system

Abstract

We provide the system. [Solution] Image receiving means, A means for extracting text information from an image using optical character recognition means, A method for automatically performing accounting entries based on extracted text information, A means of aggregating journal entry data and generating data for tax return filing, A means of providing asset formation advice based on generated data and income / expense data, A means of interacting with the user using natural language processing, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Accounting management and tax return filing operations performed by individuals require manual information input and management of each receipt, which is a significant burden. In particular, for individuals engaged in part-time jobs or investments, the management of income and expenses becomes complicated, and accurate data collection and journal entries are difficult. Additionally, due to low financial literacy, appropriate asset management and tax measures cannot be taken, and there is a problem that it is difficult to conduct optimal economic activities.

Means for Solving the Problems

[0005] This invention provides a system that can receive images of receipts and invoices sent by a user using an image receiving means, and extract text information from the images using an optical character recognition means. This reduces manual information input and includes means for automatically performing accounting entries based on the extracted text information. Furthermore, it simplifies the tax filing process by aggregating the journal entry data and generating data for tax returns. In addition, it includes means for providing the user with optimal asset formation advice based on the generated data and income and expenditure data. This advice is provided through dialogue with the user using natural language processing, so the user can operate it intuitively even without specialized knowledge.

[0006] "Image receiving means" refers to a device or software module for receiving image data transmitted by a user.

[0007] "Optical character recognition means" refers to a technology and apparatus that analyzes character information within an image and converts it into text data.

[0008] "Means of performing accounting entries" refers to processes and systems that automatically perform accounting classification and recording based on acquired data.

[0009] A "means for generating data for tax filing" refers to a system that aggregates accounting results and prepares the data necessary for tax filing in a specific format.

[0010] "Means of providing asset building advice" refers to technology that analyzes users' income and expenditure data to suggest optimal asset management methods and financial guidance.

[0011] "Means of interacting with users using natural language processing" refers to technologies that understand the user's natural language input and generate and provide appropriate responses or instructions. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system according to the present invention is designed to improve the efficiency of users' economic activities and reduce the burden of manual accounting. Users can use their personal devices to record expenses and expenditures incurred in their daily lives. Specifically, users take pictures of receipts and invoices with a device such as a smartphone or tablet and send them to the system via a chat application.

[0034] Upon receiving this image data, the server begins reading the text information using AI-OCR technology. The read information is extracted as text, sent to the server, and stored in a database. Based on this stored information, the server initiates automatic journal entries and integrates with accounting software. The server categorizes the identified expense information into the appropriate accounting categories, and all data is managed in a consistent format.

[0035] Furthermore, the server aggregates data over a certain period and organizes it into the format required for tax filing. This allows users to easily obtain the necessary documents for filing their tax returns. In addition, based on the user's income and expense data, the server uses AI to analyze the data and generate optimal advice for wealth building. This advice includes expense management, savings strategies, and investment suggestions.

[0036] Users can interact with the server's AI assistant using natural language through their device to check, modify, and inquire about data. For example, if a user asks, "Tell me my food expenses for last month," the server will provide the corresponding data. Furthermore, if a user wants to modify a specific expense, they can easily do so by giving instructions in natural language.

[0037] Through the above process, the system can manage financial data in daily life and support optimal economic activities without requiring users to possess advanced financial literacy.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users take pictures of receipts and invoices with their smartphones or other devices and send the images to the system via the LINE chat app.

[0041] Step 2:

[0042] The server receives image data from the user's terminal. The received image data is input into an optical character recognition (OCR) engine, which converts the character information into text data.

[0043] Step 3:

[0044] The server analyzes the text data extracted by OCR to identify information such as dates, amounts, and product names. Based on this, it automatically performs accounting entries and records them in the database.

[0045] Step 4:

[0046] The server aggregates the recorded journal entry data at regular intervals and generates the data format required for filing tax returns. This format is customized according to the tax laws of each country and region.

[0047] Step 5:

[0048] The server analyzes the user's income and expense data and uses AI to generate advice for wealth building. This advice is customized according to the individual's financial situation.

[0049] Step 6:

[0050] The user makes inquiries to the server's AI assistant using natural language through their device. The server receives these inquiries, checks and corrects the data, and returns feedback to the user.

[0051] Step 7:

[0052] Users review the advice and data provided by the server and, if necessary, revise their spending and plan their asset management. By continuously using the system, users can better manage their own financial situation.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In economic activity, the burden of manual accounting processes is significant, and the complexity of optimizing asset management necessitates a high level of financial literacy. To address this, a system is needed that streamlines accounting and asset management processes and provides support for making appropriate economic decisions even without specialized knowledge.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for automating accounting journal entry work based on the extracted text information. This enables the user to receive automated accounting processing via image transmission and asset building advice based on the data.

[0058] "Image receiving means" refers to a function for receiving image data sent by a user.

[0059] "Optical character recognition means" refers to a technology for analyzing character information from image data and extracting it as text data.

[0060] "Methods for automating accounting journal entries" refer to functions that use extracted text information to reduce manual work and process accounting quickly.

[0061] "Methods for generating declaration data" refers to the process of using aggregated journal entry data to organize it into a format suitable for tax filing.

[0062] "Means of providing asset building advice" refers to a function that analyzes income and expenditure information and makes useful suggestions regarding the user's asset management.

[0063] "Utilizing generative models" refers to techniques that use machine learning models to generate appropriate advice based on collected data.

[0064] "Means of interacting with users using natural language processing" refers to functions that respond to questions and instructions entered by users in natural language and provide the necessary information.

[0065] The system according to the present invention is designed to streamline users' economic activities and reduce the burden of manual accounting processing. This system is implemented using various terminals, a comprehensive server infrastructure, and advanced software technologies.

[0066] Users record their daily expenses and expenditures using their smartphones or tablets. Specifically, users take pictures of receipts and invoices and send them to the system via a chat application. The server receives this image data and extracts text information from the image using optical character recognition (OCR) technology. For example, software such as Tesseract OCR is used to convert the image data into text.

[0067] The server automatically performs accounting entries based on the extracted text information, and the resulting accounting data is stored in a database. The server also links the entry data with financial management software via an API, enabling efficient data management and analysis. Commonly available financial management software can be used for accounting and finance.

[0068] Furthermore, it can aggregate economic data over a certain period and organize it in the format required for tax filing. Based on this organized data, the server uses a generated AI model to provide users with optimal advice for wealth building. This advice includes spending management, savings strategies, and investment suggestions. This allows users to effectively manage their assets even without specialized knowledge.

[0069] Users can interact with the server's AI assistant using natural language to check and modify data. For example, by entering "Tell me my food expenses for last month" as a prompt, the server will immediately provide the corresponding data. Furthermore, users can also present specific requirements such as "I want to check my transportation expenses for this month. Also, tell me my spending for last month."

[0070] In this way, this system can support the management of financial data in daily life and facilitate efficient and effective economic activities without requiring users to possess advanced financial literacy.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user uses their device to take a picture of a receipt or invoice and sends the captured image to the server via a chat application. This provides the image data as input.

[0074] Step 2:

[0075] The server receives image data and extracts text information from the image using optical character recognition (OCR) technology. Specifically, the server uses OCR software to analyze the image data and generate text data. Here, the received image data is used as input, and the extracted text is obtained as output.

[0076] Step 3:

[0077] The server analyzes the extracted text information and organizes the data necessary for accounting entries. The server uses an algorithm to classify the text information into accounting categories and stores them in a database. This process uses the extracted text data as input and generates structured accounting data as output.

[0078] Step 4:

[0079] The server automatically performs journal entries based on structured accounting data and links the data with financial management software. The server uses an API to send data to the accounting software for integration. In this step, organized accounting data is used as input, and linked journal entry data is generated as output.

[0080] Step 5:

[0081] The server aggregates data over a specified period and organizes it in the format required for tax filing. During the aggregation process, the server uses analytical tools to integrate historical data and generate a final report. The input here is accounting data stored in a database, and the output is tax filing data.

[0082] Step 6:

[0083] The server uses an AI model based on income and expense data to generate advice for wealth building. The server inputs data into the model and generates suggestions tailored to the user's financial situation. In this step, income and expense data is used as input, and asset management advice is provided as output.

[0084] Step 7:

[0085] Users can query the AI ​​assistant using natural language through their device, and also check and correct data. For example, if a user enters the prompt "Tell me last month's food expenses," the server retrieves the corresponding information from the database and provides it. In this process, the relevant data is displayed to the user as output for the entered question.

[0086] Step 8:

[0087] The user modifies specific expense items as needed. The user enters modification instructions in natural language into the terminal, and the server parses the content and updates the information in the database. In this step, the user's instructions are used as input, and the updated accounting information is obtained as output.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In modern society, the economic activities of individuals and businesses are diversifying, making it increasingly complex to manage income and expenses and formulate asset building strategies. In particular, recording the ever-increasing number of transactions and subsequent accounting processes becomes a significant burden if relied upon manually. Furthermore, obtaining concrete and effective asset building advice based on this data is not easy. To address these needs, there is a growing demand for systems that provide automated transaction information management and effective asset building advice.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, a means for automatically performing accounting entries based on the extracted text information, and a means for proposing an asset formation strategy using generative AI technology. As a result, users can simplify daily transaction management and easily obtain effective economic activity strategies tailored to their individual asset backgrounds.

[0093] "Image receiving means" refers to a device or software that has the function of receiving image data and transferring digital data of receipts and invoices photographed by the user to a server.

[0094] "Optical character recognition means" refers to a technology that identifies character information from input image data and converts it into text, thereby converting the characters in the image into a format that can be stored in a database.

[0095] An "automated accounting journal entry system" is a process that automatically performs accounting operations based on extracted text information, and streamlines bookkeeping by classifying data into appropriate accounting categories.

[0096] A "journal entry data aggregation method" is a system or procedure for aggregating data generated through accounting processes and creating data organized in a format necessary for tax filing and other purposes.

[0097] The "asset building advice provision method" is a function that uses income and expenditure data and generation AI technology to provide users with suggestions regarding asset management and investment, offering advice optimized for the user's economic activities.

[0098] "Natural language processing technology" is a means by which computers understand human language and engage in dialogue. It analyzes user questions and instructions in natural language and generates appropriate responses.

[0099] "Electronic payment information" refers to payment information in cashless transactions, and is data that records the details of transactions performed by a user.

[0100] "Generative AI technology" is a technology that uses artificial intelligence to perform data analysis and prediction, generating optimal strategies and advice for users based on the data.

[0101] In this embodiment of the invention, the user's terminal is a smartphone or tablet. The user uses these terminals to take pictures of receipts or invoices and transmits them to the server via an image receiving means. The server uses optical character recognition means to extract text information from the image data. In this process, Google® Cloud Vision API is used as the optical character recognition technology to perform highly accurate character recognition.

[0102] The extracted text data is interfaced with accounting software (e.g., QuickBooks or Freee) via an automated accounting journal entry system and categorized into relevant accounting categories. This automates tedious bookkeeping tasks and improves efficiency.

[0103] Furthermore, a journal entry data aggregation method is used to aggregate data for a certain period and organize it in the format required for tax filing. Based on this data, the server utilizes generation AI technology (e.g., OpenAI®'s GPT-3®) to provide users with suggestions regarding asset management and investment through an asset formation advice provision method. These suggestions are displayed on the terminal for the user's reference.

[0104] Furthermore, natural language processing technology is used to enable interaction with the user. This allows users to ask questions to the server via their device, or request data verification or correction. For example, if a user prompts, "What are the expenditures for each category this month?", the server can provide the corresponding information through its AI-generated data.

[0105] This system allows users to reduce the burden of managing daily transactions while supporting effective economic activity.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user takes a picture of a receipt or invoice using a device. The input is image data. The device sends this image data to the server via an image receiving device. The output is the transfer of the image data to the server.

[0109] Step 2:

[0110] The server applies optical character recognition (OCR) to the received image data. The input is image data on the server. The server uses the Google Cloud Vision API to extract character information from the image. It performs calculations to remove noise from the data and convert it into the required string information. The output is text data.

