system

Abstract

Provide a system. The method includes: receiving a question about tax returns from a user; means for sending a query to a server; a means for the server to include an artificial intelligence module for generating answers to questions; means for returning the generated answer to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] Today, many individuals and businesses are troubled by procedures and questions related to tax returns. While these issues require the assistance of experts, accessing such professionals often takes time and is expensive. Furthermore, when searching for questions online, unreliable information is mixed in, and it can take a long time to find the right answer. This creates a lack of understanding about tax returns, making it difficult to complete the procedures accurately and efficiently. [Means for solving the problem]

[0005] The present invention provides a means for receiving questions about tax returns from users and transmitting them to a server. The server uses an artificial intelligence module to generate answers to the questions and return the answers to the users. This system allows users to quickly and accurately resolve their tax return-related concerns. Specifically, the artificial intelligence module uses natural language processing technology to analyze the questions and generate prompts. Answers are then generated based on the prompts and provided to the users, thereby streamlining the provision of information about tax returns. Another feature of the present invention is that it is designed to enable users to receive specific and detailed answers to their questions.

[0006] "User" refers to an individual or business that uses the system to enter questions about tax returns and receive answers.

[0007] "Filing a tax return" refers to the process by which individuals and companies report their income and expenses to tax authorities and calculate and pay the necessary taxes.

[0008] "Means for receiving questions" refers to an interface or software component that collects text input from a user and transmits it to the system.

[0009] A "server" refers to a computer system that receives questions submitted by users and generates and returns answers to those questions.

[0010] The "means for transmitting a question to a server" refers to a software component for transmitting a question by performing data communication from a user's terminal to a server.

[0011] "Artificial Intelligence Module" refers to a program that uses natural language processing techniques and machine learning models to generate appropriate answers to questions.

[0012] The "means for returning the generated answer to the user" refers to a software component for transmitting the generated answer from the server to the user's terminal via data communication.

[0013] "Natural language processing" refers to the technology that enables computers to understand human language and process and generate it appropriately.

[0014] "Prompt" refers to input data equivalent to an instruction or command to input a question to an artificial intelligence module and generate an answer.

[0015] "Answer" refers to a text-based response generated by an artificial intelligence module in response to a user-submitted question. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The following describes an embodiment of the present invention.

[0038] Overall system overview

[0039] The present invention provides a system that receives tax return inquiries from users and transmits them to a server, which uses an artificial intelligence module to generate answers to the inquiries and transmits the answers back to the users. This system allows users to quickly and accurately resolve their tax return inquiries.

[0040] Main components and roles

[0041] 1. Device:

[0042] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0043] Display the response from the server.

[0044] 2. User:

[0045] Use the device to enter your tax return questions.

[0046] 3. Server:

[0047] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0048] Program processing flow

[0049] 1. Terminal processing

[0050] The user uses the terminal interface to enter a question following the prompt "Ask a question about your tax return." For example, the user enters a question such as "What can I claim as an expense?" The terminal receives the question entered by the user and sends it to the server via the send_question function. The server processes the question.

[0051] 2. Server-side processing

[0052] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. It then passes the question to the generate_response function and uses the AI ​​model to generate an answer to the question. For example, in response to the question "What items can be claimed as expenses?", the AI ​​model generates the answer "Generally, stationery and travel expenses required for sales can be claimed as expenses." The server then returns this answer in JSON format as a response.

[0053] 3. Displaying the terminal

[0054] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0055] Specific examples

[0056] For example, if a user types "What can I claim as an expense?" into a terminal, the following process will occur:

[0057] 1. The user types a question into the terminal.

[0058] 2. The device sends this question to the server.

[0059] 3. The server receives the question and generates an answer using an AI model.

[0060] 4. The server generates a response and sends it back to the device.

[0061] 5. The device displays the answer to the user.

[0062] This system allows users to quickly obtain accurate information about tax returns, such as accurately understanding the procedures for filing tax returns, gathering the necessary documents, and obtaining reference information for claiming appropriate expenses.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user enters a question about their tax return, such as "What can I claim as an expense?" through a user interface.

[0066] Step 2:

[0067] The device receives the question entered by the user and prepares it to be sent to the server. Specifically, it converts the user's question into JSON format and creates an HTTP POST request.

[0068] Step 3:

[0069] The device passes the question to the server by sending a prepared HTTP POST request to a URL that points to a pre-configured API endpoint (e.g., http: / / example.com / api / ask).

[0070] Step 4:

[0071] The server receives a POST request on the / api / ask endpoint and extracts the JSON data from the request body to get the submitted question.

[0072] Step 5:

[0073] The server sets the question as a prompt to be passed to the AI ​​module, which then sends the question to the AI ​​model and generates an answer using natural language processing technology.

[0074] Step 6:

[0075] The server receives the answer returned by the AI ​​module. For example, it may generate an answer such as, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0076] Step 7:

[0077] The server sets the generated answer as a JSON format response in the HTTP response body and returns it to the terminal.

[0078] Step 8:

[0079] The device receives the response from the server, extracts the JSON data from the response body, and obtains the answer text in the response field.

[0080] Step 9:

[0081] The device displays the extracted answer text on the user interface. Specifically, it displays the answer as "AI's answer: Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0082] Step 10:

[0083] Users can check the answers displayed on the device to resolve any questions they may have about their tax return, and can also ask additional questions if necessary.

[0084] Example 1

[0085] 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."

[0086] One issue is the difficulty of providing quick and accurate answers to questions about tax returns. Conventional systems require users to consult with experts, which takes a lot of time and effort. In addition, information on the Internet can be unreliable, making it difficult for users to make appropriate decisions.

[0087] 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.

[0088] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the users, and means for the terminal to receive the answers from the server and display them to the users, thereby enabling users to quickly and accurately resolve their questions about tax returns.

[0089] A "user" is a person who enters a question about a tax return.

[0090] "Filing a tax return" refers to the procedure for calculating and paying tax on income.

[0091] A "question" refers to text entered by a user inquiring about information they would like to know about their tax return.

[0092] "Terminal" refers to a device or software that provides a user interface, receives questions from a user, sends them to a server, and displays answers from the server.

[0093] "Server" refers to a computer system on a network that receives questions sent by users, generates answers using an artificial intelligence module, and returns the generated answers to the terminal.

[0094] An "artificial intelligence module" is a computer program or system that uses natural language processing techniques to generate answers to questions.

[0095] "Natural language processing" is a technical field that uses computers to understand and process human language.

[0096] A "prompt" refers to a form of instruction or input to an artificial intelligence module that generates a specific answer.

[0097] "Answer" refers to the text of information generated by the artificial intelligence module in response to a user's question.

[0098] "Transmission" refers to the act of a terminal transferring a question to a server, or a server transferring an answer to a terminal via a network.

[0099] "Display" refers to visually showing the answer received by the terminal from the server so that the user can confirm it.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] The present invention is a system that receives questions about tax returns from users, transmits the questions to a server, and the server uses an artificial intelligence module to generate answers that are returned to the user.

[0102] System configuration and hardware / software used

[0103] The main components of the present invention are:

[0104] 1. Device:

[0105] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0106] Display the response from the server.

[0107] Specific hardware used includes computers, smartphones, tablets, etc. Software used includes web browsers and mobile apps.

[0108] 2. User:

[0109] Use the device to enter your tax return questions.

[0110] Users can use this system when they want to quickly resolve questions about tax return expense claims and procedures.

[0111] 3. Server:

[0112] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0113] Specific hardware used includes a cloud server or an on-premise server, and software includes an HTTP server, a database, and an artificial intelligence module (e.g., GPT-3 (registered trademark)).

[0114] Program processing and data processing / calculation

[0115] The terminal receives a question from the user through the user interface. The question is sent to the server through the send_question function. The question is sent using an HTTP POST request.

[0116] The server processes the received question at the / api / ask endpoint, extracts the question data from the request body, and passes it to the generate_response function, which uses a generative AI model (e.g., GPT-3) to generate an answer to the question.

[0117] The generated answer is sent back to the terminal in JSON format, and the terminal receives this response, extracts the answer text, and displays it to the user.

[0118] Specific examples

[0119] For example, if a user types "What can I claim as an expense?" into a terminal, the following will happen:

[0120] 1. The user types a question into the terminal.

[0121] 2. The device sends this question to the server.

[0122] 3. The server receives the question and generates an answer using a generative AI model (e.g., GPT-3).

[0123] 4. The server generates a response and sends it back to the device.

[0124] 5. The device displays the answer to the user.

[0125] Prompt Sentence Examples

[0126] An example prompt for the question "What can you claim as an expense?" is:

[0127] Generate accurate answers to the following questions: What can I claim as an expense?

[0128] Based on this prompt, the generative AI model will generate an appropriate answer, such as "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0129] The above is an embodiment of the present invention, which allows users to quickly and accurately resolve questions about tax returns.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Program processing steps

[0132] Step 1:

[0133] The user enters a question into the device's user interface. The entered question is in text format. Specifically, the user types "What can I claim as an expense?" into an input field in a browser or mobile app. The input field has a "Submit" button, which the user clicks to proceed to the next step.

[0134] Input: A question typed by the user (e.g., "What can I claim as an expense?")

[0135] Output: The question text entered

[0136] Step 2:

[0137] The device takes the user's input and calls the send_question function to send the question to the server. The question is sent as an HTTP POST request to the server's / api / ask endpoint, with the question text included in the request body.

[0138] Input: The question text entered by the user

[0139] Output: The question text in the HTTP POST request to the server

[0140] Step 3:

[0141] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and passes it to the generate_response function. At this point, the server holds the question text from the request.

[0142] Input: Question text included in HTTP POST request

[0143] Output: Question data passed to the generate_response function

[0144] Step 4:

[0145] The server calls a generative AI model (e.g., GPT-3) in the generate_response function to generate an answer to the question. The generative AI model uses the prompt to generate an appropriate answer to the question. For example, in response to a question about expenses, it might respond, "Generally, stationery and transportation costs required for business can be claimed as expenses."

[0146] Input: Question data passed to the generate_response function

[0147] Output: Answer text generated by the generative AI model

[0148] Step 5:

[0149] The server formats the generated answer in JSON format and sends it to the device as an HTTP response, with the answer text included in the response field of the response body.

[0150] Input: Answer text generated by the generative AI model

[0151] Output: JSON formatted HTTP response sent to the device

[0152] Step 6:

[0153] The device receives the response from the server and extracts the answer text from the response field. The extracted answer text is visually displayed to the user. The UI displays "AI answer: Generally, stationery and transportation costs required for business can be claimed as expenses."

[0154] Input: JSON format HTTP response from the server

[0155] Output: The answer text that is displayed to the user

[0156] The specific actions at each step allow users to quickly and accurately resolve their tax return questions.

[0157] (Application example 1)

[0158] 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."

[0159] Current systems for solving questions about tax returns have limitations in terms of enabling users to quickly and accurately obtain information. Furthermore, there is a lack of systems for users to resolve questions about procedures and electronic payments, which reduces user convenience. Therefore, there is a need for a system that provides quick and accurate answers to questions about tax returns and electronic payments.

[0160] 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.

[0161] In this invention, the server includes a means for an artificial intelligence module to generate answers to questions using natural language processing, a means for returning the generated answers to the user, and a means for analyzing the user's questions and sending prompts to the artificial intelligence module to provide specific answers, thereby enabling quick and accurate answers to be provided to questions from the user regarding tax returns and electronic payments.

[0162] The "means for receiving questions about tax returns from users" is an interface that allows the system to recognize and receive questions about tax returns entered by users.

[0163] The "means for transmitting a question to a server" is a communication function for transmitting a question received by a terminal to a server via a network.

[0164] "Means including an artificial intelligence module for the server to generate answers to questions" refers to a system in which an AI module installed in the server generates appropriate answers to received questions.

[0165] The "means for returning the generated answer to the user" is a function for transmitting the answer generated by the server to the user's terminal and displaying it.

[0166] The "means for receiving a question about electronic payment from a user" is an interface that allows the system to recognize and receive a question about electronic payment input by a user.

[0167] The "means for transmitting questions about electronic payment to a server" is a communication function for transmitting questions about electronic payment received by the terminal to a server via a network.

[0168] "Means including an artificial intelligence module for the server to generate answers to questions about electronic payments" refers to a system in which an AI module installed in the server generates appropriate answers to received questions about electronic payments.

[0169] The "means for returning the generated answer regarding electronic payment to the user" is a function for transmitting the answer regarding electronic payment generated by the server to the user's terminal and displaying it.

[0170] The following describes an embodiment of the present invention.

[0171] Overall system overview

[0172] The present invention is a system that receives questions from users about tax returns and electronic payments, sends them to a server, and the server uses an AI module to generate answers to the questions and returns the answers to the users. This system allows users to quickly and accurately resolve their questions about tax returns and electronic payments.

[0173] Main components and roles

[0174] 1. Device:

[0175] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0176] Display the response from the server.

[0177] 2. User:

[0178] Use the terminal to enter questions about tax returns and electronic payments.

[0179] 3. Server:

[0180] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0181] Program processing flow

[0182] Program for carrying out the invention

[0183] First, the user uses the terminal interface to input questions about tax returns or electronic payments. For example, they can input questions such as, "What can I claim as an expense?" or "At which stores can I use this electronic payment method?" The terminal then sends the input questions to the server, where they are processed.

[0184] Hardware and Software

[0185] The hardware used includes devices such as smartphones and tablets to provide the user interface, and a high-performance server to process questions and answers.

[0186] The software used is an AI module for natural language processing and an API endpoint for managing network communications. The specific AI module used is a generative AI model.

[0187] Data processing and calculation

[0188] The server receives the questions sent from the device and generates appropriate answers using the AI ​​module. The main processes performed at this time are as follows:

[0189] 1. Analyze the question data and perform natural language processing.

[0190] 2. Use a generative AI model to generate an answer based on the question.

[0191] 3. The generated answer is sent back to the device in JSON format.

[0192] Specific examples

[0193] For example, if a user types "What can I claim as an expense?", the following occurs:

[0194] 1. The user types a question into the terminal.

[0195] 2. The device sends this question to the server.

[0196] 3. The server receives the question and generates an answer using an AI model.

[0197] 4. The server generates a response and sends it back to the device.

[0198] 5. The device displays the answer to the user.

[0199] The same process also occurs when the user enters "At which stores can I use this electronic payment method?"

[0200] Example prompt sentence:

[0201] Please answer the following questions: In which stores can I use this electronic payment?

[0202] This system allows users to quickly obtain accurate information about tax returns and electronic payments. For example, users can accurately understand the procedures for filing tax returns, gather the necessary documents, and obtain reference information for claiming appropriate expenses. Users can also easily obtain information about electronic payment procedures and the stores where they can be used.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The user uses the terminal interface to input questions about tax returns and electronic payments. The user inputs specific questions such as "What can I claim as an expense?" or "At which stores can I use this electronic payment?" The input questions are temporarily stored in the terminal's memory.

[0206] Step 2:

[0207] The device sends the question entered by the user to the server via the send_question function. Specifically, the question data is converted to JSON format and sent as a POST request to the specified endpoint on the server via the Internet. In this sending process, the input data is sent to the server as a JSON object.

[0208] Step 3:

[0209] The server receives a POST request at the / api / ask endpoint and obtains the question data in JSON format from the request body. The server parses this question data and passes it to the AI ​​module to generate an appropriate answer. Specifically, the server converts the received question data into text format and inputs it to the AI ​​module as a prompt.

[0210] Step 4:

[0211] The server's AI module performs natural language processing based on the input prompt and generates an appropriate answer. The AI ​​module analyzes the question and generates an answer using a generative AI model. During this process, the question data is converted by the AI ​​into an appropriate, context-based answer.

[0212] Step 5:

[0213] The server converts the answer generated by the AI ​​module into JSON format and returns it to the device. Specifically, it stores the generated answer text in a JSON object and sends it to the user's device as an HTTP response. The output of this step is the answer data in JSON format.

[0214] Step 6:

[0215] The terminal receives the response data returned from the server and extracts the response text from the response field. The terminal displays this text on the user interface and provides the answer to the user. At this time, the terminal displays to the user, for example, "AI's response: Available at major convenience stores and supermarkets."

