system

Abstract

The system according to this embodiment aims to analyze the user's spending data and asset data and provide individually customized household financial advice. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the user's expenditure data and asset data. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that user expenditure data and asset data are not fully utilized effectively to provide individually customized household advice.

[0005] The system according to the embodiment aims to analyze user expenditure data and asset data and provide individually customized household advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects user spending data and asset data. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's spending data and asset data and provide individually customized household financial advice. [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 numbered 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 applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when three or more matters are expressed by connecting them with "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) An AI finance planner system according to an embodiment of the present invention is a customer-specific AI finance planner using an electronic payment platform. This AI finance planner system uses a generating AI to analyze payment information from the electronic payment platform and notify the user of their daily spending trends. Next, the generating AI analyzes securities and banking information from the electronic payment platform and provides advice on assets. This mechanism allows users who find household budgeting cumbersome or who have low investment literacy to easily manage their finances and manage their assets. Furthermore, it is expected that the incentive to use the electronic payment platform will increase, leading to an increase in the number of users of related services. For example, a collection unit is set up to collect user spending data and asset data. Next, an analysis unit is set up where the generating AI analyzes the collected data. Finally, a provision unit is set up to provide advice to the user based on the analysis results. This allows users to understand their spending trends and asset situation and perform optimal household budgeting and asset management. Furthermore, by incorporating a household budget app function, household budgeting can be completely automated. For example, the generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. This mechanism allows users to manage their finances and assets at their own pace. This enables the AI ​​finance planner system to efficiently collect, analyze, and provide advice on users' spending and asset data.

[0029] The AI ​​finance planner system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects user spending data and asset data. User spending data and asset data include, but are not limited to, credit card usage history and bank account balances. For example, the collection unit collects credit card usage history to understand the user's spending trends. The collection unit can also collect bank account balances to understand the user's asset situation. Furthermore, the collection unit can also collect payment information from electronic payment platforms. For example, the collection unit collects payment information from electronic payment platforms to accurately understand the user's spending data. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The generating AI analyzes the data using, for example, specific machine learning models and algorithms. For example, the generating AI analyzes the user's spending data and notifies them of daily spending trends. The generating AI can also analyze the user's asset data and provide investment advice. For example, the generating AI analyzes the user's spending data and advises on saving methods. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The service provider, for example, provides users with asset management advice based on the results analyzed by the generating AI. For instance, the service provider advises users on recommended investment destinations and risk management methods. The service provider can also incorporate a household budget app function to fully automate household financial management. For example, the service provider's generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. This enables the AI ​​finance planner system according to this embodiment to efficiently collect, analyze, and provide advice on the user's expenditure and asset data.

[0030] The data collection unit collects user spending and asset data. This data includes, but is not limited to, credit card usage history and bank account balances. Specifically, to obtain user credit card usage history, the data collection unit uses the credit card company's API and, with the user's permission, retrieves the data. This allows the unit to understand the user's monthly spending and spending trends in specific categories. The data collection unit also integrates with banks' online banking systems to obtain bank account balances, enabling real-time monitoring of the user's asset status. Furthermore, the data collection unit can collect payment information from electronic payment platforms. For example, it uses the electronic payment platform's API to retrieve user payment history, accurately understanding cash spending and electronic money usage. This allows the data collection unit to centrally collect diverse user spending data and understand comprehensive spending trends. To securely manage this data, the data collection unit uses encryption technology to protect the data and ensure user privacy. The data collection unit also regularly updates the data and provides the latest information to the analysis unit, ensuring that users always have access to the most up-to-date spending trends and asset status. This allows the data collection unit to efficiently and accurately collect user spending and asset data, thereby improving the overall system performance.