[0111] Step 3:

[0112] The server inputs text data obtained through optical character recognition into an automated accounting journal entry system. The input is text data. The server, through an interface with accounting software, classifies this text data into predetermined accounting categories. It uses a specific algorithm to map the data and performs calculations to journalize it into the appropriate accounts. The output is the journalized data.

[0113] Step 4:

[0114] The server aggregates the journalized data using a journal data aggregation mechanism and generates data in a tax return format. The input is the journalized data. The server applies a series of aggregation algorithms to format the data into the specified format. The output is the organized data for tax filing.

[0115] Step 5:

[0116] The server uses organized data and generative AI technology to generate asset building advice. The input is organized data for declaration purposes. The server runs a generative AI model, querying through prompts that generate advice based on the user's asset background, and performing calculations to propose strategies. The output is asset building advice.

[0117] Step 6:

[0118] The user interacts with the server via a terminal using natural language processing technology. Input consists of the user's natural language questions or instructions. The server, with the assistance of a generative AI model, responds to the user with appropriate information. Output consists of answers to the user's questions and verified data. Through this interaction, the user receives support for their economic activities.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] The system according to the present invention aims not only to manage financial data but also to provide an interactive experience that takes user emotions into account. Therefore, it is equipped with an emotion engine that can analyze user input and flexibly provide responses and advice tailored to individual situations.

[0121] Users can easily send images of receipts and invoices to the system via the LINE chat app on their devices. The server receives the images sent by the users and extracts text information using OCR technology. Based on this information, the server automatically performs accounting entries and saves the extracted data to a database.

[0122] As accounting data accumulates, the server aggregates the data necessary for filing tax returns and generates pre-formatted tax forms. This allows users to efficiently complete their tax payment procedures. Furthermore, the server uses the user's income and expense data to generate and provide asset building advice.

[0123] Furthermore, this system utilizes an emotion engine in its interactions with users. The emotion engine analyzes the natural language input by the user and identifies their emotional state. For example, if a user inputs the message, "I'm worried because I've been spending a lot lately," the server can identify this as anxiety. Based on this, it adjusts the tone and content of the advice, providing encouraging and reassuring feedback.

[0124] In this way, the server not only processes data but also proactively proposes drafts and responses based on the user's emotions, creating a more human-centered system. This approach allows users to enjoy value from the system that goes beyond mere digital tools.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] Users take pictures of receipts and invoices with their smartphones or tablets and send the images to the system via the LINE chat app.

[0128] Step 2:

[0129] The server receives images sent by the user. The received images are passed through an AI-OCR module, which extracts the text information within the images as text data.

[0130] Step 3:

[0131] The server analyzes the text data extracted by OCR to identify information such as the date, amount, and store name. Based on this, it performs automatic accounting entries and records the entry data in the database.

[0132] Step 4:

[0133] The server periodically aggregates multiple recorded journal entries and automatically generates the data format required for tax filing. This format complies with local and tax laws.

[0134] Step 5:

[0135] The server uses AI to generate asset building advice based on the user's income and expense data. The advice includes suggestions tailored to the user's financial situation, such as specific savings goals and investment strategies.

[0136] Step 6:

[0137] Users send questions and requests to the server from their devices using natural language. The server receives these messages and analyzes the user's emotional state from the input text through its sentiment engine.

[0138] Step 7:

[0139] Based on the user's emotions identified by the emotion engine, the server adjusts the tone and content of the advice, providing the user with appropriate feedback and suggestions.

[0140] Step 8:

[0141] Users review the advice and data provided by the server and revise their financial plans as needed. By continuously using the system, users can achieve improved financial management.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Modern financial management systems focus solely on collecting and processing data, failing to provide an interactive experience that considers the emotional aspects of the user. Therefore, there is a need to develop systems that allow users to easily organize their financial information while receiving emotionally sensitive advice.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for analyzing the user's emotions using natural language processing and providing personalized advice. This enables the user to efficiently manage their financial information and receive appropriate advice tailored to their emotions.

[0147] "Image receiving means" refers to a device or program for acquiring image data transmitted by a user and converting it into a format that can be processed by the server.

[0148] "Optical character recognition means" refers to a technology or device used to extract text information from image data, specifically one that recognizes characters by optical means and converts them into digital data.

[0149] An "accounting journal entry tool" is a system or software that automatically records, classifies, and organizes accounting transactions based on acquired text information.

[0150] A "data generation method for tax return filing" refers to a program or device for aggregating accounting journal entry data and creating tax return documents in accordance with laws and regulations.

[0151] An "asset building advice provision method" is a system or software that analyzes accumulated data and provides users with suggestions regarding asset management and investment.

[0152] "Natural language processing means" refers to technologies or programs that analyze and understand the natural language input by a user in order to facilitate smooth communication with the user.

[0153] "Emotional analysis tools" refer to algorithms and technologies that analyze user input information to identify the emotional state of a user.

[0154] An "interface" is a means or protocol for exchanging data between different systems or software.

[0155] The system according to this invention provides a means for users to efficiently manage their financial information while receiving emotion-based, interactive advice. The system includes the following configuration and operation:

[0156] Users can use the LINE chat app via their device to send images of receipts and invoices to the system. An image receiving device acquires these image data and sends them to a server. The server uses optical character recognition (OCR) to extract text information from the received image data. This process typically utilizes commonly used OCR technology, one example being cloud services.

[0157] Once text information is extracted, the server automatically records the transactions using accounting journal entry software. This process is carried out through a commercially available accounting data management program, and the data is stored in a database. The accumulated data is then used to automatically generate the necessary tax return documents through a tax return data generation system. This allows users to easily complete the tax filing process.

[0158] Furthermore, based on income and expenditure data, the server utilizes asset building advice tools to support users in achieving their goals. Natural language messages from users are analyzed through natural language processing tools, and their emotional state is identified by sentiment analysis tools. For example, if a user sends a message saying, "I want to increase my savings for the future," the server considers past spending information and presents specific saving and budgeting plans.

[0159] As an example of a prompt, if the user sends "My food expenses for this month are over budget, please tell me how I can reduce them," the system will provide optimal reduction suggestions based on the user's past data. By using a generative AI model to provide appropriate feedback in response to such prompts, it becomes possible to provide personalized support to the user.

[0160] In this way, the system can go beyond simply managing financial data and function as an interactive assistant that responds to the user's emotions and circumstances.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user opens the LINE chat app on their device and takes or selects an image of a receipt or invoice. The image is sent to the server by pressing the send button and stored on the server via an image receiving device. The input is the image data taken or selected by the user, and the output is the image file stored on the server.

[0164] Step 2:

[0165] The server extracts text information from the received image data using optical character recognition (OCR) technology. Specifically, this process utilizes OCR technology to convert characters within the image into digital text. The input is an image file stored on the server, and the output is the extracted text data.

[0166] Step 3:

[0167] The server uses the text data obtained by OCR to operate the accounting journal entry system and automatically record transactions. At this time, it connects to a database and organizes the journal entry information by category. The input is the extracted text data, and the output is the accounting journal entry information recorded in the database.

[0168] Step 4:

[0169] The server aggregates the accumulated accounting data and generates data for tax filing. This is output as a formatted tax return form, ready for the user to download. The input is accounting journal entry information recorded in the database, and the output is a digital file of the tax return form.

[0170] Step 5:

[0171] The server utilizes a system that provides asset building advice based on income and expense data and declaration data. It analyzes the user's past income and expense trends and generates asset management suggestions. The input is income and expense data obtained from the database, and the output is an advice message to the user.

[0172] Step 6:

[0173] The server receives messages sent by the user in natural language and analyzes them using natural language processing tools. It then uses sentiment analysis tools to identify the emotional state and generates a response corresponding to the prompt. The input is a text message from the user, and the output is a response message or suggestion.

[0174] Step 7:

[0175] The server uses a generative AI model to provide customized support based on user prompts. This enables specific, emotionally sensitive advice. The input is the user's prompt, and the output is a personalized feedback message.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] Traditional financial data management systems, while capable of accurately processing data, failed to provide interactive services that considered user emotions. As a result, users often did not receive adequate support for their anxieties and questions. Furthermore, the manual entry of receipts and invoices was time-consuming, making efficient asset management difficult.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring image data, means for analyzing character information from the image using an optical character recognition device, and means for automatically performing accounting processing based on the analyzed character information. As a result, users can efficiently process information from receipts and invoices without hassle, and receive interactive support that responds to the user's emotions, enabling more effective asset management.

[0181] "Means for acquiring image data" refers to a device or software that receives and stores image information transmitted by a user as digital data.

[0182] An "optical character recognition device" is a technology or device that digitally recognizes characters within an image and extracts them as text data.

[0183] "Means for analyzing character information" refers to software or hardware for processing character data obtained by an optical character recognition device and organizing and analyzing it as meaningful information.

[0184] "Methods for automatically performing accounting processing" refer to systems in which a program automatically executes accounting entries and calculations using acquired textual information.

[0185] "Revenue and expenditure data" refers to financial data such as income and expenses that constitute information about a user's asset building.

[0186] "Means of providing advice on wealth building" refers to a function that analyzes income and expenditure data and proposes the optimal asset management and saving methods for the user.

[0187] "Means of communicating with users using natural language processing" refers to technologies that understand natural language input from users and generate appropriate dialogue.

[0188] "Methods for generating responses based on user emotions" refers to technologies that detect emotions from the user's words and actions and dynamically construct dialogue content accordingly.

[0189] The system of this invention begins when a user sends image data of receipts or invoices via a chat application using a device such as a smartphone. The server acquires this data through an image receiving means. The received image data is processed by an optical character recognition (OCR) device and analyzed as character information. A Python OCR library (e.g., Tesseract) is used for this analysis.

[0190] After the textual information is analyzed, the server automatically performs accounting processing based on that data. This involves inputting accounting data using an interface with accounting software. The accounting software is used to manage the user's revenue and expenditure data and to provide advice on wealth building.

[0191] Furthermore, the server uses natural language processing to communicate with the user. This natural language processing utilizes machine learning models such as Hugging Face's Transformers, which analyze emotions based on the user's input. For example, if a user sends "I'm worried because I've been spending a lot lately," the emotion analysis program can interpret this as "anxiety."

[0192] The results of the emotion analysis are processed by a means of generating responses based on the user's emotions. This response generation uses a pre-trained generative AI model that can generate prompts that provide appropriate encouragement and advice.

[0193] For example, if a user expresses financial anxiety, the server will provide specific advice such as, "You can save XX yen per month by cutting back on eating out once a week!" Examples of prompts to input into the generative AI model include: "User message: 'I'm worried because I've been spending a lot lately.'" "Feedback generation prompt: Please think of words of encouragement and specific advice to alleviate this anxiety."

[0194] Thus, the system of the present invention efficiently manages financial data and provides optimal support while taking user emotions into consideration.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user sends image data of receipts and invoices from their smartphone using a chat app. The sent image data is then input into the server's image receiving mechanism.

[0198] Step 2:

[0199] The server analyzes the received image data using an optical character recognition (OCR) device. Specifically, it uses OCR technology (e.g., Tesseract) to extract character information from the image as text data. This process outputs text data from the image data.

[0200] Step 3:

[0201] The server processes the parsed text data using an automated accounting method. At this stage, the text data is categorized into accounting items, and the accounting data is entered through an interface with accounting software. The output of this step is stored as accounting data in the product database.

[0202] Step 4:

[0203] The server performs calculations to provide advice on wealth building based on stored revenue and expenditure data. This calculation uses algorithms that analyze assets held and the balance between income and expenses, ultimately generating advice on wealth building.

[0204] Step 5:

[0205] A message in natural language is sent from the user to the server. The server uses natural language processing technology (e.g., Hugging Face Transformers) to analyze the message and identify the emotion. Based on this input, emotion data is output.

