system

Abstract

The system according to this embodiment aims to analyze the user's purchase history and balance information and propose the optimal insurance and spending amount. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's purchase history and balance information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal insurance and monthly spending amount based on the analysis results obtained by the analysis unit. The provision unit provides the information proposed by the proposal unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, it has not been fully carried out to propose an optimal insurance or expenditure amount based on the user's purchase history and balance information, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's purchase history and balance information and propose an optimal insurance or expenditure amount.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's purchase history and balance information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes optimal insurance and monthly spending amounts based on the analysis results obtained by the analysis unit. The provision unit provides the information proposed by the proposal unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's purchase history and balance information to propose the optimal insurance and spending amount. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial planner system according to an embodiment of the present invention is a system that manages optimal insurance and monthly spending amounts by utilizing the purchase history of an electronic payment system and balance information from a digital banking system. This system is expected to promote the digitalization of payroll and secure customer retention in electronic payment services. First, the financial planner system collects the user's purchase history from the electronic payment system and balance information from the digital banking system. Next, the financial planner system uses AI to analyze the collected information and proposes optimal insurance and monthly spending amounts to the user. For example, if the financial planner system determines from the user's purchase history that a specific insurance is necessary, it will propose that insurance. The financial planner system also calculates the monthly spending amount from the user's balance information and presents it to the user. This system allows users to efficiently manage their spending and choose the optimal insurance. Furthermore, it is expected to promote the digitalization of payroll and secure customer retention in electronic payment services. The specific steps are as follows: 1. The financial planner system collects the user's purchase history from the electronic payment system and balance information from the digital banking system. 2. The financial planner system uses AI to analyze the collected information. 3. Based on the analysis results, the financial planner system proposes the most suitable insurance and monthly spending amount for the user. 4. The financial planner system provides the proposed information to the user. For example, if a user saves a fixed amount each month, the financial planner system will propose the most suitable insurance based on that savings amount. The financial planner system can also identify specific spending patterns from the user's purchase history and calculate the monthly spending amount based on those patterns. This system allows users to efficiently manage their spending and choose the most suitable insurance, thereby achieving financial stability. It is also expected to promote the digitalization of payroll and secure customer retention through electronic payment services. In this way, the financial planner system can help users achieve financial stability.

[0029] The financial planner system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's purchase history and balance information. The collection unit collects, for example, purchase history from electronic payment systems and balance information from digital banking systems. The collection unit can collect purchase history from electronic payment systems such as credit cards, debit cards, and electronic money. The collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. For example, the collection unit collects credit card usage history to understand the user's purchasing patterns. The collection unit can also collect debit card usage history to analyze the user's spending trends. The collection unit can also collect electronic money usage history to analyze the user's consumption behavior. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected purchase history and balance information to determine the user's spending patterns and the need for insurance. The analysis unit can use AI technologies such as machine learning and deep learning to analyze the user's spending patterns with high accuracy. The analysis unit analyzes, for example, monthly spending trends and spending percentages by category to understand the user's spending patterns. The analysis unit can also determine the user's insurance needs based on risk assessment and life stage. The proposal unit proposes the most suitable insurance and monthly spending amount to the user based on the analysis results obtained by the analysis unit. The proposal unit proposes the most suitable insurance to the user based on the analysis results. The proposal unit can select the most suitable insurance for the user by considering, for example, the type of insurance, premiums, and coverage. The proposal unit can also propose a monthly spending amount to the user by considering, for example, income, fixed costs, and variable costs. The proposal unit calculates the monthly spending amount based on the user's spending patterns and presents it to the user. The proposal unit can also propose insurance if it determines that a specific insurance is necessary based on the user's purchase history. The delivery unit provides the information proposed by the proposal unit to the user. The delivery unit provides, for example, information on the proposed insurance and spending amount to the user.The provisioning unit can provide information to users, for example, through web applications or mobile applications. The provisioning unit can also provide information to users, for example, using email or push notifications. The provisioning unit can display information in a format optimized for the user's device. This allows the financial planner system according to the embodiment to promote the user's financial stability. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the collection unit collects purchase history from electronic payment systems and balance information from digital banking systems, the analysis unit inputs the collected information into AI and outputs the analysis results, the proposal unit proposes the most suitable insurance and monthly spending amount to the user based on the analysis results, and the provisioning unit provides the proposed information to the user.

