system

Abstract

The system according to the embodiment aims to enable users to consult about financial worries and money plans without any psychological resistance. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation details from a user. The analysis unit analyzes the information received by the reception unit and provides advice tailored to the user's situation. The provision unit provides advice based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background 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 users find it difficult to consult others about their financial worries or money plans due to psychological resistance.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation details from a user. The analysis unit analyzes the information received by the reception unit and provides advice according to the user's situation. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to consult about financial worries and money 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 generates an AI financial planner and provides financial consultation services. This system allows users to input their desired consultation details, and the AI ​​financial planner analyzes the information and provides advice tailored to the user's situation. For example, the system can provide consultations on household budget revisions and future asset management, review the balance between income and expenses, and provide advice on saving tips and investments. It can also propose investment plans that consider the balance between risk and return. Furthermore, the AI ​​financial planner explains complex topics in simple terms to make them easier for users to understand. For example, by avoiding technical jargon and providing concrete examples, users can easily understand. This system allows users to easily consult about financial worries and financial plans that they may be hesitant to discuss with others, and receive appropriate advice. Furthermore, by providing the "value of explaining complex topics in simple terms" that traditional financial planners offer, user satisfaction is improved. For example, if a user who consults on a household budget revision practices saving money according to the AI ​​financial planner's advice and reduces their monthly expenses, they can feel the benefits. Furthermore, if a user who consults about future asset management makes an investment based on the AI ​​financial planner's suggestions and receives a return, they will be able to realize the value of that. In this way, by utilizing AI financial planners, the household finance consultation service will become more accessible to more people, and financial worries can be resolved without any psychological resistance. This allows the household finance consultation system to efficiently accept and analyze the content of users' consultations and provide appropriate advice.

[0029] A household finance consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a consultation request from a user. The consultation request may include, but is not limited to, household finance review, future asset management, income-expense balance review, savings tips, and investment advice. The reception unit receives information entered by the user in digital format, for example. The reception unit may also allow the user to input the consultation request using voice input. For example, the reception unit may convert the user's voice into text data using voice recognition technology and accept the text data as the consultation request. The analysis unit analyzes the information received by the reception unit and provides advice tailored to the user's situation. The analysis unit may perform analysis using, for example, an algorithm for reviewing the income-expense balance. The analysis unit may also perform analysis using an algorithm for proposing an investment plan that takes into account the balance between risk and return. For example, the analysis unit may apply an algorithm for reviewing the balance based on the user's income and expense data. The analysis unit may also analyze the user's past income and expense history to predict future balance. The analysis unit may also apply an algorithm for proposing an optimal investment plan based on the user's risk tolerance. The providing unit provides advice based on the analysis results obtained by the analyzing unit. The providing unit provides, for example, advice on saving tips and investments. The providing unit can also provide explanations using specific examples without using technical terms. For example, the providing unit can provide advice in everyday language, avoiding technical terms. The providing unit can also provide explanations using specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand using diagrams and graphs. This allows the household finance consultation system according to the embodiment to efficiently accept and analyze the content of the user's consultation and provide appropriate advice.

[0030] The reception unit can accept information including the user's income, expenses, assets, liabilities, and future goals. The reception unit accepts information such as the user's income, expenses, assets, liabilities, and future goals. Income includes, for example, salary income, investment income, and income from a side job. Expenses include, for example, living expenses, education expenses, and medical expenses. Assets include, for example, real estate, stocks, and savings. Liabilities include, for example, a mortgage, credit card debt, and student loans. Future goals include, for example, retirement life, children's education, and travel plans. By accepting detailed information from the user, more appropriate advice can be provided. 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 the information entered by the user into AI and have the AI ​​organize and classify the information.

[0031] The analysis unit can perform the analysis using an algorithm for reviewing the balance between income and expenditure. The analysis unit can perform the analysis using, for example, an algorithm for reviewing the balance between income and expenditure. Specific methods for reviewing the balance between income and expenditure include, for example, methods for increasing income and methods for reducing expenditure. The analysis unit can apply an algorithm for reviewing the balance based on, for example, the user's income and expenditure data. The analysis unit can also analyze the user's past income and expenditure history to predict the future balance. Furthermore, the analysis unit can review the balance between the user's income and expenditure and identify areas for saving. This allows the user to be provided with appropriate savings points by reviewing the balance between income and expenditure. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's income and expenditure data into AI and have the AI ​​review the balance.

