System

Abstract

An object of a system according to an embodiment is to enable a user to consult a financial plan without psychological resistance.SOLUTION: A system includes a reception part, an analysis part, a proposal part, and an explanation part. The receiving unit receives input information of a user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a financial plan based on the information analyzed by the analysis unit. The explanation unit explains the plan proposed by the proposal unit in an easy-to-understand manner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that people find it difficult to consult others about financial planning due to psychological resistance.

[0005] The system according to the embodiment aims to enable users to consult about financial planning without any psychological resistance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and an explanation unit. The reception unit receives input information from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a financial plan based on the information analyzed by the analysis unit. The explanation unit provides an easy-to-understand explanation of the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to consult about financial plans without any psychological resistance. [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 financial consultation system according to an embodiment of the present invention accepts and analyzes user input, proposes an optimal financial plan, and provides easy-to-understand explanations. The financial consultation system allows users to consult AI about their financial worries and financial plans, helping to resolve financial concerns that users are reluctant to discuss with others due to psychological resistance. Examples of such consultations include reviewing their financial affairs and future asset management. Users simply input their specific questions and concerns. The financial consultation system then uses AI to analyze the user's input and proposes an optimal financial plan. Examples of such proposals include advice on reviewing spending and asset management. Furthermore, the financial consultation system also provides the value of "explaining complex matters in an easy-to-understand manner," a role traditionally held by financial planners. For example, it includes a function that provides easy-to-understand explanations of technical terms and complex financial products. This allows users to deepen their understanding of their financial situation and asset management. The financial consultation system is available 24 hours a day, allowing users to consult at their convenience. For example, even if a user wants to review their financial affairs at night or on a weekend, the financial consultation system can respond immediately. This allows users to receive advice on reviewing their financial affairs and asset management at their own pace. This allows the household finance consultation system to reduce the user's psychological resistance and provide a concrete action plan. For example, it can quickly and accurately provide advice on reviewing the user's household finances and asset management, reducing the user's burden. In addition, the user can learn specific areas for improvement in their household finances, improving the effectiveness of their learning.

[0029] A household finance consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and an explanation unit. The reception unit accepts user input information. The user input information includes, but is not limited to, household information, asset information, and income information. The reception unit provides, for example, an interface through which the user inputs their household finance-related concerns and financial plans. The reception unit may also have a function that allows users to seek advice anonymously. For example, the reception unit may provide means such as anonymous chat or anonymous email. The analysis unit uses AI to analyze the information accepted by the reception unit. The analysis may be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit may perform a detailed analysis of the user's household finances and asset status and generate basic data for proposing an optimal financial plan. The proposal unit proposes an optimal financial plan based on the information analyzed by the analysis unit. The financial plan may include, for example, an investment plan, a savings plan, an insurance plan, and the like, but is not limited to these examples. For example, the proposal unit may propose advice on reviewing spending and asset management. The explanation unit provides an easy-to-understand explanation of the plan proposed by the proposal unit. The explanation may be provided, for example, by using graphs or charts, explaining technical terms, or other methods, but is not limited to these examples. For example, the explanation unit provides easy-to-understand explanations of technical terms and complex financial products. This allows the household finance consultation system according to the embodiment to accept and analyze information input by a user, propose an optimal financial plan, and provide an easy-to-understand explanation. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit may input the user's input information into AI and output the analysis results from the AI. Some or all of the above-described processing in the proposal unit may be performed, for example, using AI, or may be performed without AI. For example, the proposal unit may input basic data generated by the analysis unit into AI, and output an optimal financial plan from the AI. Some or all of the above-described processing in the explanation unit may be performed, for example, using AI, or may be performed without AI.For example, the explanation unit can input the plan proposed by the proposal unit into the AI ​​and output an easy-to-understand explanation from the AI.

[0030] The reception unit can accept input of the user's household finance concerns or money plan. Household finance concerns include, but are not limited to, spending management, debt repayment, and savings methods. Money plans include, but are not limited to, investment plans, savings plans, and insurance plans. The reception unit, for example, provides an interface through which the user inputs their household finance concerns or money plan. For example, the user can input questions such as, "Please tell me how to reduce my monthly expenses" or "How should I manage my assets for the future?" This allows the user to input their household finance concerns or money plan. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's input information to AI and output the input content from AI.

[0031] The analysis unit can perform a detailed analysis of the user's household financial situation or asset status. Examples of household financial situations include, but are not limited to, income, expenditures, savings, and debts. Examples of asset situations include, but are not limited to, real estate, stocks, deposits, and insurance. The analysis unit performs a detailed analysis of the user's household financial situation and asset status using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can perform a detailed analysis of the user's income and expenditure patterns to propose an optimal household management method. The analysis unit can also perform a detailed analysis of the user's asset status and propose an optimal asset management method. The analysis unit can also predict future income and expenditures based on the user's household financial situation and propose an optimal financial plan. This allows for a detailed analysis of the user's household financial situation and asset status. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's household financial situation and asset status into AI and output the analysis results from AI.

[0032] The suggestion unit can suggest advice on reviewing spending or asset management. Examples of reviewing spending include, but are not limited to, reducing wasteful spending and reviewing fixed costs. Examples of asset management advice include, but are not limited to, selecting investment destinations, risk management, and portfolio construction. For example, the suggestion unit can suggest an optimal household management method by considering the balance between the user's income and expenses. For example, the suggestion unit can analyze the user's expenses in detail and suggest ways to reduce wasteful spending. The suggestion unit can also suggest an optimal asset management method based on the user's asset status. For example, the suggestion unit can suggest ways to diversify the user's assets. The suggestion unit can also analyze the user's household status in detail and suggest an optimal financial plan based on future income and expenditure forecasts. This makes it possible to suggest advice on reviewing spending or asset management. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input basic data generated by the analysis unit into AI and output an optimal financial plan from the AI.

[0033] The explanation unit can provide easy-to-understand explanations of technical terms or complex financial products. Technical terms include, but are not limited to, financial terms, investment terms, and insurance terms. Complex financial products include, but are not limited to, derivatives, options, and futures trading. The explanation unit provides easy-to-understand explanations of technical terms and complex financial products, for example, by using graphs and charts, explaining technical terms, and the like. For example, the explanation unit converts technical terms into simple terms and provides easy-to-understand explanations. The explanation unit can also visually explain the workings of complex financial products. For example, the explanation unit can explain the workings of derivatives using diagrams. The explanation unit can also adjust the level of detail of the explanation depending on the user's level of understanding. For example, the explanation unit can explain technical terms in stages to make them easier for the user to understand. This allows for easy explanations of technical terms and complex financial products. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without AI. For example, the explanation unit can input a plan proposed by the proposal unit into AI, and the AI ​​can output an easy-to-understand explanation.

[0034] The reception unit can accept anonymous consultations. Anonymous consultations include, but are not limited to, anonymous chats and anonymous emails. The reception unit, for example, provides an interface through which a user can anonymously consult. For example, the reception unit provides a chat box in which a user can anonymously input financial worries and money plans. The reception unit can also have a function that allows a user to anonymously send the consultation content by email. This makes it possible to accept anonymous consultations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's anonymous consultation content into AI and output the input content from AI.

