system

Abstract

The system according to this embodiment aims to calculate income and expenditure forecasts and investment returns based on an individual's transaction history and spending patterns, and to provide financial education content and investment reports. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a prediction unit, and a generation unit. The reception unit receives user questions. The analysis unit analyzes transaction history and spending patterns based on the questions received by the reception unit. The prediction unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. The generation unit generates financial education content and investment reports based on the prediction results obtained by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the calculation of income and expenditure prediction and investment return based on an individual's transaction history and expenditure pattern has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to calculate income and expenditure prediction and investment return based on an individual's transaction history and expenditure pattern, and provide financial education content and investment reports.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a forecasting unit, and a generation unit. The reception unit receives user inquiries. The analysis unit analyzes transaction history and spending patterns based on the inquiries received by the reception unit. The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. The generation unit generates financial education content and investment reports based on the forecast results obtained by the forecasting unit. [Effects of the Invention]

[0007] The system according to this embodiment can calculate income and expenditure forecasts and investment returns based on an individual's transaction history and spending patterns, and can provide financial education content and investment reports. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal financial advisor system according to an embodiment of the present invention is a system that provides personalized financial advice using generative AI. The personal financial advisor system accepts financial questions from the user through an interactive interface. Next, the personal financial advisor system analyzes the user's transaction history and spending patterns and generates personalized financial advice. Furthermore, the personal financial advisor system calculates and presents future income and expenditure forecasts and investment return projections to the user. The personal financial advisor system also automatically generates financial education content and investment reports to support the improvement of the user's financial literacy. This enables the user to manage their assets more effectively and achieve their future financial goals. For example, the user inputs "I want to go from my home to the station." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generative AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route is generated. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video also starts navigating north. This allows users to receive navigation tailored to their direction of travel. Furthermore, the video screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows users to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it appealing to everyone. Users can receive intuitive navigation without complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. As a result, the personal financial advisor system can support users in managing their finances and achieving their future financial goals.

[0029] The personal financial advisor system according to this embodiment comprises a reception unit, an analysis unit, a forecasting unit, and a generation unit. The reception unit receives user questions. User questions include, for example, general financial questions or specific investment consultations, but are not limited to such examples. The reception unit receives user questions using, for example, a chatbot. The reception unit can also receive user questions using a voice assistant. Furthermore, the reception unit can also receive user questions through a web form. For example, the reception unit receives user questions in real time using a chatbot. The voice assistant analyzes the user's voice input and understands the content of the question. The web form saves the questions entered by the user to a database for later analysis. The analysis unit analyzes the user's transaction history and spending patterns. Transaction history includes, for example, bank transactions and credit card usage history, but is not limited to such examples. Spending patterns include, for example, monthly spending and spending by category, but is not limited to such examples. The analysis unit analyzes, for example, the user's bank transaction history and identifies spending patterns. The analysis unit can also analyze credit card usage history to understand spending trends. Furthermore, it can analyze monthly spending to understand fluctuations in spending. For example, the analysis unit identifies a user's spending patterns based on bank transaction history. Credit card usage history is categorized by spending type. Monthly spending is visualized as graphs and charts. The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. Income and expenditure forecasts may use, but are not limited to, forecasting algorithms based on historical data. Investment returns may use, but are not limited to, indicators such as ROI (Return on Investment) and IRR (Internal Rate of Return). The forecasting unit makes income and expenditure forecasts based on historical transaction data, for example. The forecasting unit can also calculate investment returns considering market trends. Furthermore, the forecasting unit can adjust forecast results based on the user's financial goals. For example, the forecasting unit predicts future income and expenditures based on historical transaction data. Market trends are calculated considering economic indicators and stock price fluctuations.The user's financial goals are reflected in the forecast results. The generation unit generates financial education content and investment reports based on the forecast results obtained by the forecast unit. Financial education content includes, but is not limited to, video tutorials and text guides. Investment reports include, but are not limited to, PDF reports and interactive web pages. The generation unit can, for example, automatically generate and provide video tutorials to the user. The generation unit can also generate text guides to help improve the user's financial literacy. The generation unit can also generate and provide investment reports to the user. For example, the generation unit generates video tutorials to provide the user with visual learning content. Text guides are provided as documents with detailed explanations. Investment reports are generated in PDF format and provided to the user in a downloadable format. Thus, the personal financial advisor system according to the embodiment can receive user questions, analyze transaction history and spending patterns, calculate income and expenditure forecasts and investment returns, and generate financial education content and investment reports.

[0030] The reception desk receives user inquiries. User inquiries include, but are not limited to, general financial questions or specific investment advice. The reception desk can, for example, use a chatbot to receive user inquiries. The chatbot uses natural language processing technology to analyze user input and generate appropriate responses. For example, if a user asks, "How can I reduce my spending this month?", the chatbot will refer to past spending data and suggest specific ways to save money. The reception desk can also receive user inquiries using a voice assistant. The voice assistant uses speech recognition technology to convert the user's voice input into text and understand the content of the question. For example, if a user asks, "How much do I need to save before my next payday?", the voice assistant will analyze the question and provide an appropriate answer. Furthermore, the reception desk can also receive user inquiries through web forms. Web forms store the questions entered by users in a database for later analysis. For example, if a user enters "My credit card payment this month is high, what should I do?" into a web form, the question will be stored in the database and later analyzed by the analytics department. This allows the reception desk to receive user inquiries through multiple interfaces and respond quickly and appropriately.

[0031] The analytics department analyzes users' transaction history and spending patterns. Transaction history includes, but is not limited to, bank transactions and credit card usage history. The analytics department collects this data and analyzes it using statistical methods and machine learning algorithms. For example, it analyzes users' bank transaction history to identify spending patterns. Specifically, it calculates the balance between monthly income and expenses and issues alerts if unusual spending occurs. It also analyzes credit card usage history and categorizes spending. For example, it divides spending into categories such as food, transportation, and entertainment, and calculates the percentage of spending in each category. Furthermore, it visualizes monthly spending as graphs and charts so that users can grasp spending trends at a glance. For example, it can display fluctuations in spending over the past six months as a line graph to identify the cause of a sudden increase in spending in a particular month. This allows the analytics department to gain a detailed understanding of users' financial situations and provide foundational data to offer appropriate advice.

