system

Abstract

The system according to the embodiment aims to analyze personal financial information and provide appropriate advice. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a user's financial information. The analysis unit analyzes the financial information input by the reception unit and determines the individual's financial situation. The generation unit generates a balance sheet based on the financial situation determined by the analysis unit. The provision unit provides specific advice based on the balance sheet generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been sufficient in efficiently analyzing personal financial information and providing appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze personal financial information and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a user's financial information. The analysis unit analyzes the financial information input by the reception unit and determines the individual's financial situation. The generation unit generates a balance sheet based on the financial situation determined by the analysis unit. The provision unit provides specific advice based on the balance sheet generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze personal financial information and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generative AI to create a robust, personalized balance sheet. The system allows users to input financial information, such as their income, expenses, assets, and liabilities. The generative AI analyzes this information to obtain a detailed understanding of the individual's financial situation and generate an optimal balance sheet. Furthermore, the generative AI provides specific advice and suggestions for improvement based on the user's financial goals. For example, it suggests ways to reduce expenses and effectively manage assets. This allows the user to accurately understand their financial situation and practice sound financial management. For example, the system provides an interface for users to input financial information, including forms for entering items such as income, expenses, assets, and liabilities. Detailed input guides are displayed for each item. Next, the generative AI uses an analytical algorithm to calculate the balance between income and expenses and evaluate the balance between assets and liabilities. Furthermore, the system provides an interface for users to set goals, and the generative AI generates advice based on those goals. For example, it specifically suggests ways to reduce expenses and manage assets. This allows the user to accurately understand their financial situation and practice sound financial management.

[0029] A financial management system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a user's financial information. The user's financial information includes, but is not limited to, income, expenses, assets, and liabilities. The reception unit provides a form for inputting, for example, income, expenses, assets, and liabilities. The reception unit can also store the financial information input by the user in a database. The analysis unit uses a generation AI to analyze the financial information input by the reception unit and grasp the individual's financial situation. The analysis unit, for example, calculates the balance between income and expenses and evaluates the balance between assets and liabilities. The generation AI can analyze the financial information using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an optimal balance sheet based on the financial situation grasped by the analysis unit. The generation unit generates a balance sheet based on, for example, the user's financial goals. The generation AI can generate an optimal balance sheet taking the user's financial goals into consideration. The provision unit provides specific advice based on the balance sheet generated by the generation unit. The providing unit provides advice, for example, specifically showing how to reduce expenses or how to manage assets. This enables the financial management system according to the embodiment to efficiently input and analyze the user's financial information, generate a balance sheet, and provide advice.

[0030] The reception unit may provide a form for inputting items such as income, expenses, assets, and liabilities. Examples of forms include, but are not limited to, text boxes and drop-down lists. For example, the reception unit may provide text boxes for inputting salary, bonuses, side job income, etc. for the income item. For expenses, it may provide drop-down lists for inputting rent, utility bills, food expenses, etc. The reception unit may also provide a form for inputting cash, deposits, real estate, etc. for the asset item. It may also provide a form for inputting mortgage loans, credit card balances, etc. for the liability item. This allows the user to enter detailed financial information.

[0031] The analysis unit can calculate the balance between income and expenses and evaluate the balance between assets and liabilities. For example, the analysis unit calculates the difference between income and expenses and evaluates the balance of income and expenses. For example, if income exceeds expenses, the balance can be evaluated as being positive. The analysis unit can also calculate the ratio of assets to liabilities and evaluate the soundness of assets. For example, if assets exceed liabilities, the assets can be evaluated as being sound. The analysis unit can also analyze the breakdown of income and expenses in detail to evaluate the balance of income and expenses. For example, the breakdown of income can be analyzed as salary, income from a side job, investment income, etc., and the breakdown of expenses can be analyzed as rent, utility costs, food costs, etc., thereby enabling a detailed understanding of the user's financial situation.

[0032] The generation unit can generate a balance sheet based on the user's financial goals. The generation unit generates an optimal balance sheet based on, for example, savings and investment goals set by the user. For example, if the user sets a goal of saving a certain amount every month, the generation unit can adjust the balance between income and expenses based on that goal and generate a balance sheet that ensures the amount of savings. The generation unit can also generate a balance sheet that optimizes asset allocation based on the user's investment goals. For example, if the user desires low-risk investments, the generation unit can generate a balance sheet that emphasizes safe assets. This makes it possible to provide a balance sheet that meets the user's goals.