[0035] The analysis unit can implement privacy protection measures. Examples of privacy protection measures include, but are not limited to, data encryption, access restrictions, and anonymization techniques. For example, the analysis unit can encrypt the user's data to prevent third parties from accessing it. The analysis unit can also limit the authority to access the user's data. For example, the analysis unit can limit access to the user's data to only those with specific authority. The analysis unit can also anonymize the user's data to prevent individuals from being identified. For example, the analysis unit can delete personal information such as the user's name and address to anonymize the data. This allows measures to be taken to protect privacy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's data into AI and output the encryption and anonymization processing from the AI.

[0036] The proposal unit can present simulation results. Examples of simulation results include, but are not limited to, income / expense simulations and risk simulations. For example, the proposal unit can predict future income / expenses based on the user's financial situation and asset status and present the results. The proposal unit can also perform risk simulations related to the user's asset management and present the results. For example, the proposal unit can predict future risks based on the user's investment plan and present the results. The proposal unit can also present simulation results for analyzing the user's financial situation in detail and proposing an optimal financial plan. This allows specific simulation results to be presented. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input basic data generated by the analysis unit into AI and output simulation results from the AI.

[0037] The reception unit can analyze the user's past consultation history and select the optimal reception method. The past consultation history includes, for example, past consultation content, consultation date and time, consultation result, etc., but is not limited to these examples. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. For example, the reception unit automatically displays the content of consultations that the user has frequently consulted about in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the content of consultations to be used in a specific time period based on the user's past consultation history. This makes it possible to select the optimal reception method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history into AI, and the AI ​​can output the selection of the optimal reception method.

[0038] The reception unit may perform filtering based on the user's current living situation and areas of interest when receiving the request. Examples of the current living situation include, but are not limited to, income, family structure, and housing situation. Examples of areas of interest include, but are not limited to, investments, savings, and insurance. The reception unit may preferentially display related consultation content based on the user's current income and expenditure situation. The reception unit may also suggest related consultation content based on the user's areas of interest. For example, the reception unit may suggest related consultation content based on the user's areas of interest (e.g., investments, savings, etc.). The reception unit may also filter optimal consultation content based on the user's living situation. For example, the reception unit may suggest optimal consultation content based on the user's family structure and living situation. This allows filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's living situation and areas of interest into AI and output the filtering results from the AI.

[0039] The reception unit can select the optimal reception means depending on the user's input method when receiving the request. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of reception means include, but are not limited to, chatbots, telephone operators, and online forms. For example, when a user inputs the consultation content by voice, the reception unit automatically converts the input content into text using voice recognition technology. Furthermore, when a user inputs the consultation content using text, the reception unit can analyze the input content in real time and provide appropriate feedback. Furthermore, when a user inputs the consultation content using an image, the reception unit can understand the content using image analysis technology and provide appropriate advice. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's input method into AI, and the AI ​​can output the selection of the optimal reception means.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the request. Past feedback includes, but is not limited to, the user's ratings, comments, and requests for improvement. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and provide an optimal interface design. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI and output a customized reception method from the AI.

[0041] The reception unit may prioritize receiving highly relevant consultations based on the user's geographical location information. Examples of geographical location information include, but are not limited to, an address, a region, and a country. Examples of highly relevant consultations include, but are not limited to, issues specific to the region and local financial products. The reception unit may prioritize displaying region-specific financial consultation content based on the user's current location. The reception unit may also suggest the nearest financial institutions and services based on the user's geographical location information. The reception unit may also prioritize receiving consultation content related to the region's economic situation based on the user's geographical location information. This allows for prioritized reception of highly relevant consultations based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information into AI and output highly relevant consultation content from the AI.

[0042] The reception unit can analyze the user's social media activity and receive related consultations. Social media activity includes, but is not limited to, post content, number of followers, and number of likes. Related consultations include, but are not limited to, consultations on the same topic or the same problem. The reception unit can suggest related consultation content based on, for example, financial posts shared by the user on social media. The reception unit can also suggest financial products or services of interest based on the user's social media activity. The reception unit can also suggest related consultation content based on the user's social media activity. In this way, related consultations can be received based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data on the user's social media activity into AI and output related consultation content from the AI.

[0043] During the analysis, the analysis unit can apply a new algorithm for a detailed analysis of the user's household finances and assets. Examples of the new algorithm include, but are not limited to, machine learning algorithms and data mining algorithms. For example, the analysis unit can apply a new algorithm for a detailed analysis of the user's income and expenditure patterns and suggest an optimal household finance management method. The analysis unit can also apply a new algorithm for a detailed analysis of the user's assets and suggest an optimal asset management method. The analysis unit can also apply a new algorithm for a future income and expenditure forecast based on the user's household finances and suggest an optimal financial plan. This allows the application of a new algorithm for a detailed analysis of the user's household finances and assets. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's household finances and assets into AI and output analysis results obtained by applying the new algorithm from the AI.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past household data. Past household data includes, for example, past income, expenses, savings, debts, etc., but is not limited to these examples. The analysis unit, for example, analyzes the current household situation in detail based on the user's past income and expense data. The analysis unit can also suggest an optimal asset management method based on the user's past asset management data. The analysis unit can also predict future income and expenditures by referring to the user's past household data. This improves the accuracy of the analysis based on the user's past household data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past household data into AI and output the analysis results from the AI.

[0045] The analysis unit may perform the analysis while taking into account the user's lifestyle and expenditure patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of expenditure patterns include, but are not limited to, monthly expenditures, weekly expenditures, and expenditures in specific categories. The analysis unit may, for example, propose an optimal household management method based on the user's lifestyle. The analysis unit may also perform a detailed analysis of the user's expenditure patterns and propose ways to reduce wasteful expenditures. The analysis unit may also propose an optimal financial plan taking into account the user's lifestyle and expenditure patterns. This allows the analysis to be performed based on the user's lifestyle and expenditure patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data on the user's lifestyle and expenditure patterns into AI and output the analysis results from the AI.

[0046] The analysis unit can perform the analysis while taking into account the geographical distribution of users. Geographical distribution includes, but is not limited to, regions, countries, and cities, for example. The analysis unit can, for example, propose a region-specific household management method based on the user's place of residence. The analysis unit can also propose an optimal asset management method taking into account the user's geographical distribution. The analysis unit can also propose an optimal financial plan based on the economic situation of the user's place of residence. This allows analysis to be performed based on the user's geographical distribution. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's geographical distribution into AI and output the analysis results from AI.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature and market data. Examples of related literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the analysis unit can compare the user's household situation with related literature and propose an optimal household management method. The analysis unit can also compare the user's asset management method with market data and propose an optimal management method. The analysis unit can also compare the user's household data with related literature and market data to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved based on the user's related literature and market data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's related literature and market data into AI and output the analysis results from AI.

