System

Abstract

An object of a system according to an embodiment is to automate and efficiently perform management and journalizing work of receipts and invoices.SOLUTION: A system includes a reception part, an analysis part, a journalizing part, a recording part, and an advice part. The reception unit receives a photograph of a receipt or a bill from a user. The analysis unit analyzes the photograph received by the reception unit and extracts necessary information. The journalizing unit automatically performs journalizing based on the information extracted by the analyzing unit. The recording unit records the information journalized by the journalizing unit. The advice unit provides advice to the user based on the data recorded by the recording unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, managing receipts and invoices and accounting for them was time-consuming and labor-intensive, making it difficult to carry out the tasks efficiently.

[0005] The system according to the embodiment aims to automate and efficiently manage receipts and invoices and perform accounting work. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a classification unit, a recording unit, and an advice unit. The reception unit receives a photo of a receipt or invoice from a user. The analysis unit analyzes the photo received by the reception unit and extracts necessary information. The classification unit automatically performs classification based on the information extracted by the analysis unit. The recording unit records the information classified by the classification unit. The advice unit provides advice to the user based on the data recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the management and accounting of receipts and invoices, enabling efficient processing. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A personal financial planner system according to an embodiment of the present invention allows users to simply attach photos of receipts and invoices via a messaging app, and AI automatically performs accounting and recording. This system can convert all data into a tax return format. It can also optionally integrate with a tax accountant-generated AI to provide appropriate advice. Furthermore, the AI ​​provides advice on optimal asset formation based on the user's income and expenditure data, and by securing the customer's income and expenditure data, it can be utilized in various financial services. This allows users to easily record expenses and smoothly file tax returns. Furthermore, by receiving advice from the tax accountant-generated AI, appropriate financial management can be performed and optimal asset formation can be achieved. Furthermore, by securing the income and expenditure data, it can be utilized in various financial services. For example, services based on the user's income and expenditure data, such as loan applications and insurance reviews, can be provided.

[0029] The personal financial planner system according to the embodiment includes a reception unit, an analysis unit, an accounting unit, a recording unit, and an advice unit. The reception unit receives a photo of a receipt or invoice from a user. The photo of the receipt or invoice from the user may be in any format, such as, but not limited to, JPEG, PNG, or PDF. The reception unit receives the photo of the receipt or invoice from the user via a messaging app, for example. The analysis unit analyzes the attached photo and reads the contents of the receipt or invoice. The analysis may be performed using, for example, but not limited to, OCR technology or an image recognition algorithm. The analysis unit may extract information from the photo, such as the date, amount, and store name, using, for example, OCR technology. The analysis unit may also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. The accounting unit automatically performs accounting based on the information extracted by the analysis unit. The automatic accounting may be performed based on, for example, but not limited to, accounting standards or category classifications. The accounting unit may, for example, assign the extracted information to an appropriate category. The journalizing unit can also appropriately classify income and expenses based on accounting standards. The recording unit records the information journalized by the journalizing unit. Recording can include, but is not limited to, storing the information in a database or in a file format. The recording unit can also convert the journalized information into a tax return format, for example. The recording unit can also store the information in a database and retrieve the data as needed. The advice unit provides advice to the user based on the data recorded by the recording unit. The advice can include, but is not limited to, tax saving strategies and investment suggestions. The advice unit can, for example, work with a tax accountant generation AI to analyze the user's income and expenditure data and provide tax saving strategies and investment advice. The advice unit can also analyze the user's income and expenditure data and suggest optimal asset formation. As a result, the personal financial planner system according to the embodiment can automatically analyze photos of the user's receipts and invoices, and perform journalizing, recording, and advice, enabling efficient financial management.

[0030] The advice unit works in cooperation with the tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving or investment advice. The tax accountant generation AI is realized, for example, using machine learning models and data analysis methods. The advice unit works in cooperation with the tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving or investment advice. For example, the tax accountant generation AI suggests tax deduction methods and investment selection criteria based on the user's income and expenditure data. The tax accountant generation AI can also analyze the user's income and expenditure data and provide optimal tax-saving measures. In this way, by working in cooperation with the tax accountant generation AI, more specialized advice can be provided to the user.

[0031] The reception unit can receive a photo of a receipt or invoice from a user through a messaging app. Examples of messaging apps include, but are not limited to, LINE (registered trademark), WhatsApp (registered trademark), and Facebook (registered trademark) Messenger. The reception unit receives a photo of a receipt or invoice from a user through the messaging app. For example, the user simply attaches a photo of the receipt or invoice to a chat on the messaging app. This allows the user to easily send a photo of a receipt or invoice by using the messaging app.

[0032] The analysis unit can analyze the attached photo and read the contents of the receipt or invoice. The analysis is performed using, for example, OCR technology or an image recognition algorithm. The analysis unit analyzes the attached photo and reads the contents of the receipt or invoice. For example, OCR technology is used to extract information such as the date, amount, and store name from the photo. The analysis unit can also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. This makes it possible to automatically read the contents of the receipt or invoice by analyzing the photo.

[0033] The journalizing unit can journalize into categories based on the extracted information. Journalizing is performed, for example, based on accounting standards or category classification. The journalizing unit journalizes into appropriate categories based on the extracted information. For example, it appropriately classifies income and expenses. The journalizing unit can also appropriately classify income and expenses based on accounting standards. This allows for automatic journalizing based on the extracted information, thereby reducing the amount of work required.

[0034] The recording unit can convert the journalized information into a tax return format. Examples of tax return formats include, but are not limited to, formats designated by the National Tax Agency and formats for electronic filing. The recording unit converts the journalized information into a tax return format. For example, the recording unit creates an accurate tax return by calculating the balance between income and expenses and applying necessary deductions. The recording unit can also store information in a database and retrieve the data as needed. This simplifies the tax return process by automatically converting the journalized information into a tax return format.

[0035] The advice unit can analyze the user's income and expenditure data and suggest asset formation. Asset formation suggestions include, for example, selecting investment targets and savings plans. The advice unit analyzes the user's income and expenditure data and suggests optimal asset formation. For example, a user with a stable income may be recommended long-term investments, while a user with high expenses may be suggested with savings methods. The advice unit can also suggest optimal investment targets and savings methods based on the user's income and expenditure data. In this way, optimal asset formation advice can be provided by analyzing the user's income and expenditure data.

[0036] The reception unit can analyze the user's past submission history and select the reception method. The past submission history includes, for example, the submission date and time and the submission content. The reception unit analyzes the user's past submission history and selects the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past submission history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method can be provided to the user by analyzing the past submission history.