[0033] The providing unit can provide advice showing how to reduce expenses or how to manage assets. The providing unit, for example, suggests specific ways to reduce the user's expenses. For example, the providing unit can suggest using energy-efficient home appliances as a way to reduce utility bills. The providing unit can also suggest planning food purchases or cooking at home as a way to reduce food expenses. The providing unit can also suggest investment strategies for risk diversification as a way to manage assets. For example, the providing unit can suggest diversifying investments across different asset classes such as stocks, bonds, and real estate. This allows the user to receive specific advice.

[0034] The reception unit can analyze the user's past financial information input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception unit can also automatically complete information previously entered by the user, simplifying the input process. This makes it possible to provide the optimal input method based on the user's past input history.

[0035] The reception unit can filter the financial information based on the user's current living situation and areas of interest when the financial information is input. For example, the reception unit can suggest appropriate input items based on the user's current living situation (e.g., student, working person, retired person, etc.). The reception unit can also preferentially display relevant input items based on the user's areas of interest (e.g., investment, savings, loan repayment, etc.). The reception unit can also hide unnecessary input items based on the user's living situation and areas of interest. This makes it possible to provide appropriate input items according to the user's living situation and areas of interest.

[0036] When inputting financial information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology and convert it into text. This allows financial information to be input using the optimal means depending on the user's input method.

[0037] When inputting financial information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can cause the user to prioritize inputting financial information related to that area (e.g., the local tax system and living expenses). Furthermore, if the user is traveling, the reception unit can also prioritize inputting financial information for the travel destination (e.g., the local currency exchange rate and local prices). Furthermore, if the user is planning to move, the reception unit can also prioritize inputting financial information related to the user's new residence (e.g., moving expenses and rent for the new residence). This allows highly relevant information to be input based on the user's geographical location information.

[0038] When financial information is input, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit automatically inputs expenditure information (e.g., the price and store of a purchased item) shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related financial information (e.g., travel expenses and event participation fees). The reception unit can also input related financial information (e.g., the cost of a meal shared with a friend) based on the activity of the user's friends on social media. This allows related information to be input based on the user's social media activity.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting financial information. For example, if the user has provided feedback on the input method in the past, the reception unit can improve the input method based on that feedback. Furthermore, if the user has reported a problem with input in the past, the reception unit can also suggest an input method to solve the problem. Furthermore, the reception unit can also preferentially provide an input method that the user has used favorably in the past, allowing the input work to be carried out smoothly. In this way, the optimal input method can be provided based on the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the financial information. For example, the analysis unit performs a detailed analysis of important financial information (e.g., income or large expenses). The analysis unit can also perform a simplified analysis of less important financial information (e.g., small expenses). The analysis unit can also adjust the display order of the analysis results based on the importance of the financial information. This makes it possible to provide detailed analysis results according to the importance of the financial information.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of financial information. For example, the analysis unit can apply an algorithm that analyzes increases or decreases in income to information about income. The analysis unit can also apply an algorithm that analyzes spending patterns to information about expenses. The analysis unit can also apply an algorithm that evaluates assets to information about assets. This makes it possible to provide appropriate analysis results depending on the category of financial information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit compares the current analysis result with the user's past analysis results to improve accuracy. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also learn the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of financial information. For example, the analysis unit prioritizes analysis of recently submitted financial information. The analysis unit can also lower the priority of analysis of financial information that was submitted earlier. The analysis unit can also adjust the display order of analysis results based on the time of submission. This allows for the prioritization of financial information based on the time of submission, enabling efficient analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of financial information. For example, the analysis unit can adjust the order of analysis taking into account the relevance between income and expenditure. The analysis unit can also adjust the order of analysis taking into account the relevance between assets and liabilities. The analysis unit can also adjust the display order of analysis results based on the relevance of financial information. This allows analysis to be performed in an appropriate order based on the relevance of financial information.

[0045] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Also, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to provide appropriate analysis results according to the user's level of expertise.

[0046] The generation unit can adjust the level of detail of the balance sheet based on the importance of the financial information when generating it. For example, the generation unit generates a detailed balance sheet for important financial information (e.g., income or large expenses). The generation unit can also generate a simplified balance sheet for less important financial information (e.g., small expenses). The generation unit can also adjust the display order of the balance sheet based on the importance of the financial information. This makes it possible to provide detailed balance sheets according to the importance of the financial information.

[0047] The generation unit can apply different generation algorithms depending on the category of financial information during generation. For example, the generation unit applies a generation algorithm that reflects increases or decreases in income to information about income. The generation unit can also apply a generation algorithm that reflects spending patterns to information about expenses. The generation unit can also apply a generation algorithm that reflects asset valuation to information about assets. This makes it possible to provide an appropriate balance sheet according to the category of financial information.