[0030] The data collection unit collects user purchase history and balance information. For example, it collects purchase history from electronic payment systems and balance information from digital banking systems. Specifically, it can collect purchase history from electronic payment systems such as credit cards, debit cards, and e-money. This allows for a detailed understanding of what goods and services users purchase and how frequently they purchase them. The data collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. This allows for a detailed analysis of users' account balances and transaction history, enabling a comprehensive analysis of their financial situation. For example, the data collection unit collects credit card usage history to understand user purchasing patterns. This allows for a detailed analysis of what goods and services users are spending on. Furthermore, it can collect debit card usage history to analyze user spending trends. This allows for an understanding of users' daily spending patterns and analysis of spending trends. It can also collect e-money usage history to analyze user consumption behavior. This allows for an understanding of when users use e-money and analysis of consumption patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses AI to analyze collected purchase history and balance information to determine the user's spending patterns and insurance needs. Specifically, it can analyze user spending patterns with high accuracy using AI technologies such as machine learning and deep learning. For example, it can analyze monthly spending trends and spending percentages by category to understand the user's spending patterns. This allows for a detailed analysis of which categories the user spends the most on. Furthermore, it can determine the user's insurance needs based on risk assessment and life stage. For example, it can propose appropriate insurance types and premiums considering the user's age, family structure, income, and spending patterns. The analysis unit can also perform long-term risk assessments and trend analyses using historical data and statistical information. For example, it can predict fluctuations in spending over a specific period based on past spending data and formulate future spending plans. In addition, it can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The proposal department proposes the most suitable insurance and monthly spending amount to the user based on the analysis results obtained by the analysis department. Specifically, it proposes the most suitable insurance to the user based on the analysis results. For example, it can select the most suitable insurance for the user by considering the type of insurance, premium, and coverage. This allows the user to choose appropriate insurance based on their life stage and risk assessment. It can also propose a monthly spending amount to the user by considering income, fixed costs, and variable costs. For example, it can calculate and present a monthly spending amount based on the user's spending patterns. This allows the user to manage their income and expenses in a balanced way and achieve financial stability. Furthermore, if it is determined from the user's purchase history that a specific insurance is necessary, it can propose that insurance. For example, if the user travels frequently, travel insurance can be proposed. This allows the user to choose insurance that suits their lifestyle. The proposal department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can analyze user reactions to the proposed insurance and spending amounts and reflect them in future proposals. This allows the proposal department to make more appropriate proposals to users and improve the overall reliability and satisfaction of the system.

[0033] The service provider delivers information proposed by the proposal provider to users. Specifically, it provides users with information on proposed insurance and expenditure amounts. For example, information can be delivered to users through web applications or mobile applications. This allows users to access the proposed information anytime, anywhere. Information can also be delivered to users using email or push notifications. This allows users to receive important information quickly. Furthermore, information can be displayed in a format optimized for the user's device. For example, the display format of information is optimized according to the device the user is using, such as a smartphone, tablet, or PC. This allows users to receive information in a highly visible format. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the delivered content. For example, user reactions to the delivered information are analyzed and reflected in future deliveries. This allows the service provider to provide users with more appropriate information and improve the overall reliability and satisfaction of the system. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, ensuring their financial stability.

[0034] The data collection unit can collect purchase history from electronic payment systems and balance information from digital banking systems. For example, the data collection unit collects purchase history from electronic payment systems such as credit cards, debit cards, and electronic money. The data collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. For example, the data collection unit collects credit card usage history to understand the user's purchasing patterns. The data collection unit can also collect debit card usage history to analyze the user's spending trends. The data collection unit can also collect electronic money usage history to analyze the user's consumption behavior. By collecting information from electronic payment systems and digital banking systems, the user's financial situation can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect purchase history from electronic payment systems and balance information from digital banking systems, input it into AI, and perform analysis.

[0035] The analysis unit can analyze the collected information using AI to determine the user's spending patterns and insurance needs. The analysis unit uses AI technologies such as machine learning and deep learning to analyze the user's spending patterns with high accuracy. The analysis unit can analyze monthly spending trends and spending percentages by category to understand the user's spending patterns. The analysis unit can also determine the user's insurance needs based on risk assessment and life stage. In this way, by using AI, the user's spending patterns and insurance needs can be determined with high accuracy. 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 inputs the collected information into the AI ​​to determine the user's spending patterns and insurance needs.

[0036] The proposal unit can suggest the most suitable insurance and monthly spending amount to the user based on the analysis results. For example, the proposal unit can suggest the most suitable insurance to the user based on the analysis results. The proposal unit can select the most suitable insurance to the user by considering factors such as the type of insurance, premiums, and coverage. The proposal unit can also suggest the monthly spending amount to the user by considering factors such as income, fixed costs, and variable costs. For example, the proposal unit can calculate the monthly spending amount based on the user's spending patterns and present it to the user. For example, if the proposal unit determines that a specific insurance is necessary based on the user's purchase history, it can suggest that insurance. In this way, by making optimal suggestions based on the analysis results, it supports the user's financial stability. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into AI and suggest the most suitable insurance and monthly spending amount to the user.

[0037] The service provider can provide the user with the proposed information. For example, the service provider can provide the user with information on proposed insurance or expenditure amounts. The service provider can provide information to the user, for example, through a web application or a mobile application. The service provider can also provide information to the user, for example, using email or push notifications. The service provider can display the information in a format optimized for the user's device. This helps the user make appropriate decisions by providing them with the proposed information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed information into AI and provide it to the user.

[0038] The data collection unit can analyze a user's past purchase history and balance information to select the optimal data collection method. For example, the data collection unit may prioritize collecting purchase history from stores and services that the user frequently uses. The data collection unit may also periodically collect user balance information and analyze it all at the end of the month. For example, the data collection unit may categorize a user's purchase history and focus on collecting data from specific categories. This allows for efficient information collection by analyzing past data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past purchase history and balance information into AI to select the optimal data collection method.