[0031] The analysis department uses a generative AI to analyze data collected by the data collection department. The generative AI analyzes the data using specific machine learning models and algorithms, for example. Specifically, it processes user spending data as time-series data and uses time-series analysis algorithms to understand daily spending trends. For example, it uses ARIMA models and LSTM (Long Short-Term Memory) networks to predict user spending patterns and detect abnormal spending or fluctuations in specific spending categories. The generative AI also uses clustering algorithms to classify user spending data by category and analyze the spending proportion and trends of each category. Furthermore, the generative AI can analyze user asset data and provide investment advice. For example, it uses portfolio optimization algorithms to optimize the user's asset allocation and propose investment strategies that consider the balance between risk and return. Based on the user's risk tolerance and investment goals, the generative AI can recommend individual investment targets and asset classes. This allows the analysis department to highly analyze the collected data and provide users with specific and practical advice. Furthermore, the generating AI can learn from user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the analysis unit to respond flexibly to user needs, thereby improving the overall reliability and usefulness of the system.

[0032] The service provider provides advice to users based on the analysis results obtained by the analysis department. Specifically, the service provider provides asset management advice to users based on the results of analysis by the generating AI. For example, the service provider may recommend a review of the user's current asset allocation or new investment opportunities. The service provider may recommend individual investment products or asset classes according to the user's risk tolerance and investment goals, and advise on risk management methods. The service provider can also integrate a household budget app function to fully automate household financial management. For example, the service provider's generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. Based on the user's income and expenditure data, the service provider provides advice on setting monthly budgets and managing expenses, and suggests saving methods and efficient fund management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can collect the results of actions taken by users following the provided advice, and the generating AI learns from this data to improve the accuracy of future advice. In addition, the service provider presents the advice in an easy-to-understand manner through the user interface, making it easy for users to understand and implement. This allows the service provider to offer users specific and practical advice, supporting their asset management and household finances.

[0033] The data collection unit can collect payment information from electronic payment platforms. For example, the data collection unit can collect payment information from electronic payment platforms to accurately understand user spending data. For example, the data collection unit can collect payment information from electronic payment platforms to understand user spending trends. The data collection unit can also collect payment information from electronic payment platforms to analyze user spending data. This allows for an accurate understanding of user spending data by collecting payment information from electronic payment platforms. Electronic payment platforms include, but are not limited to, PayPal and Apple Pay. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input payment information from electronic payment platforms into a generating AI and have the generating AI perform the collection of payment information.

[0034] The analysis unit can use a generating AI to analyze the collected data and notify the user of their daily spending trends. For example, the analysis unit can use a generating AI to analyze the collected data and notify the user of their daily spending trends. The analysis unit can also use a generating AI to analyze the user's spending data and notify the user of spending trends by category. For example, the analysis unit can use a generating AI to analyze the user's spending data and notify the user of their average spending amount. This makes it easier for users to understand their spending situation by notifying them of their daily spending trends. Daily spending trends include, but are not limited to, spending trends by category and average spending amount. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of daily spending trends.

[0035] The service provider can provide users with investment advice based on the results analyzed by the generating AI. For example, the service provider can provide users with investment recommendations and advice on risk management methods. The service provider can also provide users with investment advice based on the results analyzed by the generating AI. For example, the service provider can provide users with investment advice and enable them to manage their assets appropriately. By providing investment advice, users can manage their assets appropriately. Investment advice includes, but is not limited to, investment recommendations and risk management methods. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have the generating AI execute investment advice based on the results analyzed by the generating AI.

[0036] The service provider can incorporate a household budget app function to completely automate household financial management. For example, the service provider can incorporate a household budget app function to completely automate household financial management. For example, the service provider can have a generating AI analyze the user's income and expense data and advise on how to invest surplus funds. The service provider can also incorporate a household budget app function to completely automate household financial management. For example, the service provider can provide a household budget app function with income and expense recording and automatic classification functions. This completely automates household financial management, allowing users to manage their finances without effort. The household budget app function includes, but is not limited to, income and expense recording and automatic classification functions. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the automation of the household budget app function.

