine (e.g., GPT-4), an emotion recognition engine (e.g., EmotionAPI), and a database management system (e.g., MySQL).

[1354] Accountant's Terminal

[1355] This is a device used by accountants to input and send data on new accounting information and legal amendments. It also receives compensation information from the server. Accountant terminals are PCs or tablets with a dedicated accounting information input application installed.

[1356] System Operation

[1357] Handling user questions

[1358] 1. Input

[1359] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[1360] 2. Send

[1361] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[1362] 3. Analysis

[1363] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[1364] 4. Search

[1365] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[1366] 5. Answer generation

[1367] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[1368] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1369] 6. Transmission and Display

[1370] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1371] Information update flow

[1372] 1. Enter information

[1373] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[1374] 2. Send

[1375] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1376] 3. Data Update

[1377] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[1378] 4. Learning

[1379] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1380] 5. Providing rewards

[1381] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1382] Emotion Recognition Flow

[1383] 1. Entering emotion data

[1384] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1385] 2. Emotion analysis

[1386] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[1387] 3. Adjusting responses and reminders

[1388] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[1389] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[1390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1391] The flow of this system's program processing

[1392] Step 1: User Input

[1393] Users use the device to input their billing questions, and data such as voice and facial expressions are automatically collected for emotion recognition.

[1394] Input: The user types in a question by voice, such as "Please tell me how to process expenses." The user's facial expression is captured by the camera.

[1395] Output: Question text data and emotion recognition data (voice, facial expression)

[1396] Step 2: Sending data

[1397] The device sends the collected question data and emotion recognition data to the server in a specified format (e.g., JSON).

[1398] Input: Question data and emotion recognition data

[1399] Output: Question data and emotion recognition data in JSON format

[1400] Step 3: Analyze the data

[1401] The server analyzes the received data. A natural language processing engine is used to extract important keywords (e.g., "expenses" and "processing methods") from the question text. An emotion engine analyzes the emotion data to identify the user's emotional state.

[1402] Input: JSON-formatted question data and emotion recognition data

[1403] Output: Extracted keywords and emotional state data

[1404] Step 4: Search the database

[1405] The server searches its internal database based on the analysis results, extracting relevant FAQs, past cases, and legal and regulatory information.

[1406] Input: Extracted keywords and emotional state data

[1407] Output: Dataset of search results (FAQs, past cases, legal and regulatory information)

[1408] Step 5: Generate an answer

[1409] The server uses a natural language generation engine to generate answers based on a dataset of search results, adjusting the tone and content of the answers depending on the user's emotional state.

[1410] Input: Search result dataset, emotional state data

[1411] Output: Generated answer text

[1412] Step 6: Submit and view your responses

[1413] The server generates a response and sends it to the user's device, where the user can view it. The response is displayed in a user-friendly format.

[1414] Input: Generated answer text

[1415] Output: The answer displayed on the user's terminal

[1416] Step 7: Accountant updates information

[1417] Accountants use accountant terminals to input and submit data on new accounting information and legal changes.

[1418] Input: New accounting information and legal change data

[1419] Output: Data update request

[1420] Step 8: Update the internal database

[1421] The server saves and updates the information data received from the accountant in its internal database, ensuring that the information in the database is up to date.

[1422] Input: Data update request

[1423] Output: Updated database

[1424] Step 9: Learning new information

[1425] The server's AI engine learns from the new information and uses it in future question-answering processes.

[1426] Input: Updated database

[1427] Output: Trained AI model

[1428] Step 10: Rewarding

[1429] The server gives reward points to the accountant who provided the information, and the point information is displayed on the accountant's terminal.

[1430] Input: Information provision history

[1431] Output: Reward points awarded

[1432] The above is the specific processing flow of this system. Based on this flow, users can get quick and appropriate answers regarding accounting matters, and accountants can receive the latest information and receive compensation.

[1433] (Application example 2)

[1434] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1435] There is a demand for providing an environment in which users can smoothly answer questions and complete procedures related to checkouts at physical stores. It is also necessary to improve the quality of service by responding flexibly to users' emotions. Conventional systems have responded uniformly without considering users' emotions to meet these needs, which has prevented them from fully achieving user satisfaction.

[1436] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1437] In this invention, the server includes means for inputting transaction-related question data from a user, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating an answer based on information from the database, means for sending the generated answer to the user terminal, means for recognizing the user's emotion, and means for adjusting the content and tone of the answer based on the recognized emotion. This allows for the provision of an optimal answer based on the user's emotion, enabling smooth and personalized transaction procedures in physical stores.

[1438] "User" means any person or organization that uses this system to ask or seek advice about accounting matters.

[1439] "Question data" refers to data including questions and inquiries about accounting that users enter into the system.

[1440] "Means of analysis" refers to the means for understanding the question data entered by the user and extracting important keywords and information.

[1441] "Database" means a storage device containing accounting information, FAQs, and related legal and regulatory information.

[1442] "Answer generation means" refers to a means for creating an appropriate answer to a user's question based on information obtained from a database.

[1443] "User terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1444] "Means for recognizing emotions" refers to means for identifying a user's emotional state through data such as facial expressions and tone of voice.

[1445] "Means for adjusting the content and tone of responses" refers to means for appropriately changing the content and tone of responses depending on the user's emotional state.

[1446] "Accountants" are professionals who provide specialized accounting information to this system.

[1447] "Information data" refers to data provided by accountants, including information on new accounting information and legal changes.

[1448] An "AI engine" is an artificial intelligence engine that learns from data and generates optimal answers.

[1449] "Means for providing rewards" means means for providing rewards to accountants who provide information to the system.

[1450] "Means for setting and sending reminders" refers to a means for notifying users of deadlines for accounting procedures and important matters that they tend to forget.

[1451] MODE FOR CARRYING OUT THE INVENTION

[1452] The system for realizing this invention mainly includes three main components: a user terminal, a server, and an accountant terminal. Each component functions as follows:

[1453] User Device

[1454] User devices refer to devices such as smartphones, tablets, and PCs. User devices have the following functions:

[1455] 1. Input of question data: Users can input their accounting questions or inquiries.

[1456] 2. Emotion data capture: Emotion recognition data is acquired through the user's facial expressions and voice tone and sent to the server.

[1457] 3. View Answers: View answers and reminders sent from the server.

[1458] server

[1459] The server has the following functions:

[1460] 1. Data analysis: Question data sent by users is analyzed using a natural language processing (NLP) engine to extract important keywords.

[1461] 2. Database search: Based on the analysis results, the internal database is searched to obtain related information.

[1462] 3. Answer generation: Answers are generated based on information retrieved from the database, using a natural language generation (NLG) engine.

[1463] 4. Emotion Recognition: Use an emotion recognition engine to analyze the user's emotional state and adjust the content and tone of the response.