[0048] During analysis, the analysis unit can customize the analysis based on the user's occupation and lifestyle. Occupation includes, but is not limited to, for example, job type, industry, and position. Lifestyle includes, but is not limited to, for example, hobbies, lifestyle habits, and values. The analysis unit, for example, proposes an optimal household management method based on the user's occupation. The analysis unit can also propose an optimal asset management method taking into account the user's lifestyle. The analysis unit can also propose an optimal financial plan based on the user's occupation and lifestyle. This allows the analysis to be customized based on the user's occupation and lifestyle. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's occupation and lifestyle into AI and output the analysis results from AI.

[0049] When making a proposal, the proposal unit can propose an optimal financial plan based on the user's household financial situation and asset status. The household financial situation includes, but is not limited to, income, expenses, savings, and debt. The asset status includes, but is not limited to, real estate, stocks, deposits, and insurance. The proposal unit, for example, considers the balance between the user's income and expenses and proposes an optimal household management method. The proposal unit can also propose an optimal asset management method based on the user's asset status. The proposal unit can also analyze the user's household financial situation in detail and propose an optimal financial plan based on future income and expenditure forecasts. This makes it possible to propose an optimal financial plan based on the user's household financial situation and asset status. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the user's household financial situation and asset status into AI, and output an optimal financial plan from the AI.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, proposal content, proposal date and time, and proposal result, but are not limited to these examples. For example, the suggestion unit can suggest an optimal household management method based on the user's past proposal results. The suggestion unit can also suggest an optimal investment method based on the user's past asset management results. The suggestion unit can also refer to the user's past proposal results and propose an optimal financial plan based on future income and expenditure forecasts. This can improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past proposal results into AI and output data from the AI ​​to improve the accuracy of the proposal.

[0051] The suggestion unit may make a suggestion taking into consideration the user's lifestyle and spending patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of spending patterns include, but are not limited to, monthly spending, weekly spending, and spending in specific categories. The suggestion unit may, for example, suggest an optimal household management method based on the user's lifestyle. The suggestion unit may also perform a detailed analysis of the user's spending patterns and suggest ways to reduce wasteful spending. The suggestion unit may also propose an optimal financial plan taking into consideration the user's lifestyle and spending patterns. This allows suggestions to be made based on the user's lifestyle and spending patterns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data on the user's lifestyle and spending patterns into AI and output the suggestion results from the AI.

[0052] The suggestion unit may make a suggestion taking into account the geographical distribution of the user. Geographical distribution includes, but is not limited to, for example, regions, countries, and cities. For example, the suggestion unit may suggest a region-specific household management method based on the user's place of residence. The suggestion unit may also suggest an optimal asset management method taking into account the user's geographical distribution. The suggestion unit may also suggest an optimal financial plan based on the economic situation of the user's place of residence. This allows suggestions to be made based on the user's geographical distribution. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the user's geographical distribution into AI and output the suggestion results from AI.

[0053] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's related literature and market data. Examples of related literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the suggestion unit compares the user's household situation with related literature and proposes an optimal household management method. The suggestion unit can also compare the user's asset management method with market data and propose an optimal management method. The suggestion unit can also compare the user's household data with related literature and market data to improve the accuracy of the proposal. This allows the accuracy of the proposal to be improved based on the user's related literature and market data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's related literature and market data into AI and output the proposal results from AI.

[0054] When making a proposal, the suggestion unit can customize the proposal based on the user's occupation and lifestyle. Examples of occupation include, but are not limited to, job type, industry, and job title. Examples of lifestyle include, but are not limited to, hobbies, lifestyle habits, and values. The suggestion unit can, for example, suggest an optimal household management method based on the user's occupation. The suggestion unit can also suggest an optimal asset management method taking into account the user's lifestyle. The suggestion unit can also suggest an optimal financial plan based on the user's occupation and lifestyle. This allows the proposal to be customized based on the user's occupation and lifestyle. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's occupation and lifestyle into AI and output the proposal results from AI.

[0055] The explanation unit can apply a new algorithm to provide easy-to-understand explanations of technical terms and complex financial products during explanation. Examples of the new algorithm include, but are not limited to, machine learning algorithms and data mining algorithms. The explanation unit can, for example, convert technical terms into simpler terms and apply an algorithm to provide an easy-to-understand explanation. The explanation unit can also apply an algorithm to visually explain the mechanisms of complex financial products. The explanation unit can also apply an algorithm to adjust the level of detail of the explanation according to the user's level of understanding. This allows the application of a new algorithm to provide easy-to-understand explanations of technical terms and complex financial products. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input data on technical terms and complex financial products into AI, and output easy-to-understand explanations from the AI.

[0056] The explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results when providing an explanation. Past explanation results include, but are not limited to, the explanation content, the explanation date and time, and the explanation result. For example, the explanation unit can suggest an optimal explanation method based on the user's past explanation results. The explanation unit can also refer to the user's past explanation results and provide an explanation based on the user's level of understanding. The explanation unit can also analyze the user's past explanation results and provide an optimal explanation method. This can improve the accuracy of the explanation based on the user's past explanation results. Some or all of the above-described processing in the explanation unit can be performed using, for example, AI, or can be performed without using AI. For example, the explanation unit can input the user's past explanation results into AI and output data from the AI ​​to improve the accuracy of the explanation.

[0057] The explanation unit may provide an explanation taking into account the user's lifestyle and spending patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of spending patterns include, but are not limited to, monthly spending, weekly spending, and spending in specific categories. The explanation unit may propose an optimal explanation method based on the user's lifestyle. The explanation unit may also perform a detailed analysis of the user's spending patterns and explain ways to reduce wasteful spending. The explanation unit may also explain an optimal financial plan taking into account the user's lifestyle and spending patterns. This allows for explanation based on the user's lifestyle and spending patterns. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may input data on the user's lifestyle and spending patterns into AI and output the explanation results from the AI.

[0058] The explanation unit may provide an explanation taking into account the geographical distribution of users. Geographical distributions include, but are not limited to, regions, countries, and cities. For example, the explanation unit may explain a region-specific household management method based on the user's place of residence. The explanation unit may also explain an optimal asset management method taking into account the user's geographical distribution. The explanation unit may also explain an optimal financial plan based on the economic situation of the user's place of residence. This allows for explanations based on the user's geographical distribution. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may input data on the user's geographical distribution into AI and output the explanation results from the AI.

[0059] The explanation unit can improve the accuracy of the explanation by referring to relevant literature and market data for the user during the explanation. Examples of relevant literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the explanation unit compares the user's household situation with relevant literature and explains the optimal household management method. The explanation unit can also compare the user's asset management method with market data and explain the optimal management method. The explanation unit can also compare the user's household data with relevant literature and market data to improve the accuracy of the explanation. This allows the accuracy of the explanation to be improved based on the user's relevant literature and market data. Some or all of the above-mentioned processing in the explanation unit can be performed using, for example, AI, or can be performed without AI. For example, the explanation unit can input the user's relevant literature and market data into AI and output the explanation results from AI.