[0032] The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. Income and expenditure forecasts may use, but are not limited to, forecasting algorithms based on historical data. The forecasting unit makes income and expenditure forecasts based on historical transaction data. Specifically, it uses past income and expenditure data to predict future income and expenditures. For example, it predicts income and expenditures for the next six months based on monthly income and expenditure data for the past year. The forecasting unit can also calculate investment returns considering market trends. For example, it predicts future returns of a specific investment product by considering economic indicators and stock price fluctuations. Specifically, it evaluates investment performance using indicators such as ROI (Return on Investment) and IRR (Internal Rate of Return). Furthermore, the forecasting unit can adjust the forecast results based on the user's financial goals. For example, if the user has a goal of "buying a house in five years," the forecasting unit adjusts the income and expenditure forecast based on that goal and proposes the necessary savings amount and investment strategy. In this way, the forecasting unit can calculate income and expenditure forecasts and investment returns that meet the user's individual needs and provide specific advice.

[0033] The generation unit generates financial education content and investment reports based on the prediction results obtained by the forecasting unit. Financial education content includes, but is not limited to, video tutorials and text guides. For example, the generation unit can automatically generate and provide video tutorials to users. Specifically, it creates scenarios based on the prediction results and generates video content based on them. For example, it can create video tutorials such as "Saving Tips" and "Investment Basics" based on income and expenditure forecasts. The generation unit can also generate text guides to help improve users' financial literacy. For example, it can create and provide users with text guides that explain investment basics and risk management methods in detail. Furthermore, the generation unit can generate and provide investment reports to users. For example, it can generate simulation results of investment returns based on the prediction results as a report in PDF format and provide it to users in a downloadable format. It can also be provided as an interactive web page, allowing users to check the data in real time and perform simulations as needed. In this way, the generation unit can provide users with financial information in a visual and easy-to-understand format, supporting the improvement of users' financial knowledge and appropriate investment decisions.

[0034] The reception desk can receive financial questions through an interactive interface with the user. The reception desk can receive user questions using, for example, a chatbot. The reception desk can also receive user questions using, for example, a voice assistant. The reception desk can also receive user questions through, for example, a web form. This allows the reception desk to receive financial questions through an interactive interface with the user. The interactive interface includes, but is not limited to, chatbots and voice assistants. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input a user's question into a chatbot, which can then analyze the question and generate an answer.

[0035] The analytics unit can analyze a user's transaction history and spending patterns to generate personalized financial advice. For example, the analytics unit can analyze a user's bank transaction history to identify spending patterns. The analytics unit can also analyze credit card usage history to understand spending trends. The analytics unit can also analyze monthly spending to understand fluctuations in spending. This allows the analytics unit to analyze a user's transaction history and spending patterns to generate personalized financial advice. Personalized financial advice includes, but is not limited to, personalized investment strategies and savings suggestions. Some or all of the above processing in the analytics unit may be performed using, for example, AI, or not using AI. For example, the analytics unit can input a user's transaction history into AI, which can then analyze the transaction history to generate personalized financial advice.

[0036] The forecasting unit can calculate future profit and loss forecasts and investment returns based on the user's past trading data and market trends. For example, the forecasting unit makes profit and loss forecasts based on past trading data. The forecasting unit can also calculate investment returns considering market trends. The forecasting unit can also adjust the forecast results based on the user's financial targets. This allows the forecasting unit to calculate future profit and loss forecasts and investment returns based on the user's past trading data and market trends. Market trends include, but are not limited to, stock price fluctuations and trends in economic indicators. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input past trading data into AI, which can then calculate profit and loss forecasts and investment returns.

[0037] The generation unit can automatically generate financial education content and investment reports to help improve users' financial literacy. For example, the generation unit can automatically generate and provide video tutorials to users. The generation unit can also generate text guides to help improve users' financial literacy. The generation unit can also generate and provide investment reports to users. Thus, the generation unit can automatically generate financial education content and investment reports to help improve users' financial literacy. Financial literacy includes, but is not limited to, basic financial knowledge and fundamental investment principles. Some or all of the processes described above in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's transaction history into AI, which can then generate financial education content and investment reports.

[0038] The reception desk can analyze the user's past question history and select the optimal question reception method. For example, the reception desk can automatically display as suggestions the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest question methods to be used at specific times based on the user's past question history. In this way, the reception desk can select the optimal question reception method by analyzing the user's past question history. The optimal question reception method includes, but is not limited to, the user's past behavioral data and survey results. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0039] The reception desk can filter questions based on the user's current financial situation and areas of interest when receiving them. For example, the reception desk can prioritize receiving relevant questions based on the user's current financial situation. The reception desk can also filter relevant questions based on the user's areas of interest. The reception desk can also combine the user's financial situation and areas of interest to receive the most relevant questions. This allows the reception desk to receive highly relevant questions by filtering them based on the user's current financial situation and areas of interest. Filtering includes, but is not limited to, criteria for evaluating financial situation and methods for identifying areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's financial situation data into AI, which can then filter relevant questions.

[0040] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. The reception desk can also prioritize receiving questions related to region-specific financial issues based on the user's geographical location information. The reception desk can also prioritize receiving questions related to the regional economic situation, taking into account the user's location information. In this way, the reception desk can prioritize receiving questions that are highly relevant by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into AI, and the AI ​​can prioritize receiving relevant questions.

[0041] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can analyze the user's social media activity and accept questions related to topics of interest. For example, the reception desk can prioritize questions related to financial issues mentioned by the user on social media. For example, the reception desk can accept questions related to trends based on the user's social media activity. In this way, the reception desk can accept relevant questions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media data into AI, which can then accept relevant questions.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the transaction history. For example, the analysis unit can perform a detailed analysis on important transaction history. For example, it can perform a concise analysis on general transaction history. The analysis unit can also adjust the level of detail of its analysis in stages, depending on the importance of the transaction history. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the transaction history. The importance of a transaction history includes, but is not limited to, transaction amount and transaction frequency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the transaction history into the AI, and the AI ​​can adjust the level of detail of its analysis based on the importance.

[0043] The analysis unit can apply different analysis algorithms depending on the category of expenditure pattern during analysis. For example, the analysis unit can apply a specific analysis algorithm to expenditure patterns related to food expenses. For example, the analysis unit can apply a different analysis algorithm to expenditure patterns related to transportation expenses. The analysis unit can also select and apply the most suitable analysis algorithm for each category of expenditure pattern. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the category of expenditure pattern. Categories of expenditure patterns include, but are not limited to, food expenses, transportation expenses, and entertainment expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input expenditure pattern categories into the AI, and the AI ​​can apply an analysis algorithm appropriate to the category.