[0039] The data collection unit can filter purchase history and balance information based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the data collection unit will prioritize collecting travel-related purchase history. For example, if the user is interested in health, the data collection unit can focus on collecting health-related purchase history. For example, if the user has started a new hobby, the data collection unit can collect purchase history related to that hobby. By filtering information based on the user's lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into AI and perform filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting purchase history and balance information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history in that region. For example, if the user is traveling, the data collection unit can also focus on collecting purchase history at the travel destination. For example, if the user is at home, the data collection unit can also prioritize the collection of purchase history around the user's home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information when collecting purchase history and balance information. For example, the data collection unit can collect purchase information that the user has shared on social media. The data collection unit can also prioritize collecting purchase history of brands and stores that the user follows on social media. The data collection unit can also collect purchase history of products and services that the user has shown interest in on social media. This allows for the collection of information based on the user's interests by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into the AI ​​and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history. For example, the analysis unit can also apply a financial analysis algorithm to balance information. For example, the analysis unit can also apply a risk analysis algorithm to insurance information. By applying an analysis algorithm appropriate to the category of information, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI ​​and applies a different analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. For example, the analysis unit can also determine the priority of analysis of past information according to its importance. The analysis unit can also adjust the analysis schedule based on the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the timing of information collection into the AI ​​and determines the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the information into the AI ​​and adjust the order of analysis.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the insurance and expenditures. For example, it can provide detailed proposals for high-importance insurance, and concise proposals for lower-importance expenditures. The proposal department can also prioritize proposals based on the importance of the insurance and expenditures. This allows for efficient proposals by adjusting the level of detail according to the importance of the insurance and expenditures. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insurance and expenditures into the AI ​​and adjust the level of detail in the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the insurance and expenditure categories when making a proposal. For example, the proposal unit can apply a risk analysis algorithm to insurance. For example, the proposal unit can also apply a financial analysis algorithm to expenditures. The proposal unit can also select the optimal proposal algorithm depending on the insurance and expenditure categories. This enables highly accurate proposals by applying proposal algorithms tailored to the insurance and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the insurance and expenditure categories into the AI ​​and apply different proposal algorithms.

[0048] The proposal department can prioritize proposals based on the submission timing of insurance and expenditure details. For example, it will prioritize proposals for urgent insurance. It can also prioritize proposals for expenditure details with approaching deadlines. The proposal department can also adjust the proposal schedule based on the submission timing of insurance and expenditure details. This allows for prioritizing urgent proposals by determining the submission timing of insurance and expenditure details. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department inputs the submission timing of insurance and expenditure details into the AI ​​to determine the proposal priority.

[0049] The proposal unit can adjust the order of proposals based on the relevance of insurance and expenditure amounts when making proposals. For example, the proposal unit may prioritize proposing insurance with high relevance. For example, the proposal unit may postpone proposing expenditure amounts with low relevance. For example, the proposal unit may determine the order of proposals based on the relevance of insurance and expenditure amounts. This allows for efficient proposals by adjusting the order of proposals based on the relevance of insurance and expenditure amounts. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input the relevance of insurance and expenditure amounts into the AI ​​and adjust the order of proposals.

[0050] The information delivery unit can select the optimal delivery method by referring to the user's past response history when providing information. For example, the information delivery unit may prioritize using information delivery methods that the user has preferred in the past. For example, the information delivery unit may also select the optimal timing for information delivery based on the user's past response history. For example, the information delivery unit may analyze the user's past response history and select the most effective information delivery method. This makes it possible to select the optimal information delivery method and provide effective information by referring to the user's past response history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit may input the user's past response history into AI and select the optimal delivery method.

[0051] The information provider can customize the information provided based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the provider will prioritize providing travel-related information. If the user is interested in health, the provider can focus on providing health-related information. If the user has started a new hobby, the provider can provide information related to that hobby. By customizing the information based on the user's lifestyle and areas of interest, more relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's lifestyle and areas of interest into AI and customize the information.

[0052] The information provider can select the optimal method of information delivery by considering the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the provider can also provide a concise and highly visible display method. By considering the user's device information, the provider can provide the optimal display method and improve user convenience. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into AI and select the optimal method of information delivery.

[0053] The information provider can provide multilingual information according to the user's language settings when providing information. For example, the information provider can automatically set the language of the information based on the language settings of the user's device. For example, the information provider can also provide a language switching function if the user uses multiple languages. For example, the information provider can also provide information in a specific language if the user selects a particular language. This improves user convenience by providing multilingual information according to the user's language settings. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's language settings into AI and provide multilingual information.

[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] The financial planner system can collect and analyze users' health data in addition to their purchase history and balance information. For example, the collection unit can collect health data from the user's fitness tracker or smartwatch. The analysis unit can analyze the collected health data and propose optimal insurance and spending amounts based on the user's health status. The proposal unit can also suggest a review of health insurance if the user is in good health. This enables comprehensive financial planning that takes the user's health status into consideration.