[0032] The analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. The analysis unit may perform the analysis using, for example, an algorithm that proposes an investment plan based on the balance between risk and return. Specific methods that consider the balance between risk and return include, for example, a risk assessment method and a return prediction method. The analysis unit may apply, for example, an algorithm that proposes an optimal investment plan based on the user's risk tolerance. The analysis unit may also analyze the user's past investment history and propose an investment plan that considers the balance between risk and return. Furthermore, the analysis unit may propose an investment plan that optimizes the balance between risk and return based on the user's asset status. This allows the user to receive appropriate investment advice by proposing an investment plan that considers the balance between risk and return. 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 risk tolerance and investment history into AI and have the AI ​​execute the investment plan proposal.

[0033] The providing unit can provide advice on saving tips or investments. The providing unit provides, for example, saving tips or investment advice. Saving tips include, for example, ways to save money in daily life and ways to save energy. Investment advice includes, for example, how to select investment destinations and how to manage risk. The providing unit provides, for example, specific saving tips based on the balance between the user's income and expenses. The providing unit can also provide specific investment advice based on the user's asset status. Furthermore, the providing unit can provide specific asset management advice based on the user's future goals. This makes it possible to provide specific saving tips and investment advice to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's income and expenses into AI and have the AI ​​provide saving tips and investment advice.

[0034] The providing unit can provide explanations by citing specific examples without using technical terms. The providing unit, for example, provides explanations by citing specific examples without using technical terms. Specific examples include, for example, past success stories and failure stories. The providing unit, for example, provides advice in everyday language, avoiding technical terms. The providing unit can also provide explanations by citing specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand by using diagrams and graphs. This makes it possible to provide advice that is easier for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the user's consultation into AI and cause the AI ​​to execute explanations using specific examples.

[0035] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. The past consultation history includes, for example, the content of the user's past consultations, the consultation method used (chat, voice, etc.), and the frequency of consultations. The reception unit, for example, automatically generates related questions based on the content of the user's past consultations. The reception unit can also preferentially suggest consultation methods used by the user in the past. Furthermore, the reception unit can find specific patterns from the user's past consultation history and select the optimal reception method. This makes it possible to provide 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, or without, AI. For example, the reception unit can input the user's past consultation history data into AI and have the AI ​​select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current living situation or areas of interest. The reception unit performs filtering based on, for example, the user's current living situation or areas of interest. Living situations include, for example, family structure, income status, health status, etc. Areas of interest include, for example, hobbies, topics of interest, and areas of expertise. The reception unit, for example, preferentially receives consultation content related to the user's current income situation. The reception unit can also ask related questions based on the user's areas of interest. Furthermore, the reception unit can filter appropriate consultation content based on the user's living situation. This makes it possible to receive appropriate consultation content based on the user's living situation and areas of interest. 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 data on the user's living situation and areas of interest into AI and have the AI ​​perform filtering.

[0037] The reception unit can prioritize receiving highly relevant content based on the user's geographical location information when receiving a consultation. For example, the reception unit prioritizes receiving highly relevant content based on the user's geographical location information when receiving a consultation. Geographical location information includes, for example, GPS data, address information, etc. The reception unit prioritizes receiving related consultation content based on, for example, the economic situation of the area where the user lives. The reception unit can also prioritize receiving consultations regarding financial products and services specific to the area based on the user's geographical location information. Furthermore, the reception unit can prioritize receiving consultations regarding nearby financial institutions, taking into account the user's geographical location information. This allows the reception of highly relevant consultation content based on the user's geographical location information. 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 geographical location information into AI to determine the priority of highly relevant content.

[0038] The reception unit can analyze the user's social media activity and receive related information when receiving a consultation request. For example, the reception unit can analyze the user's social media activity and receive related information when receiving a consultation request. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the user's social media posts and prioritize receiving related consultation requests. The reception unit can also ask related questions based on the user's social media interests. Furthermore, the reception unit can analyze the user's social media activity history and suggest optimal consultation requests. This allows reception of related consultation requests based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​analyze the related information.

[0039] The analysis unit can perform a specific analysis using an algorithm for reviewing the balance between income and expenditure during the analysis. For example, the analysis unit can perform a specific analysis using an algorithm for reviewing the balance between income and expenditure during the analysis. Specific methods for reviewing the balance between income and expenditure include, for example, methods for increasing income and methods for reducing expenditure. The analysis unit can apply an algorithm for reviewing the balance based on, for example, the user's income and expenditure data. The analysis unit can also analyze the user's past income and expenditure history to predict the future balance. Furthermore, the analysis unit can review the balance between the user's income and expenditure and identify areas for saving. This allows the user to be provided with appropriate savings points by reviewing the balance between income and expenditure. 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 income and expenditure data into AI and have the AI ​​review the balance.