[0044] The analysis department can prioritize analysis based on the submission timing of transaction history. For example, the analysis department may prioritize the analysis of recent transaction history. The analysis department may also prioritize analysis based on the submission timing of important transaction history. The analysis department may also adjust the priority of analysis in stages according to the submission timing of transaction history. This allows the analysis department to provide more appropriate analysis results by prioritizing analysis based on the submission timing of transaction history. The submission timing of transaction history includes, but is not limited to, the submission date and submission frequency. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the submission timing of transaction history into AI, and the AI ​​can determine the priority of analysis based on the submission timing.

[0045] The analysis unit can adjust the order of analysis based on the relevance of transaction history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant transaction history. The analysis unit may also determine the order of analysis based on the relevance of transaction history. The analysis unit may also adjust the order of analysis step by step according to the relevance of transaction history. This allows the analysis unit to provide more appropriate analysis results by adjusting the order of analysis based on the relevance of transaction history. The relevance of transaction history includes, but is not limited to, the type of transaction and the interrelationship between transactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of transaction history into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0046] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of transaction history during the prediction process. For example, the prediction unit can analyze the interrelationships of transaction history to improve the accuracy of its predictions. The prediction unit can also adjust its prediction algorithm based on the interrelationships of transaction history. The prediction unit can also correct its prediction results by considering the interrelationships of transaction history. This allows the prediction unit to improve the accuracy of its predictions by considering the interrelationships of transaction history. Interrelationships of transaction history include, but are not limited to, transaction chains and transaction impacts. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships of transaction history into AI, which can then improve the accuracy of its predictions based on these interrelationships.

[0047] The prediction unit can make predictions by considering the attribute information of the submitter of the transaction history. For example, the prediction unit can make predictions by considering the age and occupation of the submitter of the transaction history. The prediction unit can also make predictions by considering the financial status of the submitter of the transaction history. The prediction unit can also adjust the prediction algorithm based on the attribute information of the submitter of the transaction history. This allows the prediction unit to make more appropriate predictions by considering the attribute information of the submitter of the transaction history. The attribute information of the submitter includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the submitter's attribute information into AI, and the AI ​​can make predictions based on the attribute information.

[0048] The prediction unit can make predictions while considering the geographical distribution of transaction history. For example, the prediction unit can analyze the geographical distribution of transaction history to improve the accuracy of predictions. The prediction unit can also adjust the prediction algorithm based on the geographical distribution. The prediction unit can also correct the prediction results by considering the geographical distribution. This allows the prediction unit to make more appropriate predictions by considering the geographical distribution of transaction history. Geographical distribution includes, but is not limited to, the number of transactions by region and the economic conditions of each region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of transaction history into AI, and the AI ​​can make predictions based on the geographical distribution.

[0049] The prediction unit can improve the accuracy of its predictions by referring to relevant literature in the transaction history during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature in the transaction history. The prediction unit can also adjust its prediction algorithm based on the relevant literature. The prediction unit can also correct its prediction results by considering the relevant literature. This allows the prediction unit to improve the accuracy of its predictions by referring to relevant literature in the transaction history. Relevant literature includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature from the transaction history into AI, which can then improve the accuracy of its predictions based on the relevant literature.

[0050] The generation unit can improve the accuracy of generation by considering the interrelationships of transaction history during generation. For example, the generation unit can analyze the interrelationships of transaction history to improve the accuracy of generation. The generation unit can also adjust the generation algorithm based on the interrelationships of transaction history. The generation unit can also correct the generation results by considering the interrelationships of transaction history. This allows the generation unit to improve the accuracy of generation by considering the interrelationships of transaction history. Accuracy of generation includes, but is not limited to, the accuracy and relevance of the generated content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the interrelationships of transaction history into AI, which can then improve the accuracy of generation based on the interrelationships.

[0051] The generation unit can perform generation while considering the attribute information of the submitter of the transaction history. For example, the generation unit can perform generation while considering the age and occupation of the submitter of the transaction history. The generation unit can also perform generation while considering the financial status of the submitter of the transaction history. The generation unit can also adjust the generation algorithm based on the attribute information of the submitter of the transaction history. This allows the generation unit to perform more appropriate generation by considering the attribute information of the submitter of the transaction history. The submitter's attribute information includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submitter's attribute information into AI, and the AI ​​can perform generation based on the attribute information.

[0052] The generation unit can perform generation while considering the geographical distribution of transaction history. For example, the generation unit can analyze the geographical distribution of transaction history to improve the accuracy of generation. The generation unit can also adjust the generation algorithm based on the geographical distribution. The generation unit can also correct the generation results by considering the geographical distribution. This allows the generation unit to perform more appropriate generation by considering the geographical distribution of transaction history. Geographical distribution includes, but is not limited to, the number of transactions by region and the economic conditions of each region. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the geographical distribution of transaction history into AI, and the AI ​​can perform generation based on the geographical distribution.

[0053] The generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history during the generation process. For example, the generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history. The generation unit can also adjust its generation algorithm based on the relevant literature. The generation unit can also correct the generation results by considering the relevant literature. In this way, the generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history. Relevant literature includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature in the transaction history into AI, and the AI ​​can improve the accuracy of its generation based on the relevant literature.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Personalized financial advisory systems can acquire users' health data and incorporate it into financial advice. For example, they can analyze data from a user's fitness tracker and provide investment advice based on their health status. If a user is healthy, the system can suggest high-risk investments. Conversely, if a user's health is deteriorating, it can recommend low-risk investments. Furthermore, based on the user's health data, the system can predict future medical expenses and incorporate them into financial planning. This allows users to receive optimal financial advice tailored to their health condition.

[0056] Personalized financial advisor systems can analyze users' social media activity and incorporate it into financial advice. For example, they can suggest relevant investment opportunities based on the interests and concerns users mention on social media. If a user has shown interest in a particular company or industry, they can also provide investment advice related to that company or industry. Furthermore, they can analyze users' social media activity to understand their spending trends and suggest savings based on their spending patterns. In addition, they can adjust the timing of advice delivery based on the user's social media activity times. This allows users to receive personalized financial advice based on their own interests and concerns.