[0060] The explanation unit can customize the explanation based on the user's occupation and lifestyle when providing the explanation. Occupation includes, but is not limited to, for example, job type, industry, and job title. Lifestyle includes, but is not limited to, for example, hobbies, lifestyle habits, and values. The explanation unit can, for example, explain the optimal household management method based on the user's occupation. The explanation unit can also explain the optimal asset management method taking into account the user's lifestyle. The explanation unit can also explain the optimal financial plan based on the user's occupation and lifestyle. This allows the explanation to be customized based on the user's occupation and lifestyle. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input data on the user's occupation and lifestyle into AI and output the explanation results from AI.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When accepting input information from a user, the reception unit can refer to the user's past behavioral history and provide an optimal input interface. For example, it can preferentially display input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also have a function to automatically complete content previously entered by the user. For example, it can complete current input content based on household information previously entered by the user. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past behavioral history. This makes it possible to provide an optimal input interface based on the user's past behavioral history.

[0063] When analyzing the user's household finances and asset status in detail, the analysis unit can take into account the user's lifestyle and spending patterns. For example, the analysis unit can suggest an optimal household management method based on the user's wake-up time and bedtime. The analysis unit can also analyze the user's spending patterns in detail and suggest ways to reduce wasteful spending. For example, the analysis unit can analyze the user's monthly and weekly spending and provide specific advice for reducing wasteful spending. The analysis unit can also consider the user's lifestyle and spending patterns and suggest an optimal financial plan. This allows the analysis to be performed based on the user's lifestyle and spending patterns.

[0064] When accepting information input by a user, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, the reception unit can preferentially display related consultation contents based on the user's current income and expenditure situation. The reception unit can also suggest related consultation contents based on the user's areas of interest. For example, the reception unit can suggest related consultation contents based on the user's areas of interest (e.g., investment, saving, etc.). The reception unit can also filter optimal consultation contents based on the user's living situation. For example, the reception unit can suggest optimal consultation contents based on the user's family structure and living situation. This makes it possible to perform filtering based on the user's current living situation and areas of interest.

[0065] When analyzing the user's household finances and asset status in detail, the analysis unit can customize the analysis based on the user's occupation and lifestyle. For example, the analysis unit can propose an optimal household finance management method based on the user's occupation. The analysis unit can also propose an optimal asset management method taking the user's lifestyle into consideration. For example, the analysis unit can propose optimal investment destinations based on the user's hobbies and lifestyle. The analysis unit can also propose an optimal financial plan based on the user's occupation and lifestyle. This makes it possible to customize the analysis based on the user's occupation and lifestyle.

[0066] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit can suggest an optimal household management method based on the user's past suggestion results. The suggestion unit can also suggest an optimal investment method based on the user's past asset management results. For example, the suggestion unit can suggest optimal investment destinations based on the user's past investment results. The suggestion unit can also refer to the user's past suggestion results and suggest an optimal financial plan based on future income and expenditure forecasts. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0067] The explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results. For example, the explanation unit can suggest an optimal explanation method based on the user's past explanation results. The explanation unit can also refer to the user's past explanation results and provide an explanation based on the user's level of understanding. For example, the explanation unit can adjust the explanation of technical terms or complex financial products based on the user's past explanation results. The explanation unit can also analyze the user's past explanation results and provide an optimal explanation method. This makes it possible to improve the accuracy of the explanation based on the user's past explanation results.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit receives information input by the user. The information input by the user includes household information, asset information, income information, etc. The reception unit provides an interface for the user to input their household worries and money plans, and also has a function for anonymous consultation. For example, it provides methods such as anonymous chat and anonymous email. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit conducts a detailed analysis of the user's household and asset situation and generates basic data for proposing the optimal financial plan. Step 3: The proposal unit proposes an optimal financial plan based on the information analyzed by the analysis unit. The financial plan may include investment plans, savings plans, insurance plans, etc. The proposal unit also provides advice on reviewing spending and asset management. Step 4: The explanation section explains the plan proposed by the proposal section in an easy-to-understand manner. The explanation is done by using graphs and charts, explaining technical terms, etc. The explanation section explains technical terms and complex financial products in an easy-to-understand manner.

[0070] (Example 2) A financial consultation system according to an embodiment of the present invention accepts and analyzes user input, proposes an optimal financial plan, and provides easy-to-understand explanations. The financial consultation system allows users to consult AI about their financial worries and financial plans, helping to resolve financial concerns that users are reluctant to discuss with others due to psychological resistance. Examples of such consultations include reviewing their financial affairs and future asset management. Users simply input their specific questions and concerns. The financial consultation system then uses AI to analyze the user's input and proposes an optimal financial plan. Examples of such proposals include advice on reviewing spending and asset management. Furthermore, the financial consultation system also provides the value of "explaining complex matters in an easy-to-understand manner," a role traditionally held by financial planners. For example, it includes a function that provides easy-to-understand explanations of technical terms and complex financial products. This allows users to deepen their understanding of their financial situation and asset management. The financial consultation system is available 24 hours a day, allowing users to consult at their convenience. For example, even if a user wants to review their financial affairs at night or on a weekend, the financial consultation system can respond immediately. This allows users to receive advice on reviewing their financial affairs and asset management at their own pace. This allows the household finance consultation system to reduce the user's psychological resistance and provide a concrete action plan. For example, it can quickly and accurately provide advice on reviewing the user's household finances and asset management, reducing the user's burden. In addition, the user can learn specific areas for improvement in their household finances, improving the effectiveness of their learning.

[0071] A household finance consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and an explanation unit. The reception unit accepts user input information. The user input information includes, but is not limited to, household information, asset information, and income information. The reception unit provides, for example, an interface through which the user inputs their household finance-related concerns and financial plans. The reception unit may also have a function that allows users to seek advice anonymously. For example, the reception unit may provide means such as anonymous chat or anonymous email. The analysis unit uses AI to analyze the information accepted by the reception unit. The analysis may be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit may perform a detailed analysis of the user's household finances and asset status and generate basic data for proposing an optimal financial plan. The proposal unit proposes an optimal financial plan based on the information analyzed by the analysis unit. The financial plan may include, for example, an investment plan, a savings plan, an insurance plan, and the like, but is not limited to these examples. For example, the proposal unit may propose advice on reviewing spending and asset management. The explanation unit provides an easy-to-understand explanation of the plan proposed by the proposal unit. The explanation may be provided, for example, by using graphs or charts, explaining technical terms, or other methods, but is not limited to these examples. For example, the explanation unit provides easy-to-understand explanations of technical terms and complex financial products. This allows the household finance consultation system according to the embodiment to accept and analyze information input by a user, propose an optimal financial plan, and provide an easy-to-understand explanation. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit may input the user's input information into AI and output the analysis results from the AI. Some or all of the above-described processing in the proposal unit may be performed, for example, using AI, or may be performed without AI. For example, the proposal unit may input basic data generated by the analysis unit into AI, and output an optimal financial plan from the AI. Some or all of the above-described processing in the explanation unit may be performed, for example, using AI, or may be performed without AI.For example, the explanation unit can input the plan proposed by the proposal unit into the AI ​​and output an easy-to-understand explanation from the AI.