[0206] Step 6:

[0207] The server uses a generative AI model to generate responses based on sentiment data. It generates prompt sentences appropriate to the identified sentiment and uses them to create natural dialogue documents. This step includes the operation of generating specific and appropriate responses from the prompt sentences.

[0208] Step 7:

[0209] The server sends the generated response to the user and notifies them through the chat application. As a result, the user can receive detailed support based on an understanding of their financial situation and their feelings.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] The system according to the present invention is designed to improve the efficiency of users' economic activities and reduce the burden of manual accounting. Users can use their personal devices to record expenses and expenditures incurred in their daily lives. Specifically, users take pictures of receipts and invoices with a device such as a smartphone or tablet and send them to the system via a chat application.

[0227] Upon receiving this image data, the server begins reading the text information using AI-OCR technology. The read information is extracted as text, sent to the server, and stored in a database. Based on this stored information, the server initiates automatic journal entries and integrates with accounting software. The server categorizes the identified expense information into the appropriate accounting categories, and all data is managed in a consistent format.

[0228] Furthermore, the server aggregates data over a certain period and organizes it into the format required for tax filing. This allows users to easily obtain the necessary documents for filing their tax returns. In addition, based on the user's income and expense data, the server uses AI to analyze the data and generate optimal advice for wealth building. This advice includes expense management, savings strategies, and investment suggestions.

[0229] Users can interact with the server's AI assistant using natural language through their device to check, modify, and inquire about data. For example, if a user asks, "Tell me my food expenses for last month," the server will provide the corresponding data. Furthermore, if a user wants to modify a specific expense, they can easily do so by giving instructions in natural language.

[0230] Through the above process, the system can manage financial data in daily life and support optimal economic activities without requiring users to possess advanced financial literacy.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users take pictures of receipts and invoices with their smartphones or other devices and send the images to the system via the LINE chat app.

[0234] Step 2:

[0235] The server receives image data from the user's terminal. The received image data is input into an optical character recognition (OCR) engine, which converts the character information into text data.

[0236] Step 3:

[0237] The server analyzes the text data extracted by OCR to identify information such as dates, amounts, and product names. Based on this, it automatically performs accounting entries and records them in the database.

[0238] Step 4:

[0239] The server aggregates the recorded journal entry data at regular intervals and generates the data format required for filing tax returns. This format is customized according to the tax laws of each country and region.

[0240] Step 5:

[0241] The server analyzes the user's income and expense data and uses AI to generate advice for wealth building. This advice is customized according to the individual's financial situation.

[0242] Step 6:

[0243] The user makes inquiries to the server's AI assistant using natural language through their device. The server receives these inquiries, checks and corrects the data, and returns feedback to the user.

[0244] Step 7:

[0245] Users review the advice and data provided by the server and, if necessary, revise their spending and plan their asset management. By continuously using the system, users can better manage their own financial situation.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In economic activity, the burden of manual accounting processes is significant, and the complexity of optimizing asset management necessitates a high level of financial literacy. To address this, a system is needed that streamlines accounting and asset management processes and provides support for making appropriate economic decisions even without specialized knowledge.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for automating accounting journal entry work based on the extracted text information. This enables the user to receive automated accounting processing via image transmission and asset building advice based on the data.

[0251] "Image receiving means" refers to a function for receiving image data sent by a user.

[0252] "Optical character recognition means" refers to a technology for analyzing character information from image data and extracting it as text data.

[0253] "Methods for automating accounting journal entries" refer to functions that use extracted text information to reduce manual work and process accounting quickly.

[0254] "Methods for generating declaration data" refers to the process of using aggregated journal entry data to organize it into a format suitable for tax filing.

[0255] "Means of providing asset building advice" refers to a function that analyzes income and expenditure information and makes useful suggestions regarding the user's asset management.

[0256] "Utilizing generative models" refers to techniques that use machine learning models to generate appropriate advice based on collected data.

[0257] "Means of interacting with users using natural language processing" refers to functions that respond to questions and instructions entered by users in natural language and provide the necessary information.

[0258] The system according to the present invention is designed to streamline users' economic activities and reduce the burden of manual accounting processing. This system is implemented using various terminals, a comprehensive server infrastructure, and advanced software technologies.

[0259] Users record their daily expenses and expenditures using their smartphones or tablets. Specifically, users take pictures of receipts and invoices and send them to the system via a chat application. The server receives this image data and extracts text information from the image using optical character recognition (OCR) technology. For example, software such as Tesseract OCR is used to convert the image data into text.

[0260] The server automatically performs accounting entries based on the extracted text information, and the resulting accounting data is stored in a database. The server also links the entry data with financial management software via an API, enabling efficient data management and analysis. Commonly available financial management software can be used for accounting and finance.

[0261] Furthermore, it can aggregate economic data over a certain period and organize it in the format required for tax filing. Based on this organized data, the server uses a generated AI model to provide users with optimal advice for wealth building. This advice includes spending management, savings strategies, and investment suggestions. This allows users to effectively manage their assets even without specialized knowledge.

[0262] Users can interact with the server's AI assistant using natural language to check and modify data. For example, by entering "Tell me my food expenses for last month" as a prompt, the server will immediately provide the corresponding data. Furthermore, users can also present specific requirements such as "I want to check my transportation expenses for this month. Also, tell me my spending for last month."

[0263] In this way, this system can support the management of financial data in daily life and facilitate efficient and effective economic activities without requiring users to possess advanced financial literacy.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user uses their device to take a picture of a receipt or invoice and sends the captured image to the server via a chat application. This provides the image data as input.

[0267] Step 2:

[0268] The server receives image data and extracts text information from the image using optical character recognition (OCR) technology. Specifically, the server uses OCR software to analyze the image data and generate text data. Here, the received image data is used as input, and the extracted text is obtained as output.

[0269] Step 3:

[0270] The server analyzes the extracted text information and organizes the data necessary for accounting entries. The server uses an algorithm to classify the text information into accounting categories and stores them in a database. This process uses the extracted text data as input and generates structured accounting data as output.

[0271] Step 4:

[0272] The server automatically performs journal entries based on structured accounting data and links the data with financial management software. The server uses an API to send data to the accounting software for integration. In this step, organized accounting data is used as input, and linked journal entry data is generated as output.

[0273] Step 5:

[0274] The server aggregates data over a specified period and organizes it in the format required for tax filing. During the aggregation process, the server uses analytical tools to integrate historical data and generate a final report. The input here is accounting data stored in a database, and the output is tax filing data.

[0275] Step 6:

[0276] The server uses an AI model based on income and expense data to generate advice for wealth building. The server inputs data into the model and generates suggestions tailored to the user's financial situation. In this step, income and expense data is used as input, and asset management advice is provided as output.

[0277] Step 7:

[0278] Users can query the AI ​​assistant using natural language through their device, and also check and correct data. For example, if a user enters the prompt "Tell me last month's food expenses," the server retrieves the corresponding information from the database and provides it. In this process, the relevant data is displayed to the user as output for the entered question.

[0279] Step 8:

[0280] The user modifies specific expense items as needed. The user inputs a modification instruction in natural language to the terminal, and the server analyzes the content and updates the information in the database. In this step, the user's instruction is used as the input, and the updated accounting information is obtained as the output.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In modern society, the economic activities of individuals and enterprises have diversified, and the management of their revenues and expenditures and the formulation of asset formation strategies have become more complex. In particular, the recording of daily increasing transactions and subsequent accounting processing impose a heavy burden if relying on manual labor. Also, it is not easy to obtain specific and effective advice on asset formation using these data. In response to such needs, the necessity of a system that provides automatic management of transaction information and effective asset formation advice is increasing.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes an image receiving means, a means for extracting text information from an image by an optical character recognition means, a means for automatically performing accounting entries based on the extracted text information, and a means for proposing an asset formation strategy using a generative AI technology. Thereby, the user's daily transaction management is simplified, and it becomes possible to easily obtain an effective economic activity strategy according to the individual asset background.

[0286] The "image receiving means" has a function for receiving image data and is a device or software for transferring digital data of a receipt or invoice photographed by the user to the server.

[0287] "Optical character recognition means" refers to a technology that identifies character information from input image data and converts it into text, thereby converting the characters in the image into a format that can be stored in a database.

[0288] An "automated accounting journal entry system" is a process that automatically performs accounting operations based on extracted text information, and streamlines bookkeeping by classifying data into appropriate accounting categories.

[0289] A "journal entry data aggregation method" is a system or procedure for aggregating data generated through accounting processes and creating data organized in a format necessary for tax filing and other purposes.

[0290] The "asset building advice provision method" is a function that uses income and expenditure data and generation AI technology to provide users with suggestions regarding asset management and investment, offering advice optimized for the user's economic activities.

[0291] "Natural language processing technology" is a means by which computers understand human language and engage in dialogue. It analyzes user questions and instructions in natural language and generates appropriate responses.

[0292] "Electronic payment information" refers to payment information in cashless transactions, and is data that records the details of transactions performed by a user.

[0293] "Generative AI technology" is a technology that uses artificial intelligence to perform data analysis and prediction, generating optimal strategies and advice for users based on the data.

[0294] In this embodiment of the invention, the user's terminal is a smartphone or tablet. The user uses these terminals to take pictures of receipts or invoices and transmits them to the server via an image receiving means. The server uses optical character recognition means to extract text information from the image data. In this process, the Google Cloud Vision API is used as the optical character recognition technology to perform highly accurate character recognition.

[0295] The extracted text data is interfaced with accounting software (e.g., QuickBooks or Freee) via an automated accounting journal entry system and categorized into relevant accounting categories. This automates tedious bookkeeping tasks and improves efficiency.

[0296] Furthermore, a journal entry data aggregation tool is used to aggregate data for a certain period and organize it in the format required for tax filing. Based on this data, the server utilizes generation AI technology (e.g., OpenAI's GPT-3) to provide users with asset management and investment suggestions through an asset formation advice provision tool. These suggestions are displayed on the terminal for the user's reference.

[0297] Furthermore, natural language processing technology is used to enable interaction with the user. This allows users to ask questions to the server via their device, or request data verification or correction. For example, if a user prompts, "What are the expenditures for each category this month?", the server can provide the corresponding information through its AI-generated data.

[0298] This system allows users to reduce the burden of managing daily transactions while supporting effective economic activity.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user uses a terminal to take pictures of receipts and invoices. The input is image data. The terminal sends this image data to the server through the image receiving means. The output is the transfer of image data to the server.

[0302] Step 2:

[0303] The server applies optical character recognition means to the received image data. The input is the image data on the server. The server uses the Google Cloud Vision API to extract character information from the image. Operations are performed to remove data noise and convert it into necessary string information. The output is text data.

[0304] Step 3:

[0305] The server inputs the text data obtained by optical character recognition into the automatic accounting posting means. The input is text data. The server classifies this text data into predetermined accounting categories through an interface with accounting software. Operations are performed to map the data using a specific algorithm and post it to appropriate ledger accounts. The output is posted data.

[0306] Step 4:

[0307] The server aggregates the posted data by the posted data aggregation means and generates data in the format for tax return. The input is the posted data. The server applies a series of aggregation algorithms to format the data into the specified format. The output is sorted data for filing.

[0308] Step 5:

[0309] The server uses the sorted data to utilize the generation AI technology and generates asset formation advice. The input is the sorted data for filing. The server executes the generation AI model, queries through a prompt sentence for generating advice based on the user's asset background, and performs operations to propose strategies. The output is asset formation advice.

[0310] Step 6:

[0311] The user interacts with the server via a terminal using natural language processing technology. Input consists of the user's natural language questions or instructions. The server, with the assistance of a generative AI model, responds to the user with appropriate information. Output consists of answers to the user's questions and verified data. Through this interaction, the user receives support for their economic activities.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] The system according to the present invention aims not only to manage financial data but also to provide an interactive experience that takes user emotions into account. Therefore, it is equipped with an emotion engine that can analyze user input and flexibly provide responses and advice tailored to individual situations.

[0314] Users can easily send images of receipts and invoices to the system via the LINE chat app on their devices. The server receives the images sent by the users and extracts text information using OCR technology. Based on this information, the server automatically performs accounting entries and saves the extracted data to a database.