[0216] This series of processes allows users to get quick and accurate answers to questions about tax returns and electronic payments.

[0217] 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.

[0218] The present invention relates to a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining an emotion engine that recognizes the user's emotions.

[0219] Overall system overview

[0220] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the AI ​​on the server generates a response to that question, and the emotion engine recognizes the user's emotions and adjusts the response accordingly.

[0221] Main components and roles

[0222] 1. Device:

[0223] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0224] Display the response from the server.

[0225] 2. User:

[0226] Use the device to enter your tax return questions.

[0227] Get answers that reflect your emotional state via the emotion engine.

[0228] 3. Server:

[0229] It receives questions, uses an AI module to generate appropriate answers, and sends them to the emotion engine.

[0230] Based on the results received from the emotion engine, a tailored response is sent back to the device.

[0231] The role of the emotional engine

[0232] The emotion engine analyzes user-entered questions and recognizes the emotion expressed in them, allowing the AI ​​module to adjust the tone and content of the responses it generates appropriately. For example, if the user is feeling stressed, the emotion engine will recognize this and respond more gently and reassuringly.

[0233] Program processing flow

[0234] 1. Terminal processing

[0235] The user uses the terminal interface to input a question. For example, "What can be claimed as an expense?" The terminal receives the question and sends it to the server, where it is processed.

[0236] 2. Server-side processing

[0237] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. Next, the emotion engine recognizes the emotion from the user's question, and based on that, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery needed for sales and travel expenses can be claimed as expenses." The server sends this answer to the emotion engine, which then returns the answer, appropriately adjusted by the emotion engine, in JSON format as a response.

[0238] 3. Displaying the terminal

[0239] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user. It is displayed in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses." In addition, a tone that takes the user's emotions into consideration is added.

[0240] Specific examples

[0241] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the following flow will occur:

[0242] 1. The user types a question into the terminal.

[0243] 2. The device sends this question to the server.

[0244] 3. The server receives the question and recognizes the user's emotions via the emotion engine. It recognizes that the user is "feeling stressed."

[0245] 4. The server sends the question to the AI ​​model and generates an answer.

[0246] 5. The emotion engine adjusts the generated answers based on the user's emotions, for example, "explain in a gentle tone to relieve stress."

[0247] 6. The server sends the adjusted response back to the device.

[0248] 7. The device will display the answer to the user. For example, it will display something like, "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[0249] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] A user enters a tax return question into a terminal interface, for example, "What can I claim as an expense when filing my tax return?"

[0253] Step 2:

[0254] The device retrieves the question entered by the user, converts the text data into JSON format, and prepares an HTTP POST request to send it to the server.

[0255] Step 3:

[0256] The terminal sends the prepared HTTP POST request to the server, for example, to the destination URL http: / / example.com / api / ask.

[0257] Step 4:

[0258] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and retrieves the question content.

[0259] Step 5:

[0260] The server passes the extracted question data to an emotion engine to recognize the user's emotional state, in this case the emotion engine determines an emotion such as "user's text suggests frustration."

[0261] Step 6:

[0262] The server then passes the question to the AI ​​module based on the emotional information obtained from the emotion engine, and generates the optimal answer. At the same time, it also includes information that the user is feeling frustrated as a prompt to the AI ​​model.

[0263] Step 7:

[0264] The server receives the answer returned by the AI ​​model. For example, it receives the answer text, "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0265] Step 8:

[0266] The server then passes the resulting response back to the emotion engine, which adjusts the tone and content to reflect the user's emotions, such as adding an additional encouraging message for a frustrated user.

[0267] Step 9:

[0268] The server will return the final adjusted answer as a JSON response. For example, the returned JSON might look like this: {"response": "Generally, stationery and travel expenses needed for business can be claimed as expenses. Relax and let's get on with what we need to do one step at a time."}

[0269] Step 10:

[0270] The terminal receives the response from the server and extracts the answer text in the response field from the response body.

[0271] Step 11:

[0272] The device displays the extracted answer text on the user interface. Specifically, it displays the following: "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[0273] Step 12:

[0274] Users can check the answers displayed on their device, resolve any questions they may have about their tax returns, and reduce stress with sensitive messages. They can also ask additional questions if necessary.

[0275] Example 2

[0276] 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."

[0277] It is necessary to provide not only fast and accurate answers to questions about tax returns, but also responses that take the user's emotions into consideration, thereby reducing stress and anxiety and improving satisfaction. This issue could not be resolved with conventional systems.

[0278] 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.

[0279] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to a data processing device, means including an artificial intelligence module for the data processing device to generate answers to the questions, means for returning the generated answers to the users, and an emotion recognition engine for recognizing the user's emotions and adjusting the answers, thereby enabling appropriate answers that take the user's emotions into consideration.

[0280] A "user" is a person who uses this system to ask questions about tax returns.

[0281] A "tax return" is the act of an individual or company reporting their annual income and income tax amount to tax authorities.

[0282] The "means for receiving a question" refers to an interface and function that allows the system to receive a question entered by a user.

[0283] "Data processing device" is a general term for computer hardware and software for processing user questions and generating appropriate answers.

[0284] The "means for transmitting a question to a server" is a function for transmitting question data received from a user to a server via a network.

[0285] An "artificial intelligence module" is a collection of software and algorithms that uses natural language processing techniques to generate answers to user questions.

[0286] "Generating an answer" is the process by which the artificial intelligence module creates a specific answer to provide appropriate information for the user's question.

[0287] The "means for returning the generated answer" is a function for sending the answer generated by the server back to the user.

[0288] An "emotion recognition engine" is software and algorithms for analyzing and recognizing emotions from user input.

[0289] "Adjusting the response" is the process of appropriately changing the content and tone of the generated response based on the user's emotions recognized by the emotion recognition engine.

[0290] "Natural language processing" is a general term for techniques and algorithms that allow computers to understand, interpret, and generate human language.

[0291] The present invention is a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module on the server, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining it with an emotion recognition engine that recognizes the user's emotions.

[0292] Overall system configuration

[0293] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the server generates a response to the question, and the emotion recognition engine recognizes the user's emotion and adjusts the response accordingly.

[0294] Main components and roles

[0295] 1. Device:

[0296] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0297] Display the response from the server.

[0298] 2. User:

[0299] Use the device to enter your tax return questions.

[0300] Get answers that reflect your emotional state via an emotion recognition engine.

[0301] 3. Server:

[0302] It receives questions and uses an artificial intelligence module to generate appropriate answers, which are then sent to an emotion recognition engine.

[0303] Based on the results received from the emotion recognition engine, a tailored response is sent back to the device.

[0304] The role of emotion recognition engines

[0305] The emotion recognition engine analyzes the questions entered by the user and recognizes the emotion expressed in the question. This allows the AI ​​module to appropriately adjust the tone and content of the answers it generates. For example, if the user is feeling stressed, the emotion recognition engine will recognize this and the response will be more gentle and reassuring.

[0306] Program processing flow

[0307] Specifically, a user inputs a question using the terminal interface. For example, the user inputs a question such as, "What can I claim as an expense?" The terminal receives the question input by the user and sends it to the server.

[0308] The server receives the question and uses an emotion recognition engine to recognize the emotion in the user's question. Based on the results, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0309] The emotion recognition engine then receives this response and adjusts it based on the user's emotion. For example, if the engine recognizes that the user is stressed, it adjusts the tone of the response to be gentler. The generated response is then received by the server again and sent back to the device.

[0310] Specific examples

[0311] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the process will be as follows:

[0312] 1. The user types a question into the terminal.

[0313] "I'm stressed about my tax return. What can I claim as an expense?"

[0314] 2. The device sends this question to the server.

[0315] 3. The server receives the question and recognizes the user's emotion via an emotion recognition engine.

[0316] Recognize that the user is "stressed."

[0317] 4. The server sends the question to the artificial intelligence model and generates an answer.

[0318] The answer generated is, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0319] 5. The emotion recognition engine adjusts the generated answers based on the user's emotions, for example, "Explain in a gentle tone to relieve stress."

[0320] "Just relax and take things one step at a time," he adds.

[0321] 6. The server sends the adjusted response back to the device.

[0322] 7. The terminal displays the answer to the user.

[0323] The message displayed is, "AI's answer: Generally, stationery and travel expenses necessary for business can be claimed as business expenses. Relax and just get on with what you need to do one thing at a time."

[0324] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1: User enters question

[0327] The user inputs a question using the device interface. For example, the user might input "What can be claimed as an expense?" The input data is in text format and is saved on the device as the user's question. At this stage, the input data is generated.

[0328] Step 2: The device sends the question to the server

[0329] The terminal obtains the question data entered by the user and sends it to the server as an HTTP POST request to the / api / ask endpoint. The input is text-formatted question data, and the output is converted to JSON format and sent to the server.

[0330] Step 3: The server receives the query

[0331] The server receives a POST request at the / api / ask endpoint. This request contains the question data. The server parses the received JSON data and extracts the question text. The input is the JSON data, and the output is the extracted question text.

[0332] Step 4: The server recognizes the emotion using the emotion recognition engine.

[0333] The server passes the extracted question text to an emotion recognition engine to analyze the user's emotions. The input is the question text, and the output is the recognized emotion data (e.g., "stress"). The emotion recognition engine performs the specific operation of analyzing emotions using natural language processing technology.

[0334] Step 5: The server uses an artificial intelligence model to generate an answer

[0335] The server inputs the emotion data and question text received from the emotion recognition engine into the AI ​​model to generate an appropriate answer. The input is the question text and emotion data, and the output is the generated answer text. The generative AI model performs the specific operation of generating an answer based on the prompt text.

[0336] Step 6: Emotion recognition engine adjusts the answer

[0337] The server sends the answer received from the AI ​​model to the emotion recognition engine, which adjusts the answer based on the user's emotions. The input is the answer text and emotion data, and the output is the adjusted answer text. The emotion recognition engine then performs specific actions to change the tone and content of the answer to match the user's emotions.

[0338] Step 7: The server sends the adjusted answer back to the device

[0339] The server returns the answer adjusted by the emotion recognition engine as a response in JSON format to the terminal. The input is the adjusted answer text, and the output is the response data in JSON format.

[0340] Step 8: The device displays the answer to the user

[0341] The device receives the JSON data returned from the server and extracts the answer text. The extracted answer text is displayed to the user. For example, it might be displayed as "AI's answer: Generally, stationery and travel expenses needed for sales can be claimed as expenses. Relax and proceed with the tasks one by one." The input is the JSON-formatted response data, and the output is the text to be displayed.

[0342] (Application example 2)

[0343] 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."

[0344] In autonomous vehicles, passengers and drivers are required to not only provide information but also to recognize their emotions and provide appropriate responses in a relaxed tone when they have questions or concerns during their trip.However, conventional systems are unable to properly recognize the user's emotions and adjust responses based on them, which has prevented them from fully improving user satisfaction and relaxation.

[0345] 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.

[0346] In this invention, the server includes means for receiving questions about tax returns from a user, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the user, and means including an emotion engine for the artificial intelligence module to recognize the user's emotions and adjust the answers appropriately. This makes it possible to provide relaxed answers that take into consideration the emotions of the user and reduce stress within an autonomous vehicle.

[0347] "User" refers to an individual or corporation that uses the system to enter questions about tax returns.

[0348] The "means for receiving a question" refers to an interface for transmitting a question entered by a user from a terminal to a server.

[0349] "Means for sending a question to a server" refers to a protocol for sending a question entered on a terminal to a server via a network.

[0350] "Artificial Intelligence Module" refers to a software component that uses natural language processing techniques to generate answers to user questions.

[0351] "Means for generating an answer" refers to an algorithm or process that uses an artificial intelligence module to generate an answer to a user's question.

[0352] "Means for returning the generated answer to the user" refers to a communication function for returning the answer generated by the server to the terminal via the network and displaying it to the user.

[0353] An "emotion engine" is a software module that recognizes the emotion in a user's question and adjusts the tone and content of the response.

[0354] "Natural language processing" refers to artificial intelligence techniques for understanding and generating human language.

[0355] "Means for sending prompts" refers to a mechanism that analyzes a user's question and generates and sends appropriate prompts to the artificial intelligence module based on the analysis results.

[0356] To implement this invention, a user inputs a question using a terminal in an autonomous vehicle, the question is received by a server, an artificial intelligence module generates an answer, and an emotion engine recognizes the user's emotion, adjusts the answer, and sends it back to the user.

[0357] Hardware and software used

[0358] Hardware

[0359] Terminals inside the autonomous vehicle: These can be tablets or dedicated displays that allow users to enter questions and receive answers.

[0360] software

[0361] Flask framework: Used to build server-side APIs.

[0362] Transformers library: Provides artificial intelligence modules for natural language processing.

[0363] EmotionRecognizer library: Used to build an emotion engine that recognizes emotions from user input.

[0364] Data processing and calculation

[0365] Receive user's question: The question entered by the user into the terminal is sent to the server through the terminal interface.

[0366] Question analysis and emotion recognition: The server analyzes the received question and generates an initial answer to the question using the Transformers library. It also recognizes the user's emotion from the context of the question using the EmotionRecognizer library.

[0367] Response adjustment: The emotion engine adjusts responses based on the emotions it recognizes, for example, adding a relaxed tone to responses if the user is feeling stressed.

[0368] Returning the answer: The adjusted answer is sent from the server to the device and displayed to the user.

[0369] Specific examples

[0370] Specific processing flow

[0371] 1. User input: A passenger types into the terminal, "What are some recommended tourist spots?"

[0372] 2. Emotion recognition: The server uses an emotion engine to recognize when the user is feeling stressed.

[0373] 3. Generate an initial answer: The AI ​​module (Transformers library) generates an answer such as, "The recommended tourist spot is XX."

[0374] 4. Adjusting the answer: Based on the emotion recognized by the emotion engine, the answer is adjusted to "Recommended tourist spots are ____. Relax and enjoy yourself."

[0375] 5. Display Answer: The adjusted answer is displayed on the terminal for the user to see.

[0376] Prompt Sentence Examples

[0377] For example, the following prompt sentences are used:

[0378] Q: What are some recommended tourist spots?

[0379] Emotion: Stress

[0380] Q: Where can I go with my kids?

[0381] Emotion: excitement

[0382] This allows appropriate and relaxed answers to users' questions to be provided within the self-driving vehicle, improving user satisfaction.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] The user inputs a question into the terminal.

[0386] A user uses a device such as a tablet or display to input a question such as "What are some recommended tourist spots?" This input is done through the device's interface. The question text is generated as input and sent to the next step.

[0387] Step 2:

[0388] The terminal sends a query to the server.

[0389] The terminal sends the entered question text to the server. This communication is carried out over the network, and the question text is delivered to the server as an HTTP request. The question text is taken as input and becomes the output to be sent to the server.

[0390] Step 3:

[0391] The server receives the query.

[0392] The server receives the question text sent from the terminal. It parses the question text into JSON format using the Flask framework. It receives the question JSON as input and parses it to get the question text.

[0393] Step 4:

[0394] Emotion recognition using emotion engine.

[0395] The server uses an emotion engine (EmotionRecognizer library) to recognize the user's emotion from the received question text. For example, it recognizes that the user's emotion is "stress" from the question text. It takes the question text as input and generates the user's emotion data as output.

[0396] Step 5:

[0397] Answer generation by AI module.

[0398] The server uses the Transformers library to generate an initial answer to the question text. For example, it generates an answer such as "A recommended tourist spot is ____." It takes the question text as input and generates the initial answer text as output.

[0399] Step 6:

[0400] Tailoring responses with an emotion engine.

[0401] The server uses an emotion engine to adjust the initial response text based on the user's emotions. If the user is feeling stressed, it adds a tone to the response such as "Relax and have fun." It takes the initial response text and the user's emotion data as input and generates the adjusted response text as output.

[0402] Step 7:

[0403] The adjusted response is sent back to the terminal.

[0404] The server converts the adjusted answer text back into JSON format and sends it back to the device. This communication also takes the adjusted answer text as input and returns a JSON-formatted response as output.

[0405] Step 8:

[0406] The terminal displays the answer to the user.

[0407] The device analyzes the JSON-formatted response received from the server and displays the adjusted response text to the user. For example, it might say, "A recommended tourist spot is ____. Relax and enjoy." It receives JSON-formatted response data as input and generates text to be displayed to the user as output.