[1464] 5. Accounting information update: Add / update new information provided by accountants to the database.

[1465] 6. AI engine learning: The AI ​​engine learns from the newly added information and uses it for subsequent searches and answer generation.

[1466] Accountant's Terminal

[1467] The accountant terminal is a device that allows accountants to input and submit the latest accounting information and receive compensation.

[1468] 1. Information data entry: Accountants enter information about new accounting information and legal changes.

[1469] 2. Sending information data: Send the entered information data to the server.

[1470] 3. Receiving rewards: Receive reward points in exchange for providing information.

[1471] Example of overall system operation

[1472] As a specific example of the operation of this system, consider the following scenario.

[1473] Scenario 1: User enters accounting question

[1474] 1. A user uses their smartphone to type, "How do I process expenses?"

[1475] 2. At the same time, the user's facial expression data and voice tone are automatically captured and sent to the server.

[1476] 3. The server analyzes the question data and extracts important keywords such as "expenses" and "processing methods."

[1477] 4. The server uses an emotion recognition engine to identify when the user is in a positive emotional state.

[1478] 5. The server searches the database and retrieves the relevant information.

[1479] 6. The server uses its NLG engine to generate a positive response such as, "The key to managing expenses is organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[1480] 7. The server sends the generated answer to the user's terminal, and the user checks the answer on the terminal.

[1481] Sample prompt sentence

[1482] Below are some example prompts to aid in the analysis of the emotion recognition engine and the generation of answers by the NLG engine:

[1483] "Generate instructions for credit card payment methods for when users are nervous."

[1484] "Write a description of how to process expenses for users in a positive emotional state."

[1485] These prompts are then used by a generative AI model to generate answers tailored to specific situations.

[1486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] A user uses a user device such as a smartphone or tablet to input a question about accounting, for example, "Please tell me how to process expenses." At the same time, the user device uses a camera and microphone to capture facial expression data and voice tone, and obtains this data.

[1489] Input: accounting question data, facial expression data, voice tone

[1490] Output: Question data and emotion recognition data

[1491] Step 2:

[1492] The user device sends the acquired question data and emotion recognition data to the server in a standard format such as JSON.

[1493] Input: Question data, emotion recognition data

[1494] Output: JSON formatted data sent

[1495] Step 3:

[1496] The server receives the question data and analyzes it using a natural language processing (NLP) engine. This extracts important keywords and phrases. For example, the keywords "expenses" and "processing method" are extracted.

[1497] Input: Question data

[1498] Output: Extracted keywords

[1499] Step 4:

[1500] The server analyzes the received emotion data using an emotion recognition engine, which identifies the user's emotional state (e.g., positive, negative, nervous, etc.).

[1501] Input: Emotion recognition data

[1502] Output: Identified emotional state

[1503] Step 5:

[1504] The server searches a database based on the extracted keywords and the identified emotional state. The database includes accounting-related FAQs, past cases, and relevant legal information. For example, it searches for information related to "expenses" and "processing methods."

[1505] Input: extracted keywords, identified emotional states

[1506] Output: Retrieved information

[1507] Step 6:

[1508] The server uses a natural language generation (NLG) engine to generate answers based on information obtained from the database. It adjusts the tone and content of the answer depending on the user's emotional state. For example, in a positive emotional state, it generates a positive answer such as, "Important expense management methods include organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[1509] Input: Retrieved information, identified emotional state

[1510] Output: The generated answer

[1511] Step 7:

[1512] The server sends the generated answer to the user terminal, which receives the answer and displays it on the screen, allowing the user to check and understand the provided answer.

[1513] Input: Generated Answer

[1514] Output: Answer sent to user terminal

[1515] Step 8:

[1516] The accountant's terminal inputs new accounting information and information on legal amendments and transmits it to the server. For example, the accountant inputs "the latest tax reform points."

[1517] Input: New Accounting Information

[1518] Output: Transmitted information data

[1519] Step 9:

[1520] The server saves and updates the received information data in a database, so that the database always contains the latest information.

[1521] Input: Information data

[1522] Output: Updated database

[1523] Step 10:

[1524] The server rewards the accountant who provided the information as points in the system, which are displayed on the accountant's terminal.

[1525] Input: Record of information provided

[1526] Output: Reward as points

[1527] In this way, the system provides optimal answers based on the user's emotions and efficiently updates information from accountants.

[1528] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1529] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1530] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1531] [Fourth embodiment]

[1532] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1533] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1534] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1535] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1536] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1537] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1538] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1539] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1540] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1541] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1542] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1543] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1544] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] A specific embodiment of the present invention will be described below: This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders.

[1546] System Configuration

[1547] 1. User Device

[1548] This is a device used by users to input questions and inquiries about accounting, and also has the function of displaying answers and reminders sent from the server.

[1549] 2. Server

[1550] It is a central system running an AI engine and database that analyzes the query data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants.

[1551] 3. Accountant's Terminal

[1552] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[1553] System Operation

[1554] Flow of processing user questions

[1555] 1. Input

[1556] A user uses a terminal to enter an accounting question, for example, "How do I process expenses?"

[1557] 2. Send

[1558] The user device sends the question data to the server in a format such as JSON.

[1559] 3. Analysis

[1560] The server analyzes the received question data. For example, natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1561] 4. Search

[1562] The server searches for relevant FAQs and case studies from an internal database, which contains a wealth of accounting-related information.

[1563] 5. Answer generation

[1564] The server generates the best answer for the user based on the search results, and the answer is provided in an easy-to-understand format using natural language generation (NLG).

[1565] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1566] 6. Transmission and Display

[1567] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1568] Flow of information updates from accountants

[1569] 1. Enter information

[1570] Accountants input and submit information about new accounting information and legal changes from their terminals. For example, they input "Important changes regarding the 2023 tax reform."

[1571] 2. Send

[1572] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1573] 3. Data Update

[1574] The server adds and updates the received information data to its internal database, so that the database always contains the latest information.

[1575] 4. Learning

[1576] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1577] 5. Reward Sending

[1578] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1579] This system allows users to quickly resolve their accounting concerns, while accountants can provide the latest information and receive compensation for it. This creates a win-win situation for both parties. In addition, by reminding users of important procedures, it prevents oversights and provides an environment where users can carry out accounting work with peace of mind.

[1580] The processing flow will be explained below.

[1581] Question processing flow

[1582] Step 1: User enters question

[1583] The user enters a billing question into their device.

[1584] For example: "How do you handle expenses?"

[1585] Step 2: Submit your question data

[1586] The user terminal transmits the input question data to the server.

[1587] The data is sent to the server using a format such as JSON.

[1588] Step 3: Data analysis

[1589] The server analyzes the received query data.