[0040] The analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. For example, the analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. Specific methods that consider the balance between risk and return include, for example, a risk assessment method and a return prediction method. The analysis unit may apply an algorithm that proposes an optimal investment plan based on the user's risk tolerance. The analysis unit may also analyze the user's past investment history and propose an investment plan that considers the balance between risk and return. Furthermore, the analysis unit may propose an investment plan that optimizes the balance between risk and return based on the user's financial situation. This allows the user to receive appropriate investment advice by proposing an investment plan that considers the balance between risk and return. 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 risk tolerance and investment history into AI and have the AI ​​execute the investment plan proposal.

[0041] The analysis unit can select the optimal analysis method based on the user's geographical location information during analysis. For example, the analysis unit selects the optimal analysis method based on the user's geographical location information during analysis. Geographical location information includes, for example, GPS data and address information. The analysis unit selects the optimal analysis method based on, for example, the economic situation of the area where the user lives. The analysis unit can also analyze regional financial products and services based on the user's geographical location information. Furthermore, the analysis unit can analyze nearby financial institutions taking into account the user's geographical location information. This makes it possible to provide the optimal analysis method based on the user's geographical location information. 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 geographical location information into AI and have the AI ​​select the optimal analysis method.

[0042] The analysis unit may analyze the user's social media activity during the analysis and perform analysis using related information. For example, the analysis unit may analyze the user's social media activity during the analysis and perform analysis using related information. Social media activity may include, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit may analyze the content of the user's social media posts and perform analysis using related data. The analysis unit may also perform analysis using related data based on the user's areas of interest on social media. Furthermore, the analysis unit may analyze the user's social media activity history and select an optimal analysis method. This allows analysis to be performed using related data based on the user's social media activity. 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 the user's social media activity data into AI and have the AI ​​analyze the related information.

[0043] The providing unit can provide specific savings tips or investment advice when providing advice. For example, the providing unit provides specific savings tips or investment advice when providing advice. Saving tips include, for example, ways to save money in daily life and ways to save energy. Investment advice includes, for example, how to select investment destinations and how to manage risk. The providing unit provides specific savings tips based on, for example, the balance between the user's income and expenses. The providing unit can also provide specific investment advice based on the user's asset status. Furthermore, the providing unit can provide specific asset management advice based on the user's future goals. This makes it possible to provide specific savings tips and investment advice to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's income and expenses into AI and have the AI ​​provide savings tips and investment advice.

[0044] The providing unit can provide explanations by citing specific examples without using technical terms when providing advice. For example, the providing unit can provide explanations by citing specific examples without using technical terms when providing advice. Specific examples include, for example, past success stories and failure stories. For example, the providing unit can provide advice in everyday language, avoiding technical terms. The providing unit can also provide explanations by citing specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand by using diagrams and graphs. This makes it possible to provide advice that is easier for the user to understand. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the content of the user's consultation into AI and cause the AI ​​to execute explanations by citing specific examples.

[0045] The providing unit can provide optimal advice based on the user's geographical location information when providing advice. The providing unit, for example, can provide optimal advice based on the user's geographical location information when providing advice. Geographical location information includes, for example, GPS data, address information, etc. The providing unit can provide optimal advice based on, for example, the economic situation of the area where the user lives. The providing unit can also provide advice on financial products and services specific to the area based on the user's geographical location information. Furthermore, the providing unit can provide advice on nearby financial institutions taking into account the user's geographical location information. This makes it possible to provide optimal advice based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to provide optimal advice.

[0046] The providing unit can analyze the user's social media activity and provide advice using related information when providing advice. For example, the providing unit can analyze the user's social media activity and provide advice using related information when providing advice. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. For example, the providing unit can analyze the content of the user's social media posts and provide advice using related information. The providing unit can also provide advice using related information based on the user's areas of interest on social media. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal advice. This makes it possible to provide advice using related information based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI ​​to analyze the related information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can also receive information about the user's health condition. For example, by inputting the user's health checkup results and daily health management data, advice that takes health-related expenses into consideration can be provided during household budget consultations. Based on this health data, the analysis unit can predict future medical expenses and identify savings points for maintaining health. The provision unit can provide specific advice based on the user's health condition, helping the user manage their household budget while maintaining their health.