[0072] The reception unit can accept input of the user's household finance concerns or money plan. Household finance concerns include, but are not limited to, spending management, debt repayment, and savings methods. Money plans include, but are not limited to, investment plans, savings plans, and insurance plans. The reception unit, for example, provides an interface through which the user inputs their household finance concerns or money plan. For example, the user can input questions such as, "Please tell me how to reduce my monthly expenses" or "How should I manage my assets for the future?" This allows the user to input their household finance concerns or money plan. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's input information to AI and output the input content from AI.

[0073] The analysis unit can perform a detailed analysis of the user's household financial situation or asset status. Examples of household financial situations include, but are not limited to, income, expenditures, savings, and debts. Examples of asset situations include, but are not limited to, real estate, stocks, deposits, and insurance. The analysis unit performs a detailed analysis of the user's household financial situation and asset status using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can perform a detailed analysis of the user's income and expenditure patterns to propose an optimal household management method. The analysis unit can also perform a detailed analysis of the user's asset status and propose an optimal asset management method. The analysis unit can also predict future income and expenditures based on the user's household financial situation and propose an optimal financial plan. This allows for a detailed analysis of the user's household financial situation and asset status. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's household financial situation and asset status into AI and output the analysis results from AI.

[0074] The suggestion unit can suggest advice on reviewing spending or asset management. Examples of reviewing spending include, but are not limited to, reducing wasteful spending and reviewing fixed costs. Examples of asset management advice include, but are not limited to, selecting investment destinations, risk management, and portfolio construction. For example, the suggestion unit can suggest an optimal household management method by considering the balance between the user's income and expenses. For example, the suggestion unit can analyze the user's expenses in detail and suggest ways to reduce wasteful spending. The suggestion unit can also suggest an optimal asset management method based on the user's asset status. For example, the suggestion unit can suggest ways to diversify the user's assets. The suggestion unit can also analyze the user's household status in detail and suggest an optimal financial plan based on future income and expenditure forecasts. This makes it possible to suggest advice on reviewing spending or asset management. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input basic data generated by the analysis unit into AI and output an optimal financial plan from the AI.

[0075] The explanation unit can provide easy-to-understand explanations of technical terms or complex financial products. Technical terms include, but are not limited to, financial terms, investment terms, and insurance terms. Complex financial products include, but are not limited to, derivatives, options, and futures trading. The explanation unit provides easy-to-understand explanations of technical terms and complex financial products, for example, by using graphs and charts, explaining technical terms, and the like. For example, the explanation unit converts technical terms into simple terms and provides easy-to-understand explanations. The explanation unit can also visually explain the workings of complex financial products. For example, the explanation unit can explain the workings of derivatives using diagrams. The explanation unit can also adjust the level of detail of the explanation depending on the user's level of understanding. For example, the explanation unit can explain technical terms in stages to make them easier for the user to understand. This allows for easy explanations of technical terms and complex financial products. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without AI. For example, the explanation unit can input a plan proposed by the proposal unit into AI, and the AI ​​can output an easy-to-understand explanation.

[0076] The reception unit can accept anonymous consultations. Anonymous consultations include, but are not limited to, anonymous chats and anonymous emails. The reception unit, for example, provides an interface through which a user can anonymously consult. For example, the reception unit provides a chat box in which a user can anonymously input financial worries and money plans. The reception unit can also have a function that allows a user to anonymously send the consultation content by email. This makes it possible to accept anonymous consultations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's anonymous consultation content into AI and output the input content from AI.

[0077] The analysis unit can implement privacy protection measures. Examples of privacy protection measures include, but are not limited to, data encryption, access restrictions, and anonymization techniques. For example, the analysis unit can encrypt the user's data to prevent third parties from accessing it. The analysis unit can also limit the authority to access the user's data. For example, the analysis unit can limit access to the user's data to only those with specific authority. The analysis unit can also anonymize the user's data to prevent individuals from being identified. For example, the analysis unit can delete personal information such as the user's name and address to anonymize the data. This allows measures to be taken to protect privacy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's data into AI and output the encryption and anonymization processing from the AI.

[0078] The proposal unit can present simulation results. Examples of simulation results include, but are not limited to, income / expense simulations and risk simulations. For example, the proposal unit can predict future income / expenses based on the user's financial situation and asset status and present the results. The proposal unit can also perform risk simulations related to the user's asset management and present the results. For example, the proposal unit can predict future risks based on the user's investment plan and present the results. The proposal unit can also present simulation results for analyzing the user's financial situation in detail and proposing an optimal financial plan. This allows specific simulation results to be presented. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input basic data generated by the analysis unit into AI and output simulation results from the AI.

[0079] The reception unit can estimate the user's emotion and adjust the input reception method based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the input reception method to be adjusted based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to quickly input financial concerns and financial plans. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's emotion data into the AI, and the AI ​​may output an adjustment to the input reception method.

[0080] The reception unit can analyze the user's past consultation history and select the optimal reception method. The past consultation history includes, for example, past consultation content, consultation date and time, consultation result, etc., but is not limited to these examples. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. For example, the reception unit automatically displays the content of consultations that the user has frequently consulted about in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the content of consultations to be used in a specific time period based on the user's past consultation history. This makes it possible to select the optimal reception method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history into AI, and the AI ​​can output the selection of the optimal reception method.

[0081] The reception unit may perform filtering based on the user's current living situation and areas of interest when receiving the request. Examples of the current living situation include, but are not limited to, income, family structure, and housing situation. Examples of areas of interest include, but are not limited to, investments, savings, and insurance. The reception unit may preferentially display related consultation content based on the user's current income and expenditure situation. The reception unit may also suggest related consultation content based on the user's areas of interest. For example, the reception unit may suggest related consultation content based on the user's areas of interest (e.g., investments, savings, etc.). The reception unit may also filter optimal consultation content based on the user's living situation. For example, the reception unit may suggest optimal consultation content based on the user's family structure and living situation. This allows filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's living situation and areas of interest into AI and output the filtering results from the AI.

[0082] The reception unit can select the optimal reception means depending on the user's input method when receiving the request. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of reception means include, but are not limited to, chatbots, telephone operators, and online forms. For example, when a user inputs the consultation content by voice, the reception unit automatically converts the input content into text using voice recognition technology. Furthermore, when a user inputs the consultation content using text, the reception unit can analyze the input content in real time and provide appropriate feedback. Furthermore, when a user inputs the consultation content using an image, the reception unit can understand the content using image analysis technology and provide appropriate advice. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's input method into AI, and the AI ​​can output the selection of the optimal reception means.

[0083] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the design of the input interface to be adjusted based on the estimated user's emotion. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, an AI. For example, the reception unit may input user emotion data into the AI, and the AI ​​may output adjustments to the design of the input interface.

[0084] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the request. Past feedback includes, but is not limited to, the user's ratings, comments, and requests for improvement. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and provide an optimal interface design. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI and output a customized reception method from the AI.