[0057] Personalized financial advisory systems can provide region-specific financial advice by taking into account the user's geographical location. For example, if a user lives in a particular region, the system can provide investment advice based on the economic conditions and market trends of that region. If the user is traveling, it can also provide spending management advice based on the economic conditions of their destination. Furthermore, it can provide region-specific tax and subsidy information based on the user's geographical location. In addition, it can adjust the timing of advice delivery to the optimal level, taking the user's location into consideration. This allows users to receive financial advice that is best suited to their geographical situation.

[0058] Personalized financial advisory systems can provide financial advice based on a user's life events. For example, if a user is planning to get married, the system can provide advice on wedding-related expenses and financial planning. If a user plans to have children, the system can also propose forecasts of education and childcare costs and financial planning for them. Furthermore, if a user is planning to retire, the system can provide advice on post-retirement living expenses and pension planning. In addition, it can propose optimal investment strategies based on the user's life events. This allows users to receive financial advice tailored to their specific life stages.

[0059] Personalized financial advisor systems can analyze a user's purchase history and incorporate that information into financial advice. For example, they can identify spending patterns from a user's purchase history and suggest ways to save money. If a user spends a lot in a particular category, the system can suggest ways to save money in that category. It can also predict future spending based on the user's purchase history and propose a financial plan. Furthermore, it can suggest the optimal timing for purchasing specific items based on the user's purchase history, advising on the best time to buy. This allows users to receive personalized financial advice tailored to their own purchase history.

[0060] A personal financial advisor system can provide financial advice based on the user's hobbies and interests. For example, if a user spends a lot on a particular hobby, it can suggest ways to save money related to that hobby. If a user has a specific interest, it can also suggest investment opportunities related to that interest. Furthermore, it can forecast future spending and propose a financial plan based on the user's hobbies and interests. In addition, it can adjust the timing of advice delivery to the optimal level based on the user's hobbies and interests. This allows users to receive financial advice that is best suited to their own hobbies and interests.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives user inquiries. User inquiries may include general financial questions or specific investment consultations. The reception desk uses chatbots, voice assistants, web forms, etc., to receive user inquiries. For example, chatbots receive questions in real time, voice assistants analyze voice input, and web forms store submitted questions in a database. Step 2: The analytics department analyzes the user's transaction history and spending patterns. Transaction history includes bank transactions and credit card usage history, while spending patterns include monthly spending and spending by category. The analytics department analyzes this data to understand spending patterns and trends. For example, they identify spending patterns based on bank transaction history, categorize credit card usage history, and visualize monthly spending as graphs and charts. Step 3: The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. For income and expenditure forecasts, a forecasting algorithm based on historical data is used, and for investment returns, indicators such as ROI and IRR are used. The forecasting unit makes income and expenditure forecasts based on historical transaction data, calculates investment returns considering market trends, and adjusts the forecast results based on the user's financial targets. For example, calculations are made considering economic indicators and stock price fluctuations. Step 4: The generation unit generates financial education content and investment reports based on the prediction results obtained by the prediction unit. Financial education content includes video tutorials and text guides, while investment reports include PDF reports and interactive web pages. The generation unit automatically generates video tutorials, text guides, and investment reports and provides them to the user. For example, video tutorials are provided as visual learning content, text guides are provided as documents containing detailed explanations, and investment reports are generated in PDF format.

[0063] (Example of form 2) The personal financial advisor system according to an embodiment of the present invention is a system that provides personalized financial advice using generative AI. The personal financial advisor system accepts financial questions from the user through an interactive interface. Next, the personal financial advisor system analyzes the user's transaction history and spending patterns and generates personalized financial advice. Furthermore, the personal financial advisor system calculates and presents future income and expenditure forecasts and investment return projections to the user. The personal financial advisor system also automatically generates financial education content and investment reports to support the improvement of the user's financial literacy. This enables the user to manage their assets more effectively and achieve their future financial goals. For example, the user inputs "I want to go from my home to the station." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generative AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route is generated. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video also starts navigating north. This allows users to receive navigation tailored to their direction of travel. Furthermore, the video screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows users to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it appealing to everyone. Users can receive intuitive navigation without complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. As a result, the personal financial advisor system can support users in managing their finances and achieving their future financial goals.

[0064] The personal financial advisor system according to this embodiment comprises a reception unit, an analysis unit, a forecasting unit, and a generation unit. The reception unit receives user questions. User questions include, for example, general financial questions or specific investment consultations, but are not limited to such examples. The reception unit receives user questions using, for example, a chatbot. The reception unit can also receive user questions using a voice assistant. Furthermore, the reception unit can also receive user questions through a web form. For example, the reception unit receives user questions in real time using a chatbot. The voice assistant analyzes the user's voice input and understands the content of the question. The web form saves the questions entered by the user to a database for later analysis. The analysis unit analyzes the user's transaction history and spending patterns. Transaction history includes, for example, bank transactions and credit card usage history, but is not limited to such examples. Spending patterns include, for example, monthly spending and spending by category, but is not limited to such examples. The analysis unit analyzes, for example, the user's bank transaction history and identifies spending patterns. The analysis unit can also analyze credit card usage history to understand spending trends. Furthermore, it can analyze monthly spending to understand fluctuations in spending. For example, the analysis unit identifies a user's spending patterns based on bank transaction history. Credit card usage history is categorized by spending type. Monthly spending is visualized as graphs and charts. The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. Income and expenditure forecasts may use, but are not limited to, forecasting algorithms based on historical data. Investment returns may use, but are not limited to, indicators such as ROI (Return on Investment) and IRR (Internal Rate of Return). The forecasting unit makes income and expenditure forecasts based on historical transaction data, for example. The forecasting unit can also calculate investment returns considering market trends. Furthermore, the forecasting unit can adjust forecast results based on the user's financial goals. For example, the forecasting unit predicts future income and expenditures based on historical transaction data. Market trends are calculated considering economic indicators and stock price fluctuations.The user's financial goals are reflected in the forecast results. The generation unit generates financial education content and investment reports based on the forecast results obtained by the forecast unit. Financial education content includes, but is not limited to, video tutorials and text guides. Investment reports include, but are not limited to, PDF reports and interactive web pages. The generation unit can, for example, automatically generate and provide video tutorials to the user. The generation unit can also generate text guides to help improve the user's financial literacy. The generation unit can also generate and provide investment reports to the user. For example, the generation unit generates video tutorials to provide the user with visual learning content. Text guides are provided as documents with detailed explanations. Investment reports are generated in PDF format and provided to the user in a downloadable format. Thus, the personal financial advisor system according to the embodiment can receive user questions, analyze transaction history and spending patterns, calculate income and expenditure forecasts and investment returns, and generate financial education content and investment reports.