[1590] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1591] Step 4: Database Search

[1592] The server searches its internal database based on the analysis results.

[1593] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[1594] Step 5: Answer Generation

[1595] The server generates the best answer based on the information obtained from the database.

[1596] Use natural language generation (NLG) to create answers in a user-friendly format.

[1597] For example: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1598] Step 6: Submit your response

[1599] The server transmits the generated answer to the user terminal.

[1600] The response is sent to the user's device using JSON format or similar.

[1601] Step 7: View your answers

[1602] The user terminal displays the received answer to the user.

[1603] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[1604] Information update flow

[1605] Step 1: Enter your information

[1606] Accountants enter new accounting information and legal changes on their own devices.

[1607] Example: Enter "Important changes to the 2023 tax reform."

[1608] Step 2: Send information data

[1609] The accountant terminal transmits the input information data to the server.

[1610] The data is sent to the server using a format such as JSON.

[1611] Step 3: Save and update information

[1612] The server stores and updates the information received from the accountant in a database.

[1613] Verify the accuracy of the information and update the relevant entries in our database.

[1614] Step 4: Training the AI ​​engine

[1615] The server updates and trains the AI ​​engine based on new information.

[1616] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[1617] Step 5: Offer Rewards

[1618] The server rewards the accountant who adds the information.

[1619] Rewards are awarded as points within the system and reflected in the accountant's account.

[1620] Example 1

[1621] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1622] Previously, when users had questions about accounting, it was difficult to obtain the appropriate information quickly and accurately. Furthermore, when accountants shared the latest information, the information was not properly reflected, resulting in a decline in the quality of service to users. This created a need for an efficient and reliable system for both users and accountants.

[1623] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1624] In this invention, the server includes means for inputting transaction-related question data from users, means for analyzing the question data using natural language processing technology, means for searching a database based on the analyzed data, means for generating answers using natural language generation technology, and means for sending the generated answers to the user terminal, allowing users to quickly and accurately obtain transaction-related information.

[1625] A "user terminal" is an electronic device used by a user that has the function of inputting questions or inquiries about accounting and displaying responses or reminders from the server.

[1626] The "server" is the central system where the AI ​​engine and database run, and is the device that analyzes question data received from users, searches the database, generates answers, and updates information provided by accountants.

[1627] An "accountant terminal" is an electronic device used by accountants that has the function of inputting and transmitting data related to new accounting information and legal amendments, and receiving remuneration information from a server.

[1628] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to extract the context and keywords of user inquiries.

[1629] "Natural language generation technology" is a technology in which a computer generates answers in natural, human-like language based on analyzed data.

[1630] A "database" is a system for searching, storing, and managing information, and it stores a wealth of accounting-related information.

[1631] The "search means" is a function that allows the server to search for related information from its internal database, and is a means for retrieving necessary information based on received query data.

[1632] The "answer generation means" is a function for creating an answer in a form that is easy for the user to understand, based on information obtained from the database.

[1633] "Transmission means" is a function for sending the generated answer to the user terminal, and refers to the technology for transmitting information from the server to the user terminal.

[1634] An "AI engine" is an engine that uses machine learning models to analyze and learn from data and use it for future searches and answer generation.

[1635] The "rewarding means" is a function for awarding rewards such as points that can be used within the system to accountants who provide information.

[1636] "Reminder means" is a function that notifies and reminds users of important accounting procedures.

[1637] This invention is a system that allows users to easily input questions about accounting and receive appropriate answers and procedural reminders. A specific embodiment of this system is described below.

[1638] System Configuration

[1639] 1. User Device

[1640] A device used by a user. Examples include smartphones and PCs. Through these devices, users can input questions and inquiries about accounting and view answers and reminders sent from the server.

[1641] 2. Server

[1642] This is the central system where the AI ​​engine and database run. It is responsible for analyzing the query data received from users, searching the appropriate database, generating answers, and updating the information provided by accountants. Specifically, it uses the following technologies:

[1643] Natural Language Processing (NLP) libraries: Python's spaCy and NLTK

[1644] Database management systems: MySQL, PostgreSQL, MongoDB, Elasticsearch

[1645] Natural Language Generation (NLG) engines: Generative AI models such as GPT-3

[1646] Machine learning frameworks: TensorFlow and PyTorch

[1647] 3. Accountant's Terminal

[1648] A device where accountants input and transmit data on new accounting information and legal changes. It also has the function of receiving compensation information from a server. Examples include personal computers and specialized tablet terminals.

[1649] System Operation

[1650] Handling user questions

[1651] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." The user's device then sends the question data in JSON format to the server.

[1652] The server analyzes the received question data using natural language processing technology to extract important keywords such as "expenses" and "processing methods." The server then searches its internal database for relevant FAQs and case studies. Based on the search results, the server uses a generative AI model to generate the optimal answer.

[1653] The generated answer is sent to the user's terminal, where the user can check the answer.

[1654] A user asks: "How do I process expenses?"

[1655] The system answers: "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses."

[1656] Update from your accountant

[1657] Accountants input new accounting information and information about legal amendments into their terminals and send it to the server. The server receives the information data and adds or updates it to the database.

[1658] The server's AI engine learns from new information and uses it for future searches and answer generation. Accountants who provide information are rewarded with points that can be used within the system. These points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1659] Reminder function

[1660] The system also has a function that reminds users of important accounting procedures. Reminder notifications are sent to users' devices, preventing them from missing procedures and providing an environment where they can carry out accounting work with peace of mind.

[1661] As described above, this system provides highly convenient functions for both users and accountants, enabling the rapid acquisition and provision of accurate accounting information.

[1662] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1663] Step 1:

[1664] The user types an accounting question into the terminal. The user uses a smartphone or computer to type in a specific question. For example, they might type, "Please tell me how to process expenses." This input is captured by the terminal and prepared for transmission to the server in the next step.

[1665] Step 2:

[1666] The device sends the user's question data to the server. The user's device converts the entered question data into JSON format and sends it to the server via an HTTP POST request. This data includes the user's question text.

[1667] Step 3:

[1668] The server analyzes the received question data using natural language processing technology. The server uses Python libraries such as spaCy and NLTK to extract important keywords from the question. For example, keywords such as "expenses" and "processing method" are extracted. The input is the question text, and the output is a list of keywords.

[1669] Step 4:

[1670] The server searches the database using the extracted keywords. The server uses a database management system such as MySQL or PostgreSQL to search for accounting information related to the keywords. For example, FAQs and case studies related to "expenses" are obtained as search results. The input is a list of keywords, and the output is a list of search results.