[0037] The data collection unit can analyze the user's past spending history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data using payment methods the user has frequently used in the past. The data collection unit may also select a method for collecting data at specific time periods based on the user's past spending history. The data collection unit may also analyze the user's past spending history and select the most efficient data collection method. This allows for the selection of the optimal data collection method by analyzing past spending history. Optimal data collection methods include, but are not limited to, real-time collection and periodic collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past spending history into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter expenditure and asset data based on the user's current living situation and areas of interest. For example, the data collection unit can collect only the data necessary for the user's current living situation. The data collection unit can also prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also filter the data to be collected, taking into account the user's living situation and areas of interest. This allows the collection of only the necessary data by filtering the data according to the user's living situation and areas of interest. The user's current living situation and areas of interest include, but are not limited to, occupation, hobbies, and family structure. 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 data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting expenditure and asset data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's geographical location. For example, if the user is on the move, the data collection unit can collect data based on their current location. This allows for the priority collection of highly relevant data by considering the user's geographical location. The user's geographical location includes, but is not limited to, GPS data and address information. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data when collecting expenditure data and asset data. For example, the data collection unit can analyze a user's social media activity and collect relevant expenditure data. The data collection unit can also collect asset data of interest from a user's social media activity. The data collection unit can also collect relevant data based on a user's social media activity. This allows for the efficient collection of relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI collect relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a concise analysis on data with low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Data importance includes, but is not limited to, data reliability and impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit may apply a specific algorithm to expenditure data. For example, the analysis unit may apply a different algorithm to asset data. The analysis unit can also apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Data categories include, but are not limited to, expenditure data and income data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI perform the application of analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also determine the priority of analysis based on the data submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. The data submission date includes, but is not limited to, real-time data and historical data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0045] The service provider can adjust the level of detail of the advice based on the importance of the data when providing advice. For example, the service provider can provide detailed advice for highly important data. For example, the service provider can also provide concise advice for less important data. The service provider can also adjust the level of detail of the advice according to the importance of the data. This allows for more efficient advice by adjusting the level of detail according to the importance of the data. The level of detail of the advice includes, but is not limited to, concise advice and detailed advice. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0046] The service provider can apply different advice algorithms depending on the data category when providing advice. For example, the service provider can apply a specific algorithm to provide advice for expenditure data. For example, the service provider can also apply a different algorithm to provide advice for asset data. The service provider can also apply the most suitable advice algorithm depending on the data category. This improves the accuracy of the advice by applying the most suitable advice algorithm depending on the data category. Advice algorithms include, but are not limited to, rule-based algorithms and machine learning algorithms. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data categories into a generating AI and have the generating AI apply the advice algorithm.

[0047] The service provider can prioritize advice based on the data submission date when providing advice. For example, the service provider may prioritize advice for the most recent data. For example, the service provider may postpone providing advice for older data. The service provider can also prioritize advice based on the data submission date. This allows for prioritizing advice based on the data submission date, ensuring that the most recent data is prioritized. Prioritization of advice may include, but is not limited to, data importance and urgency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data submission date into a generating AI and have the generating AI determine the priority of advice.

[0048] The advice provider can adjust the order of advice based on the relevance of the data when providing advice. For example, the provider can prioritize providing advice for highly relevant data. For example, the provider can postpone providing advice for less relevant data. The provider can also adjust the order of advice according to the relevance of the data. This allows for more efficient advice by adjusting the order of advice according to the relevance of the data. The order of advice includes, but is not limited to, examples such as in order of relevance or in order of importance. Some or all of the above processing in the advice provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the data into a generating AI and have the generating AI adjust the order of advice.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect user health data in addition to user spending and asset data. For example, it can collect data from users' fitness trackers and smartwatches to understand their health status. The data collection unit can also analyze the user's health data and provide spending and investment advice based on their health status. For example, if a user is leading a healthy lifestyle, it can advise them to increase their health-related spending. Conversely, if a user's health is deteriorating, it can provide investment advice in anticipation of increased medical expenses. This allows for the provision of appropriate advice tailored to the user's health status.

