system

Abstract

The system according to this embodiment aims to analyze an individual's income and expenditure data and provide appropriate advice based on their financial situation. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, a recommendation unit, and an advice unit. The collection unit collects income and expenditure data. The analysis unit analyzes the data collected by the collection unit. The notification unit notifies the user of the expected surplus or deficit based on the analysis results obtained by the analysis unit. The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. The advice unit makes tax deduction advice based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that personal income and expenditure data were not fully utilized effectively to grasp the financial situation and provide appropriate advice.

[0005] The system according to the embodiment aims to analyze personal income and expenditure data and provide appropriate advice according to the financial situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a recommendation unit, and an advice unit. The collection unit collects income and expenditure data. The analysis unit analyzes the data collected by the collection unit. The notification unit notifies the user of the expected surplus or deficit based on the analysis results obtained by the analysis unit. The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. The advice unit makes tax deduction advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze an individual's income and expenditure data and provide appropriate advice based on their financial situation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a personalized financial advisory system in which a generating AI learns and analyzes income and expenditure data to provide personalized information to digital payroll users. This system collects income and expenditure data, and the generating AI analyzes it to notify users mid-month of their projected surplus or deficit relative to their income. For example, if a user's expenses are higher than their income this month, the generating AI will notify them of a projected deficit and warn them. On the other hand, if there is a projected surplus, the generating AI will praise the user. Furthermore, if the projected surplus exceeds a certain threshold, the generating AI will recommend investment strategies. For example, if a user has surplus funds, the generating AI will suggest how to invest those funds. This allows users to efficiently increase their assets. In addition, once a year, during tax filing season, if there are any tax-deductible expenses, the generating AI will provide advice. For example, if a user has made expenses that qualify for medical expense deductions or charitable contribution deductions, the generating AI will notify them of this information and advise them on how to declare it during tax filing. This system is expected to generate revenue from remittance fees through the expansion of digital payroll and various fees through the expansion of financial transactions. Furthermore, it can support asset building among Japan's working population. This allows the personalized financial advisory system to automatically analyze users' income and expenditure data and provide personalized notifications, recommendations, and advice.

[0029] The personalized financial advisory system according to the embodiment comprises a data collection unit, an analysis unit, a notification unit, a recommendation unit, and an advice unit. The data collection unit collects income and expenditure data. For example, the data collection unit collects the user's income and expenditure data. The data collection unit can also automatically collect the user's income and expenditure data using generative AI. For example, the data collection unit collects the user's bank account and credit card transaction data. The data collection unit can also collect income and expenditure data manually entered by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using generative AI. For example, the analysis unit analyzes income and expenditure trends and evaluates the user's balance of income and expenditure. The analysis unit can also detect abnormal expenditure patterns using an anomaly detection algorithm. The notification unit notifies the user of the expected surplus or deficit based on the analysis results obtained by the analysis unit. The notification unit notifies the user of the expected surplus or deficit using generative AI. For example, the notification unit sends a push notification to the user's smartphone. The notification unit can also send notifications to the user's email address. The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to make investment recommendations to the user. For example, the recommendation unit may suggest to the user the purchase of mutual funds or stocks. The recommendation unit can also suggest savings plans to the user. The advice unit makes tax deduction advice based on the analysis results obtained by the analysis unit. The advice unit uses generative AI to make tax deduction advice to the user. For example, the advice unit may notify the user of expenses that are eligible for medical expense deductions or charitable contribution deductions. The advice unit can also advise the user on the procedures for filing tax returns. As a result, the personalized financial advisory system according to this embodiment can automatically analyze the user's income and expenditure data and provide personalized notifications, recommendations, and advice.

[0030] The data collection unit collects income and expenditure data. For example, it collects users' income and expenditure data. Specifically, it uses APIs to connect with financial institutions to automatically retrieve transaction data from users' bank accounts and credit cards. This eliminates the need for users to manually enter data and allows for the collection of accurate data in real time. Furthermore, the data collection unit can also automatically collect users' income and expenditure data using generative AI. By analyzing the user's transaction history and learning income and expenditure patterns, the generative AI can predict future income and expenditures. For example, if a user receives a fixed salary each month, the generative AI recognizes this pattern and predicts the next payday. Similarly, for items that users regularly spend money on (e.g., rent and utilities), the generative AI learns these patterns and can predict the next expenditure. In addition, the data collection unit can also collect income and expenditure data manually entered by users. Users can enter details of their income and expenditures through smartphone apps or web portals. This allows the data collection unit to comprehensively collect users' financial data and provide a basis for accurate analysis.