[1671] Step 5:

[1672] The server generates an answer based on the search results. The server uses a generative AI model (e.g., GPT-3) to create an answer in natural language that is easy for humans to understand. For example, an answer such as "When it comes to expense processing methods, it is important to organize receipts, create expense reports, and categorize expenses" is generated. The input is a list of search results, and the output is the generated answer.

[1673] Step 6:

[1674] The server sends the generated answer to the user terminal. The server converts the generated answer into JSON format and sends it to the user terminal as an HTTP response. The input is the generated answer, and the output is the data sent to the user terminal.

[1675] Step 7:

[1676] The user's device displays the received answer on the screen. The user's device parses the received JSON data and converts it into a format for display. The user can check the answer generated on the device. The input is the answer data from the server, and the output is the answer displayed on the screen.

[1677] This system allows users to quickly and accurately obtain accounting information, and also receives input and updates from accountants, ensuring that the latest information is always provided.

[1678] (Application example 1)

[1679] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1680] Conventional accounting question-answering systems required a lot of effort for users to ask questions about accounting, and there were problems with the accuracy and timing of answers. In particular, they lacked a function to provide regular reminders to prevent forgetting accounting procedures, making them inconvenient for users. Furthermore, there was no system for providing real-time accounting consultations, which placed a heavy burden on users. Furthermore, the compensation system for accountants was inadequate, resulting in issues with delayed information updates.

[1681] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1682] In this invention, the server includes means for inputting accounting question data from users, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating answers based on information from the database, means for transmitting the generated answers to the user terminal, means for reminding users of accounting information and procedures, and means for using a virtual currency payment platform that enables users to receive accounting consultations in real time. This allows users to quickly and efficiently obtain answers to their accounting questions and ensure that important accounting procedures are not forgotten. Furthermore, the system automates the allocation of reward points to accountants, and it is expected that the latest information will always be reflected in the database.

[1683] The "means for inputting accounting inquiry data from users" is an interface that allows users to electronically input questions or inquiries about accounting.

[1684] The "means for analyzing input question data" refers to a means for using natural language processing techniques to understand the input question and extract relevant information.

[1685] The "means for searching a database based on the analyzed data" refers to a means for searching an appropriate database based on the analyzed question to obtain related information.

[1686] "Means for generating answers based on information from a database" refers to means for generating specific answers for users using information obtained through a search.

[1687] The "means for transmitting the generated answer to the user terminal" refers to a communication means for transmitting the generated answer to the user terminal and displaying it.

[1688] "Means of reminding users about accounting information and procedures" refers to means of informing users of important accounting procedures and deadlines so that they do not forget to carry them out.

[1689] "Means of using a virtual currency payment platform that enables users to receive accounting consultations in real time" refers to means of linking with an electronic payment system that enables users to receive accounting consultations in real time.

[1690] The "means for inputting information data from accountants" is an interface that accountants use to input new accounting-related information and legal amendment information into the system.

[1691] "Means for adding and updating input information data to a database" refers to a means for saving input accounting information in a database and updating existing information to the latest version.

[1692] "Means for training AI based on information added to the database" refers to a means for training an AI model using the latest accounting information added to the database to improve the accuracy of answers.

[1693] The "means for providing rewards to accountants who provide information" refers to a means for providing rewards such as points to accountants who provide information to the system.

[1694] A specific embodiment of the present invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and reminders. A distinctive feature of this invention is that it incorporates the use of a virtual currency payment platform.

[1695] System configuration

[1696] 1. User Device

[1697] A user terminal refers to a device such as a smartphone or tablet. Through this terminal, the user inputs accounting-related question data. For example, they can type "Please tell me how to process expenses" into a text box. The input information is sent to the server in JSON format or similar.

[1698] 2. Server

[1699] The server is a central system where the AI ​​engine and database run. It analyzes the question data sent by the user and searches for relevant database information. Based on the search results, a generative AI model (e.g., OpenAI's GPT-3) is used to generate an answer. The generated answer is sent to the user's device in an appropriate format. The server also manages accounting procedure reminders and periodically sends notifications to the user.

[1700] 3. Accountant's Terminal

[1701] The accountant terminal is a device that accountants use to input and send new accounting information and legal amendments. The information input by accountants through the terminal is sent to the server, where it is added to and updated in the database. The server uses this information to train the AI ​​engine, which is then used for subsequent searches and answer generation. Accountants are also awarded reward points, which can be viewed through the accountant terminal.

[1702] Processing flow

[1703] Handling user questions

[1704] Users input and send accounting-related questions from their device. The server analyzes the received question data and performs natural language processing using a generative AI model to extract keywords and themes and search the appropriate database. Based on the search results, the server generates an answer and sends it to the user's device.

[1705] Update from your accountant

[1706] Accountants input new accounting information and information about legal changes into their terminals and send it to the server. The server adds and updates the received data to the database, and the AI ​​engine learns the new information. This improves the accuracy of answers and provides more useful information to users.

[1707] Reminder function

[1708] The server has the ability to remind users of important accounting procedures, automatically sending notifications when certain deadlines are approaching, urging users not to forget to complete the procedures.

[1709] For example, a user may enter the question "How do you handle expenses?" This question is sent to the server through an API, and the AI ​​model generates an answer using the following prompt: "User asks: 'How do you handle expenses?' Please generate an answer."

[1710] As described above, this invention is a system that allows users to quickly and efficiently consult with their accountants and receive appropriate answers and reminders. This significantly improves user convenience and also provides a mechanism for providing appropriate compensation to accountants.

[1711] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1712] Step 1:

[1713] The user's terminal inputs accounting question data from the user. For example, the user enters a question such as "Please tell me how to process expenses" into a text box, and this data is sent to the server in JSON format.

[1714] Step 2:

[1715] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods" from the question, thereby clarifying the theme and focus of the question.

[1716] Step 3:

[1717] The server searches the database based on the analyzed data. Based on the extracted keywords, the server searches its internal accounting database to retrieve relevant FAQs and past cases. At this stage, the most appropriate information is collected to answer the user's question.

[1718] Step 4:

[1719] The server generates answers based on information from the database. The server uses a generative AI model (such as OpenAI's GPT-3) to perform natural language generation (NLG) based on the collected data and creates answers in a format that is easy for the user to understand.

[1720] Step 5:

[1721] The server sends the generated answer to the user's device in JSON format, allowing the user to view the answer to their question on their device.

[1722] Step 6:

[1723] The server will remind users of accounting information and procedures. Based on the user's registered information, the server will automatically generate reminder notifications when important accounting procedures or deadlines are approaching and send them to the user's device. These notifications will help users remember to complete the procedures before the deadline.

[1724] Step 7:

[1725] The accountant's terminal inputs and sends new accounting information and legal amendment information. The accountant inputs new information from his / her terminal and sends it to the server in JSON format.