[0049] The analysis unit can review the balance between income and expenses based on the user's hobbies and lifestyle. For example, if the user's hobby is traveling, the analysis unit can suggest ways to efficiently manage travel expenses. If the user's hobby is sports, the analysis unit can provide advice on optimizing sports-related expenses. Furthermore, the analysis unit can suggest ways for the user to reduce expenses on hobbies, helping the user to allocate the savings to other important expenses.

[0050] The provider can provide advice based on the user's family structure. For example, it can suggest a method of managing household finances that takes into account children's education expenses and family medical expenses. It can also provide a long-term asset management plan based on the future goals of each family member. Furthermore, it can provide specific advice according to family life events (marriage, childbirth, moving, etc.) to stabilize household finances.

[0051] The reception unit can accept consultation requests based on the user's occupation and work style. For example, for freelancers and self-employed users, it can suggest methods for managing household finances in response to fluctuations in income. For users who work remotely, it can provide methods for optimizing expenses associated with working from home. Furthermore, it can provide advice that takes into account expenses related to specific occupations (e.g., commuting expenses and vocational training expenses).

[0052] The analysis unit can analyze a user's past consumption patterns and predict future spending. For example, it can understand a user's consumption trends from past data and predict seasonal fluctuations in spending. It can also predict spending for specific events (e.g., birthdays or the New Year holidays) and set a budget in advance. Furthermore, it can provide specific advice for reducing wasteful spending based on past consumption patterns.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit accepts the user's consultation. Consultations from users include reviewing their household finances, future asset management, reviewing the balance between income and expenses, tips for saving money, and investment advice. The reception unit accepts the information entered by the user in digital form. It can also use voice recognition technology to convert the user's voice into text data and accept it as the consultation content. Step 2: The analysis unit analyzes the information received by the reception unit and provides advice tailored to the user's situation. The analysis unit performs analysis using algorithms that review the balance between income and expenditure, and algorithms that propose investment plans that take into account the balance between risk and return. In addition, the analysis unit can analyze the user's past income and expenditure history and predict future balances. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. The provider provides advice on savings and investments, and can explain using concrete examples without using technical terms. It can also provide advice that is easy to understand visually using diagrams and graphs.

[0055] (Example 2) A financial consultation system according to an embodiment of the present invention generates an AI financial planner and provides financial consultation services. This system allows users to input their desired consultation details, and the AI ​​financial planner analyzes the information and provides advice tailored to the user's situation. For example, the system can provide consultations on household budget revisions and future asset management, review the balance between income and expenses, and provide advice on saving tips and investments. It can also propose investment plans that consider the balance between risk and return. Furthermore, the AI ​​financial planner explains complex topics in simple terms to make them easier for users to understand. For example, by avoiding technical jargon and providing concrete examples, users can easily understand. This system allows users to easily consult about financial worries and financial plans that they may be hesitant to discuss with others, and receive appropriate advice. Furthermore, by providing the "value of explaining complex topics in simple terms" that traditional financial planners offer, user satisfaction is improved. For example, if a user who consults on a household budget revision practices saving money according to the AI ​​financial planner's advice and reduces their monthly expenses, they can feel the benefits. Furthermore, if a user who consults about future asset management makes an investment based on the AI ​​financial planner's suggestions and receives a return, they will be able to realize the value of that. In this way, by utilizing AI financial planners, the household finance consultation service will become more accessible to more people, and financial worries can be resolved without any psychological resistance. This allows the household finance consultation system to efficiently accept and analyze the content of users' consultations and provide appropriate advice.

[0056] A household finance consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a consultation request from a user. The consultation request may include, but is not limited to, household finance review, future asset management, income-expense balance review, savings tips, and investment advice. The reception unit receives information entered by the user in digital format, for example. The reception unit may also allow the user to input the consultation request using voice input. For example, the reception unit may convert the user's voice into text data using voice recognition technology and accept the text data as the consultation request. The analysis unit analyzes the information received by the reception unit and provides advice tailored to the user's situation. The analysis unit may perform analysis using, for example, an algorithm for reviewing the income-expense balance. The analysis unit may also perform analysis using an algorithm for proposing an investment plan that takes into account the balance between risk and return. For example, the analysis unit may apply an algorithm for reviewing the balance based on the user's income and expense data. The analysis unit may also analyze the user's past income and expense history to predict future balance. The analysis unit may also apply an algorithm for proposing an optimal investment plan based on the user's risk tolerance. The providing unit provides advice based on the analysis results obtained by the analyzing unit. The providing unit provides, for example, advice on saving tips and investments. The providing unit can also provide explanations using specific examples without using technical terms. For example, the providing unit can provide advice in everyday language, avoiding technical terms. The providing unit can also provide explanations using specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand using diagrams and graphs. This allows the household finance consultation system according to the embodiment to efficiently accept and analyze the content of the user's consultation and provide appropriate advice.