[0408] 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.

[0409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] 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.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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."

[0424] The following describes an embodiment of the present invention.

[0425] Overall system overview

[0426] The present invention provides a system that receives tax return inquiries from users and transmits them to a server, which uses an artificial intelligence module to generate answers to the inquiries and transmits the answers back to the users. This system allows users to quickly and accurately resolve their tax return inquiries.

[0427] Main components and roles

[0428] 1. Device:

[0429] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0430] Display the response from the server.

[0431] 2. User:

[0432] Use the device to enter your tax return questions.

[0433] 3. Server:

[0434] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0435] Program processing flow

[0436] 1. Terminal processing

[0437] The user uses the terminal interface to enter a question following the prompt "Ask a question about your tax return." For example, the user enters a question such as "What can I claim as an expense?" The terminal receives the question entered by the user and sends it to the server via the send_question function. The server processes the question.

[0438] 2. Server-side processing

[0439] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. It then passes the question to the generate_response function and uses the AI ​​model to generate an answer to the question. For example, in response to the question "What items can be claimed as expenses?", the AI ​​model generates the answer "Generally, stationery and travel expenses required for sales can be claimed as expenses." The server then returns this answer in JSON format as a response.

[0440] 3. Displaying the terminal

[0441] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0442] Specific examples

[0443] For example, if a user types "What can I claim as an expense?" into a terminal, the following process will occur:

[0444] 1. The user types a question into the terminal.

[0445] 2. The device sends this question to the server.

[0446] 3. The server receives the question and generates an answer using an AI model.

[0447] 4. The server generates a response and sends it back to the device.

[0448] 5. The device displays the answer to the user.

[0449] This system allows users to quickly obtain accurate information about tax returns, such as accurately understanding the procedures for filing tax returns, gathering the necessary documents, and obtaining reference information for claiming appropriate expenses.

[0450] The processing flow will be explained below.

[0451] Step 1:

[0452] The user enters a question about their tax return, such as "What can I claim as an expense?" through a user interface.

[0453] Step 2:

[0454] The device receives the question entered by the user and prepares it to be sent to the server. Specifically, it converts the user's question into JSON format and creates an HTTP POST request.

[0455] Step 3:

[0456] The device passes the question to the server by sending a prepared HTTP POST request to a URL that points to a pre-configured API endpoint (e.g., http: / / example.com / api / ask).

[0457] Step 4:

[0458] The server receives a POST request on the / api / ask endpoint and extracts the JSON data from the request body to get the submitted question.

[0459] Step 5:

[0460] The server sets the question as a prompt to be passed to the AI ​​module, which then sends the question to the AI ​​model and generates an answer using natural language processing technology.

[0461] Step 6:

[0462] The server receives the answer returned by the AI ​​module. For example, it may generate an answer such as, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0463] Step 7:

[0464] The server sets the generated answer as a JSON format response in the HTTP response body and returns it to the terminal.

[0465] Step 8:

[0466] The device receives the response from the server, extracts the JSON data from the response body, and obtains the answer text in the response field.

[0467] Step 9:

[0468] The device displays the extracted answer text on the user interface. Specifically, it displays the answer as "AI's answer: Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0469] Step 10:

[0470] Users can check the answers displayed on the device to resolve any questions they may have about their tax return, and can also ask additional questions if necessary.

[0471] Example 1

[0472] 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."

[0473] One issue is the difficulty of providing quick and accurate answers to questions about tax returns. Conventional systems require users to consult with experts, which takes a lot of time and effort. In addition, information on the Internet can be unreliable, making it difficult for users to make appropriate decisions.

[0474] 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.

[0475] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the users, and means for the terminal to receive the answers from the server and display them to the users, thereby enabling users to quickly and accurately resolve their questions about tax returns.

[0476] A "user" is a person who enters a question about a tax return.

[0477] "Filing a tax return" refers to the procedure for calculating and paying tax on income.

[0478] A "question" refers to text entered by a user inquiring about information they would like to know about their tax return.

[0479] "Terminal" refers to a device or software that provides a user interface, receives questions from a user, sends them to a server, and displays answers from the server.

[0480] "Server" refers to a computer system on a network that receives questions sent by users, generates answers using an artificial intelligence module, and returns the generated answers to the terminal.

[0481] An "artificial intelligence module" is a computer program or system that uses natural language processing techniques to generate answers to questions.

[0482] "Natural language processing" is a technical field that uses computers to understand and process human language.

[0483] A "prompt" refers to a form of instruction or input to an artificial intelligence module that generates a specific answer.

[0484] "Answer" refers to the text of information generated by the artificial intelligence module in response to a user's question.

[0485] "Transmission" refers to the act of a terminal transferring a question to a server, or a server transferring an answer to a terminal via a network.

[0486] "Display" refers to visually showing the answer received by the terminal from the server so that the user can confirm it.

[0487] MODE FOR CARRYING OUT THE INVENTION

[0488] The present invention is a system that receives questions about tax returns from users, transmits the questions to a server, and the server uses an artificial intelligence module to generate answers that are returned to the user.

[0489] System configuration and hardware / software used

[0490] The main components of the present invention are:

[0491] 1. Device:

[0492] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0493] Display the response from the server.

[0494] Specific hardware used includes computers, smartphones, tablets, etc. Software used includes web browsers and mobile apps.

[0495] 2. User:

[0496] Use the device to enter your tax return questions.

[0497] Users can use this system when they want to quickly resolve questions about tax return expense claims and procedures.

[0498] 3. Server:

[0499] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0500] The specific hardware used includes a cloud server or an on-premise server, and the software includes an HTTP server, a database, and an artificial intelligence module (e.g., GPT-3).

[0501] Program processing and data processing / calculation

[0502] The terminal receives a question from the user through the user interface. The question is sent to the server through the send_question function. The question is sent using an HTTP POST request.

[0503] The server processes the received question at the / api / ask endpoint, extracts the question data from the request body, and passes it to the generate_response function, which uses a generative AI model (e.g., GPT-3) to generate an answer to the question.

[0504] The generated answer is sent back to the terminal in JSON format, and the terminal receives this response, extracts the answer text, and displays it to the user.

[0505] Specific examples

[0506] For example, if a user types "What can I claim as an expense?" into a terminal, the following will happen:

[0507] 1. The user types a question into the terminal.

[0508] 2. The device sends this question to the server.

[0509] 3. The server receives the question and generates an answer using a generative AI model (e.g., GPT-3).

[0510] 4. The server generates a response and sends it back to the device.

[0511] 5. The device displays the answer to the user.

[0512] Prompt Sentence Examples

[0513] An example prompt for the question "What can you claim as an expense?" is:

[0514] Generate accurate answers to the following questions: What can I claim as an expense?

[0515] Based on this prompt, the generative AI model will generate an appropriate answer, such as "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0516] The above is an embodiment of the present invention, which allows users to quickly and accurately resolve questions about tax returns.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] Program processing steps

[0519] Step 1:

[0520] The user enters a question into the device's user interface. The entered question is in text format. Specifically, the user types "What can I claim as an expense?" into an input field in a browser or mobile app. The input field has a "Submit" button, which the user clicks to proceed to the next step.

[0521] Input: A question typed by the user (e.g., "What can I claim as an expense?")

[0522] Output: The question text entered

[0523] Step 2:

[0524] The device takes the user's input and calls the send_question function to send the question to the server. The question is sent as an HTTP POST request to the server's / api / ask endpoint, with the question text included in the request body.

[0525] Input: The question text entered by the user

[0526] Output: The question text in the HTTP POST request to the server

[0527] Step 3:

[0528] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and passes it to the generate_response function. At this point, the server holds the question text from the request.

[0529] Input: Question text included in HTTP POST request

[0530] Output: Question data passed to the generate_response function

[0531] Step 4:

[0532] The server calls a generative AI model (e.g., GPT-3) in the generate_response function to generate an answer to the question. The generative AI model uses the prompt to generate an appropriate answer to the question. For example, in response to a question about expenses, it might respond, "Generally, stationery and transportation costs required for business can be claimed as expenses."

[0533] Input: Question data passed to the generate_response function

[0534] Output: Answer text generated by the generative AI model

[0535] Step 5:

[0536] The server formats the generated answer in JSON format and sends it to the device as an HTTP response, with the answer text included in the response field of the response body.

[0537] Input: Answer text generated by the generative AI model

[0538] Output: JSON formatted HTTP response sent to the device

[0539] Step 6:

[0540] The device receives the response from the server and extracts the answer text from the response field. The extracted answer text is visually displayed to the user. The UI displays "AI answer: Generally, stationery and transportation costs required for business can be claimed as expenses."

[0541] Input: JSON format HTTP response from the server

[0542] Output: The answer text that is displayed to the user

[0543] The specific actions at each step allow users to quickly and accurately resolve their tax return questions.

[0544] (Application example 1)

[0545] 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."

[0546] Current systems for solving questions about tax returns have limitations in terms of enabling users to quickly and accurately obtain information. Furthermore, there is a lack of systems for users to resolve questions about procedures and electronic payments, which reduces user convenience. Therefore, there is a need for a system that provides quick and accurate answers to questions about tax returns and electronic payments.

[0547] 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.

[0548] In this invention, the server includes a means for an artificial intelligence module to generate answers to questions using natural language processing, a means for returning the generated answers to the user, and a means for analyzing the user's questions and sending prompts to the artificial intelligence module to provide specific answers, thereby enabling quick and accurate answers to be provided to questions from the user regarding tax returns and electronic payments.

[0549] The "means for receiving questions about tax returns from users" is an interface that allows the system to recognize and receive questions about tax returns entered by users.

[0550] The "means for transmitting a question to a server" is a communication function for transmitting a question received by a terminal to a server via a network.

[0551] "Means including an artificial intelligence module for the server to generate answers to questions" refers to a system in which an AI module installed in the server generates appropriate answers to received questions.

[0552] The "means for returning the generated answer to the user" is a function for transmitting the answer generated by the server to the user's terminal and displaying it.

[0553] The "means for receiving a question about electronic payment from a user" is an interface that allows the system to recognize and receive a question about electronic payment input by a user.

[0554] The "means for transmitting questions about electronic payment to a server" is a communication function for transmitting questions about electronic payment received by the terminal to a server via a network.

[0555] "Means including an artificial intelligence module for the server to generate answers to questions about electronic payments" refers to a system in which an AI module installed in the server generates appropriate answers to received questions about electronic payments.

[0556] The "means for returning the generated answer regarding electronic payment to the user" is a function for transmitting the answer regarding electronic payment generated by the server to the user's terminal and displaying it.

[0557] The following describes an embodiment of the present invention.

[0558] Overall system overview

[0559] The present invention is a system that receives questions from users about tax returns and electronic payments, sends them to a server, and the server uses an AI module to generate answers to the questions and returns the answers to the users. This system allows users to quickly and accurately resolve their questions about tax returns and electronic payments.

[0560] Main components and roles

[0561] 1. Device:

[0562] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0563] Display the response from the server.

[0564] 2. User:

[0565] Use the terminal to enter questions about tax returns and electronic payments.

[0566] 3. Server:

[0567] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0568] Program processing flow

[0569] Program for carrying out the invention

[0570] First, the user uses the terminal interface to input questions about tax returns or electronic payments. For example, they can input questions such as, "What can I claim as an expense?" or "At which stores can I use this electronic payment method?" The terminal then sends the input questions to the server, where they are processed.

[0571] Hardware and Software

[0572] The hardware used includes devices such as smartphones and tablets to provide the user interface, and a high-performance server to process questions and answers.

[0573] The software used is an AI module for natural language processing and an API endpoint for managing network communications. The specific AI module used is a generative AI model.

[0574] Data processing and calculation

[0575] The server receives the questions sent from the device and generates appropriate answers using the AI ​​module. The main processes performed at this time are as follows:

[0576] 1. Analyze the question data and perform natural language processing.

[0577] 2. Use a generative AI model to generate an answer based on the question.

[0578] 3. The generated answer is sent back to the device in JSON format.

[0579] Specific examples

[0580] For example, if a user types "What can I claim as an expense?", the following occurs:

[0581] 1. The user types a question into the terminal.

[0582] 2. The device sends this question to the server.

[0583] 3. The server receives the question and generates an answer using an AI model.

[0584] 4. The server generates a response and sends it back to the device.

[0585] 5. The device displays the answer to the user.

[0586] The same process also occurs when the user enters "At which stores can I use this electronic payment method?"

[0587] Example prompt sentence:

[0588] Please answer the following questions: In which stores can I use this electronic payment?

[0589] This system allows users to quickly obtain accurate information about tax returns and electronic payments. For example, users can accurately understand the procedures for filing tax returns, gather the necessary documents, and obtain reference information for claiming appropriate expenses. Users can also easily obtain information about electronic payment procedures and the stores where they can be used.

[0590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0591] Step 1:

[0592] The user uses the terminal interface to input questions about tax returns and electronic payments. The user inputs specific questions such as "What can I claim as an expense?" or "At which stores can I use this electronic payment?" The input questions are temporarily stored in the terminal's memory.

[0593] Step 2:

[0594] The device sends the question entered by the user to the server via the send_question function. Specifically, the question data is converted to JSON format and sent as a POST request to the specified endpoint on the server via the Internet. In this sending process, the input data is sent to the server as a JSON object.

[0595] Step 3:

[0596] The server receives a POST request at the / api / ask endpoint and obtains the question data in JSON format from the request body. The server parses this question data and passes it to the AI ​​module to generate an appropriate answer. Specifically, the server converts the received question data into text format and inputs it to the AI ​​module as a prompt.

[0597] Step 4:

[0598] The server's AI module performs natural language processing based on the input prompt and generates an appropriate answer. The AI ​​module analyzes the question and generates an answer using a generative AI model. During this process, the question data is converted by the AI ​​into an appropriate, context-based answer.

[0599] Step 5:

[0600] The server converts the answer generated by the AI ​​module into JSON format and returns it to the device. Specifically, it stores the generated answer text in a JSON object and sends it to the user's device as an HTTP response. The output of this step is the answer data in JSON format.

[0601] Step 6:

[0602] The terminal receives the response data returned from the server and extracts the response text from the response field. The terminal displays this text on the user interface and provides the answer to the user. At this time, the terminal displays to the user, for example, "AI's response: Available at major convenience stores and supermarkets."

[0603] This series of processes allows users to get quick and accurate answers to questions about tax returns and electronic payments.

[0604] 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.

[0605] The present invention relates to a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining an emotion engine that recognizes the user's emotions.

[0606] Overall system overview

[0607] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the AI ​​on the server generates a response to that question, and the emotion engine recognizes the user's emotions and adjusts the response accordingly.

[0608] Main components and roles

[0609] 1. Device:

[0610] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0611] Display the response from the server.

[0612] 2. User:

[0613] Use the device to enter your tax return questions.

[0614] Get answers that reflect your emotional state via the emotion engine.

[0615] 3. Server:

[0616] It receives questions, uses an AI module to generate appropriate answers, and sends them to the emotion engine.

[0617] Based on the results received from the emotion engine, a tailored response is sent back to the device.

[0618] The role of the emotional engine

[0619] The emotion engine analyzes user-entered questions and recognizes the emotion expressed in them, allowing the AI ​​module to adjust the tone and content of the responses it generates appropriately. For example, if the user is feeling stressed, the emotion engine will recognize this and respond more gently and reassuringly.

[0620] Program processing flow

[0621] 1. Terminal processing

[0622] The user uses the terminal interface to input a question. For example, "What can be claimed as an expense?" The terminal receives the question and sends it to the server, where it is processed.

[0623] 2. Server-side processing

[0624] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. Next, the emotion engine recognizes the emotion from the user's question, and based on that, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery needed for sales and travel expenses can be claimed as expenses." The server sends this answer to the emotion engine, which then returns the answer, appropriately adjusted by the emotion engine, in JSON format as a response.

[0625] 3. Displaying the terminal

[0626] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user. It is displayed in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses." In addition, a tone that takes the user's emotions into consideration is added.

[0627] Specific examples

[0628] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the following flow will occur:

[0629] 1. The user types a question into the terminal.

[0630] 2. The device sends this question to the server.