[0048] The generation unit can improve the accuracy of generation by referring to the user's past balance sheets during generation. For example, the generation unit compares the current balance sheet with the user's past balance sheets to improve accuracy. The generation unit can also extract specific patterns from the user's past balance sheets and reflect them in the current generation. The generation unit can also learn from the user's past balance sheets and optimize the generation algorithm. This can improve the accuracy of generation based on the user's past balance sheets.

[0049] The generation unit can determine the priority of balance sheets based on the time of submission of financial information when generating the balance sheets. For example, the generation unit generates a balance sheet that preferentially reflects the most recently submitted financial information. The generation unit can also generate a balance sheet by lowering the priority of financial information that was submitted earlier. The generation unit can also adjust the display order of balance sheets based on the time of submission. This allows the priority to be determined based on the time of submission of financial information, making it possible to efficiently generate balance sheets.

[0050] The generation unit can adjust the order of the balance sheet based on the relevance of the financial information when generating the balance sheet. For example, the generation unit adjusts the order of the balance sheet taking into account the relevance between income and expenses. The generation unit can also adjust the order of the balance sheet taking into account the relevance between assets and liabilities. The generation unit can also adjust the display order of the balance sheet based on the relevance of the financial information. This makes it possible to provide balance sheets in an appropriate order based on the relevance of the financial information.

[0051] The generation unit can adjust the use of technical terms in the balance sheet according to the user's level of expertise during generation. For example, if the user has expertise in finance, the generation unit can generate a balance sheet that uses a lot of technical terms. Alternatively, if the user does not have expertise in finance, the generation unit can generate a balance sheet that explains things in simple terms. The generation unit can also adjust the level of detail in the balance sheet according to the user's level of expertise. This makes it possible to provide an appropriate balance sheet according to the user's level of expertise.

[0052] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the financial information. For example, the providing unit provides detailed advice for important financial information (e.g., income or large expenses). The providing unit can also provide simplified advice for less important financial information (e.g., small expenses). The providing unit can also adjust the display order of the advice depending on the importance of the financial information. This makes it possible to provide detailed advice according to the importance of the financial information.

[0053] When providing advice, the providing unit can apply different advice algorithms depending on the category of financial information. For example, the providing unit applies an advice algorithm that suggests ways to increase income to information about income. The providing unit can also apply an advice algorithm that suggests ways to reduce expenses to information about expenses. The providing unit can also apply an advice algorithm that suggests ways to manage assets to information about assets. This makes it possible to provide appropriate advice depending on the category of financial information.

[0054] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the providing unit compares the current advice based on the user's past advice results to improve accuracy. The providing unit can also extract specific patterns from the user's past advice results and reflect them in the current advice. The providing unit can also learn the user's past advice results and optimize the advice algorithm. This makes it possible to improve the accuracy of advice based on the user's past advice results.

[0055] When providing advice, the providing unit can determine the priority of advice based on the time of submission of financial information. For example, the providing unit provides advice that prioritizes the most recently submitted financial information. The providing unit can also provide advice by lowering the priority of financial information that has been submitted recently. The providing unit can also adjust the display order of advice based on the time of submission. This allows the priorities to be determined based on the time of submission of financial information, and advice to be provided efficiently.

[0056] When providing advice, the providing unit can adjust the order of advice based on the relevance of financial information. The providing unit adjusts the order of advice, for example, taking into account the relevance between income and expenditure. The providing unit can also adjust the order of advice, taking into account the relevance between assets and liabilities. The providing unit can also adjust the display order of advice based on the relevance of financial information. This makes it possible to provide advice in an appropriate order based on the relevance of financial information.

[0057] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has financial expertise, the providing unit can provide advice that uses a lot of technical terms. Also, if the user does not have financial expertise, the providing unit can provide advice that is explained in simple terms. Also, the providing unit can adjust the level of detail of the advice depending on the user's level of expertise. This makes it possible to provide appropriate advice depending on the user's level of expertise.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When a user inputs financial information, the reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can automatically complete information that the user has previously input, simplifying the input process. This makes it possible to provide the optimal input method based on the user's past input history.

[0060] When generating a balance sheet based on the user's financial goals, the generation unit can improve the accuracy of the generation by referring to the user's past balance sheets. For example, the generation unit can compare the current balance sheet with the user's past balance sheets to improve accuracy. Specific patterns can also be extracted from the user's past balance sheets and reflected in the current generation. Furthermore, the generation unit can learn from the user's past balance sheets and optimize the generation algorithm. This allows the generation accuracy to be improved based on the user's past balance sheets.