[0051] The analytics department can consider users' life events when analyzing collected data. For example, if a user is about to get married, it can predict wedding-related expenses and provide investment advice. If a user plans to have children, it can provide advice that takes into account education and childcare costs. Furthermore, if a user is planning to retire, it can provide investment advice that anticipates post-retirement living expenses. This allows for the provision of appropriate advice tailored to each user's life event.

[0052] The data collection unit can consider a user's hobbies and interests when collecting their spending and asset data. For example, the unit can prioritize collecting spending related to a user's specific hobbies. The unit can also collect relevant data based on the user's interests. The unit can also filter the data it collects, taking into account the user's hobbies and interests. This allows for more personalized advice by collecting data tailored to the user's hobbies and interests.

[0053] The analytics department can consider users' past behavioral patterns when analyzing collected data. For example, it can analyze users' past spending patterns to predict future spending. It can also analyze users' past investment results to provide advice on future investments. By considering users' past behavioral patterns, it can provide optimal advice. This allows for the provision of appropriate advice based on users' past behavioral patterns.

[0054] The data collection unit can consider the user's geographical location when collecting user spending and asset data. For example, if the user is in a specific region, the data collection unit will prioritize collecting data related to that region. The data collection unit can also collect highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can also collect data based on their current location. This allows for the priority collection of highly relevant data by considering the user's geographical location.

[0055] The analytics department can consider users' social media activity when analyzing collected data. For example, the analytics department can analyze users' social media activity and collect relevant spending data. The analytics department can also collect asset data of interest from users' social media activity. The analytics department can also collect relevant data based on users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects user spending and asset data. Specifically, it collects credit card usage history, bank account balances, and payment information from electronic payment platforms to understand the user's spending trends and asset status. Step 2: The analysis unit uses a generating AI to analyze the data collected by the collection unit. The generating AI uses specific machine learning models and algorithms to analyze the data and provide advice on the user's spending trends and asset management. Step 3: The service provider provides advice to the user based on the analysis results obtained by the analysis provider. Specifically, it provides advice on asset management, recommendations for investment destinations, methods for risk management, and also includes a function to automate household financial management.

[0058] (Example of form 2) An AI finance planner system according to an embodiment of the present invention is a customer-specific AI finance planner using an electronic payment platform. This AI finance planner system uses a generating AI to analyze payment information from the electronic payment platform and notify the user of their daily spending trends. Next, the generating AI analyzes securities and banking information from the electronic payment platform and provides advice on assets. This mechanism allows users who find household budgeting cumbersome or who have low investment literacy to easily manage their finances and manage their assets. Furthermore, it is expected that the incentive to use the electronic payment platform will increase, leading to an increase in the number of users of related services. For example, a collection unit is set up to collect user spending data and asset data. Next, an analysis unit is set up where the generating AI analyzes the collected data. Finally, a provision unit is set up to provide advice to the user based on the analysis results. This allows users to understand their spending trends and asset situation and perform optimal household budgeting and asset management. Furthermore, by incorporating a household budget app function, household budgeting can be completely automated. For example, the generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. This mechanism allows users to manage their finances and assets at their own pace. This enables the AI ​​finance planner system to efficiently collect, analyze, and provide advice on users' spending and asset data.

[0059] The AI ​​finance planner system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects user spending data and asset data. User spending data and asset data include, but are not limited to, credit card usage history and bank account balances. For example, the collection unit collects credit card usage history to understand the user's spending trends. The collection unit can also collect bank account balances to understand the user's asset situation. Furthermore, the collection unit can also collect payment information from electronic payment platforms. For example, the collection unit collects payment information from electronic payment platforms to accurately understand the user's spending data. The analysis unit uses a generating AI to analyze the data collected by the collection unit. The generating AI analyzes the data using, for example, specific machine learning models and algorithms. For example, the generating AI analyzes the user's spending data and notifies them of daily spending trends. The generating AI can also analyze the user's asset data and provide investment advice. For example, the generating AI analyzes the user's spending data and advises on saving methods. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The service provider, for example, provides users with asset management advice based on the results analyzed by the generating AI. For instance, the service provider advises users on recommended investment destinations and risk management methods. The service provider can also incorporate a household budget app function to fully automate household financial management. For example, the service provider's generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. This enables the AI ​​finance planner system according to this embodiment to efficiently collect, analyze, and provide advice on the user's expenditure and asset data.