[0631] 3. The server receives the question and recognizes the user's emotions via the emotion engine. It recognizes that the user is "feeling stressed."

[0632] 4. The server sends the question to the AI ​​model and generates an answer.

[0633] 5. The emotion engine adjusts the generated answers based on the user's emotions, for example, "explain in a gentle tone to relieve stress."

[0634] 6. The server sends the adjusted response back to the device.

[0635] 7. The device will display the answer to the user. For example, it will display something like, "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[0636] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[0637] The processing flow will be explained below.

[0638] Step 1:

[0639] A user enters a tax return question into a terminal interface, for example, "What can I claim as an expense when filing my tax return?"

[0640] Step 2:

[0641] The device retrieves the question entered by the user, converts the text data into JSON format, and prepares an HTTP POST request to send it to the server.

[0642] Step 3:

[0643] The terminal sends the prepared HTTP POST request to the server, for example, to the destination URL http: / / example.com / api / ask.

[0644] Step 4:

[0645] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and retrieves the question content.

[0646] Step 5:

[0647] The server passes the extracted question data to an emotion engine to recognize the user's emotional state, in this case the emotion engine determines an emotion such as "user's text suggests frustration."

[0648] Step 6:

[0649] The server then passes the question to the AI ​​module based on the emotional information obtained from the emotion engine, and generates the optimal answer. At the same time, it also includes information that the user is feeling frustrated as a prompt to the AI ​​model.

[0650] Step 7:

[0651] The server receives the answer returned by the AI ​​model. For example, it receives the answer text, "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0652] Step 8:

[0653] The server then passes the resulting response back to the emotion engine, which adjusts the tone and content to reflect the user's emotions, such as adding an additional encouraging message for a frustrated user.

[0654] Step 9:

[0655] The server will return the final adjusted answer as a JSON response. For example, the returned JSON might look like this: {"response": "Generally, stationery and travel expenses needed for business can be claimed as expenses. Relax and let's get on with what we need to do one step at a time."}

[0656] Step 10:

[0657] The terminal receives the response from the server and extracts the answer text in the response field from the response body.

[0658] Step 11:

[0659] The device displays the extracted answer text on the user interface. Specifically, it displays the following: "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[0660] Step 12:

[0661] Users can check the answers displayed on their device, resolve any questions they may have about their tax returns, and reduce stress with sensitive messages. They can also ask additional questions if necessary.

[0662] Example 2

[0663] 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."

[0664] It is necessary to provide not only fast and accurate answers to questions about tax returns, but also responses that take the user's emotions into consideration, thereby reducing stress and anxiety and improving satisfaction. This issue could not be resolved with conventional systems.

[0665] 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.

[0666] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to a data processing device, means including an artificial intelligence module for the data processing device to generate answers to the questions, means for returning the generated answers to the users, and an emotion recognition engine for recognizing the user's emotions and adjusting the answers, thereby enabling appropriate answers that take the user's emotions into consideration.

[0667] A "user" is a person who uses this system to ask questions about tax returns.

[0668] A "tax return" is the act of an individual or company reporting their annual income and income tax amount to tax authorities.

[0669] The "means for receiving a question" refers to an interface and function that allows the system to receive a question entered by a user.

[0670] "Data processing device" is a general term for computer hardware and software for processing user questions and generating appropriate answers.

[0671] The "means for transmitting a question to a server" is a function for transmitting question data received from a user to a server via a network.

[0672] An "artificial intelligence module" is a collection of software and algorithms that uses natural language processing techniques to generate answers to user questions.

[0673] "Generating an answer" is the process by which the artificial intelligence module creates a specific answer to provide appropriate information for the user's question.

[0674] The "means for returning the generated answer" is a function for sending the answer generated by the server back to the user.

[0675] An "emotion recognition engine" is software and algorithms for analyzing and recognizing emotions from user input.

[0676] "Adjusting the response" is the process of appropriately changing the content and tone of the generated response based on the user's emotions recognized by the emotion recognition engine.

[0677] "Natural language processing" is a general term for techniques and algorithms that allow computers to understand, interpret, and generate human language.

[0678] The present invention is a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module on the server, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining it with an emotion recognition engine that recognizes the user's emotions.

[0679] Overall system configuration

[0680] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the server generates a response to the question, and the emotion recognition engine recognizes the user's emotion and adjusts the response accordingly.

[0681] Main components and roles

[0682] 1. Device:

[0683] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0684] Display the response from the server.

[0685] 2. User:

[0686] Use the device to enter your tax return questions.

[0687] Get answers that reflect your emotional state via an emotion recognition engine.

[0688] 3. Server:

[0689] It receives questions and uses an artificial intelligence module to generate appropriate answers, which are then sent to an emotion recognition engine.

[0690] Based on the results received from the emotion recognition engine, a tailored response is sent back to the device.

[0691] The role of emotion recognition engines

[0692] The emotion recognition engine analyzes the questions entered by the user and recognizes the emotion expressed in the question. This allows the AI ​​module to appropriately adjust the tone and content of the answers it generates. For example, if the user is feeling stressed, the emotion recognition engine will recognize this and the response will be more gentle and reassuring.

[0693] Program processing flow

[0694] Specifically, a user inputs a question using the terminal interface. For example, the user inputs a question such as, "What can I claim as an expense?" The terminal receives the question input by the user and sends it to the server.

[0695] The server receives the question and uses an emotion recognition engine to recognize the emotion in the user's question. Based on the results, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0696] The emotion recognition engine then receives this response and adjusts it based on the user's emotion. For example, if the engine recognizes that the user is stressed, it adjusts the tone of the response to be gentler. The generated response is then received by the server again and sent back to the device.

[0697] Specific examples

[0698] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the process will be as follows:

[0699] 1. The user types a question into the terminal.

[0700] "I'm stressed about my tax return. What can I claim as an expense?"

[0701] 2. The device sends this question to the server.

[0702] 3. The server receives the question and recognizes the user's emotion via an emotion recognition engine.

[0703] Recognize that the user is "stressed."

[0704] 4. The server sends the question to the artificial intelligence model and generates an answer.

[0705] The answer generated is, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0706] 5. The emotion recognition engine adjusts the generated answers based on the user's emotions, for example, "Explain in a gentle tone to relieve stress."

[0707] "Just relax and take things one step at a time," he adds.

[0708] 6. The server sends the adjusted response back to the device.

[0709] 7. The terminal displays the answer to the user.

[0710] The message displayed is, "AI's answer: Generally, stationery and travel expenses necessary for business can be claimed as business expenses. Relax and just get on with what you need to do one thing at a time."

[0711] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[0712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0713] Step 1: User enters question

[0714] The user inputs a question using the device interface. For example, the user might input "What can be claimed as an expense?" The input data is in text format and is saved on the device as the user's question. At this stage, the input data is generated.

[0715] Step 2: The device sends the question to the server

[0716] The terminal obtains the question data entered by the user and sends it to the server as an HTTP POST request to the / api / ask endpoint. The input is text-formatted question data, and the output is converted to JSON format and sent to the server.

[0717] Step 3: The server receives the query

[0718] The server receives a POST request at the / api / ask endpoint. This request contains the question data. The server parses the received JSON data and extracts the question text. The input is the JSON data, and the output is the extracted question text.

[0719] Step 4: The server recognizes the emotion using the emotion recognition engine.

[0720] The server passes the extracted question text to an emotion recognition engine to analyze the user's emotions. The input is the question text, and the output is the recognized emotion data (e.g., "stress"). The emotion recognition engine performs the specific operation of analyzing emotions using natural language processing technology.

[0721] Step 5: The server uses an artificial intelligence model to generate an answer

[0722] The server inputs the emotion data and question text received from the emotion recognition engine into the AI ​​model to generate an appropriate answer. The input is the question text and emotion data, and the output is the generated answer text. The generative AI model performs the specific operation of generating an answer based on the prompt text.

[0723] Step 6: Emotion recognition engine adjusts the answer

[0724] The server sends the answer received from the AI ​​model to the emotion recognition engine, which adjusts the answer based on the user's emotions. The input is the answer text and emotion data, and the output is the adjusted answer text. The emotion recognition engine then performs specific actions to change the tone and content of the answer to match the user's emotions.

[0725] Step 7: The server sends the adjusted answer back to the device

[0726] The server returns the answer adjusted by the emotion recognition engine as a response in JSON format to the terminal. The input is the adjusted answer text, and the output is the response data in JSON format.

[0727] Step 8: The device displays the answer to the user

[0728] The device receives the JSON data returned from the server and extracts the answer text. The extracted answer text is displayed to the user. For example, it might be displayed as "AI's answer: Generally, stationery and travel expenses needed for sales can be claimed as expenses. Relax and proceed with the tasks one by one." The input is the JSON-formatted response data, and the output is the text to be displayed.

[0729] (Application example 2)

[0730] 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."

[0731] In autonomous vehicles, passengers and drivers are required to not only provide information but also to recognize their emotions and provide appropriate responses in a relaxed tone when they have questions or concerns during their trip.However, conventional systems are unable to properly recognize the user's emotions and adjust responses based on them, which has prevented them from fully improving user satisfaction and relaxation.

[0732] 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.

[0733] In this invention, the server includes means for receiving questions about tax returns from a user, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the user, and means including an emotion engine for the artificial intelligence module to recognize the user's emotions and adjust the answers appropriately. This makes it possible to provide relaxed answers that take into consideration the emotions of the user and reduce stress within an autonomous vehicle.

[0734] "User" refers to an individual or corporation that uses the system to enter questions about tax returns.

[0735] The "means for receiving a question" refers to an interface for transmitting a question entered by a user from a terminal to a server.

[0736] "Means for sending a question to a server" refers to a protocol for sending a question entered on a terminal to a server via a network.

[0737] "Artificial Intelligence Module" refers to a software component that uses natural language processing techniques to generate answers to user questions.

[0738] "Means for generating an answer" refers to an algorithm or process that uses an artificial intelligence module to generate an answer to a user's question.

[0739] "Means for returning the generated answer to the user" refers to a communication function for returning the answer generated by the server to the terminal via the network and displaying it to the user.

[0740] An "emotion engine" is a software module that recognizes the emotion in a user's question and adjusts the tone and content of the response.

[0741] "Natural language processing" refers to artificial intelligence techniques for understanding and generating human language.

[0742] "Means for sending prompts" refers to a mechanism that analyzes a user's question and generates and sends appropriate prompts to the artificial intelligence module based on the analysis results.

[0743] To implement this invention, a user inputs a question using a terminal in an autonomous vehicle, the question is received by a server, an artificial intelligence module generates an answer, and an emotion engine recognizes the user's emotion, adjusts the answer, and sends it back to the user.

[0744] Hardware and software used

[0745] Hardware

[0746] Terminals inside the autonomous vehicle: These can be tablets or dedicated displays that allow users to enter questions and receive answers.

[0747] software

[0748] Flask framework: Used to build server-side APIs.

[0749] Transformers library: Provides artificial intelligence modules for natural language processing.

[0750] EmotionRecognizer library: Used to build an emotion engine that recognizes emotions from user input.

[0751] Data processing and calculation

[0752] Receive user's question: The question entered by the user into the terminal is sent to the server through the terminal interface.

[0753] Question analysis and emotion recognition: The server analyzes the received question and generates an initial answer to the question using the Transformers library. It also recognizes the user's emotion from the context of the question using the EmotionRecognizer library.

[0754] Response adjustment: The emotion engine adjusts responses based on the emotions it recognizes, for example, adding a relaxed tone to responses if the user is feeling stressed.

[0755] Returning the answer: The adjusted answer is sent from the server to the device and displayed to the user.

[0756] Specific examples

[0757] Specific processing flow

[0758] 1. User input: A passenger types into the terminal, "What are some recommended tourist spots?"

[0759] 2. Emotion recognition: The server uses an emotion engine to recognize when the user is feeling stressed.

[0760] 3. Generate an initial answer: The AI ​​module (Transformers library) generates an answer such as, "The recommended tourist spot is XX."

[0761] 4. Adjusting the answer: Based on the emotion recognized by the emotion engine, the answer is adjusted to "Recommended tourist spots are ____. Relax and enjoy yourself."

[0762] 5. Display Answer: The adjusted answer is displayed on the terminal for the user to see.

[0763] Prompt Sentence Examples

[0764] For example, the following prompt sentences are used:

[0765] Q: What are some recommended tourist spots?

[0766] Emotion: Stress

[0767] Q: Where can I go with my kids?

[0768] Emotion: excitement

[0769] This allows appropriate and relaxed answers to users' questions to be provided within the self-driving vehicle, improving user satisfaction.

[0770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0771] Step 1:

[0772] The user inputs a question into the terminal.

[0773] A user uses a device such as a tablet or display to input a question such as "What are some recommended tourist spots?" This input is done through the device's interface. The question text is generated as input and sent to the next step.

[0774] Step 2:

[0775] The terminal sends a query to the server.

[0776] The terminal sends the entered question text to the server. This communication is carried out over the network, and the question text is delivered to the server as an HTTP request. The question text is taken as input and becomes the output to be sent to the server.

[0777] Step 3:

[0778] The server receives the query.

[0779] The server receives the question text sent from the terminal. It parses the question text into JSON format using the Flask framework. It receives the question JSON as input and parses it to get the question text.

[0780] Step 4:

[0781] Emotion recognition using emotion engine.

[0782] The server uses an emotion engine (EmotionRecognizer library) to recognize the user's emotion from the received question text. For example, it recognizes that the user's emotion is "stress" from the question text. It takes the question text as input and generates the user's emotion data as output.

[0783] Step 5:

[0784] Answer generation by AI module.

[0785] The server uses the Transformers library to generate an initial answer to the question text. For example, it generates an answer such as "A recommended tourist spot is ____." It takes the question text as input and generates the initial answer text as output.

[0786] Step 6:

[0787] Tailoring responses with an emotion engine.

[0788] The server uses an emotion engine to adjust the initial response text based on the user's emotions. If the user is feeling stressed, it adds a tone to the response such as "Relax and have fun." It takes the initial response text and the user's emotion data as input and generates the adjusted response text as output.

[0789] Step 7:

[0790] The adjusted response is sent back to the terminal.

[0791] The server converts the adjusted answer text back into JSON format and sends it back to the device. This communication also takes the adjusted answer text as input and returns a JSON-formatted response as output.

[0792] Step 8:

[0793] The terminal displays the answer to the user.

[0794] The device analyzes the JSON-formatted response received from the server and displays the adjusted response text to the user. For example, it might say, "A recommended tourist spot is ____. Relax and enjoy." It receives JSON-formatted response data as input and generates text to be displayed to the user as output.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] [Third embodiment]

[0799] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0800] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0801] 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).

[0802] 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.

[0803] 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.

[0804] 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).

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] The following describes an embodiment of the present invention.

[0812] Overall system overview

[0813] The present invention provides a system that receives tax return inquiries from users and transmits them to a server, which uses an artificial intelligence module to generate answers to the inquiries and transmits the answers back to the users. This system allows users to quickly and accurately resolve their tax return inquiries.

[0814] Main components and roles

[0815] 1. Device:

[0816] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0817] Display the response from the server.

[0818] 2. User:

[0819] Use the device to enter your tax return questions.

[0820] 3. Server:

[0821] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0822] Program processing flow

[0823] 1. Terminal processing

[0824] The user uses the terminal interface to enter a question following the prompt "Ask a question about your tax return." For example, the user enters a question such as "What can I claim as an expense?" The terminal receives the question entered by the user and sends it to the server via the send_question function. The server processes the question.

[0825] 2. Server-side processing

[0826] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. It then passes the question to the generate_response function and uses the AI ​​model to generate an answer to the question. For example, in response to the question "What items can be claimed as expenses?", the AI ​​model generates the answer "Generally, stationery and travel expenses required for sales can be claimed as expenses." The server then returns this answer in JSON format as a response.

[0827] 3. Displaying the terminal

[0828] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0829] Specific examples

[0830] For example, if a user types "What can I claim as an expense?" into a terminal, the following process will occur:

[0831] 1. The user types a question into the terminal.

[0832] 2. The device sends this question to the server.

[0833] 3. The server receives the question and generates an answer using an AI model.

[0834] 4. The server generates a response and sends it back to the device.