[0031] The analysis department analyzes the data collected by the data collection department. The analysis department uses generative AI to analyze the collected data. Specifically, it analyzes income and expenditure trends and evaluates the user's financial balance. Based on past data, the generative AI can learn the user's income and expenditure patterns and predict future financial balance. For example, if a user's income has been increasing over the past few months, the generative AI recognizes this trend and predicts that income is likely to continue increasing. Also, if a user's expenditures are increasing in a specific category (e.g., food or entertainment), the generative AI can detect this pattern and suggest a review of expenditures. Furthermore, the analysis department can also detect abnormal expenditure patterns using anomaly detection algorithms. For example, if a user makes a large expenditure that deviates significantly from their normal spending pattern, the anomaly detection algorithm can detect the anomaly and warn the user. This allows the analysis department to accurately evaluate the user's financial balance and detect abnormal expenditures early. In addition, based on the collected data, the analysis department can evaluate the user's financial behavior trends and risks and support the development of future financial plans.

[0032] The notification unit notifies users of their projected profit or loss based on the analysis results obtained by the analysis unit. The notification unit uses generative AI to notify users of their projected profit or loss. Specifically, it sends push notifications to the user's smartphone. Push notifications are highly effective because they allow users to receive important information instantly without having to open the app. The notification unit can also send notifications to the user's email address. Email notifications are suitable for providing detailed information that users can review later. Furthermore, the notification unit can customize the notification method according to the user's preferences. For example, if a user prefers SMS notifications, the notification unit can notify them of their projected profit or loss via SMS. This allows the notification unit to provide users with important information at the right time and help them take quick action. In addition, the notification unit can personalize the content of notifications. For example, it can suggest specific action plans based on the user's past behavior and current situation. This allows the notification unit to provide users with more specific and practical advice and support their financial management.

[0033] The Recommendation Department makes investment recommendations based on the analysis results obtained by the Analysis Department. The Recommendation Department uses generative AI to recommend investments to users. Specifically, it suggests the purchase of mutual funds and stocks to users. The generative AI can select the optimal investment product considering the user's risk tolerance and investment goals. For example, it suggests bonds and time deposits that can be expected to provide stable returns to users who want to avoid risk, and stocks and mutual funds that are expected to grow to users who want to take on risk and aim for high returns. The Recommendation Department can also suggest savings plans to users. For example, it can suggest a plan to save a fixed amount each month or a savings plan to achieve a specific target amount. Furthermore, the Recommendation Department can provide investment plans that are tailored to the user's life stage and future goals. For example, it suggests investment plans that anticipate long-term growth to young users and plans that emphasize stable returns to users nearing retirement. In this way, the Recommendation Department can provide optimal investment plans that meet the individual needs of users and support them in achieving their financial goals. Furthermore, the recommendation system can continuously improve its recommendations based on user feedback, enabling it to provide more accurate suggestions.

[0034] The Advice Department provides tax deduction advice based on the analysis results obtained by the Analysis Department. The Advice Department uses a generative AI to provide users with tax deduction advice. Specifically, it notifies users of expenses that are eligible for medical expense deductions and charitable contribution deductions. The generative AI can analyze the user's expenditure data and automatically identify items that are eligible for tax deductions. For example, if a user pays medical expenses, the generative AI recognizes that expense as eligible for a medical expense deduction and notifies the user. Similarly, if a user makes a donation, the generative AI recognizes that donation as eligible for a charitable contribution deduction and notifies the user. Furthermore, the Advice Department can also advise users on the procedures for filing their tax returns. For example, it provides specific guidance on how to prepare the necessary documents, the submission deadline, and how to fill out the tax return form. This allows users to file their tax returns smoothly and make the most of their tax deductions. In addition, the Advice Department can propose the optimal tax deduction plan considering the user's past filing history and current situation. This allows the Advice Department to provide users with specific and practical tax deduction advice, thereby reducing their tax burden. Furthermore, the advisory department provides up-to-date information on tax law changes and new deduction schemes, helping users to always receive the best possible tax deductions based on the latest information.

[0035] The data collection unit can analyze the user's past income and expenditure data and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also select the most efficient data collection method from the user's past data. Furthermore, the data collection unit can analyze the user's past income and expenditure patterns and suggest the optimal data collection method. This improves the efficiency of data collection by selecting the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter income and expenditure data based on the user's current living situation and areas of interest. For example, the data collection unit can filter the data to be collected according to the user's current living situation. The data collection unit can also select the data to be collected based on the user's areas of interest. Furthermore, the data collection unit can determine the priority of the data to be collected according to the user's living situation and areas of interest. This enables data collection that is tailored to the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data when collecting income and expenditure data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also determine the priority of data to collect based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their current location. This improves data accuracy by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data when collecting income and expenditure data. For example, the data collection unit can determine the priority of data to collect based on the user's social media activity. The data collection unit can also select data to collect based on the user's social media activity. Furthermore, the data collection unit can analyze the user's social media activity and collect relevant data. This improves the accuracy of the data by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a concise analysis on low-importance data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the income and expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of income and expenditure data during analysis. For example, the analysis unit can apply a specific analysis algorithm to income data. It can also apply a different analysis algorithm to expenditure data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of income and expenditure data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of income and expenditure data into a generating AI and have the generating AI apply the optimal analysis algorithm.