[0056] A financial planner system can predict a user's life events and provide appropriate advice based on their purchase history and balance information. For example, the analysis unit predicts life events such as marriage and childbirth from the user's purchase history. The proposal unit can propose necessary insurance and savings plans based on the predicted life events. The delivery unit can also provide the user with a concrete action plan to prepare for life events. This allows the user to make appropriate preparations for future life events.

[0057] The financial planner system analyzes users' purchase history and balance information, as well as their social media activity, to propose optimal insurance and spending amounts based on the user's interests. For example, the data collection unit collects posts and followed accounts from the user's social media accounts. The analysis unit analyzes the collected social media data to understand the user's interests. The proposal unit can then propose relevant insurance and spending amounts based on the user's interests. This enables financial planning that is tailored to the user's lifestyle.

[0058] The financial planner system can assess a user's environmental awareness based on their purchase history and balance information, and propose environmentally conscious spending plans. For example, the analysis unit analyzes the user's usage of eco-friendly products and services from their purchase history. The proposal unit can propose eco-friendly insurance and investment products based on the user's environmental awareness. The provision unit can also provide users with environmentally conscious spending plans and saving methods. This allows users to achieve an environmentally conscious lifestyle.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects the user's purchase history and balance information. For example, the data collection unit collects purchase history from electronic payment systems such as credit cards, debit cards, and e-money, and balance information from digital banking systems such as online banking and mobile banking. This allows the system to understand the user's purchasing patterns and spending trends. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI technology to analyze the collected purchase history and balance information to determine the user's spending patterns and insurance needs. This allows the system to understand the user's monthly spending trends and spending breakdowns by category, and to determine the need for insurance based on risk assessment and life stage. Step 3: The proposal department proposes the most suitable insurance and monthly spending amount for the user based on the analysis results obtained by the analysis department. For example, the proposal department selects the most suitable insurance for the user by considering the type of insurance, premiums, and coverage based on the analysis results. It also calculates the monthly spending amount considering income, fixed costs, and variable costs and presents it to the user. Step 4: The service provider delivers the information proposed by the proposal provider to the user. For example, the service provider provides the user with information on proposed insurance and spending amounts through a web application or mobile application. They also provide information via email and push notifications, displaying the information in a format optimized for the user's device.

[0061] (Example of form 2) The financial planner system according to an embodiment of the present invention is a system that manages optimal insurance and monthly spending amounts by utilizing the purchase history of an electronic payment system and balance information from a digital banking system. This system is expected to promote the digitalization of payroll and secure customer retention in electronic payment services. First, the financial planner system collects the user's purchase history from the electronic payment system and balance information from the digital banking system. Next, the financial planner system uses AI to analyze the collected information and proposes optimal insurance and monthly spending amounts to the user. For example, if the financial planner system determines from the user's purchase history that a specific insurance is necessary, it will propose that insurance. The financial planner system also calculates the monthly spending amount from the user's balance information and presents it to the user. This system allows users to efficiently manage their spending and choose the optimal insurance. Furthermore, it is expected to promote the digitalization of payroll and secure customer retention in electronic payment services. The specific steps are as follows: 1. The financial planner system collects the user's purchase history from the electronic payment system and balance information from the digital banking system. 2. The financial planner system uses AI to analyze the collected information. 3. Based on the analysis results, the financial planner system proposes the most suitable insurance and monthly spending amount for the user. 4. The financial planner system provides the proposed information to the user. For example, if a user saves a fixed amount each month, the financial planner system will propose the most suitable insurance based on that savings amount. The financial planner system can also identify specific spending patterns from the user's purchase history and calculate the monthly spending amount based on those patterns. This system allows users to efficiently manage their spending and choose the most suitable insurance, thereby achieving financial stability. It is also expected to promote the digitalization of payroll and secure customer retention through electronic payment services. In this way, the financial planner system can help users achieve financial stability.

[0062] The financial planner system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's purchase history and balance information. The collection unit collects, for example, purchase history from electronic payment systems and balance information from digital banking systems. The collection unit can collect purchase history from electronic payment systems such as credit cards, debit cards, and electronic money. The collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. For example, the collection unit collects credit card usage history to understand the user's purchasing patterns. The collection unit can also collect debit card usage history to analyze the user's spending trends. The collection unit can also collect electronic money usage history to analyze the user's consumption behavior. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected purchase history and balance information to determine the user's spending patterns and the need for insurance. The analysis unit can use AI technologies such as machine learning and deep learning to analyze the user's spending patterns with high accuracy. The analysis unit analyzes, for example, monthly spending trends and spending percentages by category to understand the user's spending patterns. The analysis unit can also determine the user's insurance needs based on risk assessment and life stage. The proposal unit proposes the most suitable insurance and monthly spending amount to the user based on the analysis results obtained by the analysis unit. The proposal unit proposes the most suitable insurance to the user based on the analysis results. The proposal unit can select the most suitable insurance for the user by considering, for example, the type of insurance, premiums, and coverage. The proposal unit can also propose a monthly spending amount to the user by considering, for example, income, fixed costs, and variable costs. The proposal unit calculates the monthly spending amount based on the user's spending patterns and presents it to the user. The proposal unit can also propose insurance if it determines that a specific insurance is necessary based on the user's purchase history. The delivery unit provides the information proposed by the proposal unit to the user. The delivery unit provides, for example, information on the proposed insurance and spending amount to the user.The provisioning unit can provide information to users, for example, through web applications or mobile applications. The provisioning unit can also provide information to users, for example, using email or push notifications. The provisioning unit can display information in a format optimized for the user's device. This allows the financial planner system according to the embodiment to promote the user's financial stability. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the collection unit collects purchase history from electronic payment systems and balance information from digital banking systems, the analysis unit inputs the collected information into AI and outputs the analysis results, the proposal unit proposes the most suitable insurance and monthly spending amount to the user based on the analysis results, and the provisioning unit provides the proposed information to the user.