[0085] The reception unit may prioritize receiving highly relevant consultations based on the user's geographical location information. Examples of geographical location information include, but are not limited to, an address, a region, and a country. Examples of highly relevant consultations include, but are not limited to, issues specific to the region and local financial products. The reception unit may prioritize displaying region-specific financial consultation content based on the user's current location. The reception unit may also suggest the nearest financial institutions and services based on the user's geographical location information. The reception unit may also prioritize receiving consultation content related to the region's economic situation based on the user's geographical location information. This allows for prioritized reception of highly relevant consultations based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information into AI and output highly relevant consultation content from the AI.

[0086] The reception unit can analyze the user's social media activity and receive related consultations. Social media activity includes, but is not limited to, post content, number of followers, and number of likes. Related consultations include, but are not limited to, consultations on the same topic or the same problem. The reception unit can suggest related consultation content based on, for example, financial posts shared by the user on social media. The reception unit can also suggest financial products or services of interest based on the user's social media activity. The reception unit can also suggest related consultation content based on the user's social media activity. In this way, related consultations can be received based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input data on the user's social media activity into AI and output related consultation content from the AI.

[0087] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the analysis method to be adjusted based on the estimated user's emotion. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. If the user is feeling stressed, the analysis unit can provide a visually easy-to-understand analysis result. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI ​​and output adjustments to the analysis method from the AI.

[0088] During the analysis, the analysis unit can apply a new algorithm for a detailed analysis of the user's household finances and assets. Examples of the new algorithm include, but are not limited to, machine learning algorithms and data mining algorithms. For example, the analysis unit can apply a new algorithm for a detailed analysis of the user's income and expenditure patterns and suggest an optimal household finance management method. The analysis unit can also apply a new algorithm for a detailed analysis of the user's assets and suggest an optimal asset management method. The analysis unit can also apply a new algorithm for a future income and expenditure forecast based on the user's household finances and suggest an optimal financial plan. This allows the application of a new algorithm for a detailed analysis of the user's household finances and assets. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's household finances and assets into AI and output analysis results obtained by applying the new algorithm from the AI.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past household data. Past household data includes, for example, past income, expenses, savings, debts, etc., but is not limited to these examples. The analysis unit, for example, analyzes the current household situation in detail based on the user's past income and expense data. The analysis unit can also suggest an optimal asset management method based on the user's past asset management data. The analysis unit can also predict future income and expenditures by referring to the user's past household data. This improves the accuracy of the analysis based on the user's past household data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past household data into AI and output the analysis results from the AI.

[0090] The analysis unit may perform the analysis while taking into account the user's lifestyle and expenditure patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of expenditure patterns include, but are not limited to, monthly expenditures, weekly expenditures, and expenditures in specific categories. The analysis unit may, for example, propose an optimal household management method based on the user's lifestyle. The analysis unit may also perform a detailed analysis of the user's expenditure patterns and propose ways to reduce wasteful expenditures. The analysis unit may also propose an optimal financial plan taking into account the user's lifestyle and expenditure patterns. This allows the analysis to be performed based on the user's lifestyle and expenditure patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data on the user's lifestyle and expenditure patterns into AI and output the analysis results from the AI.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the display method of the analysis results to be adjusted based on the estimated user's emotion. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI ​​and output adjustments to the display method of the analysis results from the AI.

[0092] The analysis unit can perform the analysis while taking into account the geographical distribution of users. Geographical distribution includes, but is not limited to, regions, countries, and cities, for example. The analysis unit can, for example, propose a region-specific household management method based on the user's place of residence. The analysis unit can also propose an optimal asset management method taking into account the user's geographical distribution. The analysis unit can also propose an optimal financial plan based on the economic situation of the user's place of residence. This allows analysis to be performed based on the user's geographical distribution. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's geographical distribution into AI and output the analysis results from AI.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature and market data. Examples of related literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the analysis unit can compare the user's household situation with related literature and propose an optimal household management method. The analysis unit can also compare the user's asset management method with market data and propose an optimal management method. The analysis unit can also compare the user's household data with related literature and market data to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved based on the user's related literature and market data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's related literature and market data into AI and output the analysis results from AI.

[0094] During analysis, the analysis unit can customize the analysis based on the user's occupation and lifestyle. Occupation includes, but is not limited to, for example, job type, industry, and position. Lifestyle includes, but is not limited to, for example, hobbies, lifestyle habits, and values. The analysis unit, for example, proposes an optimal household management method based on the user's occupation. The analysis unit can also propose an optimal asset management method taking into account the user's lifestyle. The analysis unit can also propose an optimal financial plan based on the user's occupation and lifestyle. This allows the analysis to be customized based on the user's occupation and lifestyle. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's occupation and lifestyle into AI and output the analysis results from AI.

[0095] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. If the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit may input user emotion data into the AI ​​and output an adjustment of the proposed expression method from the AI.

[0096] When making a proposal, the proposal unit can propose an optimal financial plan based on the user's household financial situation and asset status. The household financial situation includes, but is not limited to, income, expenses, savings, and debt. The asset status includes, but is not limited to, real estate, stocks, deposits, and insurance. The proposal unit, for example, considers the balance between the user's income and expenses and proposes an optimal household management method. The proposal unit can also propose an optimal asset management method based on the user's asset status. The proposal unit can also analyze the user's household financial situation in detail and propose an optimal financial plan based on future income and expenditure forecasts. This makes it possible to propose an optimal financial plan based on the user's household financial situation and asset status. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the user's household financial situation and asset status into AI, and output an optimal financial plan from the AI.

[0097] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, proposal content, proposal date and time, and proposal result, but are not limited to these examples. For example, the suggestion unit can suggest an optimal household management method based on the user's past proposal results. The suggestion unit can also suggest an optimal investment method based on the user's past asset management results. The suggestion unit can also refer to the user's past proposal results and propose an optimal financial plan based on future income and expenditure forecasts. This can improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past proposal results into AI and output data from the AI ​​to improve the accuracy of the proposal.

[0098] The suggestion unit may make a suggestion taking into consideration the user's lifestyle and spending patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of spending patterns include, but are not limited to, monthly spending, weekly spending, and spending in specific categories. The suggestion unit may, for example, suggest an optimal household management method based on the user's lifestyle. The suggestion unit may also perform a detailed analysis of the user's spending patterns and suggest ways to reduce wasteful spending. The suggestion unit may also propose an optimal financial plan taking into consideration the user's lifestyle and spending patterns. This allows suggestions to be made based on the user's lifestyle and spending patterns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data on the user's lifestyle and spending patterns into AI and output the suggestion results from the AI.

[0099] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the length of the suggestion to be adjusted based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can make a suggestion with visually stimulating effects. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit may input user emotion data into the AI ​​and output an adjustment to the length of the suggestion from the AI.