[0037] The reception unit can filter receipts or invoices based on the user's current project or area of ​​interest when receiving them. Current projects or areas of interest include, for example, project management tools and user interest data. The reception unit can filter receipts or invoices based on the user's current project or area of ​​interest when receiving them. For example, receipts or invoices related to projects currently underway by the user can be preferentially received. In addition, receipts and invoices related to the user's area of ​​interest can be filtered and received. Furthermore, related receipts and invoices can be received according to the progress of the user's project. In this way, by filtering based on the user's project or area of ​​interest, highly relevant information can be preferentially received.

[0038] The reception unit can select the reception means according to the user's input method when receiving a receipt or invoice. Input methods include, for example, voice input, text input, and image input. When receiving a receipt or invoice, the reception unit selects the optimal reception means according to the user's input method. For example, if the user prefers voice input, the reception unit can receive receipt or invoice information by voice. Also, if the user prefers text input, the reception unit can also receive receipt or invoice information by text. Furthermore, if the user prefers image input, the reception unit can also receive receipt or invoice information by image. This improves user convenience by selecting the optimal reception means according to the user's input method.

[0039] When receiving a receipt or invoice, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. When receiving a receipt or invoice, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, receipts and invoices related to that area are prioritized. Related information can also be prioritized based on the user's current location. Furthermore, if the user is traveling, receipts and invoices related to the user's travel destination can also be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0040] The reception unit can analyze the user's social media activity and receive related information when receiving a receipt or invoice. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. The reception unit analyzes the user's social media activity and receives related information when receiving a receipt or invoice. For example, the reception unit preferentially receives receipts and invoices related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. Furthermore, the reception unit can also receive related information by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant information.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a receipt or invoice. Past feedback includes, for example, the user's ratings and comments. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a receipt or invoice. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception method can also be improved by reflecting the user's past feedback. Furthermore, the reception method can also be customized based on the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice. Importance includes, for example, the amount of money and the frequency of the transaction. During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the receipt or invoice. For example, detailed analysis is performed on receipts or invoices with high importance. Also, simplified analysis can be performed on receipts or invoices with low importance. Furthermore, the level of detail of the analysis can be adjusted according to the importance of the receipt or invoice. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the receipt or invoice.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. Categories include, for example, expenses, sales, and purchases. During analysis, the analysis unit applies different analysis algorithms depending on the category of the receipt or invoice. For example, a specific analysis algorithm can be applied to meal-related receipts. A different analysis algorithm can also be applied to utility bills. Furthermore, the optimal analysis algorithm can be selected and applied for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, for example, past transaction data and feedback for improving the accuracy of the analysis. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. The accuracy of the analysis can also be improved by referring to the user's past analysis results. Furthermore, the efficiency of the analysis can be improved by utilizing the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the receipt or invoice. The submission time includes, for example, the submission date and the submission deadline. During analysis, the analysis unit determines the priority of analysis based on the submission time of the receipt or invoice. For example, receipts and invoices submitted earlier are analyzed with priority. Also, receipts and invoices submitted later can be postponed. Furthermore, the priority of analysis can be determined based on the submission time. In this way, by determining the priority of analysis based on the submission time, efficient analysis is possible.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of receipts or invoices. Relevance includes, for example, transaction relevance and category relevance. During analysis, the analysis unit adjusts the order of analysis based on the relevance of receipts or invoices. For example, highly relevant receipts or invoices are analyzed with priority. It is also possible to postpone less relevant receipts or invoices. Furthermore, the order of analysis can be adjusted based on the relevance of receipts or invoices. In this way, adjusting the order of analysis based on relevance enables efficient analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expert knowledge, detailed technical terms are used. Also, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0048] The journal entry unit can improve the accuracy of journal entries by taking into account the interrelationships between receipts or invoices when making journal entries. Interrelationships include, for example, transaction relevance and category relevance. The journal entry unit improves the accuracy of journal entries by taking into account the interrelationships between receipts and invoices when making journal entries. For example, it analyzes the interrelationships between receipts and invoices and makes appropriate journal entries. The accuracy of journal entries can also be improved by taking into account the interrelationships between receipts and invoices. Furthermore, the efficiency of journal entries can be improved based on the interrelationships between receipts and invoices. In this way, the accuracy of journal entries is improved by taking into account the interrelationships between receipts and invoices.

[0049] The journal entry unit can make journal entries taking into account the attribute information of the submitter of the receipt or invoice when making journal entries. The attribute information of the submitter includes, for example, age, occupation, region, etc. The journal entry unit makes journal entries taking into account the attribute information of the submitter of the receipt or invoice when making journal entries. For example, appropriate journal entries are made based on the attribute information of the submitter. The accuracy of journal entries can also be improved by taking into account the attribute information of the submitter. Furthermore, the efficiency of journal entries can also be improved by utilizing the attribute information of the submitter. In this way, appropriate journal entries can be made by taking into account the attribute information of the submitter.

[0050] When journalizing, the journal entry unit can weight the journal entries based on the submission frequency of receipts or invoices. Submission frequency includes, for example, monthly submission or annual submission. When journalizing, the journal entry unit weights the journal entries based on the submission frequency of receipts or invoices. For example, a higher weight is assigned to receipts or invoices that are submitted more frequently. Also, a lighter weight can be assigned to receipts or invoices that are submitted less frequently. Furthermore, the weighting of journal entries can be adjusted based on the submission frequency. In this way, by weighting journal entries based on the submission frequency, efficient journal entries can be achieved.

[0051] The journal entry unit can take into account the geographic distribution of receipts or invoices when making journal entries. Geographic distribution includes, for example, transaction distribution by region or by country. The journal entry unit can take into account the geographic distribution of receipts or invoices when making journal entries. For example, appropriate journal entries are made based on the place where the receipt or invoice was issued. In addition, taking geographic distribution into account can improve the accuracy of journal entries. Furthermore, the efficiency of journal entries can be improved based on the geographic distribution of receipts or invoices. In this way, appropriate journal entries can be made by taking geographic distribution into account.

[0052] The journal entry department can improve the accuracy of journal entries by referring to related literature for receipts or invoices when making journal entries. Related literature includes, for example, accounting standards and industry guidelines. The journal entry department can improve the accuracy of journal entries by referring to related literature for receipts or invoices when making journal entries. For example, appropriate journal entries are made by referring to related literature. The accuracy of journal entries can also be improved based on related literature. Furthermore, the efficiency of journal entries can be improved by utilizing related literature. In this way, the accuracy of journal entries is improved by referring to related literature.