[0065] The reception desk receives user inquiries. User inquiries include, but are not limited to, general financial questions or specific investment advice. The reception desk can, for example, use a chatbot to receive user inquiries. The chatbot uses natural language processing technology to analyze user input and generate appropriate responses. For example, if a user asks, "How can I reduce my spending this month?", the chatbot will refer to past spending data and suggest specific ways to save money. The reception desk can also receive user inquiries using a voice assistant. The voice assistant uses speech recognition technology to convert the user's voice input into text and understand the content of the question. For example, if a user asks, "How much do I need to save before my next payday?", the voice assistant will analyze the question and provide an appropriate answer. Furthermore, the reception desk can also receive user inquiries through web forms. Web forms store the questions entered by users in a database for later analysis. For example, if a user enters "My credit card payment this month is high, what should I do?" into a web form, the question will be stored in the database and later analyzed by the analytics department. This allows the reception desk to receive user inquiries through multiple interfaces and respond quickly and appropriately.

[0066] The analytics department analyzes users' transaction history and spending patterns. Transaction history includes, but is not limited to, bank transactions and credit card usage history. The analytics department collects this data and analyzes it using statistical methods and machine learning algorithms. For example, it analyzes users' bank transaction history to identify spending patterns. Specifically, it calculates the balance between monthly income and expenses and issues alerts if unusual spending occurs. It also analyzes credit card usage history and categorizes spending. For example, it divides spending into categories such as food, transportation, and entertainment, and calculates the percentage of spending in each category. Furthermore, it visualizes monthly spending as graphs and charts so that users can grasp spending trends at a glance. For example, it can display fluctuations in spending over the past six months as a line graph to identify the cause of a sudden increase in spending in a particular month. This allows the analytics department to gain a detailed understanding of users' financial situations and provide foundational data to offer appropriate advice.

[0067] The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. Income and expenditure forecasts may use, but are not limited to, forecasting algorithms based on historical data. The forecasting unit makes income and expenditure forecasts based on historical transaction data. Specifically, it uses past income and expenditure data to predict future income and expenditures. For example, it predicts income and expenditures for the next six months based on monthly income and expenditure data for the past year. The forecasting unit can also calculate investment returns considering market trends. For example, it predicts future returns of a specific investment product by considering economic indicators and stock price fluctuations. Specifically, it evaluates investment performance using indicators such as ROI (Return on Investment) and IRR (Internal Rate of Return). Furthermore, the forecasting unit can adjust the forecast results based on the user's financial goals. For example, if the user has a goal of "buying a house in five years," the forecasting unit adjusts the income and expenditure forecast based on that goal and proposes the necessary savings amount and investment strategy. In this way, the forecasting unit can calculate income and expenditure forecasts and investment returns that meet the user's individual needs and provide specific advice.

[0068] The generation unit generates financial education content and investment reports based on the prediction results obtained by the forecasting unit. Financial education content includes, but is not limited to, video tutorials and text guides. For example, the generation unit can automatically generate and provide video tutorials to users. Specifically, it creates scenarios based on the prediction results and generates video content based on them. For example, it can create video tutorials such as "Saving Tips" and "Investment Basics" based on income and expenditure forecasts. The generation unit can also generate text guides to help improve users' financial literacy. For example, it can create and provide users with text guides that explain investment basics and risk management methods in detail. Furthermore, the generation unit can generate and provide investment reports to users. For example, it can generate simulation results of investment returns based on the prediction results as a report in PDF format and provide it to users in a downloadable format. It can also be provided as an interactive web page, allowing users to check the data in real time and perform simulations as needed. In this way, the generation unit can provide users with financial information in a visual and easy-to-understand format, supporting the improvement of users' financial knowledge and appropriate investment decisions.

[0069] The reception desk can receive financial questions through an interactive interface with the user. The reception desk can receive user questions using, for example, a chatbot. The reception desk can also receive user questions using, for example, a voice assistant. The reception desk can also receive user questions through, for example, a web form. This allows the reception desk to receive financial questions through an interactive interface with the user. The interactive interface includes, but is not limited to, chatbots and voice assistants. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input a user's question into a chatbot, which can then analyze the question and generate an answer.

[0070] The analytics unit can analyze a user's transaction history and spending patterns to generate personalized financial advice. For example, the analytics unit can analyze a user's bank transaction history to identify spending patterns. The analytics unit can also analyze credit card usage history to understand spending trends. The analytics unit can also analyze monthly spending to understand fluctuations in spending. This allows the analytics unit to analyze a user's transaction history and spending patterns to generate personalized financial advice. Personalized financial advice includes, but is not limited to, personalized investment strategies and savings suggestions. Some or all of the above processing in the analytics unit may be performed using, for example, AI, or not using AI. For example, the analytics unit can input a user's transaction history into AI, which can then analyze the transaction history to generate personalized financial advice.

[0071] The forecasting unit can calculate future profit and loss forecasts and investment returns based on the user's past trading data and market trends. For example, the forecasting unit makes profit and loss forecasts based on past trading data. The forecasting unit can also calculate investment returns considering market trends. The forecasting unit can also adjust the forecast results based on the user's financial targets. This allows the forecasting unit to calculate future profit and loss forecasts and investment returns based on the user's past trading data and market trends. Market trends include, but are not limited to, stock price fluctuations and trends in economic indicators. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input past trading data into AI, which can then calculate profit and loss forecasts and investment returns.

[0072] The generation unit can automatically generate financial education content and investment reports to help improve users' financial literacy. For example, the generation unit can automatically generate and provide video tutorials to users. The generation unit can also generate text guides to help improve users' financial literacy. The generation unit can also generate and provide investment reports to users. Thus, the generation unit can automatically generate financial education content and investment reports to help improve users' financial literacy. Financial literacy includes, but is not limited to, basic financial knowledge and fundamental investment principles. Some or all of the processes described above in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's transaction history into AI, which can then generate financial education content and investment reports.

[0073] The reception desk can estimate the user's emotions and adjust the way questions are answered based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input and answer questions quickly. This allows the reception desk to answer questions more appropriately by adjusting the way questions are answered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and adjust the way questions are answered based on the result.