[0835] 5. The device displays the answer to the user.

[0836] This system allows users to quickly obtain accurate information about tax returns, such as accurately understanding the procedures for filing tax returns, gathering the necessary documents, and obtaining reference information for claiming appropriate expenses.

[0837] The processing flow will be explained below.

[0838] Step 1:

[0839] The user enters a question about their tax return, such as "What can I claim as an expense?" through a user interface.

[0840] Step 2:

[0841] The device receives the question entered by the user and prepares it to be sent to the server. Specifically, it converts the user's question into JSON format and creates an HTTP POST request.

[0842] Step 3:

[0843] The device passes the question to the server by sending a prepared HTTP POST request to a URL that points to a pre-configured API endpoint (e.g., http: / / example.com / api / ask).

[0844] Step 4:

[0845] The server receives a POST request on the / api / ask endpoint and extracts the JSON data from the request body to get the submitted question.

[0846] Step 5:

[0847] The server sets the question as a prompt to be passed to the AI ​​module, which then sends the question to the AI ​​model and generates an answer using natural language processing technology.

[0848] Step 6:

[0849] The server receives the answer returned by the AI ​​module. For example, it may generate an answer such as, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0850] Step 7:

[0851] The server sets the generated answer as a JSON format response in the HTTP response body and returns it to the terminal.

[0852] Step 8:

[0853] The device receives the response from the server, extracts the JSON data from the response body, and obtains the answer text in the response field.

[0854] Step 9:

[0855] The device displays the extracted answer text on the user interface. Specifically, it displays the answer as "AI's answer: Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[0856] Step 10:

[0857] Users can check the answers displayed on the device to resolve any questions they may have about their tax return, and can also ask additional questions if necessary.

[0858] Example 1

[0859] 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."

[0860] One issue is the difficulty of providing quick and accurate answers to questions about tax returns. Conventional systems require users to consult with experts, which takes a lot of time and effort. In addition, information on the Internet can be unreliable, making it difficult for users to make appropriate decisions.

[0861] 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.

[0862] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the users, and means for the terminal to receive the answers from the server and display them to the users, thereby enabling users to quickly and accurately resolve their questions about tax returns.

[0863] A "user" is a person who enters a question about a tax return.

[0864] "Filing a tax return" refers to the procedure for calculating and paying tax on income.

[0865] A "question" refers to text entered by a user inquiring about information they would like to know about their tax return.

[0866] "Terminal" refers to a device or software that provides a user interface, receives questions from a user, sends them to a server, and displays answers from the server.

[0867] "Server" refers to a computer system on a network that receives questions sent by users, generates answers using an artificial intelligence module, and returns the generated answers to the terminal.

[0868] An "artificial intelligence module" is a computer program or system that uses natural language processing techniques to generate answers to questions.

[0869] "Natural language processing" is a technical field that uses computers to understand and process human language.

[0870] A "prompt" refers to a form of instruction or input to an artificial intelligence module that generates a specific answer.

[0871] "Answer" refers to the text of information generated by the artificial intelligence module in response to a user's question.

[0872] "Transmission" refers to the act of a terminal transferring a question to a server, or a server transferring an answer to a terminal via a network.

[0873] "Display" refers to visually showing the answer received by the terminal from the server so that the user can confirm it.

[0874] MODE FOR CARRYING OUT THE INVENTION

[0875] The present invention is a system that receives questions about tax returns from users, transmits the questions to a server, and the server uses an artificial intelligence module to generate answers that are returned to the user.

[0876] System configuration and hardware / software used

[0877] The main components of the present invention are:

[0878] 1. Device:

[0879] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0880] Display the response from the server.

[0881] Specific hardware used includes computers, smartphones, tablets, etc. Software used includes web browsers and mobile apps.

[0882] 2. User:

[0883] Use the device to enter your tax return questions.

[0884] Users can use this system when they want to quickly resolve questions about tax return expense claims and procedures.

[0885] 3. Server:

[0886] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0887] The specific hardware used includes a cloud server or an on-premise server, and the software includes an HTTP server, a database, and an artificial intelligence module (e.g., GPT-3).

[0888] Program processing and data processing / calculation

[0889] The terminal receives a question from the user through the user interface. The question is sent to the server through the send_question function. The question is sent using an HTTP POST request.

[0890] The server processes the received question at the / api / ask endpoint, extracts the question data from the request body, and passes it to the generate_response function, which uses a generative AI model (e.g., GPT-3) to generate an answer to the question.

[0891] The generated answer is sent back to the terminal in JSON format, and the terminal receives this response, extracts the answer text, and displays it to the user.

[0892] Specific examples

[0893] For example, if a user types "What can I claim as an expense?" into a terminal, the following will happen:

[0894] 1. The user types a question into the terminal.

[0895] 2. The device sends this question to the server.

[0896] 3. The server receives the question and generates an answer using a generative AI model (e.g., GPT-3).

[0897] 4. The server generates a response and sends it back to the device.

[0898] 5. The device displays the answer to the user.

[0899] Prompt Sentence Examples

[0900] An example prompt for the question "What can you claim as an expense?" is:

[0901] Generate accurate answers to the following questions: What can I claim as an expense?

[0902] Based on this prompt, the generative AI model will generate an appropriate answer, such as "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[0903] The above is an embodiment of the present invention, which allows users to quickly and accurately resolve questions about tax returns.

[0904] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0905] Program processing steps

[0906] Step 1:

[0907] The user enters a question into the device's user interface. The entered question is in text format. Specifically, the user types "What can I claim as an expense?" into an input field in a browser or mobile app. The input field has a "Submit" button, which the user clicks to proceed to the next step.

[0908] Input: A question typed by the user (e.g., "What can I claim as an expense?")

[0909] Output: The question text entered

[0910] Step 2:

[0911] The device takes the user's input and calls the send_question function to send the question to the server. The question is sent as an HTTP POST request to the server's / api / ask endpoint, with the question text included in the request body.

[0912] Input: The question text entered by the user

[0913] Output: The question text in the HTTP POST request to the server

[0914] Step 3:

[0915] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and passes it to the generate_response function. At this point, the server holds the question text from the request.

[0916] Input: Question text included in HTTP POST request

[0917] Output: Question data passed to the generate_response function

[0918] Step 4:

[0919] The server calls a generative AI model (e.g., GPT-3) in the generate_response function to generate an answer to the question. The generative AI model uses the prompt to generate an appropriate answer to the question. For example, in response to a question about expenses, it might respond, "Generally, stationery and transportation costs required for business can be claimed as expenses."

[0920] Input: Question data passed to the generate_response function

[0921] Output: Answer text generated by the generative AI model

[0922] Step 5:

[0923] The server formats the generated answer in JSON format and sends it to the device as an HTTP response, with the answer text included in the response field of the response body.

[0924] Input: Answer text generated by the generative AI model

[0925] Output: JSON formatted HTTP response sent to the device

[0926] Step 6:

[0927] The device receives the response from the server and extracts the answer text from the response field. The extracted answer text is visually displayed to the user. The UI displays "AI answer: Generally, stationery and transportation costs required for business can be claimed as expenses."

[0928] Input: JSON format HTTP response from the server

[0929] Output: The answer text that is displayed to the user

[0930] The specific actions at each step allow users to quickly and accurately resolve their tax return questions.

[0931] (Application example 1)

[0932] 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."

[0933] Current systems for solving questions about tax returns have limitations in terms of enabling users to quickly and accurately obtain information. Furthermore, there is a lack of systems for users to resolve questions about procedures and electronic payments, which reduces user convenience. Therefore, there is a need for a system that provides quick and accurate answers to questions about tax returns and electronic payments.

[0934] 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.

[0935] In this invention, the server includes a means for an artificial intelligence module to generate answers to questions using natural language processing, a means for returning the generated answers to the user, and a means for analyzing the user's questions and sending prompts to the artificial intelligence module to provide specific answers, thereby enabling quick and accurate answers to be provided to questions from the user regarding tax returns and electronic payments.

[0936] The "means for receiving questions about tax returns from users" is an interface that allows the system to recognize and receive questions about tax returns entered by users.

[0937] The "means for transmitting a question to a server" is a communication function for transmitting a question received by a terminal to a server via a network.

[0938] "Means including an artificial intelligence module for the server to generate answers to questions" refers to a system in which an AI module installed in the server generates appropriate answers to received questions.

[0939] The "means for returning the generated answer to the user" is a function for transmitting the answer generated by the server to the user's terminal and displaying it.

[0940] The "means for receiving a question about electronic payment from a user" is an interface that allows the system to recognize and receive a question about electronic payment input by a user.

[0941] The "means for transmitting questions about electronic payment to a server" is a communication function for transmitting questions about electronic payment received by the terminal to a server via a network.

[0942] "Means including an artificial intelligence module for the server to generate answers to questions about electronic payments" refers to a system in which an AI module installed in the server generates appropriate answers to received questions about electronic payments.

[0943] The "means for returning the generated answer regarding electronic payment to the user" is a function for transmitting the answer regarding electronic payment generated by the server to the user's terminal and displaying it.

[0944] The following describes an embodiment of the present invention.

[0945] Overall system overview

[0946] The present invention is a system that receives questions from users about tax returns and electronic payments, sends them to a server, and the server uses an AI module to generate answers to the questions and returns the answers to the users. This system allows users to quickly and accurately resolve their questions about tax returns and electronic payments.

[0947] Main components and roles

[0948] 1. Device:

[0949] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0950] Display the response from the server.

[0951] 2. User:

[0952] Use the terminal to enter questions about tax returns and electronic payments.

[0953] 3. Server:

[0954] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[0955] Program processing flow

[0956] Program for carrying out the invention

[0957] First, the user uses the terminal interface to input questions about tax returns or electronic payments. For example, they can input questions such as, "What can I claim as an expense?" or "At which stores can I use this electronic payment method?" The terminal then sends the input questions to the server, where they are processed.

[0958] Hardware and Software

[0959] The hardware used includes devices such as smartphones and tablets to provide the user interface, and a high-performance server to process questions and answers.

[0960] The software used is an AI module for natural language processing and an API endpoint for managing network communications. The specific AI module used is a generative AI model.

[0961] Data processing and calculation

[0962] The server receives the questions sent from the device and generates appropriate answers using the AI ​​module. The main processes performed at this time are as follows:

[0963] 1. Analyze the question data and perform natural language processing.

[0964] 2. Use a generative AI model to generate an answer based on the question.

[0965] 3. The generated answer is sent back to the device in JSON format.

[0966] Specific examples

[0967] For example, if a user types "What can I claim as an expense?", the following occurs:

[0968] 1. The user types a question into the terminal.

[0969] 2. The device sends this question to the server.

[0970] 3. The server receives the question and generates an answer using an AI model.

[0971] 4. The server generates a response and sends it back to the device.

[0972] 5. The device displays the answer to the user.

[0973] The same process also occurs when the user enters "At which stores can I use this electronic payment method?"

[0974] Example prompt sentence:

[0975] Please answer the following questions: In which stores can I use this electronic payment?

[0976] This system allows users to quickly obtain accurate information about tax returns and electronic payments. For example, users can accurately understand the procedures for filing tax returns, gather the necessary documents, and obtain reference information for claiming appropriate expenses. Users can also easily obtain information about electronic payment procedures and the stores where they can be used.

[0977] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0978] Step 1:

[0979] The user uses the terminal interface to input questions about tax returns and electronic payments. The user inputs specific questions such as "What can I claim as an expense?" or "At which stores can I use this electronic payment?" The input questions are temporarily stored in the terminal's memory.

[0980] Step 2:

[0981] The device sends the question entered by the user to the server via the send_question function. Specifically, the question data is converted to JSON format and sent as a POST request to the specified endpoint on the server via the Internet. In this sending process, the input data is sent to the server as a JSON object.

[0982] Step 3:

[0983] The server receives a POST request at the / api / ask endpoint and obtains the question data in JSON format from the request body. The server parses this question data and passes it to the AI ​​module to generate an appropriate answer. Specifically, the server converts the received question data into text format and inputs it to the AI ​​module as a prompt.

[0984] Step 4:

[0985] The server's AI module performs natural language processing based on the input prompt and generates an appropriate answer. The AI ​​module analyzes the question and generates an answer using a generative AI model. During this process, the question data is converted by the AI ​​into an appropriate, context-based answer.

[0986] Step 5:

[0987] The server converts the answer generated by the AI ​​module into JSON format and returns it to the device. Specifically, it stores the generated answer text in a JSON object and sends it to the user's device as an HTTP response. The output of this step is the answer data in JSON format.

[0988] Step 6:

[0989] The terminal receives the response data returned from the server and extracts the response text from the response field. The terminal displays this text on the user interface and provides the answer to the user. At this time, the terminal displays to the user, for example, "AI's response: Available at major convenience stores and supermarkets."

[0990] This series of processes allows users to get quick and accurate answers to questions about tax returns and electronic payments.

[0991] 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.

[0992] The present invention relates to a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining an emotion engine that recognizes the user's emotions.

[0993] Overall system overview

[0994] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the AI ​​on the server generates a response to that question, and the emotion engine recognizes the user's emotions and adjusts the response accordingly.

[0995] Main components and roles

[0996] 1. Device:

[0997] It provides a user interface (UI), receives questions from users, and sends them to the server.

[0998] Display the response from the server.

[0999] 2. User:

[1000] Use the device to enter your tax return questions.

[1001] Get answers that reflect your emotional state via the emotion engine.

[1002] 3. Server:

[1003] It receives questions, uses an AI module to generate appropriate answers, and sends them to the emotion engine.

[1004] Based on the results received from the emotion engine, a tailored response is sent back to the device.

[1005] The role of the emotional engine

[1006] The emotion engine analyzes user-entered questions and recognizes the emotion expressed in them, allowing the AI ​​module to adjust the tone and content of the responses it generates appropriately. For example, if the user is feeling stressed, the emotion engine will recognize this and respond more gently and reassuringly.

[1007] Program processing flow

[1008] 1. Terminal processing

[1009] The user uses the terminal interface to input a question. For example, "What can be claimed as an expense?" The terminal receives the question and sends it to the server, where it is processed.

[1010] 2. Server-side processing

[1011] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. Next, the emotion engine recognizes the emotion from the user's question, and based on that, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery needed for sales and travel expenses can be claimed as expenses." The server sends this answer to the emotion engine, which then returns the answer, appropriately adjusted by the emotion engine, in JSON format as a response.

[1012] 3. Displaying the terminal

[1013] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user. It is displayed in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses." In addition, a tone that takes the user's emotions into consideration is added.

[1014] Specific examples

[1015] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the following flow will occur:

[1016] 1. The user types a question into the terminal.

[1017] 2. The device sends this question to the server.

[1018] 3. The server receives the question and recognizes the user's emotions via the emotion engine. It recognizes that the user is "feeling stressed."

[1019] 4. The server sends the question to the AI ​​model and generates an answer.

[1020] 5. The emotion engine adjusts the generated answers based on the user's emotions, for example, "explain in a gentle tone to relieve stress."

[1021] 6. The server sends the adjusted response back to the device.

[1022] 7. The device will display the answer to the user. For example, it will display something like, "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[1023] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] A user enters a tax return question into a terminal interface, for example, "What can I claim as an expense when filing my tax return?"

[1027] Step 2:

[1028] The device retrieves the question entered by the user, converts the text data into JSON format, and prepares an HTTP POST request to send it to the server.

[1029] Step 3:

[1030] The terminal sends the prepared HTTP POST request to the server, for example, to the destination URL http: / / example.com / api / ask.

[1031] Step 4:

[1032] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and retrieves the question content.

[1033] Step 5:

[1034] The server passes the extracted question data to an emotion engine to recognize the user's emotional state, in this case the emotion engine determines an emotion such as "user's text suggests frustration."

[1035] Step 6:

[1036] The server then passes the question to the AI ​​module based on the emotional information obtained from the emotion engine, and generates the optimal answer. At the same time, it also includes information that the user is feeling frustrated as a prompt to the AI ​​model.

[1037] Step 7:

[1038] The server receives the answer returned by the AI ​​model. For example, it receives the answer text, "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[1039] Step 8:

[1040] The server then passes the resulting response back to the emotion engine, which adjusts the tone and content to reflect the user's emotions, such as adding an additional encouraging message for a frustrated user.