[0315] As accounting data accumulates, the server aggregates the data necessary for filing tax returns and generates pre-formatted tax forms. This allows users to efficiently complete their tax payment procedures. Furthermore, the server uses the user's income and expense data to generate and provide asset building advice.

[0316] Furthermore, this system utilizes an emotion engine in its interactions with users. The emotion engine analyzes the natural language input by the user and identifies their emotional state. For example, if a user inputs the message, "I'm worried because I've been spending a lot lately," the server can identify this as anxiety. Based on this, it adjusts the tone and content of the advice, providing encouraging and reassuring feedback.

[0317] In this way, the server not only processes data but also proactively proposes drafts and responses based on the user's emotions, creating a more human-centered system. This approach allows users to enjoy value from the system that goes beyond mere digital tools.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users take pictures of receipts and invoices with their smartphones or tablets and send the images to the system via the LINE chat app.

[0321] Step 2:

[0322] The server receives images sent by the user. The received images are passed through an AI-OCR module, which extracts the text information within the images as text data.

[0323] Step 3:

[0324] The server analyzes the text data extracted by OCR to identify information such as the date, amount, and store name. Based on this, it performs automatic accounting entries and records the entry data in the database.

[0325] Step 4:

[0326] The server periodically aggregates multiple recorded journal entries and automatically generates the data format required for tax filing. This format complies with local and tax laws.

[0327] Step 5:

[0328] The server uses AI to generate asset building advice based on the user's income and expense data. The advice includes suggestions tailored to the user's financial situation, such as specific savings goals and investment strategies.

[0329] Step 6:

[0330] Users send questions and requests to the server from their devices using natural language. The server receives these messages and analyzes the user's emotional state from the input text through its sentiment engine.

[0331] Step 7:

[0332] Based on the user's emotions identified by the emotion engine, the server adjusts the tone and content of the advice, providing the user with appropriate feedback and suggestions.

[0333] Step 8:

[0334] Users review the advice and data provided by the server and revise their financial plans as needed. By continuously using the system, users can achieve improved financial management.

[0335] (Example 2)

[0336] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0337] Modern financial management systems focus solely on collecting and processing data, failing to provide an interactive experience that considers the emotional aspects of the user. Therefore, there is a need to develop systems that allow users to easily organize their financial information while receiving emotionally sensitive advice.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for analyzing the user's emotions using natural language processing and providing personalized advice. This enables the user to efficiently manage their financial information and receive appropriate advice tailored to their emotions.

[0340] "Image receiving means" refers to a device or program for acquiring image data transmitted by a user and converting it into a format that can be processed by the server.

[0341] "Optical character recognition means" refers to a technology or device used to extract text information from image data, specifically one that recognizes characters by optical means and converts them into digital data.

[0342] An "accounting journal entry tool" is a system or software that automatically records, classifies, and organizes accounting transactions based on acquired text information.

[0343] A "data generation method for tax return filing" refers to a program or device for aggregating accounting journal entry data and creating tax return documents in accordance with laws and regulations.

[0344] An "asset building advice provision method" is a system or software that analyzes accumulated data and provides users with suggestions regarding asset management and investment.

[0345] "Natural language processing means" refers to technologies or programs that analyze and understand the natural language input by a user in order to facilitate smooth communication with the user.

[0346] "Emotional analysis tools" refer to algorithms and technologies that analyze user input information to identify the emotional state of a user.

[0347] An "interface" is a means or protocol for exchanging data between different systems or software.

[0348] The system according to this invention provides a means for users to efficiently manage their financial information while receiving emotion-based, interactive advice. The system includes the following configuration and operation:

[0349] Users can use the LINE chat app via their device to send images of receipts and invoices to the system. An image receiving device acquires these image data and sends them to a server. The server uses optical character recognition (OCR) to extract text information from the received image data. This process typically utilizes commonly used OCR technology, one example being cloud services.

[0350] Once text information is extracted, the server automatically records the transactions using accounting journal entry software. This process is carried out through a commercially available accounting data management program, and the data is stored in a database. The accumulated data is then used to automatically generate the necessary tax return documents through a tax return data generation system. This allows users to easily complete the tax filing process.

[0351] Furthermore, based on income and expenditure data, the server utilizes asset building advice tools to support users in achieving their goals. Natural language messages from users are analyzed through natural language processing tools, and their emotional state is identified by sentiment analysis tools. For example, if a user sends a message saying, "I want to increase my savings for the future," the server considers past spending information and presents specific saving and budgeting plans.

[0352] As an example of a prompt, if the user sends "My food expenses for this month are over budget, please tell me how I can reduce them," the system will provide optimal reduction suggestions based on the user's past data. By using a generative AI model to provide appropriate feedback in response to such prompts, it becomes possible to provide personalized support to the user.

[0353] In this way, the system can go beyond simply managing financial data and function as an interactive assistant that responds to the user's emotions and circumstances.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user opens the LINE chat app on their device and takes or selects an image of a receipt or invoice. The image is sent to the server by pressing the send button and stored on the server via an image receiving device. The input is the image data taken or selected by the user, and the output is the image file stored on the server.

[0357] Step 2:

[0358] The server extracts text information from the received image data using optical character recognition (OCR) technology. Specifically, this process utilizes OCR technology to convert characters within the image into digital text. The input is an image file stored on the server, and the output is the extracted text data.

[0359] Step 3:

[0360] The server uses the text data obtained by OCR to operate the accounting journal entry system and automatically record transactions. At this time, it connects to a database and organizes the journal entry information by category. The input is the extracted text data, and the output is the accounting journal entry information recorded in the database.

[0361] Step 4:

[0362] The server aggregates the accumulated accounting data and generates data for tax filing. This is output as a formatted tax return form, ready for the user to download. The input is accounting journal entry information recorded in the database, and the output is a digital file of the tax return form.

[0363] Step 5:

[0364] The server utilizes a system that provides asset building advice based on income and expense data and declaration data. It analyzes the user's past income and expense trends and generates asset management suggestions. The input is income and expense data obtained from the database, and the output is an advice message to the user.

[0365] Step 6:

[0366] The server receives messages sent by the user in natural language and analyzes them using natural language processing tools. It then uses sentiment analysis tools to identify the emotional state and generates a response corresponding to the prompt. The input is a text message from the user, and the output is a response message or suggestion.

[0367] Step 7:

[0368] The server uses a generative AI model to provide customized support based on user prompts. This enables specific, emotionally sensitive advice. The input is the user's prompt, and the output is a personalized feedback message.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] Traditional financial data management systems, while capable of accurately processing data, failed to provide interactive services that considered user emotions. As a result, users often did not receive adequate support for their anxieties and questions. Furthermore, the manual entry of receipts and invoices was time-consuming, making efficient asset management difficult.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes means for acquiring image data, means for analyzing character information from the image using an optical character recognition device, and means for automatically performing accounting processing based on the analyzed character information. As a result, users can efficiently process information from receipts and invoices without hassle, and receive interactive support that responds to the user's emotions, enabling more effective asset management.

[0374] "Means for acquiring image data" refers to a device or software that receives and stores image information transmitted by a user as digital data.

[0375] An "optical character recognition device" is a technology or device that digitally recognizes characters within an image and extracts them as text data.

[0376] "Means for analyzing character information" refers to software or hardware for processing character data obtained by an optical character recognition device and organizing and analyzing it as meaningful information.

[0377] "Methods for automatically performing accounting processing" refer to systems in which a program automatically executes accounting entries and calculations using acquired textual information.

[0378] "Revenue and expenditure data" refers to financial data such as income and expenses that constitute information about a user's asset building.

[0379] "Means of providing advice on wealth building" refers to a function that analyzes income and expenditure data and proposes the optimal asset management and saving methods for the user.

[0380] "Means of communicating with users using natural language processing" refers to technologies that understand natural language input from users and generate appropriate dialogue.

[0381] "Methods for generating responses based on user emotions" refers to technologies that detect emotions from the user's words and actions and dynamically construct dialogue content accordingly.

[0382] The system of this invention begins when a user sends image data of receipts or invoices via a chat application using a device such as a smartphone. The server acquires this data through an image receiving means. The received image data is processed by an optical character recognition (OCR) device and analyzed as character information. A Python OCR library (e.g., Tesseract) is used for this analysis.

[0383] After the textual information is analyzed, the server automatically performs accounting processing based on that data. This involves inputting accounting data using an interface with accounting software. The accounting software is used to manage the user's revenue and expenditure data and to provide advice on wealth building.

[0384] Furthermore, the server uses natural language processing to communicate with the user. This natural language processing utilizes machine learning models such as Hugging Face's Transformers, which analyze emotions based on the user's input. For example, if a user sends "I'm worried because I've been spending a lot lately," the emotion analysis program can interpret this as "anxiety."

[0385] The results of the emotion analysis are processed by a means of generating responses based on the user's emotions. This response generation uses a pre-trained generative AI model that can generate prompts that provide appropriate encouragement and advice.

[0386] For example, if a user expresses financial anxiety, the server will provide specific advice such as, "You can save XX yen per month by cutting back on eating out once a week!" Examples of prompts to input into the generative AI model include: "User message: 'I'm worried because I've been spending a lot lately.'" "Feedback generation prompt: Please think of words of encouragement and specific advice to alleviate this anxiety."

[0387] Thus, the system of the present invention efficiently manages financial data and provides optimal support while taking user emotions into consideration.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user sends image data of receipts and invoices from their smartphone using a chat app. The sent image data is then input into the server's image receiving mechanism.

[0391] Step 2:

[0392] The server analyzes the received image data using an optical character recognition (OCR) device. Specifically, it uses OCR technology (e.g., Tesseract) to extract character information from the image as text data. This process outputs text data from the image data.

[0393] Step 3:

[0394] The server processes the parsed text data using an automated accounting method. At this stage, the text data is categorized into accounting items, and the accounting data is entered through an interface with accounting software. The output of this step is stored as accounting data in the product database.

[0395] Step 4:

[0396] The server performs calculations to provide advice on wealth building based on stored revenue and expenditure data. This calculation uses algorithms that analyze assets held and the balance between income and expenses, ultimately generating advice on wealth building.

[0397] Step 5:

[0398] A message in natural language is sent from the user to the server. The server uses natural language processing technology (e.g., Hugging Face Transformers) to analyze the message and identify the emotion. Based on this input, emotion data is output.

[0399] Step 6:

[0400] The server uses a generative AI model to generate responses based on sentiment data. It generates prompt sentences appropriate to the identified sentiment and uses them to create natural dialogue documents. This step includes the operation of generating specific and appropriate responses from the prompt sentences.

[0401] Step 7:

[0402] The server sends the generated response to the user and notifies them through the chat application. As a result, the user can receive detailed support based on an understanding of their financial situation and their feelings.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] The system according to the present invention is designed to improve the efficiency of users' economic activities and reduce the burden of manual accounting. Users can use their personal devices to record expenses and expenditures incurred in their daily lives. Specifically, users take pictures of receipts and invoices with a device such as a smartphone or tablet and send them to the system via a chat application.

[0420] Upon receiving this image data, the server begins reading the text information using AI-OCR technology. The read information is extracted as text, sent to the server, and stored in a database. Based on this stored information, the server initiates automatic journal entries and integrates with accounting software. The server categorizes the identified expense information into the appropriate accounting categories, and all data is managed in a consistent format.

[0421] Furthermore, the server aggregates data over a certain period and organizes it into the format required for tax filing. This allows users to easily obtain the necessary documents for filing their tax returns. In addition, based on the user's income and expense data, the server uses AI to analyze the data and generate optimal advice for wealth building. This advice includes expense management, savings strategies, and investment suggestions.

[0422] Users can interact with the server's AI assistant using natural language through their device to check, modify, and inquire about data. For example, if a user asks, "Tell me my food expenses for last month," the server will provide the corresponding data. Furthermore, if a user wants to modify a specific expense, they can easily do so by giving instructions in natural language.