[0074] The reception desk can analyze the user's past question history and select the optimal question reception method. For example, the reception desk can automatically display as suggestions the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest question methods to be used at specific times based on the user's past question history. In this way, the reception desk can select the optimal question reception method by analyzing the user's past question history. The optimal question reception method includes, but is not limited to, the user's past behavioral data and survey results. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0075] The reception desk can filter questions based on the user's current financial situation and areas of interest when receiving them. For example, the reception desk can prioritize receiving relevant questions based on the user's current financial situation. The reception desk can also filter relevant questions based on the user's areas of interest. The reception desk can also combine the user's financial situation and areas of interest to receive the most relevant questions. This allows the reception desk to receive highly relevant questions by filtering them based on the user's current financial situation and areas of interest. Filtering includes, but is not limited to, criteria for evaluating financial situation and methods for identifying areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's financial situation data into AI, which can then filter relevant questions.

[0076] The reception desk can estimate the user's emotions and determine the priority of questions to answer based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize important questions. If the user is relaxed, the reception desk may also prioritize detailed questions. If the user is in a hurry, the reception desk may also prioritize questions that can be answered quickly. This allows the reception desk to answer questions more appropriately by prioritizing questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of questions based on the result.

[0077] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. The reception desk can also prioritize receiving questions related to region-specific financial issues based on the user's geographical location information. The reception desk can also prioritize receiving questions related to the regional economic situation, taking into account the user's location information. In this way, the reception desk can prioritize receiving questions that are highly relevant by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into AI, and the AI ​​can prioritize receiving relevant questions.

[0078] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can analyze the user's social media activity and accept questions related to topics of interest. For example, the reception desk can prioritize questions related to financial issues mentioned by the user on social media. For example, the reception desk can accept questions related to trends based on the user's social media activity. In this way, the reception desk can accept relevant questions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media data into AI, which can then accept relevant questions.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the presentation of the analysis can be adjusted based on the result.

[0080] The analysis unit can adjust the level of detail of its analysis based on the importance of the transaction history. For example, the analysis unit can perform a detailed analysis on important transaction history. For example, it can perform a concise analysis on general transaction history. The analysis unit can also adjust the level of detail of its analysis in stages, depending on the importance of the transaction history. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the transaction history. The importance of a transaction history includes, but is not limited to, transaction amount and transaction frequency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the transaction history into the AI, and the AI ​​can adjust the level of detail of its analysis based on the importance.

[0081] The analysis unit can apply different analysis algorithms depending on the category of expenditure pattern during analysis. For example, the analysis unit can apply a specific analysis algorithm to expenditure patterns related to food expenses. For example, the analysis unit can apply a different analysis algorithm to expenditure patterns related to transportation expenses. The analysis unit can also select and apply the most suitable analysis algorithm for each category of expenditure pattern. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the category of expenditure pattern. Categories of expenditure patterns include, but are not limited to, food expenses, transportation expenses, and entertainment expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input expenditure pattern categories into the AI, and the AI ​​can apply an analysis algorithm appropriate to the category.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the length of the analysis based on the result.

[0083] The analysis department can prioritize analysis based on the submission timing of transaction history. For example, the analysis department may prioritize the analysis of recent transaction history. The analysis department may also prioritize analysis based on the submission timing of important transaction history. The analysis department may also adjust the priority of analysis in stages according to the submission timing of transaction history. This allows the analysis department to provide more appropriate analysis results by prioritizing analysis based on the submission timing of transaction history. The submission timing of transaction history includes, but is not limited to, the submission date and submission frequency. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the submission timing of transaction history into AI, and the AI ​​can determine the priority of analysis based on the submission timing.

[0084] The analysis unit can adjust the order of analysis based on the relevance of transaction history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant transaction history. The analysis unit may also determine the order of analysis based on the relevance of transaction history. The analysis unit may also adjust the order of analysis step by step according to the relevance of transaction history. This allows the analysis unit to provide more appropriate analysis results by adjusting the order of analysis based on the relevance of transaction history. The relevance of transaction history includes, but is not limited to, the type of transaction and the interrelationship between transactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of transaction history into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0085] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is tense, the prediction unit may apply conservative prediction criteria. For example, if the user is relaxed, the prediction unit may also apply detailed prediction criteria. For example, if the user is in a hurry, the prediction unit may also apply criteria to provide prediction results quickly. In this way, the prediction unit can provide more appropriate prediction results by adjusting the prediction criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the prediction criteria can be adjusted based on the result.

[0086] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of transaction history during the prediction process. For example, the prediction unit can analyze the interrelationships of transaction history to improve the accuracy of its predictions. The prediction unit can also adjust its prediction algorithm based on the interrelationships of transaction history. The prediction unit can also correct its prediction results by considering the interrelationships of transaction history. This allows the prediction unit to improve the accuracy of its predictions by considering the interrelationships of transaction history. Interrelationships of transaction history include, but are not limited to, transaction chains and transaction impacts. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships of transaction history into AI, which can then improve the accuracy of its predictions based on these interrelationships.

[0087] The prediction unit can make predictions by considering the attribute information of the submitter of the transaction history. For example, the prediction unit can make predictions by considering the age and occupation of the submitter of the transaction history. The prediction unit can also make predictions by considering the financial status of the submitter of the transaction history. The prediction unit can also adjust the prediction algorithm based on the attribute information of the submitter of the transaction history. This allows the prediction unit to make more appropriate predictions by considering the attribute information of the submitter of the transaction history. The attribute information of the submitter includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the submitter's attribute information into AI, and the AI ​​can make predictions based on the attribute information.

[0088] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is tense, the prediction unit may prioritize displaying important prediction results. For example, if the user is relaxed, the prediction unit may prioritize displaying detailed prediction results. For example, if the user is in a hurry, the prediction unit may prioritize displaying prediction results that can be quickly reviewed. In this way, the prediction unit can provide more appropriate prediction results by adjusting the display order of prediction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the order in which prediction results are displayed can be adjusted based on the result.

[0089] The prediction unit can make predictions while considering the geographical distribution of transaction history. For example, the prediction unit can analyze the geographical distribution of transaction history to improve the accuracy of predictions. The prediction unit can also adjust the prediction algorithm based on the geographical distribution. The prediction unit can also correct the prediction results by considering the geographical distribution. This allows the prediction unit to make more appropriate predictions by considering the geographical distribution of transaction history. Geographical distribution includes, but is not limited to, the number of transactions by region and the economic conditions of each region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of transaction history into AI, and the AI ​​can make predictions based on the geographical distribution.