[0061] When inputting financial information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting financial information related to that area (e.g., local tax system and cost of living). Also, if the user is traveling, the reception unit can prioritize inputting financial information for the travel destination (e.g., local currency exchange rate and local prices). Furthermore, if the user is planning to move, the reception unit can prioritize inputting financial information related to the new place of residence (e.g., moving expenses and rent for the new residence). This allows highly relevant information to be input based on the user's geographical location information.

[0062] During analysis, the analysis unit can apply different analysis algorithms depending on the category of financial information. For example, an algorithm that analyzes increases or decreases in income can be applied to information about income. Also, an algorithm that analyzes spending patterns can be applied to information about expenses. Furthermore, an algorithm that evaluates assets can be applied to information about assets. This makes it possible to provide appropriate analysis results according to the category of financial information.

[0063] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the current advice can be compared based on the user's past advice results to improve accuracy. Also, specific patterns can be extracted from the user's past advice results and reflected in the current advice. Furthermore, the advice algorithm can be optimized by learning the user's past advice results. This makes it possible to improve the accuracy of advice based on the user's past advice results.

[0064] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. On the other hand, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the level of detail of the analysis results can be adjusted according to the user's level of expertise. This makes it possible to provide appropriate analysis results according to the user's level of expertise.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit inputs the user's financial information. The user's financial information includes income, expenses, assets, liabilities, etc. The reception unit provides a form for inputting these items and can also save the financial information input by the user in a database. Step 2: The analysis unit analyzes the financial information entered by the reception unit and understands the individual's financial situation. The analysis unit calculates the balance between income and expenses and evaluates the balance between assets and liabilities. Using generation AI, the financial information is analyzed using text generation AI, multimodal generation AI, etc. Step 3: The generating unit generates a balance sheet based on the financial situation grasped by the analyzing unit. The generating unit generates an optimal balance sheet taking into account the user's financial goals. Step 4: The delivery department provides specific advice based on the balance sheet generated by the generation department. The delivery department provides specific advice on how to reduce expenses and manage assets.

[0067] (Example 2) A system according to an embodiment of the present invention uses a generative AI to create a robust, personalized balance sheet. The system allows users to input financial information, such as their income, expenses, assets, and liabilities. The generative AI analyzes this information to obtain a detailed understanding of the individual's financial situation and generate an optimal balance sheet. Furthermore, the generative AI provides specific advice and suggestions for improvement based on the user's financial goals. For example, it suggests ways to reduce expenses and effectively manage assets. This allows the user to accurately understand their financial situation and practice sound financial management. For example, the system provides an interface for users to input financial information, including forms for entering items such as income, expenses, assets, and liabilities. Detailed input guides are displayed for each item. Next, the generative AI uses an analytical algorithm to calculate the balance between income and expenses and evaluate the balance between assets and liabilities. Furthermore, the system provides an interface for users to set goals, and the generative AI generates advice based on those goals. For example, it specifically suggests ways to reduce expenses and manage assets. This allows the user to accurately understand their financial situation and practice sound financial management.

[0068] A financial management system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a user's financial information. The user's financial information includes, but is not limited to, income, expenses, assets, and liabilities. The reception unit provides a form for inputting, for example, income, expenses, assets, and liabilities. The reception unit can also store the financial information input by the user in a database. The analysis unit uses a generation AI to analyze the financial information input by the reception unit and grasp the individual's financial situation. The analysis unit, for example, calculates the balance between income and expenses and evaluates the balance between assets and liabilities. The generation AI can analyze the financial information using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an optimal balance sheet based on the financial situation grasped by the analysis unit. The generation unit generates a balance sheet based on, for example, the user's financial goals. The generation AI can generate an optimal balance sheet taking the user's financial goals into consideration. The provision unit provides specific advice based on the balance sheet generated by the generation unit. The providing unit provides advice, for example, specifically showing how to reduce expenses or how to manage assets. This enables the financial management system according to the embodiment to efficiently input and analyze the user's financial information, generate a balance sheet, and provide advice.

[0069] The reception unit may provide a form for inputting items such as income, expenses, assets, and liabilities. Examples of forms include, but are not limited to, text boxes and drop-down lists. For example, the reception unit may provide text boxes for inputting salary, bonuses, side job income, etc. for the income item. For expenses, it may provide drop-down lists for inputting rent, utility bills, food expenses, etc. The reception unit may also provide a form for inputting cash, deposits, real estate, etc. for the asset item. It may also provide a form for inputting mortgage loans, credit card balances, etc. for the liability item. This allows the user to enter detailed financial information.