[0041] The notification unit can adjust the level of detail of notifications based on the importance of income and expenditure data when sending notifications. For example, the notification unit can provide detailed notifications for high-importance data, and concise notifications for low-importance data. Furthermore, the notification unit can adjust the level of detail of notifications based on the importance of income and expenditure data. This allows for efficient notifications by adjusting the level of detail of notifications according to the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of income and expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0042] The notification unit can apply different notification algorithms depending on the category of income and expenditure data when sending notifications. For example, the notification unit can apply a specific notification algorithm to income data. It can also apply a different notification algorithm to expenditure data. Furthermore, the notification unit can select the optimal notification algorithm depending on the category of income and expenditure data. This improves the accuracy of notifications by applying the optimal notification algorithm according to the data category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal notification algorithm.

[0043] The recommendation unit can adjust the level of detail of its recommendations based on the importance of income and expenditure data. For example, it can provide detailed recommendations for highly important data, and concise recommendations for less important data. Furthermore, it can adjust the level of detail of its recommendations based on the importance of income and expenditure data. This allows for efficient recommendations by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the importance of income and expenditure data into a generating AI and have the generating AI adjust the level of detail of the recommendations.

[0044] The recommendation unit can apply different recommendation algorithms depending on the categories of income and expenditure data during the recommendation process. For example, the recommendation unit can apply a specific recommendation algorithm to income data. It can also apply a different recommendation algorithm to expenditure data. Furthermore, the recommendation unit can select the optimal recommendation algorithm depending on the categories of income and expenditure data. This improves the accuracy of recommendations by applying the optimal recommendation algorithm according to the data category. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal recommendation algorithm.

[0045] The advice unit can adjust the level of detail of its advice based on the importance of the income and expenditure data. For example, it can provide detailed advice for high-importance data and concise advice for low-importance data. Furthermore, it can adjust the level of detail of its advice based on the importance of the income and expenditure data. This allows for more efficient advice by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of the income and expenditure data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0046] The advice unit can apply different advice algorithms depending on the categories of income and expenditure data when providing advice. For example, the advice unit can apply a specific advice algorithm to income data. It can also apply a different advice algorithm to expenditure data. Furthermore, the advice unit can select the optimal advice algorithm depending on the categories of income and expenditure data. This improves the accuracy of the advice by applying the optimal advice algorithm according to the data category. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal advice algorithm.

[0047] The advice unit can prioritize advice based on the submission timing of income and expense data. For example, it may prioritize advice on data with an upcoming submission date. It can also postpone advice on data with a later submission date. Furthermore, the advice unit can prioritize advice based on the submission timing of income and expense data. This allows for more efficient advice by prioritizing advice based on data submission timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the submission timing of income and expense data into a generating AI and have the generating AI determine the priority of advice.

[0048] The advice unit can adjust the order of advice based on the relevance of income and expenditure data when providing advice. For example, the advice unit can prioritize advice on highly relevant data. It can also postpone advice on less relevant data. Furthermore, the advice unit can adjust the order of advice based on the relevance of income and expenditure data. This allows for more efficient advice by adjusting the order of advice based on the relevance of the data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance of income and expenditure data into a generating AI and have the generating AI adjust the order of advice.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect user health data and analyze it in combination with income and expenditure data. For example, it can collect user exercise levels and sleep duration to assess the impact of health status on income and expenditure. The data collection unit can also collect user dietary data and analyze whether healthy lifestyle habits contribute to financial stability. Furthermore, the data collection unit can collect user stress levels and assess how stress affects income and expenditure. This allows for the identification of the relationship between user health status and financial situation, enabling the provision of more comprehensive advice.

[0051] The notification unit can take the user's geographical location into account and provide notifications based on the economic conditions of that region. For example, if the user is in a specific region, it can provide income and expenditure advice based on the economic conditions of that region. Furthermore, if the user is traveling, the notification unit can suggest ways to manage expenses based on the economic conditions of their destination. In addition, the notification unit can provide information on region-specific benefits and discounts based on the user's geographical location. This enables the provision of personalized notifications based on the user's geographical location.

[0052] The advice section can analyze a user's past behavioral data, predict future behavior, and provide advice. For example, if a user has spent a lot of money during a particular period in the past, it can provide advice on saving money for that period. It can also provide future investment advice based on a user's past investment patterns. Furthermore, it can analyze a user's past behavioral data to predict future income and expenses and provide advice based on those predictions. This allows for the provision of personalized advice based on the user's past behavioral data.

[0053] The data collection unit can analyze users' social media activity and combine it with income and expenditure data. For example, it can identify events and trends that influence income and expenditure from users' social media posts. The data collection unit can also understand consumption trends and interests from users' social media activity and collect data based on that. Furthermore, the data collection unit can analyze users' social media activity and collect advertising and promotional information related to income and expenditure. This enables data collection based on users' social media activity.