[0057] The reception unit can accept information including the user's income, expenses, assets, liabilities, and future goals. The reception unit accepts information such as the user's income, expenses, assets, liabilities, and future goals. Income includes, for example, salary income, investment income, and income from a side job. Expenses include, for example, living expenses, education expenses, and medical expenses. Assets include, for example, real estate, stocks, and savings. Liabilities include, for example, a mortgage, credit card debt, and student loans. Future goals include, for example, retirement life, children's education, and travel plans. By accepting detailed information from the user, more appropriate advice can be provided. 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 the information entered by the user into AI and have the AI ​​organize and classify the information.

[0058] The analysis unit can perform the analysis using an algorithm for reviewing the balance between income and expenditure. The analysis unit can perform the analysis using, for example, an algorithm for reviewing the balance between income and expenditure. Specific methods for reviewing the balance between income and expenditure include, for example, methods for increasing income and methods for reducing expenditure. The analysis unit can apply an algorithm for reviewing the balance based on, for example, the user's income and expenditure data. The analysis unit can also analyze the user's past income and expenditure history to predict the future balance. Furthermore, the analysis unit can review the balance between the user's income and expenditure and identify areas for saving. This allows the user to be provided with appropriate savings points by reviewing the balance between income and expenditure. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's income and expenditure data into AI and have the AI ​​review the balance.

[0059] The analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. The analysis unit may perform the analysis using, for example, an algorithm that proposes an investment plan based on the balance between risk and return. Specific methods that consider the balance between risk and return include, for example, a risk assessment method and a return prediction method. The analysis unit may apply, for example, an algorithm that proposes an optimal investment plan based on the user's risk tolerance. The analysis unit may also analyze the user's past investment history and propose an investment plan that considers the balance between risk and return. Furthermore, the analysis unit may propose an investment plan that optimizes the balance between risk and return based on the user's asset status. This allows the user to receive appropriate investment advice by proposing an investment plan that considers the balance between risk and return. 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 risk tolerance and investment history into AI and have the AI ​​execute the investment plan proposal.

[0060] The providing unit can provide advice on saving tips or investments. The providing unit provides, for example, saving tips or investment advice. Saving tips include, for example, ways to save money in daily life and ways to save energy. Investment advice includes, for example, how to select investment destinations and how to manage risk. The providing unit provides, for example, specific saving tips based on the balance between the user's income and expenses. The providing unit can also provide specific investment advice based on the user's asset status. Furthermore, the providing unit can provide specific asset management advice based on the user's future goals. This makes it possible to provide specific saving tips and investment advice to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's income and expenses into AI and have the AI ​​provide saving tips and investment advice.

[0061] The providing unit can provide explanations by citing specific examples without using technical terms. The providing unit, for example, provides explanations by citing specific examples without using technical terms. Specific examples include, for example, past success stories and failure stories. The providing unit, for example, provides advice in everyday language, avoiding technical terms. The providing unit can also provide explanations by citing specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand by using diagrams and graphs. This makes it possible to provide advice that is easier for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the user's consultation into AI and cause the AI ​​to execute explanations using specific examples.

[0062] The reception unit can estimate the user's emotions and adjust the consultation acceptance method based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the consultation acceptance method based on the estimated user emotions. Emotions include, for example, joy, sadness, and anger. For example, if the user is feeling anxious, the reception unit may ask questions in a gentle tone to provide an interface that gives the user a sense of security. Furthermore, if the user is relaxed, the reception unit may ask detailed questions to collect more specific information. Furthermore, if the user is in a hurry, the reception unit may ask concise questions to quickly accept the consultation. This makes it possible to provide an appropriate acceptance method depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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 an AI, for example, or without an AI. For example, the reception unit may input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0063] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. The past consultation history includes, for example, the content of the user's past consultations, the consultation method used (chat, voice, etc.), and the frequency of consultations. The reception unit, for example, automatically generates related questions based on the content of the user's past consultations. The reception unit can also preferentially suggest consultation methods used by the user in the past. Furthermore, the reception unit can find specific patterns from the user's past consultation history and select the optimal reception method. This makes it possible to provide 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, or without, AI. For example, the reception unit can input the user's past consultation history data into AI and have the AI ​​select the optimal reception method.