[0053] The journal entry unit can make journal entries taking into account the market value of the receipt or invoice. Market value includes, for example, the transaction price and the appraised value. The journal entry unit makes journal entries taking into account the market value of the receipt or invoice. For example, appropriate journal entries are made for receipts or invoices with high market value. Simplified journal entries can also be made for receipts or invoices with low market value. Furthermore, the accuracy of journal entries can be improved based on market value. In this way, appropriate journal entries can be made by taking market value into account.

[0054] The recording unit can select a recording method by referring to the user's past recording history when recording. The past recording history includes, for example, past transaction data and recording format. The recording unit selects the optimal recording method by referring to the user's past recording history when recording. For example, the recording unit suggests the optimal recording method based on the user's past recording history. The user's past recording history can also be used as a reference to improve recording accuracy. Furthermore, the user's past recording history can be utilized to improve recording efficiency. In this way, the optimal recording method can be provided by referring to the past recording history.

[0055] The recording unit can customize the recording means based on the user's current living situation when recording. The current living situation includes, for example, income status and family composition. The recording unit customizes the recording means based on the user's current living situation when recording. For example, the recording unit provides the optimal recording means taking into account the user's current living situation. The accuracy of recording can also be improved based on the user's living situation. Furthermore, the efficiency of recording can be improved by reflecting the user's living situation. In this way, by customizing the recording means based on the current living situation, the optimal recording method for the user can be provided.

[0056] The recording unit can improve the recording method by reflecting user feedback during recording. Feedback includes, for example, user ratings and comments. The recording unit improves the recording method by reflecting user feedback during recording. For example, the recording method is improved based on user feedback. Also, the accuracy of recording can be improved by reflecting user feedback. Furthermore, the efficiency of recording can be improved by utilizing user feedback. In this way, the recording method can be improved by reflecting user feedback.

[0057] The recording unit can select a recording method by taking into account the user's geographical location information when recording. The geographical location information includes, for example, GPS data and location information services. The recording unit selects an optimal recording method by taking into account the user's geographical location information when recording. For example, the recording unit provides an optimal recording method based on the user's current location. Furthermore, the recording accuracy can be improved by taking into account the geographical location information. Furthermore, the recording efficiency can be improved based on the user's geographical location information. In this way, the optimal recording method can be provided by taking into account the geographical location information.

[0058] The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. For example, the recording unit can suggest the optimal recording means based on the user's social media activity. The recording accuracy can also be improved by analyzing the social media activity. Furthermore, the recording efficiency can also be improved by utilizing the user's social media activity. In this way, the optimal recording means can be provided by analyzing the social media activity.

[0059] The recording unit can customize the recording method by reflecting the user's past feedback when recording. Past feedback includes, for example, the user's ratings and comments. The recording unit customizes the recording method by reflecting the user's past feedback when recording. For example, the recording method is customized based on the user's past feedback. Also, by reflecting the user's past feedback, the accuracy of recording can be improved. Furthermore, the efficiency of recording can be improved by utilizing the user's past feedback. In this way, by reflecting the past feedback, the optimal recording method can be provided.

[0060] When providing advice, the advice unit can analyze the user's past income and expenditure data to select an advice method. The past income and expenditure data includes, for example, income, expenditure, and savings. When providing advice, the advice unit analyzes the user's past income and expenditure data to select the optimal advice method. For example, optimal advice is provided based on the user's past income and expenditure data. In addition, the accuracy of advice can be improved by analyzing the user's past income and expenditure data. Furthermore, the efficiency of advice can be improved by utilizing the user's past income and expenditure data. In this way, optimal advice can be provided by analyzing past income and expenditure data.

[0061] The advice unit can customize the means of advice based on the user's current living situation when giving advice. The current living situation includes, for example, income status and family composition. The advice unit customizes the means of advice based on the user's current living situation when giving advice. For example, the advice unit provides optimal advice taking into account the user's current living situation. The accuracy of the advice can also be improved based on the user's living situation. Furthermore, the efficiency of the advice can be improved by reflecting the user's living situation. In this way, by customizing the means of advice based on the current living situation, optimal advice can be provided to the user.

[0062] The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. Feedback includes, for example, user ratings and comments. The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. For example, the advice unit improves the method of giving advice based on user feedback. Also, the accuracy of advice can be improved by reflecting user feedback. Furthermore, the efficiency of advice can be improved by utilizing user feedback. In this way, the method of giving advice can be improved by reflecting user feedback.

[0063] The advice unit can select an advice method by taking into consideration the user's geographical location information when providing advice. Geographical location information includes, for example, GPS data and location information services. The advice unit selects an optimal advice method by taking into consideration the user's geographical location information when providing advice. For example, optimal advice is provided based on the user's current location. The accuracy of advice can also be improved by taking into consideration the geographical location information. Furthermore, the efficiency of advice can also be improved based on the user's geographical location information. In this way, optimal advice can be provided by taking into consideration the geographical location information.

[0064] When providing advice, the advice unit can analyze the user's social media activity to suggest a means of providing advice. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. When providing advice, the advice unit can analyze the user's social media activity to suggest a means of providing advice. For example, the advice unit can provide optimal advice based on the user's social media activity. The social media activity can also be analyzed to improve the accuracy of the advice. Furthermore, the user's social media activity can be utilized to improve the efficiency of the advice. In this way, optimal advice can be provided by analyzing social media activity.

[0065] The advice unit can customize the method of giving advice by reflecting the user's past feedback when giving advice. Past feedback includes, for example, the user's ratings and comments. The advice unit customizes the method of giving advice by reflecting the user's past feedback when giving advice. For example, the advice unit customizes the method of giving advice based on the user's past feedback. Also, by reflecting the user's past feedback, the accuracy of the advice can be improved. Furthermore, the efficiency of the advice can be improved by utilizing the user's past feedback. In this way, optimal advice can be provided by reflecting past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reception unit can analyze the user's past submission history and suggest the optimal reception method. For example, it can prioritize and suggest reception methods that the user has frequently used in the past. It can also select the most efficient reception method from the user's past submission history. Furthermore, it can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, it is possible to provide the optimal reception method to the user by analyzing the past submission history.

[0068] When journalizing, the journal entry unit can take into account the attribute information of the person who submitted the receipt or invoice. For example, appropriate journal entries are made based on the attribute information of the person who submitted the receipt or invoice. The accuracy of journal entries can also be improved by taking into account the attribute information of the person who submitted the receipt or invoice. Furthermore, the efficiency of journal entries can be improved by utilizing the attribute information of the person who submitted the receipt or invoice. This allows appropriate journal entries to be made by taking into account the attribute information of the person who submitted the receipt or invoice.