[0063] The data collection unit collects user purchase history and balance information. For example, it collects purchase history from electronic payment systems and balance information from digital banking systems. Specifically, it can collect purchase history from electronic payment systems such as credit cards, debit cards, and e-money. This allows for a detailed understanding of what goods and services users purchase and how frequently they purchase them. The data collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. This allows for a detailed analysis of users' account balances and transaction history, enabling a comprehensive analysis of their financial situation. For example, the data collection unit collects credit card usage history to understand user purchasing patterns. This allows for a detailed analysis of what goods and services users are spending on. Furthermore, it can collect debit card usage history to analyze user spending trends. This allows for an understanding of users' daily spending patterns and analysis of spending trends. It can also collect e-money usage history to analyze user consumption behavior. This allows for an understanding of when users use e-money and analysis of consumption patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0064] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses AI to analyze collected purchase history and balance information to determine the user's spending patterns and insurance needs. Specifically, it can analyze user spending patterns with high accuracy using AI technologies such as machine learning and deep learning. For example, it can analyze monthly spending trends and spending percentages by category to understand the user's spending patterns. This allows for a detailed analysis of which categories the user spends the most on. Furthermore, it can determine the user's insurance needs based on risk assessment and life stage. For example, it can propose appropriate insurance types and premiums considering the user's age, family structure, income, and spending patterns. The analysis unit can also perform long-term risk assessments and trend analyses using historical data and statistical information. For example, it can predict fluctuations in spending over a specific period based on past spending data and formulate future spending plans. In addition, it can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0065] The proposal department proposes the most suitable insurance and monthly spending amount to the user based on the analysis results obtained by the analysis department. Specifically, it proposes the most suitable insurance to the user based on the analysis results. For example, it can select the most suitable insurance for the user by considering the type of insurance, premium, and coverage. This allows the user to choose appropriate insurance based on their life stage and risk assessment. It can also propose a monthly spending amount to the user by considering income, fixed costs, and variable costs. For example, it can calculate and present a monthly spending amount based on the user's spending patterns. This allows the user to manage their income and expenses in a balanced way and achieve financial stability. Furthermore, if it is determined from the user's purchase history that a specific insurance is necessary, it can propose that insurance. For example, if the user travels frequently, travel insurance can be proposed. This allows the user to choose insurance that suits their lifestyle. The proposal department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can analyze user reactions to the proposed insurance and spending amounts and reflect them in future proposals. This allows the proposal department to make more appropriate proposals to users and improve the overall reliability and satisfaction of the system.

[0066] The service provider delivers information proposed by the proposal provider to users. Specifically, it provides users with information on proposed insurance and expenditure amounts. For example, information can be delivered to users through web applications or mobile applications. This allows users to access the proposed information anytime, anywhere. Information can also be delivered to users using email or push notifications. This allows users to receive important information quickly. Furthermore, information can be displayed in a format optimized for the user's device. For example, the display format of information is optimized according to the device the user is using, such as a smartphone, tablet, or PC. This allows users to receive information in a highly visible format. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the delivered content. For example, user reactions to the delivered information are analyzed and reflected in future deliveries. This allows the service provider to provide users with more appropriate information and improve the overall reliability and satisfaction of the system. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, ensuring their financial stability.

[0067] The data collection unit can collect purchase history from electronic payment systems and balance information from digital banking systems. For example, the data collection unit collects purchase history from electronic payment systems such as credit cards, debit cards, and electronic money. The data collection unit can also collect balance information from digital banking systems such as online banking and mobile banking. For example, the data collection unit collects credit card usage history to understand the user's purchasing patterns. The data collection unit can also collect debit card usage history to analyze the user's spending trends. The data collection unit can also collect electronic money usage history to analyze the user's consumption behavior. By collecting information from electronic payment systems and digital banking systems, the user's financial situation can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect purchase history from electronic payment systems and balance information from digital banking systems, input it into AI, and perform analysis.

[0068] The analysis unit can analyze the collected information using AI to determine the user's spending patterns and insurance needs. The analysis unit uses AI technologies such as machine learning and deep learning to analyze the user's spending patterns with high accuracy. The analysis unit can analyze monthly spending trends and spending percentages by category to understand the user's spending patterns. The analysis unit can also determine the user's insurance needs based on risk assessment and life stage. In this way, by using AI, the user's spending patterns and insurance needs can be determined with high accuracy. 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 inputs the collected information into the AI ​​to determine the user's spending patterns and insurance needs.