[0090] The prediction unit can improve the accuracy of its predictions by referring to relevant literature in the transaction history during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature in the transaction history. The prediction unit can also adjust its prediction algorithm based on the relevant literature. The prediction unit can also correct its prediction results by considering the relevant literature. This allows the prediction unit to improve the accuracy of its predictions by referring to relevant literature in the transaction history. Relevant literature includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature from the transaction history into AI, which can then improve the accuracy of its predictions based on the relevant literature.

[0091] The generation unit can estimate the user's emotions and determine the priority of content to generate based on the estimated emotions. For example, if the user is tense, the generation unit may prioritize generating important content. If the user is relaxed, the generation unit may also prioritize generating detailed content. If the user is in a hurry, the generation unit may also prioritize generating content that can be quickly reviewed. In this way, the generation unit can provide more appropriate content by prioritizing content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user facial expression data into a generation AI, the generation AI can estimate emotions, and the generation unit can determine the priority of content to generate based on the result.

[0092] The generation unit can improve the accuracy of generation by considering the interrelationships of transaction history during generation. For example, the generation unit can analyze the interrelationships of transaction history to improve the accuracy of generation. The generation unit can also adjust the generation algorithm based on the interrelationships of transaction history. The generation unit can also correct the generation results by considering the interrelationships of transaction history. This allows the generation unit to improve the accuracy of generation by considering the interrelationships of transaction history. Accuracy of generation includes, but is not limited to, the accuracy and relevance of the generated content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the interrelationships of transaction history into AI, which can then improve the accuracy of generation based on the interrelationships.

[0093] The generation unit can perform generation while considering the attribute information of the submitter of the transaction history. For example, the generation unit can perform generation while considering the age and occupation of the submitter of the transaction history. The generation unit can also perform generation while considering the financial status of the submitter of the transaction history. The generation unit can also adjust the generation algorithm based on the attribute information of the submitter of the transaction history. This allows the generation unit to perform more appropriate generation by considering the attribute information of the submitter of the transaction history. The submitter's attribute information includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submitter's attribute information into AI, and the AI ​​can perform generation based on the attribute information.

[0094] The generation unit can estimate the user's emotions and adjust how the generated content is displayed based on the estimated emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. If the user is in a hurry, the generation unit can also provide a display method that gets straight to the point. In this way, the generation unit can provide more appropriate content by adjusting how the content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user facial expression data into the generation AI, the generation AI can estimate emotions, and the display method of the generated content can be adjusted based on the result.

[0095] The generation unit can perform generation while considering the geographical distribution of transaction history. For example, the generation unit can analyze the geographical distribution of transaction history to improve the accuracy of generation. The generation unit can also adjust the generation algorithm based on the geographical distribution. The generation unit can also correct the generation results by considering the geographical distribution. This allows the generation unit to perform more appropriate generation by considering the geographical distribution of transaction history. Geographical distribution includes, but is not limited to, the number of transactions by region and the economic conditions of each region. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the geographical distribution of transaction history into AI, and the AI ​​can perform generation based on the geographical distribution.

[0096] The generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history during the generation process. For example, the generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history. The generation unit can also adjust its generation algorithm based on the relevant literature. The generation unit can also correct the generation results by considering the relevant literature. In this way, the generation unit can improve the accuracy of its generation by referring to relevant literature in the transaction history. Relevant literature includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature in the transaction history into AI, and the AI ​​can improve the accuracy of its generation based on the relevant literature.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] Personalized financial advisory systems can acquire users' health data and incorporate it into financial advice. For example, they can analyze data from a user's fitness tracker and provide investment advice based on their health status. If a user is healthy, the system can suggest high-risk investments. Conversely, if a user's health is deteriorating, it can recommend low-risk investments. Furthermore, based on the user's health data, the system can predict future medical expenses and incorporate them into financial planning. This allows users to receive optimal financial advice tailored to their health condition.

[0099] Personalized financial advisor systems can analyze users' social media activity and incorporate it into financial advice. For example, they can suggest relevant investment opportunities based on the interests and concerns users mention on social media. If a user has shown interest in a particular company or industry, they can also provide investment advice related to that company or industry. Furthermore, they can analyze users' social media activity to understand their spending trends and suggest savings based on their spending patterns. In addition, they can adjust the timing of advice delivery based on the user's social media activity times. This allows users to receive personalized financial advice based on their own interests and concerns.

[0100] Personalized financial advisor systems can estimate a user's emotions and adjust their advice based on those emotions. For example, if a user is stressed, they might suggest low-risk investments. If the user is relaxed, they might suggest higher-risk investments. If the user is excited, they might suggest short-term investment opportunities. Furthermore, the system can adjust the way advice is presented based on the user's emotions, making it easier to understand. This allows users to receive optimal financial advice tailored to their emotional state.

[0101] Personalized financial advisory systems can provide region-specific financial advice by taking into account the user's geographical location. For example, if a user lives in a particular region, the system can provide investment advice based on the economic conditions and market trends of that region. If the user is traveling, it can also provide spending management advice based on the economic conditions of their destination. Furthermore, it can provide region-specific tax and subsidy information based on the user's geographical location. In addition, it can adjust the timing of advice delivery to the optimal level, taking the user's location into consideration. This allows users to receive financial advice that is best suited to their geographical situation.

[0102] Personalized financial advisory systems can provide financial advice based on a user's life events. For example, if a user is planning to get married, the system can provide advice on wedding-related expenses and financial planning. If a user plans to have children, the system can also propose forecasts of education and childcare costs and financial planning for them. Furthermore, if a user is planning to retire, the system can provide advice on post-retirement living expenses and pension planning. In addition, it can propose optimal investment strategies based on the user's life events. This allows users to receive financial advice tailored to their specific life stages.

[0103] Personalized financial advisor systems can estimate a user's emotions and adjust the timing of advice based on those emotions. For example, if a user is stressed, the system may temporarily refrain from providing advice. If the user is relaxed, it may proactively provide advice. If the user is in a hurry, it may provide advice that can be understood in a short amount of time. Furthermore, it can adjust the frequency of advice based on the user's emotions, avoiding the provision of excessive information. This allows users to receive financial advice at the optimal time according to their emotional state.