[1041] Step 9:

[1042] The server will return the final adjusted answer as a JSON response. For example, the returned JSON might look like this: {"response": "Generally, stationery and travel expenses needed for business can be claimed as expenses. Relax and let's get on with what we need to do one step at a time."}

[1043] Step 10:

[1044] The terminal receives the response from the server and extracts the answer text in the response field from the response body.

[1045] Step 11:

[1046] The device displays the extracted answer text on the user interface. Specifically, it displays the following: "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[1047] Step 12:

[1048] Users can check the answers displayed on their device, resolve any questions they may have about their tax returns, and reduce stress with sensitive messages. They can also ask additional questions if necessary.

[1049] Example 2

[1050] 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."

[1051] It is necessary to provide not only fast and accurate answers to questions about tax returns, but also responses that take the user's emotions into consideration, thereby reducing stress and anxiety and improving satisfaction. This issue could not be resolved with conventional systems.

[1052] 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.

[1053] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to a data processing device, means including an artificial intelligence module for the data processing device to generate answers to the questions, means for returning the generated answers to the users, and an emotion recognition engine for recognizing the user's emotions and adjusting the answers, thereby enabling appropriate answers that take the user's emotions into consideration.

[1054] A "user" is a person who uses this system to ask questions about tax returns.

[1055] A "tax return" is the act of an individual or company reporting their annual income and income tax amount to tax authorities.

[1056] The "means for receiving a question" refers to an interface and function that allows the system to receive a question entered by a user.

[1057] "Data processing device" is a general term for computer hardware and software for processing user questions and generating appropriate answers.

[1058] The "means for transmitting a question to a server" is a function for transmitting question data received from a user to a server via a network.

[1059] An "artificial intelligence module" is a collection of software and algorithms that uses natural language processing techniques to generate answers to user questions.

[1060] "Generating an answer" is the process by which the artificial intelligence module creates a specific answer to provide appropriate information for the user's question.

[1061] The "means for returning the generated answer" is a function for sending the answer generated by the server back to the user.

[1062] An "emotion recognition engine" is software and algorithms for analyzing and recognizing emotions from user input.

[1063] "Adjusting the response" is the process of appropriately changing the content and tone of the generated response based on the user's emotions recognized by the emotion recognition engine.

[1064] "Natural language processing" is a general term for techniques and algorithms that allow computers to understand, interpret, and generate human language.

[1065] The present invention is a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module on the server, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining it with an emotion recognition engine that recognizes the user's emotions.

[1066] Overall system configuration

[1067] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the server generates a response to the question, and the emotion recognition engine recognizes the user's emotion and adjusts the response accordingly.

[1068] Main components and roles

[1069] 1. Device:

[1070] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1071] Display the response from the server.

[1072] 2. User:

[1073] Use the device to enter your tax return questions.

[1074] Get answers that reflect your emotional state via an emotion recognition engine.

[1075] 3. Server:

[1076] It receives questions and uses an artificial intelligence module to generate appropriate answers, which are then sent to an emotion recognition engine.

[1077] Based on the results received from the emotion recognition engine, a tailored response is sent back to the device.

[1078] The role of emotion recognition engines

[1079] The emotion recognition engine analyzes the questions entered by the user and recognizes the emotion expressed in the question. This allows the AI ​​module to appropriately adjust the tone and content of the answers it generates. For example, if the user is feeling stressed, the emotion recognition engine will recognize this and the response will be more gentle and reassuring.

[1080] Program processing flow

[1081] Specifically, a user inputs a question using the terminal interface. For example, the user inputs a question such as, "What can I claim as an expense?" The terminal receives the question input by the user and sends it to the server.

[1082] The server receives the question and uses an emotion recognition engine to recognize the emotion in the user's question. Based on the results, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1083] The emotion recognition engine then receives this response and adjusts it based on the user's emotion. For example, if the engine recognizes that the user is stressed, it adjusts the tone of the response to be gentler. The generated response is then received by the server again and sent back to the device.

[1084] Specific examples

[1085] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the process will be as follows:

[1086] 1. The user types a question into the terminal.

[1087] "I'm stressed about my tax return. What can I claim as an expense?"

[1088] 2. The device sends this question to the server.

[1089] 3. The server receives the question and recognizes the user's emotion via an emotion recognition engine.

[1090] Recognize that the user is "stressed."

[1091] 4. The server sends the question to the artificial intelligence model and generates an answer.

[1092] The answer generated is, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1093] 5. The emotion recognition engine adjusts the generated answers based on the user's emotions, for example, "Explain in a gentle tone to relieve stress."

[1094] "Just relax and take things one step at a time," he adds.

[1095] 6. The server sends the adjusted response back to the device.

[1096] 7. The terminal displays the answer to the user.

[1097] The message displayed is, "AI's answer: Generally, stationery and travel expenses necessary for business can be claimed as business expenses. Relax and just get on with what you need to do one thing at a time."

[1098] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[1099] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1100] Step 1: User enters question

[1101] The user inputs a question using the device interface. For example, the user might input "What can be claimed as an expense?" The input data is in text format and is saved on the device as the user's question. At this stage, the input data is generated.

[1102] Step 2: The device sends the question to the server

[1103] The terminal obtains the question data entered by the user and sends it to the server as an HTTP POST request to the / api / ask endpoint. The input is text-formatted question data, and the output is converted to JSON format and sent to the server.

[1104] Step 3: The server receives the query

[1105] The server receives a POST request at the / api / ask endpoint. This request contains the question data. The server parses the received JSON data and extracts the question text. The input is the JSON data, and the output is the extracted question text.

[1106] Step 4: The server recognizes the emotion using the emotion recognition engine.

[1107] The server passes the extracted question text to an emotion recognition engine to analyze the user's emotions. The input is the question text, and the output is the recognized emotion data (e.g., "stress"). The emotion recognition engine performs the specific operation of analyzing emotions using natural language processing technology.

[1108] Step 5: The server uses an artificial intelligence model to generate an answer

[1109] The server inputs the emotion data and question text received from the emotion recognition engine into the AI ​​model to generate an appropriate answer. The input is the question text and emotion data, and the output is the generated answer text. The generative AI model performs the specific operation of generating an answer based on the prompt text.

[1110] Step 6: Emotion recognition engine adjusts the answer

[1111] The server sends the answer received from the AI ​​model to the emotion recognition engine, which adjusts the answer based on the user's emotions. The input is the answer text and emotion data, and the output is the adjusted answer text. The emotion recognition engine then performs specific actions to change the tone and content of the answer to match the user's emotions.

[1112] Step 7: The server sends the adjusted answer back to the device

[1113] The server returns the answer adjusted by the emotion recognition engine as a response in JSON format to the terminal. The input is the adjusted answer text, and the output is the response data in JSON format.

[1114] Step 8: The device displays the answer to the user

[1115] The device receives the JSON data returned from the server and extracts the answer text. The extracted answer text is displayed to the user. For example, it might be displayed as "AI's answer: Generally, stationery and travel expenses needed for sales can be claimed as expenses. Relax and proceed with the tasks one by one." The input is the JSON-formatted response data, and the output is the text to be displayed.

[1116] (Application example 2)

[1117] 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."

[1118] In autonomous vehicles, passengers and drivers are required to not only provide information but also to recognize their emotions and provide appropriate responses in a relaxed tone when they have questions or concerns during their trip.However, conventional systems are unable to properly recognize the user's emotions and adjust responses based on them, which has prevented them from fully improving user satisfaction and relaxation.

[1119] 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.

[1120] In this invention, the server includes means for receiving questions about tax returns from a user, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the user, and means including an emotion engine for the artificial intelligence module to recognize the user's emotions and adjust the answers appropriately. This makes it possible to provide relaxed answers that take into consideration the emotions of the user and reduce stress within an autonomous vehicle.

[1121] "User" refers to an individual or corporation that uses the system to enter questions about tax returns.

[1122] The "means for receiving a question" refers to an interface for transmitting a question entered by a user from a terminal to a server.

[1123] "Means for sending a question to a server" refers to a protocol for sending a question entered on a terminal to a server via a network.

[1124] "Artificial Intelligence Module" refers to a software component that uses natural language processing techniques to generate answers to user questions.

[1125] "Means for generating an answer" refers to an algorithm or process that uses an artificial intelligence module to generate an answer to a user's question.

[1126] "Means for returning the generated answer to the user" refers to a communication function for returning the answer generated by the server to the terminal via the network and displaying it to the user.

[1127] An "emotion engine" is a software module that recognizes the emotion in a user's question and adjusts the tone and content of the response.

[1128] "Natural language processing" refers to artificial intelligence techniques for understanding and generating human language.

[1129] "Means for sending prompts" refers to a mechanism that analyzes a user's question and generates and sends appropriate prompts to the artificial intelligence module based on the analysis results.

[1130] To implement this invention, a user inputs a question using a terminal in an autonomous vehicle, the question is received by a server, an artificial intelligence module generates an answer, and an emotion engine recognizes the user's emotion, adjusts the answer, and sends it back to the user.

[1131] Hardware and software used

[1132] Hardware

[1133] Terminals inside the autonomous vehicle: These can be tablets or dedicated displays that allow users to enter questions and receive answers.

[1134] software

[1135] Flask framework: Used to build server-side APIs.

[1136] Transformers library: Provides artificial intelligence modules for natural language processing.

[1137] EmotionRecognizer library: Used to build an emotion engine that recognizes emotions from user input.

[1138] Data processing and calculation

[1139] Receive user's question: The question entered by the user into the terminal is sent to the server through the terminal interface.

[1140] Question analysis and emotion recognition: The server analyzes the received question and generates an initial answer to the question using the Transformers library. It also recognizes the user's emotion from the context of the question using the EmotionRecognizer library.

[1141] Response adjustment: The emotion engine adjusts responses based on the emotions it recognizes, for example, adding a relaxed tone to responses if the user is feeling stressed.

[1142] Returning the answer: The adjusted answer is sent from the server to the device and displayed to the user.

[1143] Specific examples

[1144] Specific processing flow

[1145] 1. User input: A passenger types into the terminal, "What are some recommended tourist spots?"

[1146] 2. Emotion recognition: The server uses an emotion engine to recognize when the user is feeling stressed.

[1147] 3. Generate an initial answer: The AI ​​module (Transformers library) generates an answer such as, "The recommended tourist spot is XX."

[1148] 4. Adjusting the answer: Based on the emotion recognized by the emotion engine, the answer is adjusted to "Recommended tourist spots are ____. Relax and enjoy yourself."

[1149] 5. Display Answer: The adjusted answer is displayed on the terminal for the user to see.

[1150] Prompt Sentence Examples

[1151] For example, the following prompt sentences are used:

[1152] Q: What are some recommended tourist spots?

[1153] Emotion: Stress

[1154] Q: Where can I go with my kids?

[1155] Emotion: excitement

[1156] This allows appropriate and relaxed answers to users' questions to be provided within the self-driving vehicle, improving user satisfaction.

[1157] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1158] Step 1:

[1159] The user inputs a question into the terminal.

[1160] A user uses a device such as a tablet or display to input a question such as "What are some recommended tourist spots?" This input is done through the device's interface. The question text is generated as input and sent to the next step.

[1161] Step 2:

[1162] The terminal sends a query to the server.

[1163] The terminal sends the entered question text to the server. This communication is carried out over the network, and the question text is delivered to the server as an HTTP request. The question text is taken as input and becomes the output to be sent to the server.

[1164] Step 3:

[1165] The server receives the query.

[1166] The server receives the question text sent from the terminal. It parses the question text into JSON format using the Flask framework. It receives the question JSON as input and parses it to get the question text.

[1167] Step 4:

[1168] Emotion recognition using emotion engine.

[1169] The server uses an emotion engine (EmotionRecognizer library) to recognize the user's emotion from the received question text. For example, it recognizes that the user's emotion is "stress" from the question text. It takes the question text as input and generates the user's emotion data as output.

[1170] Step 5:

[1171] Answer generation by AI module.

[1172] The server uses the Transformers library to generate an initial answer to the question text. For example, it generates an answer such as "A recommended tourist spot is ____." It takes the question text as input and generates the initial answer text as output.

[1173] Step 6:

[1174] Tailoring responses with an emotion engine.

[1175] The server uses an emotion engine to adjust the initial response text based on the user's emotions. If the user is feeling stressed, it adds a tone to the response such as "Relax and have fun." It takes the initial response text and the user's emotion data as input and generates the adjusted response text as output.

[1176] Step 7:

[1177] The adjusted response is sent back to the terminal.

[1178] The server converts the adjusted answer text back into JSON format and sends it back to the device. This communication also takes the adjusted answer text as input and returns a JSON-formatted response as output.

[1179] Step 8:

[1180] The terminal displays the answer to the user.

[1181] The device analyzes the JSON-formatted response received from the server and displays the adjusted response text to the user. For example, it might say, "A recommended tourist spot is ____. Relax and enjoy." It receives JSON-formatted response data as input and generates text to be displayed to the user as output.

[1182] 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.

[1183] 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.

[1184] 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.

[1185] [Fourth embodiment]

[1186] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1187] 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.

[1188] 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).

[1189] 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.

[1190] 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.

[1191] 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).

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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.

[1196] 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.

[1197] 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.

[1198] 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."

[1199] The following describes an embodiment of the present invention.

[1200] Overall system overview

[1201] The present invention provides a system that receives tax return inquiries from users and transmits them to a server, which uses an artificial intelligence module to generate answers to the inquiries and transmits the answers back to the users. This system allows users to quickly and accurately resolve their tax return inquiries.

[1202] Main components and roles

[1203] 1. Device:

[1204] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1205] Display the response from the server.

[1206] 2. User:

[1207] Use the device to enter your tax return questions.

[1208] 3. Server:

[1209] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[1210] Program processing flow

[1211] 1. Terminal processing

[1212] The user uses the terminal interface to enter a question following the prompt "Ask a question about your tax return." For example, the user enters a question such as "What can I claim as an expense?" The terminal receives the question entered by the user and sends it to the server via the send_question function. The server processes the question.

[1213] 2. Server-side processing

[1214] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. It then passes the question to the generate_response function and uses the AI ​​model to generate an answer to the question. For example, in response to the question "What items can be claimed as expenses?", the AI ​​model generates the answer "Generally, stationery and travel expenses required for sales can be claimed as expenses." The server then returns this answer in JSON format as a response.

[1215] 3. Displaying the terminal

[1216] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[1217] Specific examples

[1218] For example, if a user types "What can I claim as an expense?" into a terminal, the following process will occur:

[1219] 1. The user types a question into the terminal.

[1220] 2. The device sends this question to the server.

[1221] 3. The server receives the question and generates an answer using an AI model.

[1222] 4. The server generates a response and sends it back to the device.

[1223] 5. The device displays the answer to the user.

[1224] This system allows users to quickly obtain accurate information about tax returns, such as accurately understanding the procedures for filing tax returns, gathering the necessary documents, and obtaining reference information for claiming appropriate expenses.

[1225] The processing flow will be explained below.

[1226] Step 1:

[1227] The user enters a question about their tax return, such as "What can I claim as an expense?" through a user interface.

[1228] Step 2:

[1229] The device receives the question entered by the user and prepares it to be sent to the server. Specifically, it converts the user's question into JSON format and creates an HTTP POST request.

[1230] Step 3:

[1231] The device passes the question to the server by sending a prepared HTTP POST request to a URL that points to a pre-configured API endpoint (e.g., http: / / example.com / api / ask).

[1232] Step 4:

[1233] The server receives a POST request on the / api / ask endpoint and extracts the JSON data from the request body to get the submitted question.

[1234] Step 5:

[1235] The server sets the question as a prompt to be passed to the AI ​​module, which then sends the question to the AI ​​model and generates an answer using natural language processing technology.

[1236] Step 6:

[1237] The server receives the answer returned by the AI ​​module. For example, it may generate an answer such as, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1238] Step 7:

[1239] The server sets the generated answer as a JSON format response in the HTTP response body and returns it to the terminal.

[1240] Step 8:

[1241] The device receives the response from the server, extracts the JSON data from the response body, and obtains the answer text in the response field.