[0070] The analysis unit can calculate the balance between income and expenses and evaluate the balance between assets and liabilities. For example, the analysis unit calculates the difference between income and expenses and evaluates the balance of income and expenses. For example, if income exceeds expenses, the balance can be evaluated as being positive. The analysis unit can also calculate the ratio of assets to liabilities and evaluate the soundness of assets. For example, if assets exceed liabilities, the assets can be evaluated as being sound. The analysis unit can also analyze the breakdown of income and expenses in detail to evaluate the balance of income and expenses. For example, the breakdown of income can be analyzed as salary, income from a side job, investment income, etc., and the breakdown of expenses can be analyzed as rent, utility costs, food costs, etc., thereby enabling a detailed understanding of the user's financial situation.

[0071] The generation unit can generate a balance sheet based on the user's financial goals. The generation unit generates an optimal balance sheet based on, for example, savings and investment goals set by the user. For example, if the user sets a goal of saving a certain amount every month, the generation unit can adjust the balance between income and expenses based on that goal and generate a balance sheet that ensures the amount of savings. The generation unit can also generate a balance sheet that optimizes asset allocation based on the user's investment goals. For example, if the user desires low-risk investments, the generation unit can generate a balance sheet that emphasizes safe assets. This makes it possible to provide a balance sheet that meets the user's goals.

[0072] The providing unit can provide advice showing how to reduce expenses or how to manage assets. The providing unit, for example, suggests specific ways to reduce the user's expenses. For example, the providing unit can suggest using energy-efficient home appliances as a way to reduce utility bills. The providing unit can also suggest planning food purchases or cooking at home as a way to reduce food expenses. The providing unit can also suggest investment strategies for risk diversification as a way to manage assets. For example, the providing unit can suggest diversifying investments across different asset classes such as stocks, bonds, and real estate. This allows the user to receive specific advice.

[0073] The reception unit can estimate the user's emotions and adjust the timing of financial information input based on the emotion data. For example, if the user is feeling stressed, the reception unit can temporarily suspend input and prompt the user to input again when they are able to relax. Furthermore, if the user is relaxed, the reception unit can prompt the user to input more detailed information and collect more information. Furthermore, if the user is in a hurry, the reception unit can provide a simplified input form to allow the user to complete the input quickly. This allows financial information to be input at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The reception unit can analyze the user's past financial information input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception unit can also automatically complete information previously entered by the user, simplifying the input process. This makes it possible to provide the optimal input method based on the user's past input history.

[0075] The reception unit can filter the financial information based on the user's current living situation and areas of interest when the financial information is input. For example, the reception unit can suggest appropriate input items based on the user's current living situation (e.g., student, working person, retired person, etc.). The reception unit can also preferentially display relevant input items based on the user's areas of interest (e.g., investment, savings, loan repayment, etc.). The reception unit can also hide unnecessary input items based on the user's living situation and areas of interest. This makes it possible to provide appropriate input items according to the user's living situation and areas of interest.

[0076] When inputting financial information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology and convert it into text. This allows financial information to be input using the optimal means depending on the user's input method.

[0077] The reception unit can estimate the user's emotions and determine the priority of financial information to be input based on the emotion data. For example, if the user is feeling stressed, the reception unit can prioritize input of only important items and postpone detailed items. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed items as well. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input only the most important items and complete the input quickly. This allows financial information to be input in a priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When inputting financial information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can cause the user to prioritize inputting financial information related to that area (e.g., the local tax system and living expenses). Furthermore, if the user is traveling, the reception unit can also prioritize inputting financial information for the travel destination (e.g., the local currency exchange rate and local prices). Furthermore, if the user is planning to move, the reception unit can also prioritize inputting financial information related to the user's new residence (e.g., moving expenses and rent for the new residence). This allows highly relevant information to be input based on the user's geographical location information.