[0069] The proposal unit can suggest the most suitable insurance and monthly spending amount to the user based on the analysis results. For example, the proposal unit can suggest the most suitable insurance to the user based on the analysis results. The proposal unit can select the most suitable insurance to the user by considering factors such as the type of insurance, premiums, and coverage. The proposal unit can also suggest the monthly spending amount to the user by considering factors such as income, fixed costs, and variable costs. For example, the proposal unit can calculate the monthly spending amount based on the user's spending patterns and present it to the user. For example, if the proposal unit determines that a specific insurance is necessary based on the user's purchase history, it can suggest that insurance. In this way, by making optimal suggestions based on the analysis results, it supports the user's financial stability. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into AI and suggest the most suitable insurance and monthly spending amount to the user.

[0070] The service provider can provide the user with the proposed information. For example, the service provider can provide the user with information on proposed insurance or expenditure amounts. The service provider can provide information to the user, for example, through a web application or a mobile application. The service provider can also provide information to the user, for example, using email or push notifications. The service provider can display the information in a format optimized for the user's device. This helps the user make appropriate decisions by providing them with the proposed information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed information into AI and provide it to the user.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of collecting purchase history and balance information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the data when the user is relaxed. For example, if the user is relaxed, the data collection unit can also collect purchase history and balance information immediately. For example, if the user is in a hurry, the data collection unit can shorten the collection timing and collect the data quickly. By adjusting the collection timing according to the user's emotions, it is possible to reduce user stress and collect information efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the collection timing.

[0072] The data collection unit can analyze a user's past purchase history and balance information to select the optimal data collection method. For example, the data collection unit may prioritize collecting purchase history from stores and services that the user frequently uses. The data collection unit may also periodically collect user balance information and analyze it all at the end of the month. For example, the data collection unit may categorize a user's purchase history and focus on collecting data from specific categories. This allows for efficient information collection by analyzing past data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past purchase history and balance information into AI to select the optimal data collection method.

[0073] The data collection unit can filter purchase history and balance information based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the data collection unit will prioritize collecting travel-related purchase history. For example, if the user is interested in health, the data collection unit can focus on collecting health-related purchase history. For example, if the user has started a new hobby, the data collection unit can collect purchase history related to that hobby. By filtering information based on the user's lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into AI and perform filtering.

[0074] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important information. For example, if the user is relaxed, the data collection unit can collect all information equally. For example, if the user is in a hurry, the data collection unit can prioritize the collection of highly important information. This enables efficient information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit inputs the user's emotion data into the AI ​​to determine the priority of information.

[0075] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting purchase history and balance information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history in that region. For example, if the user is traveling, the data collection unit can also focus on collecting purchase history at the travel destination. For example, if the user is at home, the data collection unit can also prioritize the collection of purchase history around the user's home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant information.

[0076] The data collection unit can analyze the user's social media activity and collect relevant information when collecting purchase history and balance information. For example, the data collection unit can collect purchase information that the user has shared on social media. The data collection unit can also prioritize collecting purchase history of brands and stores that the user follows on social media. The data collection unit can also collect purchase history of products and services that the user has shown interest in on social media. This allows for the collection of information based on the user's interests by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and collect relevant information.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit can also provide analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 without AI. For example, the analysis unit inputs the user's emotion data into the AI ​​and adjusts the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into the AI ​​and adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history. For example, the analysis unit can also apply a financial analysis algorithm to balance information. For example, the analysis unit can also apply a risk analysis algorithm to insurance information. By applying an analysis algorithm appropriate to the category of information, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI ​​and applies a different analysis algorithm.

[0080] 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the user's emotion data into the AI ​​and adjusts the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. For example, the analysis unit can also determine the priority of analysis of past information according to its importance. The analysis unit can also adjust the analysis schedule based on the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the timing of information collection into the AI ​​and determines the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the information into the AI ​​and adjust the order of analysis.

[0083] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit can present suggestions in a format that can be quickly understood. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input user emotion data into the AI ​​and adjust the way it presents suggestions.

[0084] The proposal department can adjust the level of detail in its proposals based on the importance of the insurance and expenditures. For example, it can provide detailed proposals for high-importance insurance, and concise proposals for lower-importance expenditures. The proposal department can also prioritize proposals based on the importance of the insurance and expenditures. This allows for efficient proposals by adjusting the level of detail according to the importance of the insurance and expenditures. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insurance and expenditures into the AI ​​and adjust the level of detail in the proposals.

[0085] The proposal unit can apply different proposal algorithms depending on the insurance and expenditure categories when making a proposal. For example, the proposal unit can apply a risk analysis algorithm to insurance. For example, the proposal unit can also apply a financial analysis algorithm to expenditures. The proposal unit can also select the optimal proposal algorithm depending on the insurance and expenditure categories. This enables highly accurate proposals by applying proposal algorithms tailored to the insurance and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the insurance and expenditure categories into the AI ​​and apply different proposal algorithms.