[1242] Step 9:

[1243] The device displays the extracted answer text on the user interface. Specifically, it displays the answer as "AI's answer: Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1244] Step 10:

[1245] Users can check the answers displayed on the device to resolve any questions they may have about their tax return, and can also ask additional questions if necessary.

[1246] Example 1

[1247] 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."

[1248] One issue is the difficulty of providing quick and accurate answers to questions about tax returns. Conventional systems require users to consult with experts, which takes a lot of time and effort. In addition, information on the Internet can be unreliable, making it difficult for users to make appropriate decisions.

[1249] 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.

[1250] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the users, and means for the terminal to receive the answers from the server and display them to the users, thereby enabling users to quickly and accurately resolve their questions about tax returns.

[1251] A "user" is a person who enters a question about a tax return.

[1252] "Filing a tax return" refers to the procedure for calculating and paying tax on income.

[1253] A "question" refers to text entered by a user inquiring about information they would like to know about their tax return.

[1254] "Terminal" refers to a device or software that provides a user interface, receives questions from a user, sends them to a server, and displays answers from the server.

[1255] "Server" refers to a computer system on a network that receives questions sent by users, generates answers using an artificial intelligence module, and returns the generated answers to the terminal.

[1256] An "artificial intelligence module" is a computer program or system that uses natural language processing techniques to generate answers to questions.

[1257] "Natural language processing" is a technical field that uses computers to understand and process human language.

[1258] A "prompt" refers to a form of instruction or input to an artificial intelligence module that generates a specific answer.

[1259] "Answer" refers to the text of information generated by the artificial intelligence module in response to a user's question.

[1260] "Transmission" refers to the act of a terminal transferring a question to a server, or a server transferring an answer to a terminal via a network.

[1261] "Display" refers to visually showing the answer received by the terminal from the server so that the user can confirm it.

[1262] MODE FOR CARRYING OUT THE INVENTION

[1263] The present invention is a system that receives questions about tax returns from users, transmits the questions to a server, and the server uses an artificial intelligence module to generate answers that are returned to the user.

[1264] System configuration and hardware / software used

[1265] The main components of the present invention are:

[1266] 1. Device:

[1267] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1268] Display the response from the server.

[1269] Specific hardware used includes computers, smartphones, tablets, etc. Software used includes web browsers and mobile apps.

[1270] 2. User:

[1271] Use the device to enter your tax return questions.

[1272] Users can use this system when they want to quickly resolve questions about tax return expense claims and procedures.

[1273] 3. Server:

[1274] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[1275] The specific hardware used includes a cloud server or an on-premise server, and the software includes an HTTP server, a database, and an artificial intelligence module (e.g., GPT-3).

[1276] Program processing and data processing / calculation

[1277] The terminal receives a question from the user through the user interface. The question is sent to the server through the send_question function. The question is sent using an HTTP POST request.

[1278] The server processes the received question at the / api / ask endpoint, extracts the question data from the request body, and passes it to the generate_response function, which uses a generative AI model (e.g., GPT-3) to generate an answer to the question.

[1279] The generated answer is sent back to the terminal in JSON format, and the terminal receives this response, extracts the answer text, and displays it to the user.

[1280] Specific examples

[1281] For example, if a user types "What can I claim as an expense?" into a terminal, the following will happen:

[1282] 1. The user types a question into the terminal.

[1283] 2. The device sends this question to the server.

[1284] 3. The server receives the question and generates an answer using a generative AI model (e.g., GPT-3).

[1285] 4. The server generates a response and sends it back to the device.

[1286] 5. The device displays the answer to the user.

[1287] Prompt Sentence Examples

[1288] An example prompt for the question "What can you claim as an expense?" is:

[1289] Generate accurate answers to the following questions: What can I claim as an expense?

[1290] Based on this prompt, the generative AI model will generate an appropriate answer, such as "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[1291] The above is an embodiment of the present invention, which allows users to quickly and accurately resolve questions about tax returns.

[1292] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1293] Program processing steps

[1294] Step 1:

[1295] The user enters a question into the device's user interface. The entered question is in text format. Specifically, the user types "What can I claim as an expense?" into an input field in a browser or mobile app. The input field has a "Submit" button, which the user clicks to proceed to the next step.

[1296] Input: A question typed by the user (e.g., "What can I claim as an expense?")

[1297] Output: The question text entered

[1298] Step 2:

[1299] The device takes the user's input and calls the send_question function to send the question to the server. The question is sent as an HTTP POST request to the server's / api / ask endpoint, with the question text included in the request body.

[1300] Input: The question text entered by the user

[1301] Output: The question text in the HTTP POST request to the server

[1302] Step 3:

[1303] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and passes it to the generate_response function. At this point, the server holds the question text from the request.

[1304] Input: Question text included in HTTP POST request

[1305] Output: Question data passed to the generate_response function

[1306] Step 4:

[1307] The server calls a generative AI model (e.g., GPT-3) in the generate_response function to generate an answer to the question. The generative AI model uses the prompt to generate an appropriate answer to the question. For example, in response to a question about expenses, it might respond, "Generally, stationery and transportation costs required for business can be claimed as expenses."

[1308] Input: Question data passed to the generate_response function

[1309] Output: Answer text generated by the generative AI model

[1310] Step 5:

[1311] The server formats the generated answer in JSON format and sends it to the device as an HTTP response, with the answer text included in the response field of the response body.

[1312] Input: Answer text generated by the generative AI model

[1313] Output: JSON formatted HTTP response sent to the device

[1314] Step 6:

[1315] The device receives the response from the server and extracts the answer text from the response field. The extracted answer text is visually displayed to the user. The UI displays "AI answer: Generally, stationery and transportation costs required for business can be claimed as expenses."

[1316] Input: JSON format HTTP response from the server

[1317] Output: The answer text that is displayed to the user

[1318] The specific actions at each step allow users to quickly and accurately resolve their tax return questions.

[1319] (Application example 1)

[1320] 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."

[1321] Current systems for solving questions about tax returns have limitations in terms of enabling users to quickly and accurately obtain information. Furthermore, there is a lack of systems for users to resolve questions about procedures and electronic payments, which reduces user convenience. Therefore, there is a need for a system that provides quick and accurate answers to questions about tax returns and electronic payments.

[1322] 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.

[1323] In this invention, the server includes a means for an artificial intelligence module to generate answers to questions using natural language processing, a means for returning the generated answers to the user, and a means for analyzing the user's questions and sending prompts to the artificial intelligence module to provide specific answers, thereby enabling quick and accurate answers to be provided to questions from the user regarding tax returns and electronic payments.

[1324] The "means for receiving questions about tax returns from users" is an interface that allows the system to recognize and receive questions about tax returns entered by users.

[1325] The "means for transmitting a question to a server" is a communication function for transmitting a question received by a terminal to a server via a network.

[1326] "Means including an artificial intelligence module for the server to generate answers to questions" refers to a system in which an AI module installed in the server generates appropriate answers to received questions.

[1327] The "means for returning the generated answer to the user" is a function for transmitting the answer generated by the server to the user's terminal and displaying it.

[1328] The "means for receiving a question about electronic payment from a user" is an interface that allows the system to recognize and receive a question about electronic payment input by a user.

[1329] The "means for transmitting questions about electronic payment to a server" is a communication function for transmitting questions about electronic payment received by the terminal to a server via a network.

[1330] "Means including an artificial intelligence module for the server to generate answers to questions about electronic payments" refers to a system in which an AI module installed in the server generates appropriate answers to received questions about electronic payments.

[1331] The "means for returning the generated answer regarding electronic payment to the user" is a function for transmitting the answer regarding electronic payment generated by the server to the user's terminal and displaying it.

[1332] The following describes an embodiment of the present invention.

[1333] Overall system overview

[1334] The present invention is a system that receives questions from users about tax returns and electronic payments, sends them to a server, and the server uses an AI module to generate answers to the questions and returns the answers to the users. This system allows users to quickly and accurately resolve their questions about tax returns and electronic payments.

[1335] Main components and roles

[1336] 1. Device:

[1337] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1338] Display the response from the server.

[1339] 2. User:

[1340] Use the terminal to enter questions about tax returns and electronic payments.

[1341] 3. Server:

[1342] It receives the question, uses an AI module to generate an appropriate answer, and sends it back to the device.

[1343] Program processing flow

[1344] Program for carrying out the invention

[1345] First, the user uses the terminal interface to input questions about tax returns or electronic payments. For example, they can input questions such as, "What can I claim as an expense?" or "At which stores can I use this electronic payment method?" The terminal then sends the input questions to the server, where they are processed.

[1346] Hardware and Software

[1347] The hardware used includes devices such as smartphones and tablets to provide the user interface, and a high-performance server to process questions and answers.

[1348] The software used is an AI module for natural language processing and an API endpoint for managing network communications. The specific AI module used is a generative AI model.

[1349] Data processing and calculation

[1350] The server receives the questions sent from the device and generates appropriate answers using the AI ​​module. The main processes performed at this time are as follows:

[1351] 1. Analyze the question data and perform natural language processing.

[1352] 2. Use a generative AI model to generate an answer based on the question.

[1353] 3. The generated answer is sent back to the device in JSON format.

[1354] Specific examples

[1355] For example, if a user types "What can I claim as an expense?", the following occurs:

[1356] 1. The user types a question into the terminal.

[1357] 2. The device sends this question to the server.

[1358] 3. The server receives the question and generates an answer using an AI model.

[1359] 4. The server generates a response and sends it back to the device.

[1360] 5. The device displays the answer to the user.

[1361] The same process also occurs when the user enters "At which stores can I use this electronic payment method?"

[1362] Example prompt sentence:

[1363] Please answer the following questions: In which stores can I use this electronic payment?

[1364] This system allows users to quickly obtain accurate information about tax returns and electronic payments. For example, users can accurately understand the procedures for filing tax returns, gather the necessary documents, and obtain reference information for claiming appropriate expenses. Users can also easily obtain information about electronic payment procedures and the stores where they can be used.

[1365] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1366] Step 1:

[1367] The user uses the terminal interface to input questions about tax returns and electronic payments. The user inputs specific questions such as "What can I claim as an expense?" or "At which stores can I use this electronic payment?" The input questions are temporarily stored in the terminal's memory.

[1368] Step 2:

[1369] The device sends the question entered by the user to the server via the send_question function. Specifically, the question data is converted to JSON format and sent as a POST request to the specified endpoint on the server via the Internet. In this sending process, the input data is sent to the server as a JSON object.

[1370] Step 3:

[1371] The server receives a POST request at the / api / ask endpoint and obtains the question data in JSON format from the request body. The server parses this question data and passes it to the AI ​​module to generate an appropriate answer. Specifically, the server converts the received question data into text format and inputs it to the AI ​​module as a prompt.

[1372] Step 4:

[1373] The server's AI module performs natural language processing based on the input prompt and generates an appropriate answer. The AI ​​module analyzes the question and generates an answer using a generative AI model. During this process, the question data is converted by the AI ​​into an appropriate, context-based answer.

[1374] Step 5:

[1375] The server converts the answer generated by the AI ​​module into JSON format and returns it to the device. Specifically, it stores the generated answer text in a JSON object and sends it to the user's device as an HTTP response. The output of this step is the answer data in JSON format.

[1376] Step 6:

[1377] The terminal receives the response data returned from the server and extracts the response text from the response field. The terminal displays this text on the user interface and provides the answer to the user. At this time, the terminal displays to the user, for example, "AI's response: Available at major convenience stores and supermarkets."

[1378] This series of processes allows users to get quick and accurate answers to questions about tax returns and electronic payments.

[1379] 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.

[1380] The present invention relates to a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining an emotion engine that recognizes the user's emotions.

[1381] Overall system overview

[1382] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the AI ​​on the server generates a response to that question, and the emotion engine recognizes the user's emotions and adjusts the response accordingly.

[1383] Main components and roles

[1384] 1. Device:

[1385] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1386] Display the response from the server.

[1387] 2. User:

[1388] Use the device to enter your tax return questions.

[1389] Get answers that reflect your emotional state via the emotion engine.

[1390] 3. Server:

[1391] It receives questions, uses an AI module to generate appropriate answers, and sends them to the emotion engine.

[1392] Based on the results received from the emotion engine, a tailored response is sent back to the device.

[1393] The role of the emotional engine

[1394] The emotion engine analyzes user-entered questions and recognizes the emotion expressed in them, allowing the AI ​​module to adjust the tone and content of the responses it generates appropriately. For example, if the user is feeling stressed, the emotion engine will recognize this and respond more gently and reassuringly.

[1395] Program processing flow

[1396] 1. Terminal processing

[1397] The user uses the terminal interface to input a question. For example, "What can be claimed as an expense?" The terminal receives the question and sends it to the server, where it is processed.

[1398] 2. Server-side processing

[1399] The server receives a POST request at the / api / ask endpoint and retrieves the question data in JSON format from the request body. Next, the emotion engine recognizes the emotion from the user's question, and based on that, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery needed for sales and travel expenses can be claimed as expenses." The server sends this answer to the emotion engine, which then returns the answer, appropriately adjusted by the emotion engine, in JSON format as a response.

[1400] 3. Displaying the terminal

[1401] The device receives the response from the server, extracts the response text from the response field, and displays this response to the user. It is displayed in the form of "AI response: Generally, stationery and travel expenses necessary for business can be claimed as expenses." In addition, a tone that takes the user's emotions into consideration is added.

[1402] Specific examples

[1403] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the following flow will occur:

[1404] 1. The user types a question into the terminal.

[1405] 2. The device sends this question to the server.

[1406] 3. The server receives the question and recognizes the user's emotions via the emotion engine. It recognizes that the user is "feeling stressed."

[1407] 4. The server sends the question to the AI ​​model and generates an answer.

[1408] 5. The emotion engine adjusts the generated answers based on the user's emotions, for example, "explain in a gentle tone to relieve stress."

[1409] 6. The server sends the adjusted response back to the device.

[1410] 7. The device will display the answer to the user. For example, it will display something like, "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[1411] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[1412] The processing flow will be explained below.

[1413] Step 1:

[1414] A user enters a tax return question into a terminal interface, for example, "What can I claim as an expense when filing my tax return?"

[1415] Step 2:

[1416] The device retrieves the question entered by the user, converts the text data into JSON format, and prepares an HTTP POST request to send it to the server.

[1417] Step 3:

[1418] The terminal sends the prepared HTTP POST request to the server, for example, to the destination URL http: / / example.com / api / ask.

[1419] Step 4:

[1420] The server receives a POST request at the / api / ask endpoint, extracts the question data in JSON format from the request body, and retrieves the question content.

[1421] Step 5:

[1422] The server passes the extracted question data to an emotion engine to recognize the user's emotional state, in this case the emotion engine determines an emotion such as "user's text suggests frustration."

[1423] Step 6:

[1424] The server then passes the question to the AI ​​module based on the emotional information obtained from the emotion engine, and generates the optimal answer. At the same time, it also includes information that the user is feeling frustrated as a prompt to the AI ​​model.

[1425] Step 7:

[1426] The server receives the answer returned by the AI ​​model. For example, it receives the answer text, "Generally, stationery and travel expenses necessary for business can be claimed as expenses."

[1427] Step 8:

[1428] The server then passes the resulting response back to the emotion engine, which adjusts the tone and content to reflect the user's emotions, such as adding an additional encouraging message for a frustrated user.

[1429] Step 9:

[1430] The server will return the final adjusted answer as a JSON response. For example, the returned JSON might look like this: {"response": "Generally, stationery and travel expenses needed for business can be claimed as expenses. Relax and let's get on with what we need to do one step at a time."}

[1431] Step 10:

[1432] The terminal receives the response from the server and extracts the answer text in the response field from the response body.

[1433] Step 11:

[1434] The device displays the extracted answer text on the user interface. Specifically, it displays the following: "AI's answer: Generally, stationery and travel expenses necessary for sales can be claimed as business expenses. Relax and proceed with the tasks one by one."

[1435] Step 12:

[1436] Users can check the answers displayed on their device, resolve any questions they may have about their tax returns, and reduce stress with sensitive messages. They can also ask additional questions if necessary.

[1437] Example 2

[1438] 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."