[0079] When financial information is input, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit automatically inputs expenditure information (e.g., the price and store of a purchased item) shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related financial information (e.g., travel expenses and event participation fees). The reception unit can also input related financial information (e.g., the cost of a meal shared with a friend) based on the activity of the user's friends on social media. This allows related information to be input based on the user's social media activity.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when inputting financial information. For example, if the user has provided feedback on the input method in the past, the reception unit can improve the input method based on that feedback. Furthermore, if the user has reported a problem with input in the past, the reception unit can also suggest an input method to solve the problem. Furthermore, the reception unit can also preferentially provide an input method that the user has used favorably in the past, allowing the input work to be carried out smoothly. In this way, the optimal input method can be provided based on the user's past feedback.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is expressed based on the emotion data. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results and display more information. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. This makes it possible to provide analysis results in an appropriate expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the financial information. For example, the analysis unit performs a detailed analysis of important financial information (e.g., income or large expenses). The analysis unit can also perform a simplified analysis of less important financial information (e.g., small expenses). The analysis unit can also adjust the display order of the analysis results based on the importance of the financial information. This makes it possible to provide detailed analysis results according to the importance of the financial information.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of financial information. For example, the analysis unit can apply an algorithm that analyzes increases or decreases in income to information about income. The analysis unit can also apply an algorithm that analyzes spending patterns to information about expenses. The analysis unit can also apply an algorithm that evaluates assets to information about assets. This makes it possible to provide appropriate analysis results depending on the category of financial information.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit compares the current analysis result with the user's past analysis results to improve accuracy. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also learn the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotion data. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This allows the analysis result to be provided at an appropriate length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of financial information. For example, the analysis unit prioritizes analysis of recently submitted financial information. The analysis unit can also lower the priority of analysis of financial information that was submitted earlier. The analysis unit can also adjust the display order of analysis results based on the time of submission. This allows for the prioritization of financial information based on the time of submission, enabling efficient analysis.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of financial information. For example, the analysis unit can adjust the order of analysis taking into account the relevance between income and expenditure. The analysis unit can also adjust the order of analysis taking into account the relevance between assets and liabilities. The analysis unit can also adjust the display order of analysis results based on the relevance of financial information. This allows analysis to be performed in an appropriate order based on the relevance of financial information.

[0088] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Also, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to provide appropriate analysis results according to the user's level of expertise.

[0089] The generation unit can estimate the user's emotions and adjust the balance sheet generation method based on the emotion data. For example, if the user is relaxed, the generation unit generates a balance sheet that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a balance sheet that emphasizes the shortest route. If the user is excited, the generation unit can also generate a balance sheet with visually stimulating effects. This makes it possible to generate a balance sheet in an appropriate manner according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The generation unit can adjust the level of detail of the balance sheet based on the importance of the financial information when generating it. For example, the generation unit generates a detailed balance sheet for important financial information (e.g., income or large expenses). The generation unit can also generate a simplified balance sheet for less important financial information (e.g., small expenses). The generation unit can also adjust the display order of the balance sheet based on the importance of the financial information. This makes it possible to provide detailed balance sheets according to the importance of the financial information.

[0091] The generation unit can apply different generation algorithms depending on the category of financial information during generation. For example, the generation unit applies a generation algorithm that reflects increases or decreases in income to information about income. The generation unit can also apply a generation algorithm that reflects spending patterns to information about expenses. The generation unit can also apply a generation algorithm that reflects asset valuation to information about assets. This makes it possible to provide an appropriate balance sheet according to the category of financial information.

[0092] The generation unit can improve the accuracy of generation by referring to the user's past balance sheets during generation. For example, the generation unit compares the current balance sheet with the user's past balance sheets to improve accuracy. The generation unit can also extract specific patterns from the user's past balance sheets and reflect them in the current generation. The generation unit can also learn from the user's past balance sheets and optimize the generation algorithm. This can improve the accuracy of generation based on the user's past balance sheets.

[0093] The generation unit can estimate the user's emotions and adjust the length of the balance sheet based on the emotion data. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point balance sheet. If the user is relaxed, the generation unit can also generate a longer balance sheet with detailed explanations. If the user is excited, the generation unit can also generate a balance sheet with visually stimulating effects. This allows the balance sheet to be provided with an appropriate length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The generation unit can determine the priority of balance sheets based on the time of submission of financial information when generating the balance sheets. For example, the generation unit generates a balance sheet that preferentially reflects the most recently submitted financial information. The generation unit can also generate a balance sheet by lowering the priority of financial information that was submitted earlier. The generation unit can also adjust the display order of balance sheets based on the time of submission. This allows the priority to be determined based on the time of submission of financial information, making it possible to efficiently generate balance sheets.

[0095] The generation unit can adjust the order of the balance sheet based on the relevance of the financial information when generating the balance sheet. For example, the generation unit adjusts the order of the balance sheet taking into account the relevance between income and expenses. The generation unit can also adjust the order of the balance sheet taking into account the relevance between assets and liabilities. The generation unit can also adjust the display order of the balance sheet based on the relevance of the financial information. This makes it possible to provide balance sheets in an appropriate order based on the relevance of the financial information.