[0069] When receiving receipts or invoices, the reception unit can filter them based on the user's current project or area of ​​interest. For example, receipts and invoices related to a project currently underway by the user are preferentially received. The reception unit can also filter and receive receipts and invoices related to the user's area of ​​interest. Furthermore, it can also receive related receipts and invoices according to the progress of the user's project. In this way, by filtering based on the user's project or area of ​​interest, highly relevant information can be preferentially received.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. For example, a specific analysis algorithm can be applied to meal-related receipts. A different analysis algorithm can also be applied to utility bills. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category.

[0071] The journal entry unit can improve the accuracy of journal entries by taking into account the interrelationships between receipts or invoices when making journal entries. For example, it analyzes the interrelationships between receipts and invoices and makes appropriate journal entries. It can also improve the accuracy of journal entries by taking into account the relevance of receipts and invoices. Furthermore, it can improve the efficiency of journal entries based on the interrelationships between receipts and invoices. In this way, the accuracy of journal entries is improved by taking into account the interrelationships between receipts and invoices.

[0072] When recording, the recording unit can select a recording method by referring to the user's past recording history. For example, the recording unit can suggest an optimal recording method based on the user's past recording history. The recording accuracy can also be improved by referring to the user's past recording history. Furthermore, the recording efficiency can be improved by utilizing the user's past recording history. In this way, the optimal recording method can be provided by referring to the past recording history.

[0073] When giving advice, the advice unit can analyze the user's past income and expenditure data and select the advice method. For example, the advice unit can provide optimal advice based on the user's past income and expenditure data. The advice unit can also analyze the user's past income and expenditure data to improve the accuracy of the advice. Furthermore, the advice unit can utilize the user's past income and expenditure data to improve the efficiency of the advice. In this way, the advice unit can provide optimal advice by analyzing the past income and expenditure data.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The reception unit receives a photo of a receipt or invoice from a user. The photo of the receipt or invoice from the user may be in a format such as, but not limited to, JPEG, PNG, or PDF. The reception unit may receive the photo of the receipt or invoice from the user via, for example, a messaging app. Step 2: The analysis unit analyzes the photo received by the reception unit and reads the contents of the receipt or invoice. The analysis is performed using, for example, OCR technology or an image recognition algorithm, but is not limited to these examples. For example, the analysis unit uses OCR technology to extract information such as the date, amount, and store name from the photo. The analysis unit can also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. Step 3: The journalizing unit automatically performs journalizing based on the information extracted by the analysis unit. The automatic journalizing is performed, for example, based on accounting standards or category classification, but is not limited to these examples. The journalizing unit, for example, performs journalizing into appropriate categories based on the extracted information. The journalizing unit can also appropriately classify income and expenses based on accounting standards. Step 4: The recording unit records the information journalized by the journalizing unit. Recording includes, but is not limited to, storing the information in a database or in a file format. For example, the recording unit converts the journalized information into a tax return format. The recording unit can also store the information in a database and retrieve the data as needed. Step 5: The advice unit provides advice to the user based on the data recorded by the recording unit. The advice may include, but is not limited to, tax-saving strategies and investment suggestions. The advice unit may, for example, work with a tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving strategies and investment advice. The advice unit may also analyze the user's income and expenditure data and suggest optimal asset formation.

[0076] (Example 2) A personal financial planner system according to an embodiment of the present invention allows users to simply attach photos of receipts and invoices via a messaging app, and AI automatically performs accounting and recording. This system can convert all data into a tax return format. It can also optionally integrate with a tax accountant-generated AI to provide appropriate advice. Furthermore, the AI ​​provides advice on optimal asset formation based on the user's income and expenditure data, and by securing the customer's income and expenditure data, it can be utilized in various financial services. This allows users to easily record expenses and smoothly file tax returns. Furthermore, by receiving advice from the tax accountant-generated AI, appropriate financial management can be performed and optimal asset formation can be achieved. Furthermore, by securing the income and expenditure data, it can be utilized in various financial services. For example, services based on the user's income and expenditure data, such as loan applications and insurance reviews, can be provided.

[0077] The personal financial planner system according to the embodiment includes a reception unit, an analysis unit, an accounting unit, a recording unit, and an advice unit. The reception unit receives a photo of a receipt or invoice from a user. The photo of the receipt or invoice from the user may be in any format, such as, but not limited to, JPEG, PNG, or PDF. The reception unit receives the photo of the receipt or invoice from the user via a messaging app, for example. The analysis unit analyzes the attached photo and reads the contents of the receipt or invoice. The analysis may be performed using, for example, but not limited to, OCR technology or an image recognition algorithm. The analysis unit may extract information from the photo, such as the date, amount, and store name, using, for example, OCR technology. The analysis unit may also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. The accounting unit automatically performs accounting based on the information extracted by the analysis unit. The automatic accounting may be performed based on, for example, but not limited to, accounting standards or category classifications. The accounting unit may, for example, assign the extracted information to an appropriate category. The journalizing unit can also appropriately classify income and expenses based on accounting standards. The recording unit records the information journalized by the journalizing unit. Recording can include, but is not limited to, storing the information in a database or in a file format. The recording unit can also convert the journalized information into a tax return format, for example. The recording unit can also store the information in a database and retrieve the data as needed. The advice unit provides advice to the user based on the data recorded by the recording unit. The advice can include, but is not limited to, tax saving strategies and investment suggestions. The advice unit can, for example, work with a tax accountant generation AI to analyze the user's income and expenditure data and provide tax saving strategies and investment advice. The advice unit can also analyze the user's income and expenditure data and suggest optimal asset formation. As a result, the personal financial planner system according to the embodiment can automatically analyze photos of the user's receipts and invoices, and perform journalizing, recording, and advice, enabling efficient financial management.

[0078] The advice unit works in cooperation with the tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving or investment advice. The tax accountant generation AI is realized, for example, using machine learning models and data analysis methods. The advice unit works in cooperation with the tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving or investment advice. For example, the tax accountant generation AI suggests tax deduction methods and investment selection criteria based on the user's income and expenditure data. The tax accountant generation AI can also analyze the user's income and expenditure data and provide optimal tax-saving measures. In this way, by working in cooperation with the tax accountant generation AI, more specialized advice can be provided to the user.

[0079] The reception unit can receive a photo of a receipt or invoice from a user through a messaging app. Examples of messaging apps include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. The reception unit receives a photo of a receipt or invoice from a user through the messaging app. For example, the user simply attaches a photo of the receipt or invoice to a chat on the messaging app. This allows the user to easily send a photo of a receipt or invoice by using the messaging app.