[1439] It is necessary to provide not only fast and accurate answers to questions about tax returns, but also responses that take the user's emotions into consideration, thereby reducing stress and anxiety and improving satisfaction. This issue could not be resolved with conventional systems.

[1440] 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.

[1441] In this invention, the server includes means for receiving questions about tax returns from users, means for transmitting the questions to a data processing device, means including an artificial intelligence module for the data processing device to generate answers to the questions, means for returning the generated answers to the users, and an emotion recognition engine for recognizing the user's emotions and adjusting the answers, thereby enabling appropriate answers that take the user's emotions into consideration.

[1442] A "user" is a person who uses this system to ask questions about tax returns.

[1443] A "tax return" is the act of an individual or company reporting their annual income and income tax amount to tax authorities.

[1444] The "means for receiving a question" refers to an interface and function that allows the system to receive a question entered by a user.

[1445] "Data processing device" is a general term for computer hardware and software for processing user questions and generating appropriate answers.

[1446] The "means for transmitting a question to a server" is a function for transmitting question data received from a user to a server via a network.

[1447] An "artificial intelligence module" is a collection of software and algorithms that uses natural language processing techniques to generate answers to user questions.

[1448] "Generating an answer" is the process by which the artificial intelligence module creates a specific answer to provide appropriate information for the user's question.

[1449] The "means for returning the generated answer" is a function for sending the answer generated by the server back to the user.

[1450] An "emotion recognition engine" is software and algorithms for analyzing and recognizing emotions from user input.

[1451] "Adjusting the response" is the process of appropriately changing the content and tone of the generated response based on the user's emotions recognized by the emotion recognition engine.

[1452] "Natural language processing" is a general term for techniques and algorithms that allow computers to understand, interpret, and generate human language.

[1453] The present invention is a system that receives questions about tax returns from users, sends them to a server, generates answers to the questions using an artificial intelligence module on the server, and returns the answers to the users. The present invention further improves the accuracy and appropriateness of answers to users by combining it with an emotion recognition engine that recognizes the user's emotions.

[1454] Overall system configuration

[1455] The system of the present invention consists of three main components: a terminal, a user, and a server. The user uses the terminal to ask a question, the server generates a response to the question, and the emotion recognition engine recognizes the user's emotion and adjusts the response accordingly.

[1456] Main components and roles

[1457] 1. Device:

[1458] It provides a user interface (UI), receives questions from users, and sends them to the server.

[1459] Display the response from the server.

[1460] 2. User:

[1461] Use the device to enter your tax return questions.

[1462] Get answers that reflect your emotional state via an emotion recognition engine.

[1463] 3. Server:

[1464] It receives questions and uses an artificial intelligence module to generate appropriate answers, which are then sent to an emotion recognition engine.

[1465] Based on the results received from the emotion recognition engine, a tailored response is sent back to the device.

[1466] The role of emotion recognition engines

[1467] The emotion recognition engine analyzes the questions entered by the user and recognizes the emotion expressed in the question. This allows the AI ​​module to appropriately adjust the tone and content of the answers it generates. For example, if the user is feeling stressed, the emotion recognition engine will recognize this and the response will be more gentle and reassuring.

[1468] Program processing flow

[1469] Specifically, a user inputs a question using the terminal interface. For example, the user inputs a question such as, "What can I claim as an expense?" The terminal receives the question input by the user and sends it to the server.

[1470] The server receives the question and uses an emotion recognition engine to recognize the emotion in the user's question. Based on the results, the AI ​​module generates an answer. For example, in response to the question, "What items can be claimed as expenses?", the AI ​​model generates the answer, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1471] The emotion recognition engine then receives this response and adjusts it based on the user's emotion. For example, if the engine recognizes that the user is stressed, it adjusts the tone of the response to be gentler. The generated response is then received by the server again and sent back to the device.

[1472] Specific examples

[1473] For example, if a user types into a terminal, "I'm stressed about filing my tax return. What can I claim as an expense?", the process will be as follows:

[1474] 1. The user types a question into the terminal.

[1475] "I'm stressed about my tax return. What can I claim as an expense?"

[1476] 2. The device sends this question to the server.

[1477] 3. The server receives the question and recognizes the user's emotion via an emotion recognition engine.

[1478] Recognize that the user is "stressed."

[1479] 4. The server sends the question to the artificial intelligence model and generates an answer.

[1480] The answer generated is, "Generally, stationery and transportation costs necessary for business can be claimed as expenses."

[1481] 5. The emotion recognition engine adjusts the generated answers based on the user's emotions, for example, "Explain in a gentle tone to relieve stress."

[1482] "Just relax and take things one step at a time," he adds.

[1483] 6. The server sends the adjusted response back to the device.

[1484] 7. The terminal displays the answer to the user.

[1485] The message displayed is, "AI's answer: Generally, stationery and travel expenses necessary for business can be claimed as business expenses. Relax and just get on with what you need to do one thing at a time."

[1486] This system not only allows users to quickly obtain information about tax returns, but also provides answers that take their feelings into consideration, thereby increasing user satisfaction and reducing anxiety and stress about filing tax returns.

[1487] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1488] Step 1: User enters question

[1489] The user inputs a question using the device interface. For example, the user might input "What can be claimed as an expense?" The input data is in text format and is saved on the device as the user's question. At this stage, the input data is generated.

[1490] Step 2: The device sends the question to the server

[1491] The terminal obtains the question data entered by the user and sends it to the server as an HTTP POST request to the / api / ask endpoint. The input is text-formatted question data, and the output is converted to JSON format and sent to the server.

[1492] Step 3: The server receives the query

[1493] The server receives a POST request at the / api / ask endpoint. This request contains the question data. The server parses the received JSON data and extracts the question text. The input is the JSON data, and the output is the extracted question text.

[1494] Step 4: The server recognizes the emotion using the emotion recognition engine.

[1495] The server passes the extracted question text to an emotion recognition engine to analyze the user's emotions. The input is the question text, and the output is the recognized emotion data (e.g., "stress"). The emotion recognition engine performs the specific operation of analyzing emotions using natural language processing technology.

[1496] Step 5: The server uses an artificial intelligence model to generate an answer

[1497] The server inputs the emotion data and question text received from the emotion recognition engine into the AI ​​model to generate an appropriate answer. The input is the question text and emotion data, and the output is the generated answer text. The generative AI model performs the specific operation of generating an answer based on the prompt text.

[1498] Step 6: Emotion recognition engine adjusts the answer

[1499] The server sends the answer received from the AI ​​model to the emotion recognition engine, which adjusts the answer based on the user's emotions. The input is the answer text and emotion data, and the output is the adjusted answer text. The emotion recognition engine then performs specific actions to change the tone and content of the answer to match the user's emotions.

[1500] Step 7: The server sends the adjusted answer back to the device

[1501] The server returns the answer adjusted by the emotion recognition engine as a response in JSON format to the terminal. The input is the adjusted answer text, and the output is the response data in JSON format.

[1502] Step 8: The device displays the answer to the user

[1503] The device receives the JSON data returned from the server and extracts the answer text. The extracted answer text is displayed to the user. For example, it might be displayed as "AI's answer: Generally, stationery and travel expenses needed for sales can be claimed as expenses. Relax and proceed with the tasks one by one." The input is the JSON-formatted response data, and the output is the text to be displayed.

[1504] (Application example 2)

[1505] 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."

[1506] In autonomous vehicles, passengers and drivers are required to not only provide information but also to recognize their emotions and provide appropriate responses in a relaxed tone when they have questions or concerns during their trip.However, conventional systems are unable to properly recognize the user's emotions and adjust responses based on them, which has prevented them from fully improving user satisfaction and relaxation.

[1507] 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.

[1508] In this invention, the server includes means for receiving questions about tax returns from a user, means for transmitting the questions to the server, means including an artificial intelligence module for the server to generate answers to the questions, means for returning the generated answers to the user, and means including an emotion engine for the artificial intelligence module to recognize the user's emotions and adjust the answers appropriately. This makes it possible to provide relaxed answers that take into consideration the emotions of the user and reduce stress within an autonomous vehicle.

[1509] "User" refers to an individual or corporation that uses the system to enter questions about tax returns.

[1510] The "means for receiving a question" refers to an interface for transmitting a question entered by a user from a terminal to a server.

[1511] "Means for sending a question to a server" refers to a protocol for sending a question entered on a terminal to a server via a network.

[1512] "Artificial Intelligence Module" refers to a software component that uses natural language processing techniques to generate answers to user questions.

[1513] "Means for generating an answer" refers to an algorithm or process that uses an artificial intelligence module to generate an answer to a user's question.

[1514] "Means for returning the generated answer to the user" refers to a communication function for returning the answer generated by the server to the terminal via the network and displaying it to the user.

[1515] An "emotion engine" is a software module that recognizes the emotion in a user's question and adjusts the tone and content of the response.

[1516] "Natural language processing" refers to artificial intelligence techniques for understanding and generating human language.

[1517] "Means for sending prompts" refers to a mechanism that analyzes a user's question and generates and sends appropriate prompts to the artificial intelligence module based on the analysis results.

[1518] To implement this invention, a user inputs a question using a terminal in an autonomous vehicle, the question is received by a server, an artificial intelligence module generates an answer, and an emotion engine recognizes the user's emotion, adjusts the answer, and sends it back to the user.

[1519] Hardware and software used

[1520] Hardware

[1521] Terminals inside the autonomous vehicle: These can be tablets or dedicated displays that allow users to enter questions and receive answers.

[1522] software

[1523] Flask framework: Used to build server-side APIs.

[1524] Transformers library: Provides artificial intelligence modules for natural language processing.

[1525] EmotionRecognizer library: Used to build an emotion engine that recognizes emotions from user input.

[1526] Data processing and calculation

[1527] Receive user's question: The question entered by the user into the terminal is sent to the server through the terminal interface.

[1528] Question analysis and emotion recognition: The server analyzes the received question and generates an initial answer to the question using the Transformers library. It also recognizes the user's emotion from the context of the question using the EmotionRecognizer library.

[1529] Response adjustment: The emotion engine adjusts responses based on the emotions it recognizes, for example, adding a relaxed tone to responses if the user is feeling stressed.

[1530] Returning the answer: The adjusted answer is sent from the server to the device and displayed to the user.

[1531] Specific examples

[1532] Specific processing flow

[1533] 1. User input: A passenger types into the terminal, "What are some recommended tourist spots?"

[1534] 2. Emotion recognition: The server uses an emotion engine to recognize when the user is feeling stressed.

[1535] 3. Generate an initial answer: The AI ​​module (Transformers library) generates an answer such as, "The recommended tourist spot is XX."

[1536] 4. Adjusting the answer: Based on the emotion recognized by the emotion engine, the answer is adjusted to "Recommended tourist spots are ____. Relax and enjoy yourself."

[1537] 5. Display Answer: The adjusted answer is displayed on the terminal for the user to see.

[1538] Prompt Sentence Examples

[1539] For example, the following prompt sentences are used:

[1540] Q: What are some recommended tourist spots?

[1541] Emotion: Stress

[1542] Q: Where can I go with my kids?

[1543] Emotion: excitement

[1544] This allows appropriate and relaxed answers to users' questions to be provided within the self-driving vehicle, improving user satisfaction.

[1545] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1546] Step 1:

[1547] The user inputs a question into the terminal.

[1548] A user uses a device such as a tablet or display to input a question such as "What are some recommended tourist spots?" This input is done through the device's interface. The question text is generated as input and sent to the next step.

[1549] Step 2:

[1550] The terminal sends a query to the server.

[1551] The terminal sends the entered question text to the server. This communication is carried out over the network, and the question text is delivered to the server as an HTTP request. The question text is taken as input and becomes the output to be sent to the server.

[1552] Step 3:

[1553] The server receives the query.

[1554] The server receives the question text sent from the terminal. It parses the question text into JSON format using the Flask framework. It receives the question JSON as input and parses it to get the question text.

[1555] Step 4:

[1556] Emotion recognition using emotion engine.

[1557] The server uses an emotion engine (EmotionRecognizer library) to recognize the user's emotion from the received question text. For example, it recognizes that the user's emotion is "stress" from the question text. It takes the question text as input and generates the user's emotion data as output.

[1558] Step 5:

[1559] Answer generation by AI module.

[1560] The server uses the Transformers library to generate an initial answer to the question text. For example, it generates an answer such as "A recommended tourist spot is ____." It takes the question text as input and generates the initial answer text as output.

[1561] Step 6:

[1562] Tailoring responses with an emotion engine.

[1563] The server uses an emotion engine to adjust the initial response text based on the user's emotions. If the user is feeling stressed, it adds a tone to the response such as "Relax and have fun." It takes the initial response text and the user's emotion data as input and generates the adjusted response text as output.

[1564] Step 7:

[1565] The adjusted response is sent back to the terminal.

[1566] The server converts the adjusted answer text back into JSON format and sends it back to the device. This communication also takes the adjusted answer text as input and returns a JSON-formatted response as output.

[1567] Step 8:

[1568] The terminal displays the answer to the user.

[1569] The device analyzes the JSON-formatted response received from the server and displays the adjusted response text to the user. For example, it might say, "A recommended tourist spot is ____. Relax and enjoy." It receives JSON-formatted response data as input and generates text to be displayed to the user as output.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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).

[1577] 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.

[1578] 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."

[1579] 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.

[1580] 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).

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] The following is further disclosed regarding the above embodiment.

[1592] (Claim 1)

[1593] means for receiving tax return inquiries from users;

[1594] means for sending a query to a server;

[1595] a means for the server to include an artificial intelligence module for generating answers to questions;

[1596] means for returning the generated answer to the user;

[1597] A system including:

[1598] (Claim 2)

[1599] 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

[1600] (Claim 3)

[1601] 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer.

[1602] "Example 1"

[1603] (Claim 1)

[1604] means for receiving tax return inquiries from users;

[1605] means for sending a query to a server;

[1606] a means for the server to include an artificial intelligence module for generating answers to questions;

[1607] means for returning the generated answer to the user;

[1608] means for the terminal to receive a response from the server and display it to the user;

[1609] A system including:

[1610] (Claim 2)

[1611] 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

[1612] (Claim 3)

[1613] 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer.

[1614] "Application Example 1"

[1615] (Claim 1)

[1616] means for receiving tax return inquiries from users;

[1617] means for sending a query to a server;

[1618] a means for the server to include an artificial intelligence module for generating answers to questions;

[1619] means for returning the generated answer to the user;

[1620] means for receiving inquiries from a user regarding electronic payments;

[1621] means for transmitting electronic payment queries to a server;

[1622] a means for the server to include an artificial intelligence module for generating answers to questions about electronic payments;

[1623] means for returning a generated electronic payment response to the user;

[1624] A system including:

[1625] (Claim 2)

[1626] 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

[1627] (Claim 3)

[1628] 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer.

[1629] "Example 2: Combining Emotion Engines"

[1630] (Claim 1)

[1631] means for receiving tax return inquiries from users;

[1632] means for transmitting a query to a data processing device;

[1633] means for the data processing device to include an artificial intelligence module for generating answers to questions;

[1634] means for returning the generated answer to the user;

[1635] means including an emotion recognition engine for recognizing the user's emotions and adjusting responses;

[1636] A system including:

[1637] (Claim 2)

[1638] 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

[1639] (Claim 3)

[1640] 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer.

[1641] "Application example 2 when combining emotion engines"

[1642] (Claim 1)

[1643] means for receiving tax return inquiries from users;

[1644] means for sending a query to a server;

[1645] a means for the server to include an artificial intelligence module for generating answers to questions;

[1646] means for returning the generated answer to the user;

[1647] a means for the artificial intelligence module to include an emotion engine that recognizes the user's emotions and adjusts responses accordingly;

[1648] A system including:

[1649] (Claim 2)

[1650] 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

[1651] (Claim 3)

[1652] 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer. [Explanation of symbols]

[1653] 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. means for receiving tax return inquiries from users; means for sending a query to a server; a means for the server to include an artificial intelligence module for generating answers to questions; means for returning the generated answer to the user; A system including:

2. 10. The system of claim 1, wherein the artificial intelligence module utilizes natural language processing to generate answers to questions.

3. 10. The system of claim 1, further comprising means for analyzing a user's question and sending prompts to an artificial intelligence module to provide a specific answer.

Images