[0054] The notification unit can analyze a user's past notification history and select the most suitable notification method. For example, if a user has previously preferred receiving email notifications, it will prioritize email notifications. Similarly, if a user has previously preferred receiving push notifications, it can prioritize push notifications. Furthermore, it can analyze the user's past notification history to select the optimal notification timing. This allows the system to provide the most suitable notification method based on the user's past notification history.

[0055] The advisory department can provide advice based on the user's geographical location, taking into account regional tax and financial systems. For example, if a user is in a specific region, it can provide tax-saving advice based on that region's tax system. Furthermore, if a user is traveling, the advisory department can provide investment advice based on the financial system of their destination. In addition, the advisory department can suggest regional financial products and services based on the user's geographical location. This allows for the provision of personalized advice tailored to the user's geographical location.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects income and expenditure data. For example, it collects transaction data from the user's bank account and credit card. It can also collect income and expenditure data manually entered by the user. Furthermore, it is possible to automatically collect the user's income and expenditure data using generation AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes income and expenditure trends to evaluate the user's financial balance. It can also detect abnormal expenditure patterns using anomaly detection algorithms. It is also possible to analyze the collected data using generative AI. Step 3: The notification unit notifies the user of the projected profit or loss based on the analysis results obtained by the analysis unit. For example, it sends a push notification to the user's smartphone. It can also send a notification to the user's email address. It is also possible to use generative AI to notify the user of the projected profit or loss. Step 4: The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. For example, it may suggest to the user the purchase of mutual funds or stocks. It can also suggest savings plans to the user. It is also possible to make investment recommendations to the user using generative AI. Step 5: The advice unit provides tax deduction advice based on the analysis results obtained by the analysis unit. For example, it notifies the user of expenses that are eligible for medical expense deductions or charitable contribution deductions. It can also advise the user on the procedures for filing tax returns. It is also possible to provide tax deduction advice to the user using generative AI.

[0058] (Example of form 2) An embodiment of the present invention provides a personalized financial advisory system in which a generating AI learns and analyzes income and expenditure data to provide personalized information to digital payroll users. This system collects income and expenditure data, and the generating AI analyzes it to notify users mid-month of their projected surplus or deficit relative to their income. For example, if a user's expenses are higher than their income this month, the generating AI will notify them of a projected deficit and warn them. On the other hand, if there is a projected surplus, the generating AI will praise the user. Furthermore, if the projected surplus exceeds a certain threshold, the generating AI will recommend investment strategies. For example, if a user has surplus funds, the generating AI will suggest how to invest those funds. This allows users to efficiently increase their assets. In addition, once a year, during tax filing season, if there are any tax-deductible expenses, the generating AI will provide advice. For example, if a user has made expenses that qualify for medical expense deductions or charitable contribution deductions, the generating AI will notify them of this information and advise them on how to declare it during tax filing. This system is expected to generate revenue from remittance fees through the expansion of digital payroll and various fees through the expansion of financial transactions. Furthermore, it can support asset building among Japan's working population. This allows the personalized financial advisory system to automatically analyze users' income and expenditure data and provide personalized notifications, recommendations, and advice.

[0059] The personalized financial advisory system according to the embodiment comprises a data collection unit, an analysis unit, a notification unit, a recommendation unit, and an advice unit. The data collection unit collects income and expenditure data. For example, the data collection unit collects the user's income and expenditure data. The data collection unit can also automatically collect the user's income and expenditure data using generative AI. For example, the data collection unit collects the user's bank account and credit card transaction data. The data collection unit can also collect income and expenditure data manually entered by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using generative AI. For example, the analysis unit analyzes income and expenditure trends and evaluates the user's balance of income and expenditure. The analysis unit can also detect abnormal expenditure patterns using an anomaly detection algorithm. The notification unit notifies the user of the expected surplus or deficit based on the analysis results obtained by the analysis unit. The notification unit notifies the user of the expected surplus or deficit using generative AI. For example, the notification unit sends a push notification to the user's smartphone. The notification unit can also send notifications to the user's email address. The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to make investment recommendations to the user. For example, the recommendation unit may suggest to the user the purchase of mutual funds or stocks. The recommendation unit can also suggest savings plans to the user. The advice unit makes tax deduction advice based on the analysis results obtained by the analysis unit. The advice unit uses generative AI to make tax deduction advice to the user. For example, the advice unit may notify the user of expenses that are eligible for medical expense deductions or charitable contribution deductions. The advice unit can also advise the user on the procedures for filing tax returns. As a result, the personalized financial advisory system according to this embodiment can automatically analyze the user's income and expenditure data and provide personalized notifications, recommendations, and advice.