[1726] Step 8:

[1727] The server adds and updates the entered information data to the database. The server saves the new information received from the accountant in the database and updates the existing information.

[1728] Step 9:

[1729] The server trains the AI ​​engine based on the information added to the database, and uses the new information to train the generative AI model, improving the accuracy of future question answers.

[1730] Step 10:

[1731] The server rewards the accountant who provided the information. The server calculates reward points based on the database and awards points to the accountant according to the information provided. The accountant can check their reward through the terminal.

[1732] This process step allows users to get quick and accurate accounting answers, ensures important procedures are not forgotten, and ensures that accountants are compensated accordingly, ensuring that information is not overdue.

[1733] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1734] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. Furthermore, it has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[1735] System Configuration

[1736] 1. User Device

[1737] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, tone of voice, etc.).

[1738] 2. Server

[1739] It is a central system running an AI engine, an emotion engine, and a database, which analyzes the question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes the user's emotions and adjusts the behavior of the entire system accordingly.

[1740] 3. Accountant's Terminal

[1741] A device used by accountants to input and submit data on new accounting information and legal changes, and to receive remuneration information from the server.

[1742] System Operation

[1743] Flow of processing user questions

[1744] 1. Input

[1745] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[1746] 2. Send

[1747] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[1748] 3. Analysis

[1749] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[1750] 4. Search

[1751] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[1752] 5. Answer generation

[1753] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[1754] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1755] 6. Transmission and Display

[1756] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1757] Information update flow

[1758] 1. Enter information

[1759] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[1760] 2. Send

[1761] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1762] 3. Data Update

[1763] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[1764] 4. Learning

[1765] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1766] 5. Providing rewards

[1767] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1768] Emotion Recognition Flow

[1769] 1. Entering emotion data

[1770] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1771] 2. Emotion analysis

[1772] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[1773] 3. Adjusting responses and reminders

[1774] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[1775] In this way, the system not only helps users solve their accounting problems quickly, but also provides more personalized responses through emotion recognition, improving user satisfaction and encouraging accountants to provide up-to-date information and get paid for it.

[1776] The processing flow will be explained below.

[1777] Question processing flow using emotion engine

[1778] Step 1: User enters question

[1779] The user enters a billing question into their device.

[1780] For example: "How do you handle expenses?"

[1781] Step 2: Submit your question data

[1782] The user terminal transmits the input question data to the server.

[1783] The data is sent to the server using a format such as JSON.

[1784] Step 3: Sending emotion data

[1785] The user terminal transmits emotion recognition data such as voice input and facial expression data to the server.

[1786] Emotion data is also sent to the server in JSON format.

[1787] Step 4: Analyze the Question Data

[1788] The server analyzes the received query data.

[1789] Natural language processing (NLP) is used to extract important keywords such as "expenses" and "processing methods."

[1790] Step 5: Analyze the sentiment data

[1791] The server analyzes the received emotion data using an emotion engine.

[1792] It uses voice tone analysis and facial expression analysis to identify the user's emotional state (e.g., joy, stress, tension, etc.).

[1793] Step 6: Database Search

[1794] The server searches its internal database based on the analysis results and emotional state.

[1795] Search our database for accounting FAQs, past cases, and relevant laws and regulations.

[1796] Step 7: Answer Generation

[1797] The server generates the best answer based on the information obtained from the database.

[1798] Use natural language generation (NLG) to create answers in a user-friendly format.

[1799] The tone and content of the response is adjusted depending on the user's emotional state.

[1800] For example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1801] Step 8: Submit your response

[1802] The server transmits the generated answer to the user terminal.

[1803] The response is sent to the user's device using JSON format or similar.

[1804] Step 9: View your answers

[1805] The user terminal displays the received answer to the user.

[1806] Display responses in an easy-to-read format (for example, a web page or an in-app message).

[1807] Information update flow

[1808] Step 1: Enter your information

[1809] Accountants input new accounting information and information about legal changes into their terminals.

[1810] Example: Enter "Important changes to the 2023 tax reform."

[1811] Step 2: Send information data

[1812] The accountant terminal transmits the input information data to the server.

[1813] The data is sent to the server using a format such as JSON.

[1814] Step 3: Save and update information

[1815] The server stores and updates the information received from the accountant in a database.

[1816] Verify the accuracy of the information and update the relevant entries in our database.

[1817] Step 4: Training the AI ​​engine

[1818] The server updates and trains the AI ​​engine based on new information.

[1819] Machine learning algorithms are used to improve the accuracy of answers by taking new information into account.

[1820] Step 5: Offer Rewards

[1821] The server rewards the accountant who adds the information.

[1822] Rewards are awarded as points within the system and reflected in the accountant's account.

[1823] Adjusting emotion recognition

[1824] Step 1: Input emotion data

[1825] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1826] Step 2: Sentiment Analysis

[1827] The server's emotion engine analyzes the received emotion data and identifies the user's emotional state.

[1828] For example, if a user is feeling stressed, their emotional state is identified.

[1829] Step 3: Adjusting responses and reminders

[1830] The server adjusts the tone and content of the responses and reminders it generates based on the results of sentiment analysis.

[1831] For example, users who are feeling stressed will receive answers and reminders in a more considerate tone.

[1832] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[1833] Example 2

[1834] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1835] Conventional accounting consultation systems have the problem that they provide uniform answers to users' accounting questions and are unable to provide personalized responses based on the user's emotional state. Additionally, the latest accounting information and legal amendments provided by accountants are not updated immediately, making it difficult for users to obtain the latest and accurate information.

[1836] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1837] In this invention, the server includes a means for inputting accounting question data and emotion recognition data from a user, a means for analyzing the input data and extracting important keywords and the user's emotional state, a means for searching a database based on the analyzed data and emotional state, a means for generating an answer based on the user's emotional state based on information obtained from the database, and a means for transmitting the generated answer to a user terminal and displaying it. This allows users to receive prompt and appropriate answers to their accounting questions and also enables personalized responses based on the user's emotional state. In addition, the latest accounting information and legal amendments provided by accountants are quickly updated in the system, ensuring that the latest and most accurate information is always provided.

[1838] "Accounting question data from users" refers to specific accounting-related questions that users input into the system.

[1839] "Emotion recognition data" refers to data that represents a user's emotional state, obtained from the user's voice, facial expressions, etc.

[1840] "Means for analyzing" refers to software and hardware functions for identifying important keywords and the user's emotional state based on input data.

[1841] "Means for searching the database" refers to the algorithms and associated hardware for searching the internal database for appropriate information based on the analyzed data.

[1842] "Answer generation means" refers to software and hardware capabilities for generating answers to user questions based on information retrieved from a database and using natural language generation techniques.

[1843] "User Device" means the device (e.g., smartphone, tablet, PC) used by a User to enter accounting questions and receive answers.