[0064] The reception unit can perform filtering based on the user's current living situation or areas of interest. The reception unit performs filtering based on, for example, the user's current living situation or areas of interest. Living situations include, for example, family structure, income status, health status, etc. Areas of interest include, for example, hobbies, topics of interest, and areas of expertise. The reception unit, for example, preferentially receives consultation content related to the user's current income situation. The reception unit can also ask related questions based on the user's areas of interest. Furthermore, the reception unit can filter appropriate consultation content based on the user's living situation. This makes it possible to receive appropriate consultation content based on the user's living situation and areas of interest. 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 data on the user's living situation and areas of interest into AI and have the AI ​​perform filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of the consultation contents to be accepted based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the consultation contents to be accepted based on the estimated user emotions. Emotions include, for example, joy, sadness, and anger. For example, when the user is stressed, the reception unit prioritizes accepting consultation contents with high urgency. Furthermore, when the user is relaxed, the reception unit can prioritize accepting detailed consultation contents. Furthermore, when the user is in a hurry, the reception unit can prioritize accepting brief consultation contents. This makes it possible to determine the priority of the consultation contents according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into an AI and have the AI ​​execute emotion estimation.

[0066] The reception unit can prioritize receiving highly relevant content based on the user's geographical location information when receiving a consultation. For example, the reception unit prioritizes receiving highly relevant content based on the user's geographical location information when receiving a consultation. Geographical location information includes, for example, GPS data, address information, etc. The reception unit prioritizes receiving related consultation content based on, for example, the economic situation of the area where the user lives. The reception unit can also prioritize receiving consultations regarding financial products and services specific to the area based on the user's geographical location information. Furthermore, the reception unit can prioritize receiving consultations regarding nearby financial institutions, taking into account the user's geographical location information. This allows the reception of highly relevant consultation content based on the user's geographical location information. 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 geographical location information into AI to determine the priority of highly relevant content.

[0067] The reception unit can analyze the user's social media activity and receive related information when receiving a consultation request. For example, the reception unit can analyze the user's social media activity and receive related information when receiving a consultation request. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the user's social media posts and prioritize receiving related consultation requests. The reception unit can also ask related questions based on the user's social media interests. Furthermore, the reception unit can analyze the user's social media activity history and suggest optimal consultation requests. This allows reception of related consultation requests based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​analyze the related information.

[0068] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. Emotions include, for example, joy, sadness, and anger. For example, if the user is feeling anxious, the analysis unit explains the analysis results in a gentle tone. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows appropriate analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0069] The analysis unit can perform a specific analysis using an algorithm for reviewing the balance between income and expenditure during the analysis. For example, the analysis unit can perform a specific analysis using an algorithm for reviewing the balance between income and expenditure during the analysis. Specific methods for reviewing the balance between income and expenditure include, for example, methods for increasing income and methods for reducing expenditure. The analysis unit can apply an algorithm for reviewing the balance based on, for example, the user's income and expenditure data. The analysis unit can also analyze the user's past income and expenditure history to predict the future balance. Furthermore, the analysis unit can review the balance between the user's income and expenditure and identify areas for saving. This allows the user to be provided with appropriate savings points by reviewing the balance between income and expenditure. 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 income and expenditure data into AI and have the AI ​​review the balance.

[0070] The analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. For example, the analysis unit may perform the analysis using an algorithm that proposes an investment plan based on the balance between risk and return. Specific methods that consider the balance between risk and return include, for example, a risk assessment method and a return prediction method. The analysis unit may apply an algorithm that proposes an optimal investment plan based on the user's risk tolerance. The analysis unit may also analyze the user's past investment history and propose an investment plan that considers the balance between risk and return. Furthermore, the analysis unit may propose an investment plan that optimizes the balance between risk and return based on the user's financial situation. This allows the user to receive appropriate investment advice by proposing an investment plan that considers the balance between risk and return. 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 risk tolerance and investment history into AI and have the AI ​​execute the investment plan proposal.

[0071] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priority based on the estimated user emotions. Emotions include, for example, joy, sadness, and anger. For example, if the user is feeling anxious, the analysis unit prioritizes analysis with a high degree of urgency. Furthermore, if the user is relaxed, the analysis unit can prioritize detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize concise analysis. This allows the analysis priority to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0072] The analysis unit can select the optimal analysis method based on the user's geographical location information during analysis. For example, the analysis unit selects the optimal analysis method based on the user's geographical location information during analysis. Geographical location information includes, for example, GPS data and address information. The analysis unit selects the optimal analysis method based on, for example, the economic situation of the area where the user lives. The analysis unit can also analyze regional financial products and services based on the user's geographical location information. Furthermore, the analysis unit can analyze nearby financial institutions taking into account the user's geographical location information. This makes it possible to provide the optimal analysis method based on the user's geographical location information. 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 geographical location information into AI and have the AI ​​select the optimal analysis method.