[0060] The data collection unit collects income and expenditure data. For example, it collects users' income and expenditure data. Specifically, it uses APIs to connect with financial institutions to automatically retrieve transaction data from users' bank accounts and credit cards. This eliminates the need for users to manually enter data and allows for the collection of accurate data in real time. Furthermore, the data collection unit can also automatically collect users' income and expenditure data using generative AI. By analyzing the user's transaction history and learning income and expenditure patterns, the generative AI can predict future income and expenditures. For example, if a user receives a fixed salary each month, the generative AI recognizes this pattern and predicts the next payday. Similarly, for items that users regularly spend money on (e.g., rent and utilities), the generative AI learns these patterns and can predict the next expenditure. In addition, the data collection unit can also collect income and expenditure data manually entered by users. Users can enter details of their income and expenditures through smartphone apps or web portals. This allows the data collection unit to comprehensively collect users' financial data and provide a basis for accurate analysis.

[0061] The analysis department analyzes the data collected by the data collection department. The analysis department uses generative AI to analyze the collected data. Specifically, it analyzes income and expenditure trends and evaluates the user's financial balance. Based on past data, the generative AI can learn the user's income and expenditure patterns and predict future financial balance. For example, if a user's income has been increasing over the past few months, the generative AI recognizes this trend and predicts that income is likely to continue increasing. Also, if a user's expenditures are increasing in a specific category (e.g., food or entertainment), the generative AI can detect this pattern and suggest a review of expenditures. Furthermore, the analysis department can also detect abnormal expenditure patterns using anomaly detection algorithms. For example, if a user makes a large expenditure that deviates significantly from their normal spending pattern, the anomaly detection algorithm can detect the anomaly and warn the user. This allows the analysis department to accurately evaluate the user's financial balance and detect abnormal expenditures early. In addition, based on the collected data, the analysis department can evaluate the user's financial behavior trends and risks and support the development of future financial plans.

[0062] The notification unit notifies users of their projected profit or loss based on the analysis results obtained by the analysis unit. The notification unit uses generative AI to notify users of their projected profit or loss. Specifically, it sends push notifications to the user's smartphone. Push notifications are highly effective because they allow users to receive important information instantly without having to open the app. The notification unit can also send notifications to the user's email address. Email notifications are suitable for providing detailed information that users can review later. Furthermore, the notification unit can customize the notification method according to the user's preferences. For example, if a user prefers SMS notifications, the notification unit can notify them of their projected profit or loss via SMS. This allows the notification unit to provide users with important information at the right time and help them take quick action. In addition, the notification unit can personalize the content of notifications. For example, it can suggest specific action plans based on the user's past behavior and current situation. This allows the notification unit to provide users with more specific and practical advice and support their financial management.

[0063] The Recommendation Department makes investment recommendations based on the analysis results obtained by the Analysis Department. The Recommendation Department uses generative AI to recommend investments to users. Specifically, it suggests the purchase of mutual funds and stocks to users. The generative AI can select the optimal investment product considering the user's risk tolerance and investment goals. For example, it suggests bonds and time deposits that can be expected to provide stable returns to users who want to avoid risk, and stocks and mutual funds that are expected to grow to users who want to take on risk and aim for high returns. The Recommendation Department can also suggest savings plans to users. For example, it can suggest a plan to save a fixed amount each month or a savings plan to achieve a specific target amount. Furthermore, the Recommendation Department can provide investment plans that are tailored to the user's life stage and future goals. For example, it suggests investment plans that anticipate long-term growth to young users and plans that emphasize stable returns to users nearing retirement. In this way, the Recommendation Department can provide optimal investment plans that meet the individual needs of users and support them in achieving their financial goals. Furthermore, the recommendation system can continuously improve its recommendations based on user feedback, enabling it to provide more accurate suggestions.

[0064] The Advice Department provides tax deduction advice based on the analysis results obtained by the Analysis Department. The Advice Department uses a generative AI to provide users with tax deduction advice. Specifically, it notifies users of expenses that are eligible for medical expense deductions and charitable contribution deductions. The generative AI can analyze the user's expenditure data and automatically identify items that are eligible for tax deductions. For example, if a user pays medical expenses, the generative AI recognizes that expense as eligible for a medical expense deduction and notifies the user. Similarly, if a user makes a donation, the generative AI recognizes that donation as eligible for a charitable contribution deduction and notifies the user. Furthermore, the Advice Department can also advise users on the procedures for filing their tax returns. For example, it provides specific guidance on how to prepare the necessary documents, the submission deadline, and how to fill out the tax return form. This allows users to file their tax returns smoothly and make the most of their tax deductions. In addition, the Advice Department can propose the optimal tax deduction plan considering the user's past filing history and current situation. This allows the Advice Department to provide users with specific and practical tax deduction advice, thereby reducing their tax burden. Furthermore, the advisory department provides up-to-date information on tax law changes and new deduction schemes, helping users to always receive the best possible tax deductions based on the latest information.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of income and expenditure data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to improve data accuracy. Furthermore, if the user is busy, the data collection unit can adjust the collection timing to reduce the user's burden. This improves the accuracy of data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0066] The data collection unit can analyze the user's past income and expenditure data and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also select the most efficient data collection method from the user's past data. Furthermore, the data collection unit can analyze the user's past income and expenditure patterns and suggest the optimal data collection method. This improves the efficiency of data collection by selecting the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into a generating AI and have the generating AI select the optimal data collection method.