[0100] The suggestion unit may make a suggestion taking into account the geographical distribution of the user. Geographical distribution includes, but is not limited to, for example, regions, countries, and cities. For example, the suggestion unit may suggest a region-specific household management method based on the user's place of residence. The suggestion unit may also suggest an optimal asset management method taking into account the user's geographical distribution. The suggestion unit may also suggest an optimal financial plan based on the economic situation of the user's place of residence. This allows suggestions to be made based on the user's geographical distribution. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the user's geographical distribution into AI and output the suggestion results from AI.

[0101] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's related literature and market data. Examples of related literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the suggestion unit compares the user's household situation with related literature and proposes an optimal household management method. The suggestion unit can also compare the user's asset management method with market data and propose an optimal management method. The suggestion unit can also compare the user's household data with related literature and market data to improve the accuracy of the proposal. This allows the accuracy of the proposal to be improved based on the user's related literature and market data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's related literature and market data into AI and output the proposal results from AI.

[0102] When making a proposal, the suggestion unit can customize the proposal based on the user's occupation and lifestyle. Examples of occupation include, but are not limited to, job type, industry, and job title. Examples of lifestyle include, but are not limited to, hobbies, lifestyle habits, and values. The suggestion unit can, for example, suggest an optimal household management method based on the user's occupation. The suggestion unit can also suggest an optimal asset management method taking into account the user's lifestyle. The suggestion unit can also suggest an optimal financial plan based on the user's occupation and lifestyle. This allows the proposal to be customized based on the user's occupation and lifestyle. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's occupation and lifestyle into AI and output the proposal results from AI.

[0103] The explanation unit can estimate the user's emotion and adjust the explanation method based on the estimated user's emotion. For example, the explanation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the explanation unit calculates an emotion score based on changes in facial expression. The explanation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the explanation unit analyzes the tone and speed of the voice and calculates an emotion score. The explanation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the explanation unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the explanation method to be adjusted based on the estimated user's emotion. For example, if the user is nervous, the explanation unit can provide a simple, highly visible explanation method. If the user is relaxed, the explanation unit can provide an explanation method that includes detailed information. If the user is in a hurry, the explanation unit can provide an explanation method that focuses on the main points. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the explanation unit may be performed using, or without, an AI. For example, the explanation unit may input user emotion data into the AI ​​and output adjustments to the explanation method from the AI.

[0104] The explanation unit can apply a new algorithm to provide easy-to-understand explanations of technical terms and complex financial products during explanation. Examples of the new algorithm include, but are not limited to, machine learning algorithms and data mining algorithms. The explanation unit can, for example, convert technical terms into simpler terms and apply an algorithm to provide an easy-to-understand explanation. The explanation unit can also apply an algorithm to visually explain the mechanisms of complex financial products. The explanation unit can also apply an algorithm to adjust the level of detail of the explanation according to the user's level of understanding. This allows the application of a new algorithm to provide easy-to-understand explanations of technical terms and complex financial products. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input data on technical terms and complex financial products into AI, and output easy-to-understand explanations from the AI.

[0105] The explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results when providing an explanation. Past explanation results include, but are not limited to, the explanation content, the explanation date and time, and the explanation result. For example, the explanation unit can suggest an optimal explanation method based on the user's past explanation results. The explanation unit can also refer to the user's past explanation results and provide an explanation based on the user's level of understanding. The explanation unit can also analyze the user's past explanation results and provide an optimal explanation method. This can improve the accuracy of the explanation based on the user's past explanation results. Some or all of the above-described processing in the explanation unit can be performed using, for example, AI, or can be performed without using AI. For example, the explanation unit can input the user's past explanation results into AI and output data from the AI ​​to improve the accuracy of the explanation.

[0106] The explanation unit may provide an explanation taking into account the user's lifestyle and spending patterns. Examples of lifestyle include, but are not limited to, wake-up time, bedtime, and meal times. Examples of spending patterns include, but are not limited to, monthly spending, weekly spending, and spending in specific categories. The explanation unit may propose an optimal explanation method based on the user's lifestyle. The explanation unit may also perform a detailed analysis of the user's spending patterns and explain ways to reduce wasteful spending. The explanation unit may also explain an optimal financial plan taking into account the user's lifestyle and spending patterns. This allows for explanation based on the user's lifestyle and spending patterns. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may input data on the user's lifestyle and spending patterns into AI and output the explanation results from the AI.

[0107] The explanation unit can estimate the user's emotion and adjust the length of the explanation based on the estimated user's emotion. For example, the explanation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the explanation unit calculates an emotion score based on changes in facial expression. The explanation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the explanation unit analyzes the tone and speed of the voice and calculates an emotion score. The explanation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the explanation unit calculates an emotion score based on heart rate fluctuations. This allows the user's emotion to be estimated and the length of the explanation to be adjusted based on the estimated user's emotion. For example, if the user is in a hurry, the explanation unit can provide a short, to-the-point explanation. If the user is relaxed, the explanation unit can provide a longer explanation including detailed explanations. If the user is excited, the explanation unit can provide an explanation with visually stimulating effects. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the explanation unit may be performed using, or without, an AI. For example, the explanation unit may input user emotion data into the AI ​​and output an adjustment to the length of the explanation from the AI.

[0108] The explanation unit may provide an explanation taking into account the geographical distribution of users. Geographical distributions include, but are not limited to, regions, countries, and cities. For example, the explanation unit may explain a region-specific household management method based on the user's place of residence. The explanation unit may also explain an optimal asset management method taking into account the user's geographical distribution. The explanation unit may also explain an optimal financial plan based on the economic situation of the user's place of residence. This allows for explanations based on the user's geographical distribution. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may input data on the user's geographical distribution into AI and output the explanation results from the AI.

[0109] The explanation unit can improve the accuracy of the explanation by referring to relevant literature and market data for the user during the explanation. Examples of relevant literature include, but are not limited to, academic papers, industry reports, and books. Examples of market data include, but are not limited to, stock price data, economic indicators, and consumer survey data. For example, the explanation unit compares the user's household situation with relevant literature and explains the optimal household management method. The explanation unit can also compare the user's asset management method with market data and explain the optimal management method. The explanation unit can also compare the user's household data with relevant literature and market data to improve the accuracy of the explanation. This allows the accuracy of the explanation to be improved based on the user's relevant literature and market data. Some or all of the above-mentioned processing in the explanation unit can be performed using, for example, AI, or can be performed without AI. For example, the explanation unit can input the user's relevant literature and market data into AI and output the explanation results from AI.