[1844] "Information data from accountants" refers to the latest accounting information and data on legal changes provided to the system by accountants.

[1845] "Means for adding and updating the database" refers to the software and hardware functions for storing information data entered by accountants in the internal database and updating existing data.

[1846] "Means for training the AI ​​engine" refers to the algorithms and related hardware that allow the AI ​​to learn from the information added to the database and use it in future question-answering processes.

[1847] "Means for awarding rewards" refers to the software and hardware functions for adding reward points within the system to accountants who provide information, and for managing and displaying these points.

[1848] "Means for setting and sending reminders" refers to software and hardware functions that notify users of important accounting deadlines and send such reminder information to the user's device.

[1849] A specific embodiment of this invention is described below. This system allows users to easily ask questions about accounting and receive appropriate answers and procedural reminders. It also has the function of recognizing the user's emotions and adjusting the answers and reminders based on those emotions.

[1850] System Configuration

[1851] The system consists of the following main components:

[1852] User Device

[1853] This is a device used by users to input questions and inquiries about accounting. It also has the function of displaying answers and reminders sent from the server. It also has the function of inputting data for emotion recognition (facial expressions, voice tone, etc.). Specifically, smartphones, tablets, PCs, etc. are used as user devices. An accounting consultation application and emotion recognition software such as Emotion SDK are installed on the user device.

[1854] server

[1855] This is a central system running an AI engine, an emotion engine, and a database. It analyzes question data received from users, searches the appropriate database, generates answers, and updates the information provided by accountants. It also recognizes user emotions and adjusts the behavior of the entire system accordingly. The server runs on a virtual machine in the cloud (e.g., AWS EC2). The software used includes a natural language processing engine (e.g., GPT-4), an emotion recognition engine (e.g., EmotionAPI), and a database management system (e.g., MySQL).

[1856] Accountant's Terminal

[1857] This is a device used by accountants to input and send data on new accounting information and legal amendments. It also receives compensation information from the server. Accountant terminals are PCs or tablets with a dedicated accounting information input application installed.

[1858] System Operation

[1859] Handling user questions

[1860] 1. Input

[1861] The user uses the device to input a question about accounting, for example, "Please tell me how to process expenses." Data for emotion recognition (e.g., voice input and facial expression data) is automatically sent to the server.

[1862] 2. Send

[1863] The user device sends question data and emotion recognition data to the server in a format such as JSON.

[1864] 3. Analysis

[1865] The server analyzes the received question data and uses natural language processing (NLP) to extract important keywords such as "expenses" and "processing methods." In parallel, the emotion engine analyzes the emotion recognition data and identifies the user's emotional state.

[1866] 4. Search

[1867] The server searches an internal database based on the analysis results and emotional state, searching for accounting-related FAQs, past cases, and relevant laws and regulations.

[1868] 5. Answer generation

[1869] The server generates the best answer based on information retrieved from the database. The answer is presented in a user-friendly format using natural language generation (NLG), and the tone and content of the answer are adjusted depending on the user's emotional state.

[1870] Example: For a user in a positive emotional state, generate a positive response such as, "Organizing receipts, creating expense reports, and categorizing expenses are important ways to handle expenses. Let's get started!"

[1871] 6. Transmission and Display

[1872] The server generates an answer and sends it to the user's device, which displays it to the user, who can then check the answer on the device.

[1873] Information update flow

[1874] 1. Enter information

[1875] An accountant enters new accounting information or legal changes into the accountant's terminal. For example, they enter "Important changes regarding the 2023 tax reform."

[1876] 2. Send

[1877] The accountant's terminal sends the information data to the server, which is also sent in a format such as JSON.

[1878] 3. Data Update

[1879] The server stores and updates the received information data in its internal database, ensuring that the database always contains the latest information.

[1880] 4. Learning

[1881] The server's AI engine learns from the new information and uses it for subsequent searches and answer generation.

[1882] 5. Providing rewards

[1883] The server rewards the accountant who provided the information in the form of points within the system. The points are displayed on the accountant's terminal and can later be used for advertising, etc.

[1884] Emotion Recognition Flow

[1885] 1. Entering emotion data

[1886] As soon as the user enters a question, data for recognizing the user's emotions (facial expressions, tone of voice, etc.) is automatically sent from the user's device to the server.

[1887] 2. Emotion analysis

[1888] The emotion engine on the server analyzes the received emotion data and identifies the user's emotional state, for example, if the user is feeling stressed, that emotional state is identified.

[1889] 3. Adjusting responses and reminders

[1890] The server adjusts the tone and content of the generated replies and reminders based on the analysis results of the emotion engine, so that users who are feeling stressed will receive replies and reminders in a kinder and more considerate tone.

[1891] In this way, the system not only allows users to quickly resolve their accounting concerns, but also provides more personalized responses through emotion recognition, which increases user satisfaction and creates an environment where accountants can provide up-to-date information and be compensated for it.

[1892] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1893] The flow of this system's program processing

[1894] Step 1: User Input

[1895] Users use the device to input their billing questions, and data such as voice and facial expressions are automatically collected for emotion recognition.

[1896] Input: The user types in a question by voice, such as "Please tell me how to process expenses." The user's facial expression is captured by the camera.

[1897] Output: Question text data and emotion recognition data (voice, facial expression)

[1898] Step 2: Sending data

[1899] The device sends the collected question data and emotion recognition data to the server in a specified format (e.g., JSON).

[1900] Input: Question data and emotion recognition data

[1901] Output: Question data and emotion recognition data in JSON format

[1902] Step 3: Analyze the data

[1903] The server analyzes the received data. A natural language processing engine is used to extract important keywords (e.g., "expenses" and "processing methods") from the question text. An emotion engine analyzes the emotion data to identify the user's emotional state.

[1904] Input: JSON-formatted question data and emotion recognition data

[1905] Output: Extracted keywords and emotional state data

[1906] Step 4: Search the database

[1907] The server searches its internal database based on the analysis results, extracting relevant FAQs, past cases, and legal and regulatory information.

[1908] Input: Extracted keywords and emotional state data

[1909] Output: Dataset of search results (FAQs, past cases, legal and regulatory information)

[1910] Step 5: Generate an answer

[1911] The server uses a natural language generation engine to generate answers based on a dataset of search results, adjusting the tone and content of the answers depending on the user's emotional state.

[1912] Input: Search result dataset, emotional state data

[1913] Output: Generated answer text

[1914] Step 6: Submit and view your responses

[1915] The server generates a response and sends it to the user's device, where the user can view it. The response is displayed in a user-friendly format.

[1916] Input: Generated answer text

[1917] Output: The answer displayed on the user's terminal

[1918] Step 7: Accountant updates information

[1919] Accountants use accountant terminals to input and submit data on new accounting information and legal changes.