[0096] The generation unit can adjust the use of technical terms in the balance sheet according to the user's level of expertise during generation. For example, if the user has expertise in finance, the generation unit can generate a balance sheet that uses a lot of technical terms. Alternatively, if the user does not have expertise in finance, the generation unit can generate a balance sheet that explains things in simple terms. The generation unit can also adjust the level of detail in the balance sheet according to the user's level of expertise. This makes it possible to provide an appropriate balance sheet according to the user's level of expertise.

[0097] The providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the emotion data. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice and display more information. Furthermore, if the user is in a hurry, the providing unit can provide concise advice that focuses on the main points. This makes it possible to provide advice in an appropriate way of expression according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the financial information. For example, the providing unit provides detailed advice for important financial information (e.g., income or large expenses). The providing unit can also provide simplified advice for less important financial information (e.g., small expenses). The providing unit can also adjust the display order of the advice depending on the importance of the financial information. This makes it possible to provide detailed advice according to the importance of the financial information.

[0099] When providing advice, the providing unit can apply different advice algorithms depending on the category of financial information. For example, the providing unit applies an advice algorithm that suggests ways to increase income to information about income. The providing unit can also apply an advice algorithm that suggests ways to reduce expenses to information about expenses. The providing unit can also apply an advice algorithm that suggests ways to manage assets to information about assets. This makes it possible to provide appropriate advice depending on the category of financial information.

[0100] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the providing unit compares the current advice based on the user's past advice results to improve accuracy. The providing unit can also extract specific patterns from the user's past advice results and reflect them in the current advice. The providing unit can also learn the user's past advice results and optimize the advice algorithm. This makes it possible to improve the accuracy of advice based on the user's past advice results.

[0101] The providing unit can estimate the user's emotions and adjust the length of advice based on the emotion data. For example, if the user is in a hurry, the providing unit can provide short, to-the-point advice. If the user is relaxed, the providing unit can also provide longer advice with detailed explanations. If the user is excited, the providing unit can also provide advice with visually stimulating effects. This makes it possible to provide advice of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] When providing advice, the providing unit can determine the priority of advice based on the time of submission of financial information. For example, the providing unit provides advice that prioritizes the most recently submitted financial information. The providing unit can also provide advice by lowering the priority of financial information that has been submitted recently. The providing unit can also adjust the display order of advice based on the time of submission. This allows the priorities to be determined based on the time of submission of financial information, and advice to be provided efficiently.

[0103] When providing advice, the providing unit can adjust the order of advice based on the relevance of financial information. The providing unit adjusts the order of advice, for example, taking into account the relevance between income and expenditure. The providing unit can also adjust the order of advice, taking into account the relevance between assets and liabilities. The providing unit can also adjust the display order of advice based on the relevance of financial information. This makes it possible to provide advice in an appropriate order based on the relevance of financial information.

[0104] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has financial expertise, the providing unit can provide advice that uses a lot of technical terms. Also, if the user does not have financial expertise, the providing unit can provide advice that is explained in simple terms. Also, the providing unit can adjust the level of detail of the advice depending on the user's level of expertise. This makes it possible to provide appropriate advice depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to input financial information such as income, expenses, assets, and liabilities. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input financial information using a generation AI to grasp the individual's financial situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal balance sheet based on the analysis results. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with specific advice based on the generated balance sheet. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input financial information such as income, expenses, assets, and liabilities by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input financial information using a generation AI to grasp the individual's financial situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal balance sheet based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with specific advice based on the generated balance sheet. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to input financial information such as income, expenses, assets, and liabilities by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input financial information using a generation AI to understand the individual's financial situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal balance sheet based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the user with specific advice based on the generated balance sheet. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to input financial information such as income, expenses, assets, and liabilities by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input financial information using a generation AI to grasp the individual's financial situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal balance sheet based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with specific advice based on the generated balance sheet.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When a user inputs financial information, the reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can automatically complete information that the user has previously input, simplifying the input process. This makes it possible to provide the optimal input method based on the user's past input history.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotion data. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results and display more information. Furthermore, if the user is in a hurry, it can provide concise analysis results that focus on the main points. This allows the analysis results to be presented in an appropriate way according to the user's emotions.