[0067] The data collection unit can filter income and expenditure data based on the user's current living situation and areas of interest. For example, the data collection unit can filter the data to be collected according to the user's current living situation. The data collection unit can also select the data to be collected based on the user's areas of interest. Furthermore, the data collection unit can determine the priority of the data to be collected according to the user's living situation and areas of interest. This enables data collection that is tailored to the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. Conversely, if the user is relaxed, the data collection unit can prioritize collecting more important data. Furthermore, if the user is busy, the data collection unit can adjust the priority of the data to be collected to reduce the user's burden. This improves the efficiency of data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0069] The data collection unit can prioritize the collection of highly relevant data when collecting income and expenditure data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also determine the priority of data to collect based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their current location. This improves data accuracy by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0070] The data collection unit can analyze the user's social media activity and collect relevant data when collecting income and expenditure data. For example, the data collection unit can determine the priority of data to collect based on the user's social media activity. The data collection unit can also select data to collect based on the user's social media activity. Furthermore, the data collection unit can analyze the user's social media activity and collect relevant data. This improves the accuracy of the data by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a concise analysis on low-importance data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the income and expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the category of income and expenditure data during analysis. For example, the analysis unit can apply a specific analysis algorithm to income data. It can also apply a different analysis algorithm to expenditure data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of income and expenditure data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of income and expenditure data into a generating AI and have the generating AI apply the optimal analysis algorithm.

[0074] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the user is relaxed, the notification unit can provide a detailed notification. If the user is stressed, the notification unit can provide a concise notification. Furthermore, if the user is excited, the notification unit can provide a visually stimulating notification. By adjusting the way notifications are presented according to the user's emotions, notifications that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the way notifications are presented.

[0075] The notification unit can adjust the level of detail of notifications based on the importance of income and expenditure data when sending notifications. For example, the notification unit can provide detailed notifications for high-importance data, and concise notifications for low-importance data. Furthermore, the notification unit can adjust the level of detail of notifications based on the importance of income and expenditure data. This allows for efficient notifications by adjusting the level of detail of notifications according to the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of income and expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0076] The notification unit can apply different notification algorithms depending on the category of income and expenditure data when sending notifications. For example, the notification unit can apply a specific notification algorithm to income data. It can also apply a different notification algorithm to expenditure data. Furthermore, the notification unit can select the optimal notification algorithm depending on the category of income and expenditure data. This improves the accuracy of notifications by applying the optimal notification algorithm according to the data category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal notification algorithm.

[0077] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is stressed, the recommendation unit can provide concise recommendations. Furthermore, if the user is excited, the recommendation unit can provide visually stimulating recommendations. By adjusting the way recommendations are presented according to the user's emotions, recommendations that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0078] The recommendation unit can adjust the level of detail of its recommendations based on the importance of income and expenditure data. For example, it can provide detailed recommendations for highly important data, and concise recommendations for less important data. Furthermore, it can adjust the level of detail of its recommendations based on the importance of income and expenditure data. This allows for efficient recommendations by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the importance of income and expenditure data into a generating AI and have the generating AI adjust the level of detail of the recommendations.

[0079] The recommendation unit can apply different recommendation algorithms depending on the categories of income and expenditure data during the recommendation process. For example, the recommendation unit can apply a specific recommendation algorithm to income data. It can also apply a different recommendation algorithm to expenditure data. Furthermore, the recommendation unit can select the optimal recommendation algorithm depending on the categories of income and expenditure data. This improves the accuracy of recommendations by applying the optimal recommendation algorithm according to the data category. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal recommendation algorithm.

[0080] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is stressed, the advice unit can provide concise advice. Furthermore, if the user is excited, the advice unit can provide visually stimulating advice. By adjusting the way advice is presented according to the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents the advice.

[0081] The advice unit can adjust the level of detail of its advice based on the importance of the income and expenditure data. For example, it can provide detailed advice for high-importance data and concise advice for low-importance data. Furthermore, it can adjust the level of detail of its advice based on the importance of the income and expenditure data. This allows for more efficient advice by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of the income and expenditure data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0082] The advice unit can apply different advice algorithms depending on the categories of income and expenditure data when providing advice. For example, the advice unit can apply a specific advice algorithm to income data. It can also apply a different advice algorithm to expenditure data. Furthermore, the advice unit can select the optimal advice algorithm depending on the categories of income and expenditure data. This improves the accuracy of the advice by applying the optimal advice algorithm according to the data category. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the categories of income and expenditure data into a generating AI and have the generating AI execute the application of the optimal advice algorithm.