[0073] The analysis unit may analyze the user's social media activity during the analysis and perform analysis using related information. For example, the analysis unit may analyze the user's social media activity during the analysis and perform analysis using related information. Social media activity may include, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit may analyze the content of the user's social media posts and perform analysis using related data. The analysis unit may also perform analysis using related data based on the user's areas of interest on social media. Furthermore, the analysis unit may analyze the user's social media activity history and select an optimal analysis method. This allows analysis to be performed using related data based on the user's social media activity. 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 the user's social media activity data into AI and have the AI ​​analyze the related information.

[0074] The providing unit can estimate the user's emotion and adjust the method of providing advice based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the method of providing advice based on the estimated user's emotion. Emotions include, for example, joy, sadness, and anger. For example, if the user is feeling anxious, the providing unit can provide advice in a gentle tone. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can provide concise advice. This makes it possible to provide appropriate advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and cause the AI ​​to estimate the emotion.

[0075] The providing unit can provide specific savings tips or investment advice when providing advice. For example, the providing unit provides specific savings tips or investment advice when providing advice. Saving tips include, for example, ways to save money in daily life and ways to save energy. Investment advice includes, for example, how to select investment destinations and how to manage risk. The providing unit provides specific savings tips based on, for example, the balance between the user's income and expenses. The providing unit can also provide specific investment advice based on the user's asset status. Furthermore, the providing unit can provide specific asset management advice based on the user's future goals. This makes it possible to provide specific savings tips and investment advice to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's income and expenses into AI and have the AI ​​provide savings tips and investment advice.

[0076] The providing unit can provide explanations by citing specific examples without using technical terms when providing advice. For example, the providing unit can provide explanations by citing specific examples without using technical terms when providing advice. Specific examples include, for example, past success stories and failure stories. For example, the providing unit can provide advice in everyday language, avoiding technical terms. The providing unit can also provide explanations by citing specific examples to make the advice easier for the user to understand. Furthermore, the providing unit can provide advice that is visually easy to understand by using diagrams and graphs. This makes it possible to provide advice that is easier for the user to understand. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the content of the user's consultation into AI and cause the AI ​​to execute explanations by citing specific examples.

[0077] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated user emotions. Emotions include, for example, joy, sadness, and anger. For example, if the user is feeling anxious, the providing unit can prioritize advice with a high level of urgency. Furthermore, if the user is relaxed, the providing unit can prioritize detailed advice. Furthermore, if the user is in a hurry, the providing unit can prioritize concise advice. This makes it possible to determine the priority of advice based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI and cause the AI ​​to perform emotion estimation.

[0078] The providing unit can provide optimal advice based on the user's geographical location information when providing advice. The providing unit, for example, can provide optimal advice based on the user's geographical location information when providing advice. Geographical location information includes, for example, GPS data, address information, etc. The providing unit can provide optimal advice based on, for example, the economic situation of the area where the user lives. The providing unit can also provide advice on financial products and services specific to the area based on the user's geographical location information. Furthermore, the providing unit can provide advice on nearby financial institutions taking into account the user's geographical location information. This makes it possible to provide optimal advice based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to provide optimal advice.

[0079] The providing unit can analyze the user's social media activity and provide advice using related information when providing advice. For example, the providing unit can analyze the user's social media activity and provide advice using related information when providing advice. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. For example, the providing unit can analyze the content of the user's social media posts and provide advice using related information. The providing unit can also provide advice using related information based on the user's areas of interest on social media. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal advice. This makes it possible to provide advice using related information based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI ​​to analyze the related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives information entered by the user in digital form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs analysis using an algorithm to review the balance between income and expenditure. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides advice based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs the consultation content using voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs analysis using an algorithm that proposes an investment plan that takes into account the balance between risk and return. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides advice based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs the consultation content using voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and applies an algorithm to review the balance based on data on the user's income and expenditure. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides advice based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs the consultation content using voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and applies an algorithm that proposes an optimal investment plan based on the user's risk tolerance. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides advice based on the analysis results.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can also receive information about the user's health condition. For example, by inputting the user's health checkup results and daily health management data, advice that takes health-related expenses into consideration can be provided during household budget consultations. Based on this health data, the analysis unit can predict future medical expenses and identify savings points for maintaining health. The provision unit can provide specific advice based on the user's health condition, helping the user manage their household budget while maintaining their health.