[0108] When generating a balance sheet based on the user's financial goals, the generation unit can improve the accuracy of the generation by referring to the user's past balance sheets. For example, the generation unit can compare the current balance sheet with the user's past balance sheets to improve accuracy. Specific patterns can also be extracted from the user's past balance sheets and reflected in the current generation. Furthermore, the generation unit can learn from the user's past balance sheets and optimize the generation algorithm. This allows the generation accuracy to be improved based on the user's past balance sheets.

[0109] The providing unit can estimate the user's emotions and adjust the way advice is expressed based on the emotion data. For example, if the user is nervous, simple, highly visible advice can be provided. If the user is relaxed, detailed advice can be provided and more information can be displayed. Furthermore, if the user is in a hurry, concise advice that focuses on the main points can be provided. This makes it possible to provide advice in an appropriate way according to the user's emotions.

[0110] When inputting financial information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting financial information related to that area (e.g., local tax system and cost of living). Also, if the user is traveling, the reception unit can prioritize inputting financial information for the travel destination (e.g., local currency exchange rate and local prices). Furthermore, if the user is planning to move, the reception unit can prioritize inputting financial information related to the new place of residence (e.g., moving expenses and rent for the new residence). This allows highly relevant information to be input based on the user's geographical location information.

[0111] During analysis, the analysis unit can apply different analysis algorithms depending on the category of financial information. For example, an algorithm that analyzes increases or decreases in income can be applied to information about income. Also, an algorithm that analyzes spending patterns can be applied to information about expenses. Furthermore, an algorithm that evaluates assets can be applied to information about assets. This makes it possible to provide appropriate analysis results according to the category of financial information.

[0112] The generation unit can estimate the user's emotions and adjust the balance sheet generation method based on the emotion data. For example, if the user is relaxed, a balance sheet that progresses at a leisurely pace can be generated. If the user is in a hurry, a balance sheet that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a balance sheet with visually stimulating effects can be generated. In this way, a balance sheet can be generated in an appropriate manner according to the user's emotions.

[0113] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the current advice can be compared based on the user's past advice results to improve accuracy. Also, specific patterns can be extracted from the user's past advice results and reflected in the current advice. Furthermore, the advice algorithm can be optimized by learning the user's past advice results. This makes it possible to improve the accuracy of advice based on the user's past advice results.

[0114] The reception unit can estimate the user's emotions and determine the priority of financial information to be entered based on the emotion data. For example, if the user is feeling stressed, the reception unit can prioritize inputting only important items and postpone detailed items. Also, if the user is relaxed, the reception unit can encourage inputting detailed items as well. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input only the most important items and complete the input quickly. This allows financial information to be entered in a priority order according to the user's emotions.

[0115] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. On the other hand, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the level of detail of the analysis results can be adjusted according to the user's level of expertise. This makes it possible to provide appropriate analysis results according to the user's level of expertise.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit inputs the user's financial information. The user's financial information includes income, expenses, assets, liabilities, etc. The reception unit provides a form for inputting these items and can also save the financial information input by the user in a database. Step 2: The analysis unit analyzes the financial information entered by the reception unit and understands the individual's financial situation. The analysis unit calculates the balance between income and expenses and evaluates the balance between assets and liabilities. Using generation AI, the financial information is analyzed using text generation AI, multimodal generation AI, etc. Step 3: The generating unit generates a balance sheet based on the financial situation grasped by the analyzing unit. The generating unit generates an optimal balance sheet taking into account the user's financial goals. Step 4: The delivery department provides specific advice based on the balance sheet generated by the generation department. The delivery department provides specific advice on how to reduce expenses and manage assets.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting financial information of a user; an analysis unit that analyzes the financial information input by the reception unit and grasps the individual's financial situation; a generation unit that generates a balance sheet based on the financial situation grasped by the analysis unit; a providing unit that provides specific advice based on the balance sheet generated by the generating unit. A system characterized by:

2. The reception unit Provide a form for entering income, expenses, assets, and liabilities 2. The system of claim 1.

3. The analysis unit Calculate the balance of income and expenses and assess the balance of assets and liabilities 2. The system of claim 1.

4. The generation unit Generate a balance sheet based on your financial goals 2. The system of claim 1.

5. The providing unit Providing advice on how to reduce expenses or manage assets 2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the timing of financial information input based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past financial information input history and select the optimal input method 2. The system of claim 1.

8. The reception unit Filtering financial information based on the user's current life situation and interests 2. The system of claim 1.

9. The reception unit When entering financial information, select the optimal input method depending on the user's input method.

2. The system of claim 1.

10. The reception unit Inferring user emotions and prioritizing financial information to be entered based on the estimated user emotions 2. The system of claim 1.

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