[0083] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is stressed, the advice unit can also provide concise advice. Furthermore, if the user is excited, the advice unit can provide visually stimulating advice. By adjusting the length of the advice according to the user's emotions, the advice can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0084] The advice unit can prioritize advice based on the submission timing of income and expense data. For example, it may prioritize advice on data with an upcoming submission date. It can also postpone advice on data with a later submission date. Furthermore, the advice unit can prioritize advice based on the submission timing of income and expense data. This allows for more efficient advice by prioritizing advice based on data submission timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the submission timing of income and expense data into a generating AI and have the generating AI determine the priority of advice.

[0085] The advice unit can adjust the order of advice based on the relevance of income and expenditure data when providing advice. For example, the advice unit can prioritize advice on highly relevant data. It can also postpone advice on less relevant data. Furthermore, the advice unit can adjust the order of advice based on the relevance of income and expenditure data. This allows for more efficient advice by adjusting the order of advice based on the relevance of the data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance of income and expenditure data into a generating AI and have the generating AI adjust the order of advice.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can collect user health data and analyze it in combination with income and expenditure data. For example, it can collect user exercise levels and sleep duration to assess the impact of health status on income and expenditure. The data collection unit can also collect user dietary data and analyze whether healthy lifestyle habits contribute to financial stability. Furthermore, the data collection unit can collect user stress levels and assess how stress affects income and expenditure. This allows for the identification of the relationship between user health status and financial situation, enabling the provision of more comprehensive advice.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis results can be presented in a concise summary. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is excited, the analysis results can be presented using visually appealing graphs and charts. This allows the analysis results to be delivered in the most appropriate way according to the user's emotions.

[0089] The notification unit can take the user's geographical location into account and provide notifications based on the economic conditions of that region. For example, if the user is in a specific region, it can provide income and expenditure advice based on the economic conditions of that region. Furthermore, if the user is traveling, the notification unit can suggest ways to manage expenses based on the economic conditions of their destination. In addition, the notification unit can provide information on region-specific benefits and discounts based on the user's geographical location. This enables the provision of personalized notifications based on the user's geographical location.

[0090] The recommendation system can estimate the user's emotions and adjust the recommendations based on those emotions. For example, if a user is stressed, it can suggest a relaxing investment plan. If the user is relaxed, it can suggest a high-risk but high-return investment plan. Furthermore, if the user is excited, it can suggest a short-term investment plan. This allows the system to provide the optimal investment plan tailored to the user's emotions.

[0091] The advice section can analyze a user's past behavioral data, predict future behavior, and provide advice. For example, if a user has spent a lot of money during a particular period in the past, it can provide advice on saving money for that period. It can also provide future investment advice based on a user's past investment patterns. Furthermore, it can analyze a user's past behavioral data to predict future income and expenses and provide advice based on those predictions. This allows for the provision of personalized advice based on the user's past behavioral data.

[0092] The data collection unit can analyze users' social media activity and combine it with income and expenditure data. For example, it can identify events and trends that influence income and expenditure from users' social media posts. The data collection unit can also understand consumption trends and interests from users' social media activity and collect data based on that. Furthermore, the data collection unit can analyze users' social media activity and collect advertising and promotional information related to income and expenditure. This enables data collection based on users' social media activity.

[0093] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those estimates. For example, if the user is stressed, the analysis can be delayed and performed when the user is relaxed. Conversely, if the user is relaxed, the analysis can be sped up to provide results quickly. Furthermore, if the user is busy, the timing of the analysis can be adjusted to reduce the user's burden. This allows for analysis to be performed at the optimal time according to the user's emotions.

[0094] The notification unit can analyze a user's past notification history and select the most suitable notification method. For example, if a user has previously preferred receiving email notifications, it will prioritize email notifications. Similarly, if a user has previously preferred receiving push notifications, it can prioritize push notifications. Furthermore, it can analyze the user's past notification history to select the optimal notification timing. This allows the system to provide the most suitable notification method based on the user's past notification history.

[0095] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if a user is stressed, recommendations can be delayed until the user is relaxed. Conversely, if the user is relaxed, recommendations can be brought forward to provide quick suggestions. Furthermore, if the user is busy, the timing of recommendations can be adjusted to reduce the user's burden. This allows for recommendations to be made at the optimal time according to the user's emotions.