[0060] The data collection unit collects user spending and asset data. This data includes, but is not limited to, credit card usage history and bank account balances. Specifically, to obtain user credit card usage history, the data collection unit uses the credit card company's API and, with the user's permission, retrieves the data. This allows the unit to understand the user's monthly spending and spending trends in specific categories. The data collection unit also integrates with banks' online banking systems to obtain bank account balances, enabling real-time monitoring of the user's asset status. Furthermore, the data collection unit can collect payment information from electronic payment platforms. For example, it uses the electronic payment platform's API to retrieve user payment history, accurately understanding cash spending and electronic money usage. This allows the data collection unit to centrally collect diverse user spending data and understand comprehensive spending trends. To securely manage this data, the data collection unit uses encryption technology to protect the data and ensure user privacy. The data collection unit also regularly updates the data and provides the latest information to the analysis unit, ensuring that users always have access to the most up-to-date spending trends and asset status. This allows the data collection unit to efficiently and accurately collect user spending and asset data, thereby improving the overall system performance.

[0061] The analysis department uses a generative AI to analyze data collected by the data collection department. The generative AI analyzes the data using specific machine learning models and algorithms, for example. Specifically, it processes user spending data as time-series data and uses time-series analysis algorithms to understand daily spending trends. For example, it uses ARIMA models and LSTM (Long Short-Term Memory) networks to predict user spending patterns and detect abnormal spending or fluctuations in specific spending categories. The generative AI also uses clustering algorithms to classify user spending data by category and analyze the spending proportion and trends of each category. Furthermore, the generative AI can analyze user asset data and provide investment advice. For example, it uses portfolio optimization algorithms to optimize the user's asset allocation and propose investment strategies that consider the balance between risk and return. Based on the user's risk tolerance and investment goals, the generative AI can recommend individual investment targets and asset classes. This allows the analysis department to highly analyze the collected data and provide users with specific and practical advice. Furthermore, the generating AI can learn from user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the analysis unit to respond flexibly to user needs, thereby improving the overall reliability and usefulness of the system.

[0062] The service provider provides advice to users based on the analysis results obtained by the analysis department. Specifically, the service provider provides asset management advice to users based on the results of analysis by the generating AI. For example, the service provider may recommend a review of the user's current asset allocation or new investment opportunities. The service provider may recommend individual investment products or asset classes according to the user's risk tolerance and investment goals, and advise on risk management methods. The service provider can also integrate a household budget app function to fully automate household financial management. For example, the service provider's generating AI analyzes the user's income and expenditure data and advises on how to invest surplus funds. Based on the user's income and expenditure data, the service provider provides advice on setting monthly budgets and managing expenses, and suggests saving methods and efficient fund management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can collect the results of actions taken by users following the provided advice, and the generating AI learns from this data to improve the accuracy of future advice. In addition, the service provider presents the advice in an easy-to-understand manner through the user interface, making it easy for users to understand and implement. This allows the service provider to offer users specific and practical advice, supporting their asset management and household finances.

[0063] The data collection unit can collect payment information from electronic payment platforms. For example, the data collection unit can collect payment information from electronic payment platforms to accurately understand user spending data. For example, the data collection unit can collect payment information from electronic payment platforms to understand user spending trends. The data collection unit can also collect payment information from electronic payment platforms to analyze user spending data. This allows for an accurate understanding of user spending data by collecting payment information from electronic payment platforms. Electronic payment platforms include, but are not limited to, PayPal and Apple Pay. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input payment information from electronic payment platforms into a generating AI and have the generating AI perform the collection of payment information.