[0110] The explanation unit can customize the explanation based on the user's occupation and lifestyle when providing the explanation. Occupation includes, but is not limited to, for example, job type, industry, and job title. Lifestyle includes, but is not limited to, for example, hobbies, lifestyle habits, and values. The explanation unit can, for example, explain the optimal household management method based on the user's occupation. The explanation unit can also explain the optimal asset management method taking into account the user's lifestyle. The explanation unit can also explain the optimal financial plan based on the user's occupation and lifestyle. This allows the explanation to be customized based on the user's occupation and lifestyle. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input data on the user's occupation and lifestyle into AI and output the explanation results from AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and explanation 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 can detect the user's facial expression and voice using the camera 42 and microphone 38B of the smart device 14 and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the user. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal financial plan. The explanation unit is realized by the control unit 46A of the smart device 14 and explains the proposed plan in an easy-to-understand manner. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and explanation 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 can detect the user's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the user. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal financial plan. The explanation unit is realized by the control unit 46A of the smart glasses 214 and explains the proposed plan in an easy-to-understand manner. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and explanation 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 can detect the user's facial expression and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and estimate the emotion using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the user. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal financial plan. The explanation unit is realized by the control unit 46A of the headset-type terminal 314 and explains the proposed plan in an easy-to-understand manner. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and explanation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can detect the user's facial expression and voice using the camera 42 and microphone 238 of the robot 414 and estimate the user's emotions using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the user. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal financial plan. The explanation unit is realized by the control unit 46A of the robot 414 and explains the proposed plan in an easy-to-understand manner.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] When accepting input information from a user, the reception unit can refer to the user's past behavioral history and provide an optimal input interface. For example, it can preferentially display input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also have a function to automatically complete content previously entered by the user. For example, it can complete current input content based on household information previously entered by the user. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past behavioral history. This makes it possible to provide an optimal input interface based on the user's past behavioral history.

[0113] When analyzing the user's household finances and asset status in detail, the analysis unit can take into account the user's lifestyle and spending patterns. For example, the analysis unit can suggest an optimal household management method based on the user's wake-up time and bedtime. The analysis unit can also analyze the user's spending patterns in detail and suggest ways to reduce wasteful spending. For example, the analysis unit can analyze the user's monthly and weekly spending and provide specific advice for reducing wasteful spending. The analysis unit can also consider the user's lifestyle and spending patterns and suggest an optimal financial plan. This allows the analysis to be performed based on the user's lifestyle and spending patterns.

[0114] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. On the other hand, if the user is relaxed, the suggestion unit can also provide a suggestion method including detailed information. For example, if the user is relaxed, the suggestion unit can suggest a detailed investment plan or savings plan. On the other hand, if the user is in a hurry, the suggestion unit can also provide a suggestion method that gets to the point. For example, if the user is in a hurry, the suggestion unit can make short and to the point suggestions. In this way, the way suggestions are expressed can be adjusted based on the user's emotions.

[0115] The explanation unit can estimate the user's emotions and adjust the explanation method based on the estimated user's emotions. For example, if the user is nervous, the explanation unit can provide a simple and highly visible explanation method. Also, if the user is relaxed, the explanation unit can provide an explanation method including detailed information. For example, if the user is relaxed, the explanation unit can explain a detailed investment plan or savings plan. Also, if the user is in a hurry, the explanation unit can provide an explanation method that focuses on the main points. For example, if the user is in a hurry, the explanation unit can provide a short and to-the-point explanation. In this way, the explanation method can be adjusted based on the user's emotions.

[0116] When accepting information input by a user, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, the reception unit can preferentially display related consultation contents based on the user's current income and expenditure situation. The reception unit can also suggest related consultation contents based on the user's areas of interest. For example, the reception unit can suggest related consultation contents based on the user's areas of interest (e.g., investment, saving, etc.). The reception unit can also filter optimal consultation contents based on the user's living situation. For example, the reception unit can suggest optimal consultation contents based on the user's family structure and living situation. This makes it possible to perform filtering based on the user's current living situation and areas of interest.

[0117] When analyzing the user's household finances and asset status in detail, the analysis unit can customize the analysis based on the user's occupation and lifestyle. For example, the analysis unit can propose an optimal household finance management method based on the user's occupation. The analysis unit can also propose an optimal asset management method taking the user's lifestyle into consideration. For example, the analysis unit can propose optimal investment destinations based on the user's hobbies and lifestyle. The analysis unit can also propose an optimal financial plan based on the user's occupation and lifestyle. This makes it possible to customize the analysis based on the user's occupation and lifestyle.

[0118] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. On the other hand, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, if the user is relaxed, the suggestion unit can provide detailed investment plans or savings plans. On the other hand, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. In this way, the length of the suggestions can be adjusted based on the user's emotions.

[0119] The explanation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated user's emotions. For example, if the user is in a hurry, the explanation unit can provide a short, to-the-point explanation. On the other hand, if the user is relaxed, the explanation unit can provide a longer explanation including detailed information. For example, if the user is relaxed, the explanation unit can provide a detailed investment plan or savings plan. On the other hand, if the user is excited, the explanation unit can provide an explanation with visually stimulating effects. In this way, the length of the explanation can be adjusted based on the user's emotions.

[0120] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit can suggest an optimal household management method based on the user's past suggestion results. The suggestion unit can also suggest an optimal investment method based on the user's past asset management results. For example, the suggestion unit can suggest optimal investment destinations based on the user's past investment results. The suggestion unit can also refer to the user's past suggestion results and suggest an optimal financial plan based on future income and expenditure forecasts. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0121] The explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results. For example, the explanation unit can suggest an optimal explanation method based on the user's past explanation results. The explanation unit can also refer to the user's past explanation results and provide an explanation based on the user's level of understanding. For example, the explanation unit can adjust the explanation of technical terms or complex financial products based on the user's past explanation results. The explanation unit can also analyze the user's past explanation results and provide an optimal explanation method. This makes it possible to improve the accuracy of the explanation based on the user's past explanation results.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives information input by the user. The information input by the user includes household information, asset information, income information, etc. The reception unit provides an interface for the user to input their household worries and money plans, and also has a function for anonymous consultation. For example, it provides methods such as anonymous chat and anonymous email. Step 2: The analysis unit uses AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit conducts a detailed analysis of the user's household and asset situation and generates basic data for proposing the optimal financial plan. Step 3: The proposal unit proposes an optimal financial plan based on the information analyzed by the analysis unit. The financial plan may include investment plans, savings plans, insurance plans, etc. The proposal unit also provides advice on reviewing spending and asset management. Step 4: The explanation section explains the plan proposed by the proposal section in an easy-to-understand manner. The explanation is done by using graphs and charts, explaining technical terms, etc. The explanation section explains technical terms and complex financial products in an easy-to-understand manner.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 AI 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 AI 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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, in order to avoid confusion and to 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input information from a user; an analysis unit that analyzes the information received by the reception unit; a proposal unit that proposes a financial plan based on the information analyzed by the analysis unit; An explanation unit that explains the plan proposed by the proposal unit in an easy-to-understand manner. A system characterized by:

2. The reception unit Accepts input of user's financial worries or money plans 2. The system of claim 1.

3. The analysis unit Conduct detailed analysis of your financial situation or assets 2. The system of claim 1.

4. The proposal unit Propose spending reviews or asset management advice 2. The system of claim 1.

5. The explanation section Explain technical terms or complex financial products in an easy-to-understand way 2. The system of claim 1.

6. The reception unit Accepting anonymous consultations 2. The system of claim 1.

7. The analysis unit Take measures to protect privacy 2. The system of claim 1.

8. The proposal unit Presenting the simulation results 2. The system of claim 1.

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