[0104] Personalized financial advisor systems can analyze a user's purchase history and incorporate that information into financial advice. For example, they can identify spending patterns from a user's purchase history and suggest ways to save money. If a user spends a lot in a particular category, the system can suggest ways to save money in that category. It can also predict future spending based on the user's purchase history and propose a financial plan. Furthermore, it can suggest the optimal timing for purchasing specific items based on the user's purchase history, advising on the best time to buy. This allows users to receive personalized financial advice tailored to their own purchase history.

[0105] Personalized financial advisor systems can estimate a user's emotions and personalize their advice based on those emotions. For example, if a user is stressed, they might suggest low-risk investments. If the user is relaxed, they might suggest higher-risk investments. If the user is excited, they might suggest short-term investment opportunities. Furthermore, the system can adjust the way advice is presented based on the user's emotions, making it easier to understand. This allows users to receive optimal financial advice tailored to their emotional state.

[0106] A personal financial advisor system can provide financial advice based on the user's hobbies and interests. For example, if a user spends a lot on a particular hobby, it can suggest ways to save money related to that hobby. If a user has a specific interest, it can also suggest investment opportunities related to that interest. Furthermore, it can forecast future spending and propose a financial plan based on the user's hobbies and interests. In addition, it can adjust the timing of advice delivery to the optimal level based on the user's hobbies and interests. This allows users to receive financial advice that is best suited to their own hobbies and interests.

[0107] Personalized financial advisor systems can estimate a user's emotions and prioritize advice based on those emotions. For example, if a user is stressed, important advice can be prioritized. If a user is relaxed, detailed advice can be prioritized. If a user is in a hurry, advice that can be quickly reviewed can be prioritized. Furthermore, the system can adjust the order in which advice is delivered based on the user's emotions to provide more effective advice. This allows users to receive optimal financial advice tailored to their emotional state.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives user inquiries. User inquiries may include general financial questions or specific investment consultations. The reception desk uses chatbots, voice assistants, web forms, etc., to receive user inquiries. For example, chatbots receive questions in real time, voice assistants analyze voice input, and web forms store submitted questions in a database. Step 2: The analytics department analyzes the user's transaction history and spending patterns. Transaction history includes bank transactions and credit card usage history, while spending patterns include monthly spending and spending by category. The analytics department analyzes this data to understand spending patterns and trends. For example, they identify spending patterns based on bank transaction history, categorize credit card usage history, and visualize monthly spending as graphs and charts. Step 3: The forecasting unit calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the analysis unit. For income and expenditure forecasts, a forecasting algorithm based on historical data is used, and for investment returns, indicators such as ROI and IRR are used. The forecasting unit makes income and expenditure forecasts based on historical transaction data, calculates investment returns considering market trends, and adjusts the forecast results based on the user's financial targets. For example, calculations are made considering economic indicators and stock price fluctuations. Step 4: The generation unit generates financial education content and investment reports based on the prediction results obtained by the prediction unit. Financial education content includes video tutorials and text guides, while investment reports include PDF reports and interactive web pages. The generation unit automatically generates video tutorials, text guides, and investment reports and provides them to the user. For example, video tutorials are provided as visual learning content, text guides are provided as documents containing detailed explanations, and investment reports are generated in PDF format.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, forecasting unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user questions using a chatbot or voice assistant on the smart device 14. The analysis unit analyzes the user's transaction history and spending patterns using the specific processing unit 290 of the data processing unit 12. The forecasting unit calculates income and expenditure forecasts and investment returns using the specific processing unit 290 of the data processing unit 12. The generation unit generates financial education content and investment reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, forecasting unit, and generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user questions using the chatbot or voice assistant of the smart glasses 214. The analysis unit analyzes the user's transaction history and spending patterns using the specific processing unit 290 of the data processing unit 12. The forecasting unit calculates income and expenditure forecasts and investment returns using the specific processing unit 290 of the data processing unit 12. The generation unit generates financial education content and investment reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, forecasting unit, and generation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user questions using a chatbot or voice assistant on the headset terminal 314. The analysis unit analyzes the user's transaction history and spending patterns using the specific processing unit 290 of the data processing unit 12. The forecasting unit calculates income and expenditure forecasts and investment returns using the specific processing unit 290 of the data processing unit 12. The generation unit generates financial education content and investment reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, forecasting unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user questions using the chatbot or voice assistant of the robot 414. The analysis unit analyzes the user's transaction history and spending patterns using the specific processing unit 290 of the data processing unit 12. The forecasting unit calculates income and expenditure forecasts and investment returns using the specific processing unit 290 of the data processing unit 12. The generation unit generates financial education content and investment reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices or control units is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception desk that handles user inquiries, An analysis unit analyzes transaction history and spending patterns based on questions received by the reception unit, A forecasting unit that calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the aforementioned analysis unit, The system includes a generation unit that generates financial education content and investment reports based on the prediction results obtained by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts financial questions through an interactive interface with the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It analyzes the user's transaction history and spending patterns to generate personalized financial advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Based on the user's past trading data and market trends, it calculates future profit and loss forecasts and investment returns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We automatically generate financial education content and investment reports to support users in improving their financial literacy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of spending pattern. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, the priority of the analysis is determined based on when the transaction history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, When making predictions, we improve the accuracy of the predictions by considering the interrelationships of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, the attribute information of the person submitting the transaction history is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, the geographical distribution of transaction history is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, we improve the accuracy of our predictions by referring to relevant literature in the transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates user sentiment and determines the priority of content to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the accuracy of the generation is improved by considering the interrelationships of transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the attribute information of the person submitting the transaction history is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the geographical distribution of transaction history is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the accuracy of the data is improved by referencing relevant literature in the transaction history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles user inquiries, An analysis unit analyzes transaction history and spending patterns based on questions received by the reception unit, A forecasting unit that calculates income and expenditure forecasts and investment returns based on the analysis results obtained by the aforementioned analysis unit, The system includes a generation unit that generates financial education content and investment reports based on the prediction results obtained by the prediction unit. A system characterized by the following features.

2. The aforementioned reception unit is It accepts financial questions through an interactive interface with the user. The system according to feature 1.

3. The aforementioned analysis unit is It analyzes the user's transaction history and spending patterns to generate personalized financial advice. The system according to feature 1.

4. The prediction unit, Based on the user's past trading data and market trends, it calculates future profit and loss forecasts and investment returns. The system according to feature 1.

5. The generating unit is We automatically generate financial education content and investment reports to support users in improving their financial literacy. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system according to feature 1.

8. The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current financial situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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