[0080] The analysis unit can analyze the attached photo and read the contents of the receipt or invoice. The analysis is performed using, for example, OCR technology or an image recognition algorithm. The analysis unit analyzes the attached photo and reads the contents of the receipt or invoice. For example, OCR technology is used to extract information such as the date, amount, and store name from the photo. The analysis unit can also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. This makes it possible to automatically read the contents of the receipt or invoice by analyzing the photo.

[0081] The journalizing unit can journalize into categories based on the extracted information. Journalizing is performed, for example, based on accounting standards or category classification. The journalizing unit journalizes into appropriate categories based on the extracted information. For example, it appropriately classifies income and expenses. The journalizing unit can also appropriately classify income and expenses based on accounting standards. This allows for automatic journalizing based on the extracted information, thereby reducing the amount of work required.

[0082] The recording unit can convert the journalized information into a tax return format. Examples of tax return formats include, but are not limited to, formats designated by the National Tax Agency and formats for electronic filing. The recording unit converts the journalized information into a tax return format. For example, the recording unit creates an accurate tax return by calculating the balance between income and expenses and applying necessary deductions. The recording unit can also store information in a database and retrieve the data as needed. This simplifies the tax return process by automatically converting the journalized information into a tax return format.

[0083] The advice unit can analyze the user's income and expenditure data and suggest asset formation. Asset formation suggestions include, for example, selecting investment targets and savings plans. The advice unit analyzes the user's income and expenditure data and suggests optimal asset formation. For example, a user with a stable income may be recommended long-term investments, while a user with high expenses may be suggested with savings methods. The advice unit can also suggest optimal investment targets and savings methods based on the user's income and expenditure data. In this way, optimal asset formation advice can be provided by analyzing the user's income and expenditure data.

[0084] The reception unit can estimate the user's emotions and adjust the timing of receiving photos of receipts and invoices based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. User emotions include, for example, stress, relaxation, and busyness. The reception unit estimates the user's emotions and adjusts the timing of receiving photos of receipts and invoices based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit adjusts the timing to receive photos of receipts and invoices during a time when the user is relaxed. Furthermore, if the user is busy, the reception timing can be adjusted so that the reception is completed in a short time. Furthermore, if the user is relaxed, the reception timing can be adjusted to ensure time to enter detailed information. In this way, the reception timing can be adjusted according to the user's emotions, thereby reducing the burden on the user.

[0085] The reception unit can analyze the user's past submission history and select the reception method. The past submission history includes, for example, the submission date and time and the submission content. The reception unit analyzes the user's past submission history and selects the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past submission history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method can be provided to the user by analyzing the past submission history.

[0086] The reception unit can filter receipts or invoices based on the user's current project or area of ​​interest when receiving them. Current projects or areas of interest include, for example, project management tools and user interest data. The reception unit can filter receipts or invoices based on the user's current project or area of ​​interest when receiving them. For example, receipts or invoices related to projects currently underway by the user can be preferentially received. In addition, receipts and invoices related to the user's area of ​​interest can be filtered and received. Furthermore, related receipts and invoices can be received according to the progress of the user's project. In this way, by filtering based on the user's project or area of ​​interest, highly relevant information can be preferentially received.

[0087] The reception unit can select the reception means according to the user's input method when receiving a receipt or invoice. Input methods include, for example, voice input, text input, and image input. When receiving a receipt or invoice, the reception unit selects the optimal reception means according to the user's input method. For example, if the user prefers voice input, the reception unit can receive receipt or invoice information by voice. Also, if the user prefers text input, the reception unit can also receive receipt or invoice information by text. Furthermore, if the user prefers image input, the reception unit can also receive receipt or invoice information by image. This improves user convenience by selecting the optimal reception means according to the user's input method.

[0088] The reception unit can estimate the user's emotions and determine the priority of receipts and invoices to be received based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. User emotions include, for example, stress, relaxation, and busyness. The reception unit estimates the user's emotions and determines the priority of receipts and invoices to be received based on the estimated user's emotions. For example, if the user is feeling stressed, receipts and invoices of lower importance can be postponed. Also, if the user is relaxed, receipts and invoices of higher importance can be received with higher priority. Furthermore, if the user is in a hurry, receipts and invoices that require quick processing can be received with higher priority. In this way, by determining the priority according to the user's emotions, important information can be processed with higher priority.

[0089] When receiving a receipt or invoice, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. When receiving a receipt or invoice, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, receipts and invoices related to that area are prioritized. Related information can also be prioritized based on the user's current location. Furthermore, if the user is traveling, receipts and invoices related to the user's travel destination can also be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0090] The reception unit can analyze the user's social media activity and receive related information when receiving a receipt or invoice. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. The reception unit analyzes the user's social media activity and receives related information when receiving a receipt or invoice. For example, the reception unit preferentially receives receipts and invoices related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. Furthermore, the reception unit can also receive related information by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant information.

[0091] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a receipt or invoice. Past feedback includes, for example, the user's ratings and comments. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a receipt or invoice. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception method can also be improved by reflecting the user's past feedback. Furthermore, the reception method can also be customized based on the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0092] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. User emotions include, for example, stress, relaxation, and busyness. The analysis unit estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice. Importance includes, for example, the amount of money and the frequency of the transaction. During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the receipt or invoice. For example, detailed analysis is performed on receipts or invoices with high importance. Also, simplified analysis can be performed on receipts or invoices with low importance. Furthermore, the level of detail of the analysis can be adjusted according to the importance of the receipt or invoice. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the receipt or invoice.

[0094] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. Categories include, for example, expenses, sales, and purchases. During analysis, the analysis unit applies different analysis algorithms depending on the category of the receipt or invoice. For example, a specific analysis algorithm can be applied to meal-related receipts. A different analysis algorithm can also be applied to utility bills. Furthermore, the optimal analysis algorithm can be selected and applied for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, for example, past transaction data and feedback for improving the accuracy of the analysis. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. The accuracy of the analysis can also be improved by referring to the user's past analysis results. Furthermore, the efficiency of the analysis can be improved by utilizing the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. User emotions include, for example, stress, relaxation, and busyness. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, an analysis result with visually stimulating effects can be provided. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide the user with the optimal analysis result.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the receipt or invoice. The submission time includes, for example, the submission date and the submission deadline. During analysis, the analysis unit determines the priority of analysis based on the submission time of the receipt or invoice. For example, receipts and invoices submitted earlier are analyzed with priority. Also, receipts and invoices submitted later can be postponed. Furthermore, the priority of analysis can be determined based on the submission time. In this way, by determining the priority of analysis based on the submission time, efficient analysis is possible.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of receipts or invoices. Relevance includes, for example, transaction relevance and category relevance. During analysis, the analysis unit adjusts the order of analysis based on the relevance of receipts or invoices. For example, highly relevant receipts or invoices are analyzed with priority. It is also possible to postpone less relevant receipts or invoices. Furthermore, the order of analysis can be adjusted based on the relevance of receipts or invoices. In this way, adjusting the order of analysis based on relevance enables efficient analysis.