[0086] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit may provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit may provide suggestions in a format that can be quickly understood. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into the AI ​​and adjusts the length of the suggestions.

[0087] The proposal department can prioritize proposals based on the submission timing of insurance and expenditure details. For example, it will prioritize proposals for urgent insurance. It can also prioritize proposals for expenditure details with approaching deadlines. The proposal department can also adjust the proposal schedule based on the submission timing of insurance and expenditure details. This allows for prioritizing urgent proposals by determining the submission timing of insurance and expenditure details. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department inputs the submission timing of insurance and expenditure details into the AI ​​to determine the proposal priority.

[0088] The proposal unit can adjust the order of proposals based on the relevance of insurance and expenditure amounts when making proposals. For example, the proposal unit may prioritize proposing insurance with high relevance. For example, the proposal unit may postpone proposing expenditure amounts with low relevance. For example, the proposal unit may determine the order of proposals based on the relevance of insurance and expenditure amounts. This allows for efficient proposals by adjusting the order of proposals based on the relevance of insurance and expenditure amounts. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input the relevance of insurance and expenditure amounts into the AI ​​and adjust the order of proposals.

[0089] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed information. If the user is stressed, the information provider can also provide concise and to-the-point information. If the user is in a hurry, the information provider can also provide information in a format that can be quickly understood. In this way, by adjusting the method of information delivery according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into AI and adjust the method of information delivery.

[0090] The information delivery unit can select the optimal delivery method by referring to the user's past response history when providing information. For example, the information delivery unit may prioritize using information delivery methods that the user has preferred in the past. For example, the information delivery unit may also select the optimal timing for information delivery based on the user's past response history. For example, the information delivery unit may analyze the user's past response history and select the most effective information delivery method. This makes it possible to select the optimal information delivery method and provide effective information by referring to the user's past response history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit may input the user's past response history into AI and select the optimal delivery method.

[0091] The information provider can customize the information provided based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the provider will prioritize providing travel-related information. If the user is interested in health, the provider can focus on providing health-related information. If the user has started a new hobby, the provider can provide information related to that hobby. By customizing the information based on the user's lifestyle and areas of interest, more relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's lifestyle and areas of interest into AI and customize the information.

[0092] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information delivery unit may postpone the delivery of less important information. For example, if the user is relaxed, the information delivery unit may deliver all information equally. For example, if the user is in a hurry, the information delivery unit may prioritize the delivery of highly important information. This enables efficient information delivery by determining the priority of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit inputs user emotion data into AI to determine the priority of information delivery.

[0093] The information provider can select the optimal method of information delivery by considering the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the provider can also provide a concise and highly visible display method. By considering the user's device information, the provider can provide the optimal display method and improve user convenience. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into AI and select the optimal method of information delivery.

[0094] The information provider can provide multilingual information according to the user's language settings when providing information. For example, the information provider can automatically set the language of the information based on the language settings of the user's device. For example, the information provider can also provide a language switching function if the user uses multiple languages. For example, the information provider can also provide information in a specific language if the user selects a particular language. This improves user convenience by providing multilingual information according to the user's language settings. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's language settings into AI and provide multilingual information.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The financial planner system can collect and analyze users' health data in addition to their purchase history and balance information. For example, the collection unit can collect health data from the user's fitness tracker or smartwatch. The analysis unit can analyze the collected health data and propose optimal insurance and spending amounts based on the user's health status. The proposal unit can also suggest a review of health insurance if the user is in good health. This enables comprehensive financial planning that takes the user's health status into consideration.

[0097] A financial planner system can predict a user's life events and provide appropriate advice based on their purchase history and balance information. For example, the analysis unit predicts life events such as marriage and childbirth from the user's purchase history. The proposal unit can propose necessary insurance and savings plans based on the predicted life events. The delivery unit can also provide the user with a concrete action plan to prepare for life events. This allows the user to make appropriate preparations for future life events.

[0098] The financial planner system analyzes users' purchase history and balance information, as well as their social media activity, to propose optimal insurance and spending amounts based on the user's interests. For example, the data collection unit collects posts and followed accounts from the user's social media accounts. The analysis unit analyzes the collected social media data to understand the user's interests. The proposal unit can then propose relevant insurance and spending amounts based on the user's interests. This enables financial planning that is tailored to the user's lifestyle.

[0099] The financial planner system can assess a user's environmental awareness based on their purchase history and balance information, and propose environmentally conscious spending plans. For example, the analysis unit analyzes the user's usage of eco-friendly products and services from their purchase history. The proposal unit can propose eco-friendly insurance and investment products based on the user's environmental awareness. The provision unit can also provide users with environmentally conscious spending plans and saving methods. This allows users to achieve an environmentally conscious lifestyle.

[0100] The financial planner system can estimate a user's stress level based on their purchase history and balance information, and propose spending plans that help reduce stress. For example, the analysis unit analyzes signs of stress from the user's purchase history. The proposal unit can propose spending plans related to relaxation and hobbies based on the user's stress level. The delivery unit can also provide the user with specific action plans that help reduce stress. This allows the user to reduce stress and maintain a healthy lifestyle.