[0423] Through the above process, the system can manage financial data in daily life and support optimal economic activities without requiring users to possess advanced financial literacy.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] Users take pictures of receipts and invoices with their smartphones or other devices and send the images to the system via the LINE chat app.

[0427] Step 2:

[0428] The server receives image data from the user's terminal. The received image data is input into an optical character recognition (OCR) engine, which converts the character information into text data.

[0429] Step 3:

[0430] The server analyzes the text data extracted by OCR to identify information such as dates, amounts, and product names. Based on this, it automatically performs accounting entries and records them in the database.

[0431] Step 4:

[0432] The server aggregates the recorded journal entry data at regular intervals and generates the data format required for filing tax returns. This format is customized according to the tax laws of each country and region.

[0433] Step 5:

[0434] The server analyzes the user's income and expense data and uses AI to generate advice for wealth building. This advice is customized according to the individual's financial situation.

[0435] Step 6:

[0436] The user makes inquiries to the server's AI assistant using natural language through their device. The server receives these inquiries, checks and corrects the data, and returns feedback to the user.

[0437] Step 7:

[0438] Users review the advice and data provided by the server and, if necessary, revise their spending and plan their asset management. By continuously using the system, users can better manage their own financial situation.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] In economic activity, the burden of manual accounting processes is significant, and the complexity of optimizing asset management necessitates a high level of financial literacy. To address this, a system is needed that streamlines accounting and asset management processes and provides support for making appropriate economic decisions even without specialized knowledge.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for automating accounting journal entry work based on the extracted text information. This enables the user to receive automated accounting processing via image transmission and asset building advice based on the data.

[0444] "Image receiving means" refers to a function for receiving image data sent by a user.

[0445] "Optical character recognition means" refers to a technology for analyzing character information from image data and extracting it as text data.

[0446] "Methods for automating accounting journal entries" refer to functions that use extracted text information to reduce manual work and process accounting quickly.

[0447] "Methods for generating declaration data" refers to the process of using aggregated journal entry data to organize it into a format suitable for tax filing.

[0448] "Means of providing asset building advice" refers to a function that analyzes income and expenditure information and makes useful suggestions regarding the user's asset management.

[0449] "Utilizing generative models" refers to techniques that use machine learning models to generate appropriate advice based on collected data.

[0450] "Means of interacting with users using natural language processing" refers to functions that respond to questions and instructions entered by users in natural language and provide the necessary information.

[0451] The system according to the present invention is designed to streamline users' economic activities and reduce the burden of manual accounting processing. This system is implemented using various terminals, a comprehensive server infrastructure, and advanced software technologies.

[0452] Users record their daily expenses and expenditures using their smartphones or tablets. Specifically, users take pictures of receipts and invoices and send them to the system via a chat application. The server receives this image data and extracts text information from the image using optical character recognition (OCR) technology. For example, software such as Tesseract OCR is used to convert the image data into text.

[0453] The server automatically performs accounting entries based on the extracted text information, and the resulting accounting data is stored in a database. The server also links the entry data with financial management software via an API, enabling efficient data management and analysis. Commonly available financial management software can be used for accounting and finance.

[0454] Furthermore, it can aggregate economic data over a certain period and organize it in the format required for tax filing. Based on this organized data, the server uses a generated AI model to provide users with optimal advice for wealth building. This advice includes spending management, savings strategies, and investment suggestions. This allows users to effectively manage their assets even without specialized knowledge.

[0455] Users can interact with the server's AI assistant using natural language to check and modify data. For example, by entering "Tell me my food expenses for last month" as a prompt, the server will immediately provide the corresponding data. Furthermore, users can also present specific requirements such as "I want to check my transportation expenses for this month. Also, tell me my spending for last month."

[0456] In this way, this system can support the management of financial data in daily life and facilitate efficient and effective economic activities without requiring users to possess advanced financial literacy.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The user uses their device to take a picture of a receipt or invoice and sends the captured image to the server via a chat application. This provides the image data as input.

[0460] Step 2:

[0461] The server receives image data and extracts text information from the image using optical character recognition (OCR) technology. Specifically, the server uses OCR software to analyze the image data and generate text data. Here, the received image data is used as input, and the extracted text is obtained as output.

[0462] Step 3:

[0463] The server analyzes the extracted text information and organizes the data necessary for accounting entries. The server uses an algorithm to classify the text information into accounting categories and stores them in a database. This process uses the extracted text data as input and generates structured accounting data as output.

[0464] Step 4:

[0465] The server automatically performs journal entries based on structured accounting data and links the data with financial management software. The server uses an API to send data to the accounting software for integration. In this step, organized accounting data is used as input, and linked journal entry data is generated as output.

[0466] Step 5:

[0467] The server aggregates data over a specified period and organizes it in the format required for tax filing. During the aggregation process, the server uses analytical tools to integrate historical data and generate a final report. The input here is accounting data stored in a database, and the output is tax filing data.

[0468] Step 6:

[0469] The server uses an AI model based on income and expense data to generate advice for wealth building. The server inputs data into the model and generates suggestions tailored to the user's financial situation. In this step, income and expense data is used as input, and asset management advice is provided as output.

[0470] Step 7:

[0471] Users can query the AI ​​assistant using natural language through their device, and also check and correct data. For example, if a user enters the prompt "Tell me last month's food expenses," the server retrieves the corresponding information from the database and provides it. In this process, the relevant data is displayed to the user as output for the entered question.

[0472] Step 8:

[0473] The user modifies specific expense items as needed. The user enters modification instructions in natural language into the terminal, and the server parses the content and updates the information in the database. In this step, the user's instructions are used as input, and the updated accounting information is obtained as output.

[0474] (Application Example 1)

[0475] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0476] In modern society, the economic activities of individuals and businesses are diversifying, making it increasingly complex to manage income and expenses and formulate asset building strategies. In particular, recording the ever-increasing number of transactions and subsequent accounting processes becomes a significant burden if relied upon manually. Furthermore, obtaining concrete and effective asset building advice based on this data is not easy. To address these needs, there is a growing demand for systems that provide automated transaction information management and effective asset building advice.

[0477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0478] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, a means for automatically performing accounting entries based on the extracted text information, and a means for proposing an asset formation strategy using generative AI technology. As a result, users can simplify daily transaction management and easily obtain effective economic activity strategies tailored to their individual asset backgrounds.

[0479] "Image receiving means" refers to a device or software that has the function of receiving image data and transferring digital data of receipts and invoices photographed by the user to a server.

[0480] "Optical character recognition means" refers to a technology that identifies character information from input image data and converts it into text, thereby converting the characters in the image into a format that can be stored in a database.

[0481] An "automated accounting journal entry system" is a process that automatically performs accounting operations based on extracted text information, and streamlines bookkeeping by classifying data into appropriate accounting categories.

[0482] A "journal entry data aggregation method" is a system or procedure for aggregating data generated through accounting processes and creating data organized in a format necessary for tax filing and other purposes.

[0483] The "asset building advice provision method" is a function that uses income and expenditure data and generation AI technology to provide users with suggestions regarding asset management and investment, offering advice optimized for the user's economic activities.

[0484] "Natural language processing technology" is a means by which computers understand human language and engage in dialogue. It analyzes user questions and instructions in natural language and generates appropriate responses.

[0485] "Electronic payment information" refers to payment information in cashless transactions, and is data that records the details of transactions performed by a user.

[0486] "Generative AI technology" is a technology that uses artificial intelligence to perform data analysis and prediction, generating optimal strategies and advice for users based on the data.

[0487] In this embodiment of the invention, the user's terminal is a smartphone or tablet. The user uses these terminals to take pictures of receipts or invoices and transmits them to the server via an image receiving means. The server uses optical character recognition means to extract text information from the image data. In this process, the Google Cloud Vision API is used as the optical character recognition technology to perform highly accurate character recognition.

[0488] The extracted text data is interfaced with accounting software (e.g., QuickBooks or Freee) via an automated accounting journal entry system and categorized into relevant accounting categories. This automates tedious bookkeeping tasks and improves efficiency.

[0489] Furthermore, a journal entry data aggregation tool is used to aggregate data for a certain period and organize it in the format required for tax filing. Based on this data, the server utilizes generation AI technology (e.g., OpenAI's GPT-3) to provide users with asset management and investment suggestions through an asset formation advice provision tool. These suggestions are displayed on the terminal for the user's reference.

[0490] Furthermore, natural language processing technology is used to enable interaction with the user. This allows users to ask questions to the server via their device, or request data verification or correction. For example, if a user prompts, "What are the expenditures for each category this month?", the server can provide the corresponding information through its AI-generated data.

[0491] This system allows users to reduce the burden of managing daily transactions while supporting effective economic activity.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The user takes a picture of a receipt or invoice using a device. The input is image data. The device sends this image data to the server via an image receiving device. The output is the transfer of the image data to the server.

[0495] Step 2:

[0496] The server applies optical character recognition (OCR) to the received image data. The input is image data on the server. The server uses the Google Cloud Vision API to extract character information from the image. It performs calculations to remove noise from the data and convert it into the required string information. The output is text data.

[0497] Step 3:

[0498] The server inputs text data obtained through optical character recognition into an automated accounting journal entry system. The input is text data. The server, through an interface with accounting software, classifies this text data into predetermined accounting categories. It uses a specific algorithm to map the data and performs calculations to journalize it into the appropriate accounts. The output is the journalized data.

[0499] Step 4:

[0500] The server aggregates the journalized data using a journal data aggregation mechanism and generates data in a tax return format. The input is the journalized data. The server applies a series of aggregation algorithms to format the data into the specified format. The output is the organized data for tax filing.

[0501] Step 5:

[0502] The server uses organized data and generative AI technology to generate asset building advice. The input is organized data for declaration purposes. The server runs a generative AI model, querying through prompts that generate advice based on the user's asset background, and performing calculations to propose strategies. The output is asset building advice.

[0503] Step 6:

[0504] The user interacts with the server via a terminal using natural language processing technology. Input consists of the user's natural language questions or instructions. The server, with the assistance of a generative AI model, responds to the user with appropriate information. Output consists of answers to the user's questions and verified data. Through this interaction, the user receives support for their economic activities.

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] The system according to the present invention aims not only to manage financial data but also to provide an interactive experience that takes user emotions into account. Therefore, it is equipped with an emotion engine that can analyze user input and flexibly provide responses and advice tailored to individual situations.

[0507] Users can easily send images of receipts and invoices to the system via the LINE chat app on their devices. The server receives the images sent by the users and extracts text information using OCR technology. Based on this information, the server automatically performs accounting entries and saves the extracted data to a database.

[0508] As accounting data accumulates, the server aggregates the data necessary for filing tax returns and generates pre-formatted tax forms. This allows users to efficiently complete their tax payment procedures. Furthermore, the server uses the user's income and expense data to generate and provide asset building advice.

[0509] Furthermore, this system utilizes an emotion engine in its interactions with users. The emotion engine analyzes the natural language input by the user and identifies their emotional state. For example, if a user inputs the message, "I'm worried because I've been spending a lot lately," the server can identify this as anxiety. Based on this, it adjusts the tone and content of the advice, providing encouraging and reassuring feedback.

[0510] In this way, the server not only processes data but also proactively proposes drafts and responses based on the user's emotions, creating a more human-centered system. This approach allows users to enjoy value from the system that goes beyond mere digital tools.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] Users take pictures of receipts and invoices with their smartphones or tablets and send the images to the system via the LINE chat app.

[0514] Step 2:

[0515] The server receives images sent by the user. The received images are passed through an AI-OCR module, which extracts the text information within the images as text data.

[0516] Step 3:

[0517] The server analyzes the text data extracted by OCR to identify information such as the date, amount, and store name. Based on this, it performs automatic accounting entries and records the entry data in the database.

[0518] Step 4:

[0519] The server periodically aggregates multiple recorded journal entries and automatically generates the data format required for tax filing. This format complies with local and tax laws.