[0099] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expert knowledge, detailed technical terms are used. Also, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0100] The sorting unit can estimate the user's emotions and adjust the sorting criteria based on the estimated user's emotions. The sorting unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. The user's emotions include, for example, stress, relaxation, busyness, etc. The sorting unit estimates the user's emotions and adjusts the sorting criteria based on the estimated user's emotions. For example, if the user is feeling stressed, simple sorting criteria can be applied. Also, if the user is relaxed, detailed sorting criteria can be applied. Furthermore, if the user is in a hurry, criteria that allow sorting to be completed quickly can be applied. In this way, by adjusting the sorting criteria according to the user's emotions, it is possible to provide the optimal sorting for the user.

[0101] The journal entry unit can improve the accuracy of journal entries by taking into account the interrelationships between receipts or invoices when making journal entries. Interrelationships include, for example, transaction relevance and category relevance. The journal entry unit improves the accuracy of journal entries by taking into account the interrelationships between receipts and invoices when making journal entries. For example, it analyzes the interrelationships between receipts and invoices and makes appropriate journal entries. The accuracy of journal entries can also be improved by taking into account the interrelationships between receipts and invoices. Furthermore, the efficiency of journal entries can be improved based on the interrelationships between receipts and invoices. In this way, the accuracy of journal entries is improved by taking into account the interrelationships between receipts and invoices.

[0102] The journal entry unit can make journal entries taking into account the attribute information of the submitter of the receipt or invoice when making journal entries. The attribute information of the submitter includes, for example, age, occupation, region, etc. The journal entry unit makes journal entries taking into account the attribute information of the submitter of the receipt or invoice when making journal entries. For example, appropriate journal entries are made based on the attribute information of the submitter. The accuracy of journal entries can also be improved by taking into account the attribute information of the submitter. Furthermore, the efficiency of journal entries can also be improved by utilizing the attribute information of the submitter. In this way, appropriate journal entries can be made by taking into account the attribute information of the submitter.

[0103] When journalizing, the journal entry unit can weight the journal entries based on the submission frequency of receipts or invoices. Submission frequency includes, for example, monthly submission or annual submission. When journalizing, the journal entry unit weights the journal entries based on the submission frequency of receipts or invoices. For example, a higher weight is assigned to receipts or invoices that are submitted more frequently. Also, a lighter weight can be assigned to receipts or invoices that are submitted less frequently. Furthermore, the weighting of journal entries can be adjusted based on the submission frequency. In this way, by weighting journal entries based on the submission frequency, efficient journal entries can be achieved.

[0104] The journalizing unit can estimate the user's emotions and adjust the order in which journalizing results are displayed based on the estimated user's emotions. The journalizing unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. User emotions include, for example, stress, relaxation, and busyness. The journalizing unit estimates the user's emotions and adjusts the order in which journalizing results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, journalizing results with lower importance can be postponed. Also, if the user is relaxed, detailed journalizing results can be displayed with priority. Furthermore, if the user is in a hurry, journalizing results that require quick confirmation can be displayed with priority. In this way, by adjusting the display order of journalizing results according to the user's emotions, it is possible to provide optimal information to the user.

[0105] The journal entry unit can take into account the geographic distribution of receipts or invoices when making journal entries. Geographic distribution includes, for example, transaction distribution by region or by country. The journal entry unit can take into account the geographic distribution of receipts or invoices when making journal entries. For example, appropriate journal entries are made based on the place where the receipt or invoice was issued. In addition, taking geographic distribution into account can improve the accuracy of journal entries. Furthermore, the efficiency of journal entries can be improved based on the geographic distribution of receipts or invoices. In this way, appropriate journal entries can be made by taking geographic distribution into account.

[0106] The journal entry department can improve the accuracy of journal entries by referring to related literature for receipts or invoices when making journal entries. Related literature includes, for example, accounting standards and industry guidelines. The journal entry department can improve the accuracy of journal entries by referring to related literature for receipts or invoices when making journal entries. For example, appropriate journal entries are made by referring to related literature. The accuracy of journal entries can also be improved based on related literature. Furthermore, the efficiency of journal entries can be improved by utilizing related literature. In this way, the accuracy of journal entries is improved by referring to related literature.

[0107] The journal entry unit can make journal entries taking into account the market value of the receipt or invoice. Market value includes, for example, the transaction price and the appraised value. The journal entry unit makes journal entries taking into account the market value of the receipt or invoice. For example, appropriate journal entries are made for receipts or invoices with high market value. Simplified journal entries can also be made for receipts or invoices with low market value. Furthermore, the accuracy of journal entries can be improved based on market value. In this way, appropriate journal entries can be made by taking market value into account.

[0108] The recording unit can estimate the user's emotion and adjust the recording method based on the estimated user's emotion. The recording unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. The user's emotion includes, for example, stress, relaxation, busyness, etc. The recording unit estimates the user's emotion and adjusts the recording method based on the estimated user's emotion. For example, if the user is feeling stressed, a simple recording method can be provided. Also, if the user is relaxed, a detailed recording method can be provided. Furthermore, if the user is in a hurry, a method that allows for quick completion of recording can be provided. In this way, by adjusting the recording method according to the user's emotion, it is possible to provide the optimal recording method for the user.

[0109] The recording unit can select a recording method by referring to the user's past recording history when recording. The past recording history includes, for example, past transaction data and recording format. The recording unit selects the optimal recording method by referring to the user's past recording history when recording. For example, the recording unit suggests the optimal recording method based on the user's past recording history. The user's past recording history can also be used as a reference to improve recording accuracy. Furthermore, the user's past recording history can be utilized to improve recording efficiency. In this way, the optimal recording method can be provided by referring to the past recording history.

[0110] The recording unit can customize the recording means based on the user's current living situation when recording. The current living situation includes, for example, income status and family composition. The recording unit customizes the recording means based on the user's current living situation when recording. For example, the recording unit provides the optimal recording means taking into account the user's current living situation. The accuracy of recording can also be improved based on the user's living situation. Furthermore, the efficiency of recording can be improved by reflecting the user's living situation. In this way, by customizing the recording means based on the current living situation, the optimal recording method for the user can be provided.