[0082] The analysis unit can review the balance between income and expenses based on the user's hobbies and lifestyle. For example, if the user's hobby is traveling, the analysis unit can suggest ways to efficiently manage travel expenses. If the user's hobby is sports, the analysis unit can provide advice on optimizing sports-related expenses. Furthermore, the analysis unit can suggest ways for the user to reduce expenses on hobbies, helping the user to allocate the savings to other important expenses.

[0083] The provider can provide advice based on the user's family structure. For example, it can suggest a method of managing household finances that takes into account children's education expenses and family medical expenses. It can also provide a long-term asset management plan based on the future goals of each family member. Furthermore, it can provide specific advice according to family life events (marriage, childbirth, moving, etc.) to stabilize household finances.

[0084] The reception unit can accept consultation requests based on the user's occupation and work style. For example, for freelancers and self-employed users, it can suggest methods for managing household finances in response to fluctuations in income. For users who work remotely, it can provide methods for optimizing expenses associated with working from home. Furthermore, it can provide advice that takes into account expenses related to specific occupations (e.g., commuting expenses and vocational training expenses).

[0085] The analysis unit can analyze a user's past consumption patterns and predict future spending. For example, it can understand a user's consumption trends from past data and predict seasonal fluctuations in spending. It can also predict spending for specific events (e.g., birthdays or the New Year holidays) and set a budget in advance. Furthermore, it can provide specific advice for reducing wasteful spending based on past consumption patterns.

[0086] The providing unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest a saving method or investment plan that will help the user relax. Also, if the user is feeling happy, the providing unit can provide positive advice to help the user maintain that emotion. Furthermore, if the user is feeling anxious, the providing unit can provide specific advice that will give the user a sense of security.

[0087] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, the analysis can be performed when the user is relaxed and provide detailed results. Alternatively, when the user is busy, the analysis can be provided in a concise manner. Furthermore, if the user is feeling stressed, the analysis can be temporarily delayed so that the user can receive the results in a relaxed state.

[0088] The providing unit can estimate the user's emotion and adjust the format of the advice based on the estimated user's emotion. For example, if the user prefers visual information, the advice can be provided using graphs or diagrams. If the user prefers text-based information, the advice can be provided in detailed sentences. Furthermore, if the user prefers audio advice, the advice can be provided using voice synthesis technology.

[0089] The reception unit can estimate the user's emotions and determine the priority of consultation contents to be received based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize receiving consultation contents with high urgency. Also, if the user is relaxed, it can prioritize receiving consultation contents with detailed details. Furthermore, if the user is in a hurry, it can prioritize receiving consultation contents with concise details.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis results can be explained in a gentle tone. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results can be provided.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts the user's consultation. Consultations from users include reviewing their household finances, future asset management, reviewing the balance between income and expenses, tips for saving money, and investment advice. The reception unit accepts the information entered by the user in digital form. It can also use voice recognition technology to convert the user's voice into text data and accept it as the consultation content. Step 2: The analysis unit analyzes the information received by the reception unit and provides advice tailored to the user's situation. The analysis unit performs analysis using algorithms that review the balance between income and expenditure, and algorithms that propose investment plans that take into account the balance between risk and return. In addition, the analysis unit can analyze the user's past income and expenditure history and predict future balances. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. The provider provides advice on savings and investments, and can explain using concrete examples without using technical terms. It can also provide advice that is easy to understand visually using diagrams and graphs.

[0093] 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.

[0094] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 inquiries from users; an analysis unit that analyzes the information received by the reception unit and provides advice according to the user's situation; a providing unit that provides advice based on the analysis result obtained by the analyzing unit. A system characterized by:

2. The reception unit Accepts information including the user's income, expenses, assets, liabilities, and future goals 2. The system of claim 1.

3. The analysis unit Analysis using algorithms to rebalance income and expenses 2. The system of claim 1.

4. The analysis unit The analysis is performed using an algorithm that proposes an investment plan based on the balance of risk and return.

2. The system of claim 1.

5. The providing unit Offering savings tips or investment advice 2. The system of claim 1.

6. The providing unit Explain using concrete examples without using technical terms 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way the consultation is received based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past consultation history and select the most appropriate reception method 2. The system of claim 1.

9. The reception unit Filtering based on the user's current life situation or interests 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the consultation content to be accepted based on the estimated user emotions.

2. The system of claim 1.

Images