[0520] Step 5:

[0521] The server uses AI to generate asset building advice based on the user's income and expense data. The advice includes suggestions tailored to the user's financial situation, such as specific savings goals and investment strategies.

[0522] Step 6:

[0523] Users send questions and requests to the server from their devices using natural language. The server receives these messages and analyzes the user's emotional state from the input text through its sentiment engine.

[0524] Step 7:

[0525] Based on the user's emotions identified by the emotion engine, the server adjusts the tone and content of the advice, providing the user with appropriate feedback and suggestions.

[0526] Step 8:

[0527] Users review the advice and data provided by the server and revise their financial plans as needed. By continuously using the system, users can achieve improved financial management.

[0528] (Example 2)

[0529] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] Modern financial management systems focus solely on collecting and processing data, failing to provide an interactive experience that considers the emotional aspects of the user. Therefore, there is a need to develop systems that allow users to easily organize their financial information while receiving emotionally sensitive advice.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for analyzing the user's emotions using natural language processing and providing personalized advice. This enables the user to efficiently manage their financial information and receive appropriate advice tailored to their emotions.

[0533] "Image receiving means" refers to a device or program for acquiring image data transmitted by a user and converting it into a format that can be processed by the server.

[0534] "Optical character recognition means" refers to a technology or device used to extract text information from image data, specifically one that recognizes characters by optical means and converts them into digital data.

[0535] An "accounting journal entry tool" is a system or software that automatically records, classifies, and organizes accounting transactions based on acquired text information.

[0536] A "data generation method for tax return filing" refers to a program or device for aggregating accounting journal entry data and creating tax return documents in accordance with laws and regulations.

[0537] An "asset building advice provision method" is a system or software that analyzes accumulated data and provides users with suggestions regarding asset management and investment.

[0538] "Natural language processing means" refers to technologies or programs that analyze and understand the natural language input by a user in order to facilitate smooth communication with the user.

[0539] "Emotional analysis tools" refer to algorithms and technologies that analyze user input information to identify the emotional state of a user.

[0540] An "interface" is a means or protocol for exchanging data between different systems or software.

[0541] The system according to this invention provides a means for users to efficiently manage their financial information while receiving emotion-based, interactive advice. The system includes the following configuration and operation:

[0542] Users can use the LINE chat app via their device to send images of receipts and invoices to the system. An image receiving device acquires these image data and sends them to a server. The server uses optical character recognition (OCR) to extract text information from the received image data. This process typically utilizes commonly used OCR technology, one example being cloud services.

[0543] Once text information is extracted, the server automatically records the transactions using accounting journal entry software. This process is carried out through a commercially available accounting data management program, and the data is stored in a database. The accumulated data is then used to automatically generate the necessary tax return documents through a tax return data generation system. This allows users to easily complete the tax filing process.

[0544] Furthermore, based on income and expenditure data, the server utilizes asset building advice tools to support users in achieving their goals. Natural language messages from users are analyzed through natural language processing tools, and their emotional state is identified by sentiment analysis tools. For example, if a user sends a message saying, "I want to increase my savings for the future," the server considers past spending information and presents specific saving and budgeting plans.

[0545] As an example of a prompt, if the user sends "My food expenses for this month are over budget, please tell me how I can reduce them," the system will provide optimal reduction suggestions based on the user's past data. By using a generative AI model to provide appropriate feedback in response to such prompts, it becomes possible to provide personalized support to the user.

[0546] In this way, the system can go beyond simply managing financial data and function as an interactive assistant that responds to the user's emotions and circumstances.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user opens the LINE chat app on their device and takes or selects an image of a receipt or invoice. The image is sent to the server by pressing the send button and stored on the server via an image receiving device. The input is the image data taken or selected by the user, and the output is the image file stored on the server.

[0550] Step 2:

[0551] The server extracts text information from the received image data using optical character recognition (OCR) technology. Specifically, this process utilizes OCR technology to convert characters within the image into digital text. The input is an image file stored on the server, and the output is the extracted text data.

[0552] Step 3:

[0553] The server uses the text data obtained by OCR to operate the accounting journal entry system and automatically record transactions. At this time, it connects to a database and organizes the journal entry information by category. The input is the extracted text data, and the output is the accounting journal entry information recorded in the database.

[0554] Step 4:

[0555] The server aggregates the accumulated accounting data and generates data for tax filing. This is output as a formatted tax return form, ready for the user to download. The input is accounting journal entry information recorded in the database, and the output is a digital file of the tax return form.

[0556] Step 5:

[0557] The server utilizes a system that provides asset building advice based on income and expense data and declaration data. It analyzes the user's past income and expense trends and generates asset management suggestions. The input is income and expense data obtained from the database, and the output is an advice message to the user.

[0558] Step 6:

[0559] The server receives messages sent by the user in natural language and analyzes them using natural language processing tools. It then uses sentiment analysis tools to identify the emotional state and generates a response corresponding to the prompt. The input is a text message from the user, and the output is a response message or suggestion.

[0560] Step 7:

[0561] The server uses a generative AI model to provide customized support based on user prompts. This enables specific, emotionally sensitive advice. The input is the user's prompt, and the output is a personalized feedback message.

[0562] (Application Example 2)

[0563] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] Traditional financial data management systems, while capable of accurately processing data, failed to provide interactive services that considered user emotions. As a result, users often did not receive adequate support for their anxieties and questions. Furthermore, the manual entry of receipts and invoices was time-consuming, making efficient asset management difficult.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes means for acquiring image data, means for analyzing character information from the image using an optical character recognition device, and means for automatically performing accounting processing based on the analyzed character information. As a result, users can efficiently process information from receipts and invoices without hassle, and receive interactive support that responds to the user's emotions, enabling more effective asset management.

[0567] "Means for acquiring image data" refers to a device or software that receives and stores image information transmitted by a user as digital data.

[0568] An "optical character recognition device" is a technology or device that digitally recognizes characters within an image and extracts them as text data.

[0569] "Means for analyzing character information" refers to software or hardware for processing character data obtained by an optical character recognition device and organizing and analyzing it as meaningful information.

[0570] "Methods for automatically performing accounting processing" refer to systems in which a program automatically executes accounting entries and calculations using acquired textual information.

[0571] "Revenue and expenditure data" refers to financial data such as income and expenses that constitute information about a user's asset building.

[0572] "Means of providing advice on wealth building" refers to a function that analyzes income and expenditure data and proposes the optimal asset management and saving methods for the user.

[0573] "Means of communicating with users using natural language processing" refers to technologies that understand natural language input from users and generate appropriate dialogue.

[0574] "Methods for generating responses based on user emotions" refers to technologies that detect emotions from the user's words and actions and dynamically construct dialogue content accordingly.

[0575] The system of this invention begins when a user sends image data of receipts or invoices via a chat application using a device such as a smartphone. The server acquires this data through an image receiving means. The received image data is processed by an optical character recognition (OCR) device and analyzed as character information. A Python OCR library (e.g., Tesseract) is used for this analysis.

[0576] After the textual information is analyzed, the server automatically performs accounting processing based on that data. This involves inputting accounting data using an interface with accounting software. The accounting software is used to manage the user's revenue and expenditure data and to provide advice on wealth building.

[0577] Furthermore, the server uses natural language processing to communicate with the user. This natural language processing utilizes machine learning models such as Hugging Face's Transformers, which analyze emotions based on the user's input. For example, if a user sends "I'm worried because I've been spending a lot lately," the emotion analysis program can interpret this as "anxiety."

[0578] The results of the emotion analysis are processed by a means of generating responses based on the user's emotions. This response generation uses a pre-trained generative AI model that can generate prompts that provide appropriate encouragement and advice.

[0579] For example, if a user expresses financial anxiety, the server will provide specific advice such as, "You can save XX yen per month by cutting back on eating out once a week!" Examples of prompts to input into the generative AI model include: "User message: 'I'm worried because I've been spending a lot lately.'" "Feedback generation prompt: Please think of words of encouragement and specific advice to alleviate this anxiety."

[0580] Thus, the system of the present invention efficiently manages financial data and provides optimal support while taking user emotions into consideration.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user sends image data of receipts and invoices from their smartphone using a chat app. The sent image data is then input into the server's image receiving mechanism.

[0584] Step 2:

[0585] The server analyzes the received image data using an optical character recognition (OCR) device. Specifically, it uses OCR technology (e.g., Tesseract) to extract character information from the image as text data. This process outputs text data from the image data.

[0586] Step 3:

[0587] The server processes the parsed text data using an automated accounting method. At this stage, the text data is categorized into accounting items, and the accounting data is entered through an interface with accounting software. The output of this step is stored as accounting data in the product database.

[0588] Step 4:

[0589] The server performs calculations to provide advice on wealth building based on stored revenue and expenditure data. This calculation uses algorithms that analyze assets held and the balance between income and expenses, ultimately generating advice on wealth building.

[0590] Step 5:

[0591] A message in natural language is sent from the user to the server. The server uses natural language processing technology (e.g., Hugging Face Transformers) to analyze the message and identify the emotion. Based on this input, emotion data is output.

[0592] Step 6:

[0593] The server uses a generative AI model to generate responses based on sentiment data. It generates prompt sentences appropriate to the identified sentiment and uses them to create natural dialogue documents. This step includes the operation of generating specific and appropriate responses from the prompt sentences.

[0594] Step 7:

[0595] The server sends the generated response to the user and notifies them through the chat application. As a result, the user can receive detailed support based on an understanding of their financial situation and their feelings.

[0596] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0597] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0598] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0602] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0604] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0606] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0607] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0608] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0609] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0610] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0611] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0612] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] The system according to the present invention is designed to improve the efficiency of users' economic activities and reduce the burden of manual accounting. Users can use their personal devices to record expenses and expenditures incurred in their daily lives. Specifically, users take pictures of receipts and invoices with a device such as a smartphone or tablet and send them to the system via a chat application.

[0614] Upon receiving this image data, the server begins reading the text information using AI-OCR technology. The read information is extracted as text, sent to the server, and stored in a database. Based on this stored information, the server initiates automatic journal entries and integrates with accounting software. The server categorizes the identified expense information into the appropriate accounting categories, and all data is managed in a consistent format.

[0615] Furthermore, the server aggregates data over a certain period and organizes it into the format required for tax filing. This allows users to easily obtain the necessary documents for filing their tax returns. In addition, based on the user's income and expense data, the server uses AI to analyze the data and generate optimal advice for wealth building. This advice includes expense management, savings strategies, and investment suggestions.

[0616] Users can interact with the server's AI assistant using natural language through their device to check, modify, and inquire about data. For example, if a user asks, "Tell me my food expenses for last month," the server will provide the corresponding data. Furthermore, if a user wants to modify a specific expense, they can easily do so by giving instructions in natural language.

[0617] Through the above process, the system can manage financial data in daily life and support optimal economic activities without requiring users to possess advanced financial literacy.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users take pictures of receipts and invoices with their smartphones or other devices and send the images to the system via the LINE chat app.

[0621] Step 2:

[0622] The server receives image data from the user's terminal. The received image data is input into an optical character recognition (OCR) engine, which converts the character information into text data.

[0623] Step 3:

[0624] The server analyzes the text data extracted by OCR to identify information such as dates, amounts, and product names. Based on this, it automatically performs accounting entries and records them in the database.

[0625] Step 4:

[0626] The server aggregates the recorded journal entry data at regular intervals and generates the data format required for filing tax returns. This format is customized according to the tax laws of each country and region.

[0627] Step 5:

[0628] The server analyzes the user's income and expense data and uses AI to generate advice for wealth building. This advice is customized according to the individual's financial situation.

[0629] Step 6:

[0630] The user makes inquiries to the server's AI assistant using natural language through their device. The server receives these inquiries, checks and corrects the data, and returns feedback to the user.