[0111] The recording unit can improve the recording method by reflecting user feedback during recording. Feedback includes, for example, user ratings and comments. The recording unit improves the recording method by reflecting user feedback during recording. For example, the recording method is improved based on user feedback. Also, the accuracy of recording can be improved by reflecting user feedback. Furthermore, the efficiency of recording can be improved by utilizing user feedback. In this way, the recording method can be improved by reflecting user feedback.

[0112] The recording unit can estimate the user's emotions and determine the recording priority based on the estimated user's emotions. The recording unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. The user's emotions include, for example, stress, relaxation, busyness, etc. The recording unit estimates the user's emotions and determines the recording priority based on the estimated user's emotions. For example, if the user is feeling stressed, recordings of less importance can be postponed. Also, if the user is relaxed, recordings of more importance can be prioritized. Furthermore, if the user is in a hurry, information that needs to be recorded quickly can be prioritized. In this way, by determining the recording priority according to the user's emotions, important information can be preferentially recorded.

[0113] The recording unit can select a recording method by taking into account the user's geographical location information when recording. The geographical location information includes, for example, GPS data and location information services. The recording unit selects an optimal recording method by taking into account the user's geographical location information when recording. For example, the recording unit provides an optimal recording method based on the user's current location. Furthermore, the recording accuracy can be improved by taking into account the geographical location information. Furthermore, the recording efficiency can be improved based on the user's geographical location information. In this way, the optimal recording method can be provided by taking into account the geographical location information.

[0114] The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. For example, the recording unit can suggest the optimal recording means based on the user's social media activity. The recording accuracy can also be improved by analyzing the social media activity. Furthermore, the recording efficiency can also be improved by utilizing the user's social media activity. In this way, the optimal recording means can be provided by analyzing the social media activity.

[0115] The recording unit can customize the recording method by reflecting the user's past feedback when recording. Past feedback includes, for example, the user's ratings and comments. The recording unit customizes the recording method by reflecting the user's past feedback when recording. For example, the recording method is customized based on the user's past feedback. Also, by reflecting the user's past feedback, the accuracy of recording can be improved. Furthermore, the efficiency of recording can be improved by utilizing the user's past feedback. In this way, by reflecting the past feedback, the optimal recording method can be provided.

[0116] The advice unit can estimate the user's emotion and adjust the method of advice based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. The user's emotion includes, for example, stress, relaxation, busyness, etc. The advice unit estimates the user's emotion and adjusts the method of advice based on the estimated user's emotion. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-follow advice. Also, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide advice that can be quickly followed. In this way, by adjusting the method of advice according to the user's emotion, it is possible to provide the user with the most suitable advice.

[0117] When providing advice, the advice unit can analyze the user's past income and expenditure data to select an advice method. The past income and expenditure data includes, for example, income, expenditure, and savings. When providing advice, the advice unit analyzes the user's past income and expenditure data to select the optimal advice method. For example, optimal advice is provided based on the user's past income and expenditure data. In addition, the accuracy of advice can be improved by analyzing the user's past income and expenditure data. Furthermore, the efficiency of advice can be improved by utilizing the user's past income and expenditure data. In this way, optimal advice can be provided by analyzing past income and expenditure data.

[0118] The advice unit can customize the means of advice based on the user's current living situation when giving advice. The current living situation includes, for example, income status and family composition. The advice unit customizes the means of advice based on the user's current living situation when giving advice. For example, the advice unit provides optimal advice taking into account the user's current living situation. The accuracy of the advice can also be improved based on the user's living situation. Furthermore, the efficiency of the advice can be improved by reflecting the user's living situation. In this way, by customizing the means of advice based on the current living situation, optimal advice can be provided to the user.

[0119] The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. Feedback includes, for example, user ratings and comments. The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. For example, the advice unit improves the method of giving advice based on user feedback. Also, the accuracy of advice can be improved by reflecting user feedback. Furthermore, the efficiency of advice can be improved by utilizing user feedback. In this way, the method of giving advice can be improved by reflecting user feedback.

[0120] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The advice unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. The user's emotions include, for example, stress, relaxation, busyness, etc. The advice unit estimates the user's emotions and determines the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, less important advice can be postponed. Also, if the user is relaxed, more important advice can be provided preferentially. Furthermore, if the user is in a hurry, advice that needs to be implemented quickly can be provided preferentially. In this way, by determining the priority of advice according to the user's emotions, more important advice can be provided preferentially.

[0121] The advice unit can select an advice method by taking into consideration the user's geographical location information when providing advice. Geographical location information includes, for example, GPS data and location information services. The advice unit selects an optimal advice method by taking into consideration the user's geographical location information when providing advice. For example, optimal advice is provided based on the user's current location. The accuracy of advice can also be improved by taking into consideration the geographical location information. Furthermore, the efficiency of advice can also be improved based on the user's geographical location information. In this way, optimal advice can be provided by taking into consideration the geographical location information.

[0122] When providing advice, the advice unit can analyze the user's social media activity to suggest a means of providing advice. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. When providing advice, the advice unit can analyze the user's social media activity to suggest a means of providing advice. For example, the advice unit can provide optimal advice based on the user's social media activity. The social media activity can also be analyzed to improve the accuracy of the advice. Furthermore, the user's social media activity can be utilized to improve the efficiency of the advice. In this way, optimal advice can be provided by analyzing social media activity.

[0123] The advice unit can customize the method of giving advice by reflecting the user's past feedback when giving advice. Past feedback includes, for example, the user's ratings and comments. The advice unit customizes the method of giving advice by reflecting the user's past feedback when giving advice. For example, the advice unit customizes the method of giving advice based on the user's past feedback. Also, by reflecting the user's past feedback, the accuracy of the advice can be improved. Furthermore, the efficiency of the advice can be improved by utilizing the user's past feedback. In this way, optimal advice can be provided by reflecting past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, sorting unit, recording unit, and advice unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives photos of receipts and invoices from a user using the reception device 38 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the photos using OCR technology or an image recognition algorithm. The sorting unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically sorts the data based on the analyzed information. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and records the sorted information in the database 24. The advice unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the recorded data. Furthermore, the reception unit can estimate the user's emotions using the camera 42 and microphone 38B of the smart device 14 and adjust the timing of receiving the advice. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, sorting unit, recording unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives photos of receipts or invoices from a user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using OCR technology or an image recognition algorithm. The sorting unit is realized by the specific processing unit 290 of the data processing device 12 and automatically sorts the data based on the analyzed information. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the sorted information in the database 24. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the recorded data. Furthermore, the reception unit can estimate the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjust the timing of reception. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, sorting unit, recording unit, and advice unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives photos of receipts and invoices from a user using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using OCR technology or an image recognition algorithm. The sorting unit is realized by the specific processing unit 290 of the data processing device 12 and automatically sorts the data based on the analyzed information. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the sorted information in the database 24. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the recorded data. Furthermore, the reception unit can estimate the user's emotions using the camera 42 and microphone 238 of the headset terminal 314 and adjust the timing of receiving the advice. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, sorting unit, recording unit, and advice unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives photos of receipts and invoices from users using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using OCR technology or an image recognition algorithm. The sorting unit is realized by the specific processing unit 290 of the data processing device 12 and automatically sorts the data based on the analyzed information. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the sorted information in the database 24. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the recorded data. Furthermore, the reception unit can estimate the user's emotions using the camera 42 and microphone 238 of the robot 414 and adjust the timing of reception.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The reception unit can analyze the user's past submission history and suggest the optimal reception method. For example, it can prioritize and suggest reception methods that the user has frequently used in the past. It can also select the most efficient reception method from the user's past submission history. Furthermore, it can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, it is possible to provide the optimal reception method to the user by analyzing the past submission history.