[0096] The advisory department can provide advice based on the user's geographical location, taking into account regional tax and financial systems. For example, if a user is in a specific region, it can provide tax-saving advice based on that region's tax system. Furthermore, if a user is traveling, the advisory department can provide investment advice based on the financial system of their destination. In addition, the advisory department can suggest regional financial products and services based on the user's geographical location. This allows for the provision of personalized advice tailored to the user's geographical location.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects income and expenditure data. For example, it collects transaction data from the user's bank account and credit card. It can also collect income and expenditure data manually entered by the user. Furthermore, it is possible to automatically collect the user's income and expenditure data using generation AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes income and expenditure trends to evaluate the user's financial balance. It can also detect abnormal expenditure patterns using anomaly detection algorithms. It is also possible to analyze the collected data using generative AI. Step 3: The notification unit notifies the user of the projected profit or loss based on the analysis results obtained by the analysis unit. For example, it sends a push notification to the user's smartphone. It can also send a notification to the user's email address. It is also possible to use generative AI to notify the user of the projected profit or loss. Step 4: The recommendation unit makes investment recommendations based on the analysis results obtained by the analysis unit. For example, it may suggest to the user the purchase of mutual funds or stocks. It can also suggest savings plans to the user. It is also possible to make investment recommendations to the user using generative AI. Step 5: The advice unit provides tax deduction advice based on the analysis results obtained by the analysis unit. For example, it notifies the user of expenses that are eligible for medical expense deductions or charitable contribution deductions. It can also advise the user on the procedures for filing tax returns. It is also possible to provide tax deduction advice to the user using generative AI.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, recommendation unit, and advice unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect the user's income and expenditure data using the control unit 46A of the smart device 14, or it can be automatically collected using generated AI by the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze the collected data using the specific processing unit 290 of the data processing unit 12 and evaluate income and expenditure trends. The notification unit can notify the user of the expected surplus or deficit using the control unit 46A of the smart device 14. The recommendation unit can recommend asset management using the specific processing unit 290 of the data processing unit 12 and suggest the user purchase investment trusts or stocks. The advice unit can provide tax deduction advice using the specific processing unit 290 of the data processing unit 12 and notify the user of expenditures eligible for medical expense deductions or charitable contribution deductions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, recommendation unit, and advice unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect the user's income and expenditure data using the control unit 46A of the smart glasses 214, or it can be automatically collected using generated AI by the identification processing unit 290 of the data processing unit 12. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing unit 12 and evaluate income and expenditure trends. The notification unit can notify the user of the expected surplus or deficit using the control unit 46A of the smart glasses 214. The recommendation unit can recommend asset management using the identification processing unit 290 of the data processing unit 12 and suggest the user purchase investment trusts or stocks. The advice unit can provide tax deduction advice using the identification processing unit 290 of the data processing unit 12 and notify the user of expenditures eligible for medical expense deductions or charitable contribution deductions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, recommendation unit, and advice unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect the user's income and expenditure data using the control unit 46A of the headset terminal 314, or it can be automatically collected using generated AI by the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze the collected data using the specific processing unit 290 of the data processing unit 12 and evaluate income and expenditure trends. The notification unit can notify the user of the expected surplus or deficit using the control unit 46A of the headset terminal 314. The recommendation unit can recommend asset management using the specific processing unit 290 of the data processing unit 12 and suggest the user purchase investment trusts or stocks. The advice unit can provide tax deduction advice using the specific processing unit 290 of the data processing unit 12 and notify the user of expenditures eligible for medical expense deductions or charitable contribution deductions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, recommendation unit, and advice unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect the user's income and expenditure data by the control unit 46A of the robot 414, or it can be automatically collected using generated AI by the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze the collected data by the specific processing unit 290 of the data processing unit 12 and evaluate the trends in income and expenditure. The notification unit can notify the user of the expected surplus or deficit by the control unit 46A of the robot 414. The recommendation unit can recommend asset management by the specific processing unit 290 of the data processing unit 12 and suggest the user purchase investment trusts or stocks. The advice unit can provide tax deduction advice by the specific processing unit 290 of the data processing unit 12 and notify the user of expenditures eligible for medical expense deductions or charitable contribution deductions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A collection unit that collects income and expenditure data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit that notifies the forecast of a surplus or deficit based on the analysis results obtained by the aforementioned analysis unit, A recommendation unit that makes recommendations for asset management based on the analysis results obtained by the aforementioned analysis unit, The system includes an advice unit that provides tax deduction advice based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate user sentiment and adjust the timing of income and expenditure data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past income and expenditure data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting income and expenditure data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting income and expenditure data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting income and expenditure data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of your income and expense data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the categories of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of your income and expense data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, we prioritize the advice based on when income and expense data is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects income and expenditure data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit that notifies the forecast of a surplus or deficit based on the analysis results obtained by the aforementioned analysis unit, A recommendation unit that makes recommendations for asset management based on the analysis results obtained by the aforementioned analysis unit, The system includes an advice unit that provides tax deduction advice based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate user sentiment and adjust the timing of income and expenditure data collection based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past income and expenditure data to select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting income and expenditure data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting income and expenditure data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting income and expenditure data, analyze users' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the income and expenditure data. The system according to feature 1.

10. The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of income and expenditure data. The system according to feature 1.

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