[0631] Step 7:

[0632] Users review the advice and data provided by the server and, if necessary, revise their spending and plan their asset management. By continuously using the system, users can better manage their own financial situation.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In economic activity, the burden of manual accounting processes is significant, and the complexity of optimizing asset management necessitates a high level of financial literacy. To address this, a system is needed that streamlines accounting and asset management processes and provides support for making appropriate economic decisions even without specialized knowledge.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for automating accounting journal entry work based on the extracted text information. This enables the user to receive automated accounting processing via image transmission and asset building advice based on the data.

[0638] "Image receiving means" refers to a function for receiving image data sent by a user.

[0639] "Optical character recognition means" refers to a technology for analyzing character information from image data and extracting it as text data.

[0640] "Methods for automating accounting journal entries" refer to functions that use extracted text information to reduce manual work and process accounting quickly.

[0641] "Methods for generating declaration data" refers to the process of using aggregated journal entry data to organize it into a format suitable for tax filing.

[0642] "Means of providing asset building advice" refers to a function that analyzes income and expenditure information and makes useful suggestions regarding the user's asset management.

[0643] "Utilizing generative models" refers to techniques that use machine learning models to generate appropriate advice based on collected data.

[0644] "Means of interacting with users using natural language processing" refers to functions that respond to questions and instructions entered by users in natural language and provide the necessary information.

[0645] The system according to the present invention is designed to streamline users' economic activities and reduce the burden of manual accounting processing. This system is implemented using various terminals, a comprehensive server infrastructure, and advanced software technologies.

[0646] Users record their daily expenses and expenditures using their smartphones or tablets. Specifically, users take pictures of receipts and invoices and send them to the system via a chat application. The server receives this image data and extracts text information from the image using optical character recognition (OCR) technology. For example, software such as Tesseract OCR is used to convert the image data into text.

[0647] The server automatically performs accounting entries based on the extracted text information, and the resulting accounting data is stored in a database. The server also links the entry data with financial management software via an API, enabling efficient data management and analysis. Commonly available financial management software can be used for accounting and finance.

[0648] Furthermore, it can aggregate economic data over a certain period and organize it in the format required for tax filing. Based on this organized data, the server uses a generated AI model to provide users with optimal advice for wealth building. This advice includes spending management, savings strategies, and investment suggestions. This allows users to effectively manage their assets even without specialized knowledge.

[0649] Users can interact with the server's AI assistant using natural language to check and modify data. For example, by entering "Tell me my food expenses for last month" as a prompt, the server will immediately provide the corresponding data. Furthermore, users can also present specific requirements such as "I want to check my transportation expenses for this month. Also, tell me my spending for last month."

[0650] In this way, this system can support the management of financial data in daily life and facilitate efficient and effective economic activities without requiring users to possess advanced financial literacy.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The user uses their device to take a picture of a receipt or invoice and sends the captured image to the server via a chat application. This provides the image data as input.

[0654] Step 2:

[0655] The server receives image data and extracts text information from the image using optical character recognition (OCR) technology. Specifically, the server uses OCR software to analyze the image data and generate text data. Here, the received image data is used as input, and the extracted text is obtained as output.

[0656] Step 3:

[0657] The server analyzes the extracted text information and organizes the data necessary for accounting entries. The server uses an algorithm to classify the text information into accounting categories and stores them in a database. This process uses the extracted text data as input and generates structured accounting data as output.

[0658] Step 4:

[0659] The server automatically performs journal entries based on structured accounting data and links the data with financial management software. The server uses an API to send data to the accounting software for integration. In this step, organized accounting data is used as input, and linked journal entry data is generated as output.

[0660] Step 5:

[0661] The server aggregates data over a specified period and organizes it in the format required for tax filing. During the aggregation process, the server uses analytical tools to integrate historical data and generate a final report. The input here is accounting data stored in a database, and the output is tax filing data.

[0662] Step 6:

[0663] The server uses an AI model based on income and expense data to generate advice for wealth building. The server inputs data into the model and generates suggestions tailored to the user's financial situation. In this step, income and expense data is used as input, and asset management advice is provided as output.

[0664] Step 7:

[0665] Users can query the AI ​​assistant using natural language through their device, and also check and correct data. For example, if a user enters the prompt "Tell me last month's food expenses," the server retrieves the corresponding information from the database and provides it. In this process, the relevant data is displayed to the user as output for the entered question.

[0666] Step 8:

[0667] The user modifies specific expense items as needed. The user enters modification instructions in natural language into the terminal, and the server parses the content and updates the information in the database. In this step, the user's instructions are used as input, and the updated accounting information is obtained as output.

[0668] (Application Example 1)

[0669] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] In modern society, the economic activities of individuals and businesses are diversifying, making it increasingly complex to manage income and expenses and formulate asset building strategies. In particular, recording the ever-increasing number of transactions and subsequent accounting processes becomes a significant burden if relied upon manually. Furthermore, obtaining concrete and effective asset building advice based on this data is not easy. To address these needs, there is a growing demand for systems that provide automated transaction information management and effective asset building advice.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0672] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, a means for automatically performing accounting entries based on the extracted text information, and a means for proposing an asset formation strategy using generative AI technology. As a result, users can simplify daily transaction management and easily obtain effective economic activity strategies tailored to their individual asset backgrounds.

[0673] "Image receiving means" refers to a device or software that has the function of receiving image data and transferring digital data of receipts and invoices photographed by the user to a server.

[0674] "Optical character recognition means" refers to a technology that identifies character information from input image data and converts it into text, thereby converting the characters in the image into a format that can be stored in a database.

[0675] An "automated accounting journal entry system" is a process that automatically performs accounting operations based on extracted text information, and streamlines bookkeeping by classifying data into appropriate accounting categories.

[0676] A "journal entry data aggregation method" is a system or procedure for aggregating data generated through accounting processes and creating data organized in a format necessary for tax filing and other purposes.

[0677] The "asset building advice provision method" is a function that uses income and expenditure data and generation AI technology to provide users with suggestions regarding asset management and investment, offering advice optimized for the user's economic activities.

[0678] "Natural language processing technology" is a means by which computers understand human language and engage in dialogue. It analyzes user questions and instructions in natural language and generates appropriate responses.

[0679] "Electronic payment information" refers to payment information in cashless transactions, and is data that records the details of transactions performed by a user.

[0680] "Generative AI technology" is a technology that uses artificial intelligence to perform data analysis and prediction, generating optimal strategies and advice for users based on the data.

[0681] In this embodiment of the invention, the user's terminal is a smartphone or tablet. The user uses these terminals to take pictures of receipts or invoices and transmits them to the server via an image receiving means. The server uses optical character recognition means to extract text information from the image data. In this process, the Google Cloud Vision API is used as the optical character recognition technology to perform highly accurate character recognition.

[0682] The extracted text data is interfaced with accounting software (e.g., QuickBooks or Freee) via an automated accounting journal entry system and categorized into relevant accounting categories. This automates tedious bookkeeping tasks and improves efficiency.

[0683] Furthermore, a journal entry data aggregation tool is used to aggregate data for a certain period and organize it in the format required for tax filing. Based on this data, the server utilizes generation AI technology (e.g., OpenAI's GPT-3) to provide users with asset management and investment suggestions through an asset formation advice provision tool. These suggestions are displayed on the terminal for the user's reference.

[0684] Furthermore, natural language processing technology is used to enable interaction with the user. This allows users to ask questions to the server via their device, or request data verification or correction. For example, if a user prompts, "What are the expenditures for each category this month?", the server can provide the corresponding information through its AI-generated data.

[0685] This system allows users to reduce the burden of managing daily transactions while supporting effective economic activity.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The user takes a picture of a receipt or invoice using a device. The input is image data. The device sends this image data to the server via an image receiving device. The output is the transfer of the image data to the server.

[0689] Step 2:

[0690] The server applies optical character recognition (OCR) to the received image data. The input is image data on the server. The server uses the Google Cloud Vision API to extract character information from the image. It performs calculations to remove noise from the data and convert it into the required string information. The output is text data.

[0691] Step 3:

[0692] The server inputs text data obtained through optical character recognition into an automated accounting journal entry system. The input is text data. The server, through an interface with accounting software, classifies this text data into predetermined accounting categories. It uses a specific algorithm to map the data and performs calculations to journalize it into the appropriate accounts. The output is the journalized data.

[0693] Step 4:

[0694] The server aggregates the journalized data using a journal data aggregation mechanism and generates data in a tax return format. The input is the journalized data. The server applies a series of aggregation algorithms to format the data into the specified format. The output is the organized data for tax filing.

[0695] Step 5:

[0696] The server uses organized data and generative AI technology to generate asset building advice. The input is organized data for declaration purposes. The server runs a generative AI model, querying through prompts that generate advice based on the user's asset background, and performing calculations to propose strategies. The output is asset building advice.

[0697] Step 6:

[0698] The user interacts with the server via a terminal using natural language processing technology. Input consists of the user's natural language questions or instructions. The server, with the assistance of a generative AI model, responds to the user with appropriate information. Output consists of answers to the user's questions and verified data. Through this interaction, the user receives support for their economic activities.

[0699] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0700] The system according to the present invention aims not only to manage financial data but also to provide an interactive experience that takes user emotions into account. Therefore, it is equipped with an emotion engine that can analyze user input and flexibly provide responses and advice tailored to individual situations.

[0701] Users can easily send images of receipts and invoices to the system via the LINE chat app on their devices. The server receives the images sent by the users and extracts text information using OCR technology. Based on this information, the server automatically performs accounting entries and saves the extracted data to a database.

[0702] As accounting data accumulates, the server aggregates the data necessary for filing tax returns and generates pre-formatted tax forms. This allows users to efficiently complete their tax payment procedures. Furthermore, the server uses the user's income and expense data to generate and provide asset building advice.

[0703] Furthermore, this system utilizes an emotion engine in its interactions with users. The emotion engine analyzes the natural language input by the user and identifies their emotional state. For example, if a user inputs the message, "I'm worried because I've been spending a lot lately," the server can identify this as anxiety. Based on this, it adjusts the tone and content of the advice, providing encouraging and reassuring feedback.

[0704] In this way, the server not only processes data but also proactively proposes drafts and responses based on the user's emotions, creating a more human-centered system. This approach allows users to enjoy value from the system that goes beyond mere digital tools.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] Users take pictures of receipts and invoices with their smartphones or tablets and send the images to the system via the LINE chat app.

[0708] Step 2:

[0709] The server receives images sent by the user. The received images are passed through an AI-OCR module, which extracts the text information within the images as text data.

[0710] Step 3:

[0711] The server analyzes the text data extracted by OCR to identify information such as the date, amount, and store name. Based on this, it performs automatic accounting entries and records the entry data in the database.

[0712] Step 4:

[0713] The server periodically aggregates multiple recorded journal entries and automatically generates the data format required for tax filing. This format complies with local and tax laws.

[0714] Step 5:

[0715] The server uses AI to generate asset building advice based on the user's income and expense data. The advice includes suggestions tailored to the user's financial situation, such as specific savings goals and investment strategies.

[0716] Step 6:

[0717] Users send questions and requests to the server from their devices using natural language. The server receives these messages and analyzes the user's emotional state from the input text through its sentiment engine.

[0718] Step 7:

[0719] Based on the user's emotions identified by the emotion engine, the server adjusts the tone and content of the advice, providing the user with appropriate feedback and suggestions.

[0720] Step 8:

[0721] Users review the advice and data provided by the server and revise their financial plans as needed. By continuously using the system, users can achieve improved financial management.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Modern financial management systems focus solely on collecting and processing data, failing to provide an interactive experience that considers the emotional aspects of the user. Therefore, there is a need to develop systems that allow users to easily organize their financial information while receiving emotionally sensitive advice.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes an image receiving means, an optical character recognition means for extracting text information from the image, and a means for analyzing the user's emotions using natural language processing and providing personalized advice. This enables the user to efficiently manage their financial information and receive appropriate advice tailored to their emotions.

[0727] "Image receiving means" refers to a device or program for acquiring image data transmitted by a user and converting it into a format that can be processed by the server.

[0728] "Optical character recognition means" refers to a technology or device used to extract text information from image data, specifically one that recognizes characters by optical means and converts them into digital data.