[1920] Input: New accounting information and legal change data

[1921] Output: Data update request

[1922] Step 8: Update the internal database

[1923] The server saves and updates the information data received from the accountant in its internal database, ensuring that the information in the database is up to date.

[1924] Input: Data update request

[1925] Output: Updated database

[1926] Step 9: Learning new information

[1927] The server's AI engine learns from the new information and uses it in future question-answering processes.

[1928] Input: Updated database

[1929] Output: Trained AI model

[1930] Step 10: Rewarding

[1931] The server gives reward points to the accountant who provided the information, and the point information is displayed on the accountant's terminal.

[1932] Input: Information provision history

[1933] Output: Reward points awarded

[1934] The above is the specific processing flow of this system. Based on this flow, users can get quick and appropriate answers regarding accounting matters, and accountants can receive the latest information and receive compensation.

[1935] (Application example 2)

[1936] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1937] There is a demand for providing an environment in which users can smoothly answer questions and complete procedures related to checkouts at physical stores. It is also necessary to improve the quality of service by responding flexibly to users' emotions. Conventional systems have responded uniformly without considering users' emotions to meet these needs, which has prevented them from fully achieving user satisfaction.

[1938] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1939] In this invention, the server includes means for inputting transaction-related question data from a user, means for analyzing the input question data, means for searching a database based on the analyzed data, means for generating an answer based on information from the database, means for sending the generated answer to the user terminal, means for recognizing the user's emotion, and means for adjusting the content and tone of the answer based on the recognized emotion. This allows for the provision of an optimal answer based on the user's emotion, enabling smooth and personalized transaction procedures in physical stores.

[1940] "User" means any person or organization that uses this system to ask or seek advice about accounting matters.

[1941] "Question data" refers to data including questions and inquiries about accounting that users enter into the system.

[1942] "Means of analysis" refers to the means for understanding the question data entered by the user and extracting important keywords and information.

[1943] "Database" means a storage device containing accounting information, FAQs, and related legal and regulatory information.

[1944] "Answer generation means" refers to a means for creating an appropriate answer to a user's question based on information obtained from a database.

[1945] "User terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1946] "Means for recognizing emotions" refers to means for identifying a user's emotional state through data such as facial expressions and tone of voice.

[1947] "Means for adjusting the content and tone of responses" refers to means for appropriately changing the content and tone of responses depending on the user's emotional state.

[1948] "Accountants" are professionals who provide specialized accounting information to this system.

[1949] "Information data" refers to data provided by accountants, including information on new accounting information and legal changes.

[1950] An "AI engine" is an artificial intelligence engine that learns from data and generates optimal answers.

[1951] "Means for providing rewards" means means for providing rewards to accountants who provide information to the system.

[1952] "Means for setting and sending reminders" refers to a means for notifying users of deadlines for accounting procedures and important matters that they tend to forget.

[1953] MODE FOR CARRYING OUT THE INVENTION

[1954] The system for realizing this invention mainly includes three main components: a user terminal, a server, and an accountant terminal. Each component functions as follows:

[1955] User Device

[1956] User devices refer to devices such as smartphones, tablets, and PCs. User devices have the following functions:

[1957] 1. Input of question data: Users can input their accounting questions or inquiries.

[1958] 2. Emotion data capture: Emotion recognition data is acquired through the user's facial expressions and voice tone and sent to the server.

[1959] 3. View Answers: View answers and reminders sent from the server.

[1960] server

[1961] The server has the following functions:

[1962] 1. Data analysis: Question data sent by users is analyzed using a natural language processing (NLP) engine to extract important keywords.

[1963] 2. Database search: Based on the analysis results, the internal database is searched to obtain related information.

[1964] 3. Answer generation: Answers are generated based on information retrieved from the database, using a natural language generation (NLG) engine.

[1965] 4. Emotion Recognition: Use an emotion recognition engine to analyze the user's emotional state and adjust the content and tone of the response.

[1966] 5. Accounting information update: Add / update new information provided by accountants to the database.

[1967] 6. AI engine learning: The AI ​​engine learns from the newly added information and uses it for subsequent searches and answer generation.

[1968] Accountant's Terminal

[1969] The accountant terminal is a device that allows accountants to input and submit the latest accounting information and receive compensation.

[1970] 1. Information data entry: Accountants enter information about new accounting information and legal changes.

[1971] 2. Sending information data: Send the entered information data to the server.

[1972] 3. Receiving rewards: Receive reward points in exchange for providing information.

[1973] Example of overall system operation

[1974] As a specific example of the operation of this system, consider the following scenario.

[1975] Scenario 1: User enters accounting question

[1976] 1. A user uses their smartphone to type, "How do I process expenses?"

[1977] 2. At the same time, the user's facial expression data and voice tone are automatically captured and sent to the server.

[1978] 3. The server analyzes the question data and extracts important keywords such as "expenses" and "processing methods."

[1979] 4. The server uses an emotion recognition engine to identify when the user is in a positive emotional state.

[1980] 5. The server searches the database and retrieves the relevant information.

[1981] 6. The server uses its NLG engine to generate a positive response such as, "The key to managing expenses is organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[1982] 7. The server sends the generated answer to the user's terminal, and the user checks the answer on the terminal.

[1983] Sample prompt sentence

[1984] Below are some example prompts to aid in the analysis of the emotion recognition engine and the generation of answers by the NLG engine:

[1985] "Generate instructions for credit card payment methods for when users are nervous."

[1986] "Write a description of how to process expenses for users in a positive emotional state."

[1987] These prompts are then used by a generative AI model to generate answers tailored to specific situations.

[1988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1989] Step 1:

[1990] A user uses a user device such as a smartphone or tablet to input a question about accounting, for example, "Please tell me how to process expenses." At the same time, the user device uses a camera and microphone to capture facial expression data and voice tone, and obtains this data.

[1991] Input: accounting question data, facial expression data, voice tone

[1992] Output: Question data and emotion recognition data

[1993] Step 2:

[1994] The user device sends the acquired question data and emotion recognition data to the server in a standard format such as JSON.

[1995] Input: Question data, emotion recognition data

[1996] Output: JSON formatted data sent

[1997] Step 3:

[1998] The server receives the question data and analyzes it using a natural language processing (NLP) engine. This extracts important keywords and phrases. For example, the keywords "expenses" and "processing method" are extracted.

[1999] Input: Question data

[2000] Output: Extracted keywords

[2001] Step 4:

[2002] The server analyzes the received emotion data using an emotion recognition engine, which identifies the user's emotional state (e.g., positive, negative, nervous, etc.).