[0101] The financial planner system can estimate a user's emotions based on their purchase history and balance information, and propose spending plans tailored to those emotions. For example, the analysis unit analyzes emotional fluctuations from the user's purchase history. The proposal unit can then propose spending plans related to relaxation and entertainment based on the user's emotions. The delivery unit can also provide the user with specific action plans tailored to their emotions. This allows the user to execute an appropriate spending plan that aligns with their emotions.

[0102] The financial planner system can estimate a user's emotions based on their purchase history and balance information, and then propose insurance tailored to those emotions. For example, the analysis unit analyzes emotional fluctuations from the user's purchase history. The proposal unit can then propose insurance to mitigate risk based on the user's emotions. The delivery unit can also provide the user with a specific insurance plan tailored to their emotions. This allows the user to select insurance that is appropriate for their emotions.

[0103] The financial planner system can estimate a user's emotions based on their purchase history and balance information, and propose an investment plan tailored to those emotions. For example, the analysis unit analyzes emotional fluctuations from the user's purchase history. The proposal unit can propose an investment plan to mitigate risk based on the user's emotions. The provision unit can also provide the user with a specific investment plan tailored to their emotions. This allows the user to execute an appropriate investment plan that aligns with their emotions.

[0104] A financial planner system can estimate a user's emotions based on their purchase history and balance information, and propose a savings plan tailored to those emotions. For example, the analysis unit analyzes emotional fluctuations from the user's purchase history. The proposal unit can propose a savings plan to mitigate risk based on the user's emotions. The provision unit can also provide the user with a specific savings plan tailored to their emotions. This allows the user to implement an appropriate savings plan that aligns with their emotions.

[0105] The financial planner system can estimate a user's emotions based on their purchase history and balance information, and propose spending limits that are appropriate to those emotions. For example, the analysis unit analyzes fluctuations in the user's emotions from their purchase history. The proposal unit can then propose spending limits based on the user's emotions. The provision unit can also provide the user with a specific spending limit plan tailored to their emotions. This allows the user to implement appropriate spending limits that are appropriate to their emotions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects the user's purchase history and balance information. For example, the data collection unit collects purchase history from electronic payment systems such as credit cards, debit cards, and e-money, and balance information from digital banking systems such as online banking and mobile banking. This allows the system to understand the user's purchasing patterns and spending trends. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI technology to analyze the collected purchase history and balance information to determine the user's spending patterns and insurance needs. This allows the system to understand the user's monthly spending trends and spending breakdowns by category, and to determine the need for insurance based on risk assessment and life stage. Step 3: The proposal department proposes the most suitable insurance and monthly spending amount for the user based on the analysis results obtained by the analysis department. For example, the proposal department selects the most suitable insurance for the user by considering the type of insurance, premiums, and coverage based on the analysis results. It also calculates the monthly spending amount considering income, fixed costs, and variable costs and presents it to the user. Step 4: The service provider delivers the information proposed by the proposal provider to the user. For example, the service provider provides the user with information on proposed insurance and spending amounts through a web application or mobile application. They also provide information via email and push notifications, displaying the information in a format optimized for the user's device.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects purchase history from an electronic payment system and balance information from a digital banking system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable insurance and monthly spending amount to the user based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed information to the user. 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.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects purchase history from an electronic payment system and balance information from a digital banking system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable insurance and monthly spending amount to the user based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the proposed information to the user. 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.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects purchase history from an electronic payment system and balance information from a digital banking system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the collected information is analyzed by AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the most suitable insurance and monthly spending amount to the user based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314, and provides the proposed information to the user. 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.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects purchase history from an electronic payment system and balance information from a digital banking system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and the AI ​​analyzes the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable insurance and monthly spending amount to the user based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the proposed information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A collection unit that collects user purchase history and balance information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal insurance and monthly spending amount, The system comprises a provisioning unit that provides the user with the information proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect purchase history from electronic payment systems and balance information from digital banking systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed using AI to determine the user's spending patterns and insurance needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the most suitable insurance and monthly spending amount for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the proposed information to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting purchase history and balance information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past purchase history and balance information to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting purchase history and balance information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting purchase history and balance information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting purchase history and balance information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, 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, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of insurance and expenditure amounts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the insurance and expenditure categories. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of submission of insurance and expenditure figures. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of insurance and expenditure amounts. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, the system selects the optimal method of delivery by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, customize the information based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the optimal method of delivery is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we will provide multilingual information according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user purchase history and balance information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal insurance and monthly spending amount, The system comprises a provisioning unit that provides the user with the information proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect purchase history from electronic payment systems and balance information from digital banking systems. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed using AI to determine the user's spending patterns and insurance needs. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the most suitable insurance and monthly spending amount for the user. The system according to feature 1.

5. The aforementioned supply unit is, Provide the proposed information to the user. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting purchase history and balance information based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past purchase history and balance information to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting purchase history and balance information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting purchase history and balance information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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