[0126] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide analysis results that are concise. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0127] When journalizing, the journal entry unit can take into account the attribute information of the person who submitted the receipt or invoice. For example, appropriate journal entries are made based on the attribute information of the person who submitted the receipt or invoice. The accuracy of journal entries can also be improved by taking into account the attribute information of the person who submitted the receipt or invoice. Furthermore, the efficiency of journal entries can be improved by utilizing the attribute information of the person who submitted the receipt or invoice. This allows appropriate journal entries to be made by taking into account the attribute information of the person who submitted the receipt or invoice.

[0128] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple recording method can be provided. If the user is relaxed, a detailed recording method can be provided. Furthermore, if the user is in a hurry, a method that allows for quick completion of recording can be provided. In this way, the recording method can be adjusted according to the user's emotions, thereby providing the optimal recording method for the user.

[0129] The advice unit can estimate the user's emotions and adjust the method of advice based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-follow advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, advice that can be quickly followed can be provided. In this way, by adjusting the method of advice according to the user's emotions, it is possible to provide the most suitable advice for the user.

[0130] When receiving receipts or invoices, the reception unit can filter them based on the user's current project or area of ​​interest. For example, receipts and invoices related to a project currently underway by the user are preferentially received. The reception unit can also filter and receive receipts and invoices related to the user's area of ​​interest. Furthermore, it can also receive related receipts and invoices according to the progress of the user's project. In this way, by filtering based on the user's project or area of ​​interest, highly relevant information can be preferentially received.

[0131] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. For example, a specific analysis algorithm can be applied to meal-related receipts. A different analysis algorithm can also be applied to utility bills. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each category.

[0132] The journal entry unit can improve the accuracy of journal entries by taking into account the interrelationships between receipts or invoices when making journal entries. For example, it analyzes the interrelationships between receipts and invoices and makes appropriate journal entries. It can also improve the accuracy of journal entries by taking into account the relevance of receipts and invoices. Furthermore, it can improve the efficiency of journal entries based on the interrelationships between receipts and invoices. In this way, the accuracy of journal entries is improved by taking into account the interrelationships between receipts and invoices.

[0133] When recording, the recording unit can select a recording method by referring to the user's past recording history. For example, the recording unit can suggest an optimal recording method based on the user's past recording history. The recording accuracy can also be improved by referring to the user's past recording history. Furthermore, the recording efficiency can be improved by utilizing the user's past recording history. In this way, the optimal recording method can be provided by referring to the past recording history.

[0134] When giving advice, the advice unit can analyze the user's past income and expenditure data and select the advice method. For example, the advice unit can provide optimal advice based on the user's past income and expenditure data. The advice unit can also analyze the user's past income and expenditure data to improve the accuracy of the advice. Furthermore, the advice unit can utilize the user's past income and expenditure data to improve the efficiency of the advice. In this way, the advice unit can provide optimal advice by analyzing the past income and expenditure data.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The reception unit receives a photo of a receipt or invoice from a user. The photo of the receipt or invoice from the user may be in a format such as, but not limited to, JPEG, PNG, or PDF. The reception unit may receive the photo of the receipt or invoice from the user via, for example, a messaging app. Step 2: The analysis unit analyzes the photo received by the reception unit and reads the contents of the receipt or invoice. The analysis is performed using, for example, OCR technology or an image recognition algorithm, but is not limited to these examples. For example, the analysis unit uses OCR technology to extract information such as the date, amount, and store name from the photo. The analysis unit can also automatically analyze the contents of the receipt or invoice using an image recognition algorithm. Step 3: The journalizing unit automatically performs journalizing based on the information extracted by the analysis unit. The automatic journalizing is performed, for example, based on accounting standards or category classification, but is not limited to these examples. The journalizing unit, for example, performs journalizing into appropriate categories based on the extracted information. The journalizing unit can also appropriately classify income and expenses based on accounting standards. Step 4: The recording unit records the information journalized by the journalizing unit. Recording includes, but is not limited to, storing the information in a database or in a file format. For example, the recording unit converts the journalized information into a tax return format. The recording unit can also store the information in a database and retrieve the data as needed. Step 5: The advice unit provides advice to the user based on the data recorded by the recording unit. The advice may include, but is not limited to, tax-saving strategies and investment suggestions. The advice unit may, for example, work with a tax accountant generation AI to analyze the user's income and expenditure data and provide tax-saving strategies and investment advice. The advice unit may also analyze the user's income and expenditure data and suggest optimal asset formation.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] 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.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] 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.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a photo of a receipt or invoice from a user; an analysis unit that analyzes the photograph received by the reception unit and extracts necessary information; a journalizing unit that automatically performs journalizing based on the information extracted by the analyzing unit; a recording unit that records the information sorted by the sorting unit; an advice unit that provides advice to a user based on the data recorded by the recording unit. A system characterized by:

2. The advice unit In collaboration with tax accountant-generated AI, the system analyzes users' income and expenditure data and provides tax-saving measures or investment advice.

2. The system of claim 1.

3. The reception unit Accept photos of receipts or invoices from users through messaging apps 2. The system of claim 1.

4. The analysis unit Analyze attached photos to read receipts or invoices 2. The system of claim 1.

5. The sorting unit Categorize based on extracted information 2. The system of claim 1.

6. The recording unit Convert the journalized information into tax return format 2. The system of claim 1.

7. The advice unit Analyzes users' income and expenditure data and suggests asset formation 2. The system of claim 1.

8. The reception unit Estimate user emotions and adjust the timing of accepting photos of receipts or invoices based on the estimated user emotions 2. The system of claim 1.

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