[2003] Input: Emotion recognition data

[2004] Output: Identified emotional state

[2005] Step 5:

[2006] The server searches a database based on the extracted keywords and the identified emotional state. The database includes accounting-related FAQs, past cases, and relevant legal information. For example, it searches for information related to "expenses" and "processing methods."

[2007] Input: extracted keywords, identified emotional states

[2008] Output: Retrieved information

[2009] Step 6:

[2010] The server uses a natural language generation (NLG) engine to generate answers based on information obtained from the database. It adjusts the tone and content of the answer depending on the user's emotional state. For example, in a positive emotional state, it generates a positive answer such as, "Important expense management methods include organizing receipts, creating expense reports, and categorizing expenses. Let's get started!"

[2011] Input: Retrieved information, identified emotional state

[2012] Output: The generated answer

[2013] Step 7:

[2014] The server sends the generated answer to the user terminal, which receives the answer and displays it on the screen, allowing the user to check and understand the provided answer.

[2015] Input: Generated Answer

[2016] Output: Answer sent to user terminal

[2017] Step 8:

[2018] The accountant's terminal inputs new accounting information and information on legal amendments and transmits it to the server. For example, the accountant inputs "the latest tax reform points."

[2019] Input: New Accounting Information

[2020] Output: Transmitted information data

[2021] Step 9:

[2022] The server saves and updates the received information data in a database, so that the database always contains the latest information.

[2023] Input: Information data

[2024] Output: Updated database

[2025] Step 10:

[2026] The server rewards the accountant who provided the information as points in the system, which are displayed on the accountant's terminal.

[2027] Input: Record of information provided

[2028] Output: Reward as points

[2029] In this way, the system provides optimal answers based on the user's emotions and efficiently updates information from accountants.

[2030] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2031] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2032] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2033] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2034] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2035] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2036] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2037] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2038] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2039] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2040] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2041] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2042] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2043] 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.

[2044] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2045] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2046] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2047] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2048] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2049] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2050] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2051] The following is further disclosed regarding the above embodiment.

[2052] (Claim 1)

[2053] a means for inputting accounting question data from a user;

[2054] means for analyzing input question data;

[2055] A means for searching the database based on the analyzed data;

[2056] a means for generating an answer based on information from the database;

[2057] means for transmitting the generated answer to a user terminal;

[2058] A system including:

[2059] (Claim 2)

[2060] a means for inputting information data from an accountant;

[2061] A means for adding and updating input information data to a database;

[2062] A means to train the AI ​​engine based on the information added to the database, and

[2063] a means of rewarding accountants who provide information;

[2064] The system of claim 1 further comprising:

[2065] (Claim 3)

[2066] 10. The system of claim 1, further comprising means for setting and sending reminders to the user regarding checkout procedures.

[2067] "Example 1"

[2068] (Claim 1)

[2069] a means for inputting accounting question data from a user;

[2070] means for analyzing input question data;

[2071] A means for searching the database based on the analyzed data;

[2072] a means for generating an answer based on information from the database;

[2073] means for transmitting the generated answer to a user terminal;

[2074] A system including:

[2075] (Claim 2)

[2076] a means for inputting information data from an accountant;

[2077] A means for adding and updating input information data to a database;

[2078] a means for training a learning model based on the information added to the database;

[2079] a means of rewarding accountants who provide information;

[2080] The system of claim 1 further comprising:

[2081] (Claim 3)

[2082] A means for analyzing question data using natural language processing technology;

[2083] a means for generating an answer using natural language generation technology;

[2084] The system of claim 1 further comprising:

[2085] (Claim 4)

[2086] 10. The system of claim 1, further comprising means for setting and sending reminders to the user regarding checkout procedures.

[2087] "Application Example 1"

[2088] (Claim 1)

[2089] a means for inputting accounting question data from a user;

[2090] means for analyzing input question data;

[2091] A means for searching the database based on the analyzed data;

[2092] a means for generating an answer based on information from the database;

[2093] means for transmitting the generated answer to a user terminal;

[2094] A means of reminding you about accounting information and procedures,

[2095] A means to use a cryptocurrency payment platform that allows users to receive real-time accounting consultations;

[2096] A system including:

[2097] (Claim 2)

[2098] a means for inputting information data from an accountant;

[2099] A means for adding and updating input information data to a database;

[2100] A means to train AI based on the information added to the database, and

[2101] a means of rewarding accountants who provide information;

[2102] The system of claim 1 further comprising:

[2103] (Claim 3)

[2104] 10. The system of claim 1, further comprising means for setting and sending reminders to the user regarding checkout procedures.

[2105] "Example 2: Combining Emotion Engines"

[2106] (Claim 1)

[2107] A means for inputting accounting question data and emotion recognition data from a user;

[2108] A means for analyzing the input data and extracting important keywords and the user's emotional state;

[2109] a means for searching the database based on the analyzed data and the emotional state;

[2110] A means for generating an answer according to the emotional state of the user based on information obtained from the database;

[2111] means for transmitting the generated answer to a user terminal and displaying it;

[2112] A system including:

[2113] (Claim 2)

[2114] a means for inputting information data from an accountant;

[2115] a means for adding and updating input information data to a database;

[2116] A means to train the AI ​​engine based on the information added to the database, and

[2117] a means of rewarding accountants who provide information;

[2118] The system of claim 1 further comprising:

[2119] (Claim 3)

[2120] 10. The system of claim 1, further comprising means for setting and sending checkout procedure reminders to the user.

[2121] "Application example 2 when combining emotion engines"

[2122] (Claim 1)

[2123] a means for inputting accounting question data from a user;

[2124] means for analyzing input question data;

[2125] A means for searching the database based on the analyzed data;

[2126] a means for generating an answer based on information from the database;

[2127] means for transmitting the generated answer to a user terminal;

[2128] a means of recognizing a user's emotions;

[2129] a means of adjusting the content and tone of responses based on perceived sentiment;

[2130] A system including:

[2131] (Claim 2)

[2132] a means for inputting information data from an accountant;

[2133] A means for adding and updating input information data to a database;

[2134] A means to train the AI ​​engine based on the information added to the database, and

[2135] a means of rewarding accountants who provide information;

[2136] The system of claim 1 further comprising:

[2137] (Claim 3)

[2138] 10. The system of claim 1, further comprising means for setting and sending reminders to the user regarding checkout procedures. [Explanation of symbols]

[2139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting accounting question data from a user; means for analyzing input question data; A means for searching the database based on the analyzed data; a means for generating an answer based on information from the database; means for transmitting the generated answer to a user terminal; A system including:

2. a means for inputting information data from an accountant; A means for adding and updating input information data to a database; A means to train the AI ​​engine based on the information added to the database, and a means of rewarding accountants who provide information; The system of claim 1 further comprising:

3. The system according to claim 1, further comprising means for setting and sending reminders for checkout procedures to the user.

Images