[0729] An "accounting journal entry tool" is a system or software that automatically records, classifies, and organizes accounting transactions based on acquired text information.

[0730] A "data generation method for tax return filing" refers to a program or device for aggregating accounting journal entry data and creating tax return documents in accordance with laws and regulations.

[0731] An "asset building advice provision method" is a system or software that analyzes accumulated data and provides users with suggestions regarding asset management and investment.

[0732] "Natural language processing means" refers to technologies or programs that analyze and understand the natural language input by a user in order to facilitate smooth communication with the user.

[0733] "Emotional analysis tools" refer to algorithms and technologies that analyze user input information to identify the emotional state of a user.

[0734] An "interface" is a means or protocol for exchanging data between different systems or software.

[0735] The system according to this invention provides a means for users to efficiently manage their financial information while receiving emotion-based, interactive advice. The system includes the following configuration and operation:

[0736] Users can use the LINE chat app via their device to send images of receipts and invoices to the system. An image receiving device acquires these image data and sends them to a server. The server uses optical character recognition (OCR) to extract text information from the received image data. This process typically utilizes commonly used OCR technology, one example being cloud services.

[0737] Once text information is extracted, the server automatically records the transactions using accounting journal entry software. This process is carried out through a commercially available accounting data management program, and the data is stored in a database. The accumulated data is then used to automatically generate the necessary tax return documents through a tax return data generation system. This allows users to easily complete the tax filing process.

[0738] Furthermore, based on income and expenditure data, the server utilizes asset building advice tools to support users in achieving their goals. Natural language messages from users are analyzed through natural language processing tools, and their emotional state is identified by sentiment analysis tools. For example, if a user sends a message saying, "I want to increase my savings for the future," the server considers past spending information and presents specific saving and budgeting plans.

[0739] As an example of a prompt, if the user sends "My food expenses for this month are over budget, please tell me how I can reduce them," the system will provide optimal reduction suggestions based on the user's past data. By using a generative AI model to provide appropriate feedback in response to such prompts, it becomes possible to provide personalized support to the user.

[0740] In this way, the system can go beyond simply managing financial data and function as an interactive assistant that responds to the user's emotions and circumstances.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The user opens the LINE chat app on their device and takes or selects an image of a receipt or invoice. The image is sent to the server by pressing the send button and stored on the server via an image receiving device. The input is the image data taken or selected by the user, and the output is the image file stored on the server.

[0744] Step 2:

[0745] The server extracts text information from the received image data using optical character recognition (OCR) technology. Specifically, this process utilizes OCR technology to convert characters within the image into digital text. The input is an image file stored on the server, and the output is the extracted text data.

[0746] Step 3:

[0747] The server uses the text data obtained by OCR to operate the accounting journal entry system and automatically record transactions. At this time, it connects to a database and organizes the journal entry information by category. The input is the extracted text data, and the output is the accounting journal entry information recorded in the database.

[0748] Step 4:

[0749] The server aggregates the accumulated accounting data and generates data for tax filing. This is output as a formatted tax return form, ready for the user to download. The input is accounting journal entry information recorded in the database, and the output is a digital file of the tax return form.

[0750] Step 5:

[0751] The server utilizes a system that provides asset building advice based on income and expense data and declaration data. It analyzes the user's past income and expense trends and generates asset management suggestions. The input is income and expense data obtained from the database, and the output is an advice message to the user.

[0752] Step 6:

[0753] The server receives messages sent by the user in natural language and analyzes them using natural language processing tools. It then uses sentiment analysis tools to identify the emotional state and generates a response corresponding to the prompt. The input is a text message from the user, and the output is a response message or suggestion.

[0754] Step 7:

[0755] The server uses a generative AI model to provide customized support based on user prompts. This enables specific, emotionally sensitive advice. The input is the user's prompt, and the output is a personalized feedback message.

[0756] (Application Example 2)

[0757] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Traditional financial data management systems, while capable of accurately processing data, failed to provide interactive services that considered user emotions. As a result, users often did not receive adequate support for their anxieties and questions. Furthermore, the manual entry of receipts and invoices was time-consuming, making efficient asset management difficult.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0760] In this invention, the server includes means for acquiring image data, means for analyzing character information from the image using an optical character recognition device, and means for automatically performing accounting processing based on the analyzed character information. As a result, users can efficiently process information from receipts and invoices without hassle, and receive interactive support that responds to the user's emotions, enabling more effective asset management.

[0761] "Means for acquiring image data" refers to a device or software that receives and stores image information transmitted by a user as digital data.

[0762] An "optical character recognition device" is a technology or device that digitally recognizes characters within an image and extracts them as text data.

[0763] "Means for analyzing character information" refers to software or hardware for processing character data obtained by an optical character recognition device and organizing and analyzing it as meaningful information.

[0764] "Methods for automatically performing accounting processing" refer to systems in which a program automatically executes accounting entries and calculations using acquired textual information.

[0765] "Revenue and expenditure data" refers to financial data such as income and expenses that constitute information about a user's asset building.

[0766] "Means of providing advice on wealth building" refers to a function that analyzes income and expenditure data and proposes the optimal asset management and saving methods for the user.

[0767] "Means of communicating with users using natural language processing" refers to technologies that understand natural language input from users and generate appropriate dialogue.

[0768] "Methods for generating responses based on user emotions" refers to technologies that detect emotions from the user's words and actions and dynamically construct dialogue content accordingly.

[0769] The system of this invention begins when a user sends image data of receipts or invoices via a chat application using a device such as a smartphone. The server acquires this data through an image receiving means. The received image data is processed by an optical character recognition (OCR) device and analyzed as character information. A Python OCR library (e.g., Tesseract) is used for this analysis.

[0770] After the textual information is analyzed, the server automatically performs accounting processing based on that data. This involves inputting accounting data using an interface with accounting software. The accounting software is used to manage the user's revenue and expenditure data and to provide advice on wealth building.

[0771] Furthermore, the server uses natural language processing to communicate with the user. This natural language processing utilizes machine learning models such as Hugging Face's Transformers, which analyze emotions based on the user's input. For example, if a user sends "I'm worried because I've been spending a lot lately," the emotion analysis program can interpret this as "anxiety."

[0772] The results of the emotion analysis are processed by a means of generating responses based on the user's emotions. This response generation uses a pre-trained generative AI model that can generate prompts that provide appropriate encouragement and advice.

[0773] For example, if a user expresses financial anxiety, the server will provide specific advice such as, "You can save XX yen per month by cutting back on eating out once a week!" Examples of prompts to input into the generative AI model include: "User message: 'I'm worried because I've been spending a lot lately.'" "Feedback generation prompt: Please think of words of encouragement and specific advice to alleviate this anxiety."

[0774] Thus, the system of the present invention efficiently manages financial data and provides optimal support while taking user emotions into consideration.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The user sends image data of receipts and invoices from their smartphone using a chat app. The sent image data is then input into the server's image receiving mechanism.

[0778] Step 2:

[0779] The server analyzes the received image data using an optical character recognition (OCR) device. Specifically, it uses OCR technology (e.g., Tesseract) to extract character information from the image as text data. This process outputs text data from the image data.

[0780] Step 3:

[0781] The server processes the parsed text data using an automated accounting method. At this stage, the text data is categorized into accounting items, and the accounting data is entered through an interface with accounting software. The output of this step is stored as accounting data in the product database.

[0782] Step 4:

[0783] The server performs calculations to provide advice on wealth building based on stored revenue and expenditure data. This calculation uses algorithms that analyze assets held and the balance between income and expenses, ultimately generating advice on wealth building.

[0784] Step 5:

[0785] A message in natural language is sent from the user to the server. The server uses natural language processing technology (e.g., Hugging Face Transformers) to analyze the message and identify the emotion. Based on this input, emotion data is output.

[0786] Step 6:

[0787] The server uses a generative AI model to generate responses based on sentiment data. It generates prompt sentences appropriate to the identified sentiment and uses them to create natural dialogue documents. This step includes the operation of generating specific and appropriate responses from the prompt sentences.

[0788] Step 7:

[0789] The server sends the generated response to the user and notifies them through the chat application. As a result, the user can receive detailed support based on an understanding of their financial situation and their feelings.

[0790] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0791] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0792] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0793] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0794] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0795] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0796] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0797] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0798] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0799] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0800] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0801] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0802] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0803] 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.

[0804] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0805] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0806] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0807] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0808] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0809] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0810] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] Image receiving means,

[0814] A means for extracting text information from an image using optical character recognition means,

[0815] A method for automatically performing accounting entries based on extracted text information,

[0816] A means of aggregating journal entry data and generating data for tax return filing,

[0817] A means of providing asset formation advice based on generated data and income / expense data,

[0818] A means of interacting with the user using natural language processing,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, characterized in that it performs character identification and noise removal using optical character recognition means.

[0822] (Claim 3)

[0823] The system according to claim 1, characterized in that the means for performing accounting entries is to input journal entry data through an interface with accounting software.

[0824] "Example 1"

[0825] (Claim 1)

[0826] Image receiving means,

[0827] A means for extracting text information from an image using optical character recognition means,

[0828] A method for automating accounting journal entry work based on extracted text information,

[0829] A means of aggregating journal entry data and generating data for tax declaration,

[0830] A means of providing asset formation advice based on generated data and income / expense information,

[0831] A means of creating user-optimized advice using generative models,

[0832] A means of using natural language processing to interact with users and enable them to verify and modify data,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, characterized in that it performs character identification and noise removal using optical character recognition means.

[0836] (Claim 3)

[0837] The system according to claim 1, characterized in that the means for performing accounting entries is to input journal entry data through an interface with financial management software.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] Image receiving means,

[0841] A means for extracting text information from an image using optical character recognition means,

[0842] A method for automatically performing accounting entries based on extracted text information,

[0843] A means of aggregating journal entry data and generating data for tax return filing,

[0844] A means of providing asset formation advice based on generated data and income / expense data,

[0845] A means of interacting with users using natural language processing technology,

[0846] A means of automatically collecting and analyzing transaction data from electronic payment information,

[0847] A means of proposing asset formation strategies using generative AI technology,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, characterized in that it performs character identification and noise removal using optical character recognition means.

[0851] (Claim 3)

[0852] The system according to claim 1, characterized in that the means for performing accounting entries is to input journal entry data through an interface with accounting software.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] Image receiving means,

[0856] A means for extracting text information from an image using optical character recognition means,

[0857] A method for automatically performing accounting entries based on extracted text information,

[0858] A means of aggregating journal entry data and generating data for tax return filing,

[0859] A means of providing asset formation advice based on generated data and income / expense data,

[0860] A means of analyzing user emotions using natural language processing and providing personalized advice,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that it performs character identification and noise removal using optical character recognition means, as well as sentiment analysis.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the means for performing accounting entries inputs journal entry data through an interface with an accounting data management program and generates user-optimized advice.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] Means for acquiring image data,

[0869] A means for analyzing character information from an image using an optical character recognition device,

[0870] A means for automatically performing accounting processing based on analyzed textual information,

[0871] A means of aggregating accounting data to generate data for tax filing,

[0872] A means of providing advice on wealth building based on revenue and expenditure data,

[0873] A means of communicating with users using natural language processing,

[0874] A means of generating responses based on the user's emotions,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, characterized in that it performs text discrimination and noise elimination using an optical character recognition device.

[0878] (Claim 3)

[0879] The system according to claim 1, characterized in that the means for performing accounting processing supplies accounting data through a connection with an automated accounting program. [Explanation of symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image receiving means, A means for extracting text information from an image using optical character recognition means, A method for automatically performing accounting entries based on extracted text information, A means of aggregating journal entry data and generating data for tax return filing, A means of providing asset formation advice based on generated data and income / expense data, A means of interacting with the user using natural language processing, A system that includes this.

2. The system according to claim 1, characterized in that it performs character identification and noise removal using optical character recognition means.

3. The system according to claim 1, characterized in that the means for performing accounting entries inputs journal entry data through an interface with accounting software.

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