[0064] The analysis unit can use a generating AI to analyze the collected data and notify the user of their daily spending trends. For example, the analysis unit can use a generating AI to analyze the collected data and notify the user of their daily spending trends. The analysis unit can also use a generating AI to analyze the user's spending data and notify the user of spending trends by category. For example, the analysis unit can use a generating AI to analyze the user's spending data and notify the user of their average spending amount. This makes it easier for users to understand their spending situation by notifying them of their daily spending trends. Daily spending trends include, but are not limited to, spending trends by category and average spending amount. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of daily spending trends.

[0065] The service provider can provide users with investment advice based on the results analyzed by the generating AI. For example, the service provider can provide users with investment recommendations and advice on risk management methods. The service provider can also provide users with investment advice based on the results analyzed by the generating AI. For example, the service provider can provide users with investment advice and enable them to manage their assets appropriately. By providing investment advice, users can manage their assets appropriately. Investment advice includes, but is not limited to, investment recommendations and risk management methods. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have the generating AI execute investment advice based on the results analyzed by the generating AI.

[0066] The service provider can incorporate a household budget app function to completely automate household financial management. For example, the service provider can incorporate a household budget app function to completely automate household financial management. For example, the service provider can have a generating AI analyze the user's income and expense data and advise on how to invest surplus funds. The service provider can also incorporate a household budget app function to completely automate household financial management. For example, the service provider can provide a household budget app function with income and expense recording and automatic classification functions. This completely automates household financial management, allowing users to manage their finances without effort. The household budget app function includes, but is not limited to, income and expense recording and automatic classification functions. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the automation of the household budget app function.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection for expenditure and asset data based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can also advance the collection timing and collect data in real time. For example, if the user is in a hurry, the data collection unit can adjust the collection timing and collect data when the user is calm. This reduces the burden on the user by adjusting the collection timing according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0068] The data collection unit can analyze the user's past spending history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data using payment methods the user has frequently used in the past. The data collection unit may also select a method for collecting data at specific time periods based on the user's past spending history. The data collection unit may also analyze the user's past spending history and select the most efficient data collection method. This allows for the selection of the optimal data collection method by analyzing past spending history. Optimal data collection methods include, but are not limited to, real-time collection and periodic collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past spending history into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit can filter expenditure and asset data based on the user's current living situation and areas of interest. For example, the data collection unit can collect only the data necessary for the user's current living situation. The data collection unit can also prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also filter the data to be collected, taking into account the user's living situation and areas of interest. This allows the collection of only the necessary data by filtering the data according to the user's living situation and areas of interest. The user's current living situation and areas of interest include, but are not limited to, occupation, hobbies, and family structure. 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 data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. For example, if the user is relaxed, the data collection unit may prioritize collecting more important data. For example, if the user is in a hurry, the data collection unit may quickly collect more important data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. The priority of data to collect includes, but is not limited to, data importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The 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 can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting expenditure and asset data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's geographical location. For example, if the user is on the move, the data collection unit can collect data based on their current location. This allows for the priority collection of highly relevant data by considering the user's geographical location. The user's geographical location includes, but is not limited to, GPS data and address information. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze a user's social media activity and collect relevant data when collecting expenditure data and asset data. For example, the data collection unit can analyze a user's social media activity and collect relevant expenditure data. The data collection unit can also collect asset data of interest from a user's social media activity. The data collection unit can also collect relevant data based on a user's social media activity. This allows for the efficient collection of relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a user's social media activity into a generating AI and have the generating AI collect relevant data.

[0073] 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 provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Presentation methods of the analysis include, but are not limited to, graphs and text reports. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and 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 can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a concise analysis on data with low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Data importance includes, but is not limited to, data reliability and impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit may apply a specific algorithm to expenditure data. For example, the analysis unit may apply a different algorithm to asset data. The analysis unit can also apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Data categories include, but are not limited to, expenditure data and income data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI perform the application of analysis algorithms.

[0076] 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 a detailed analysis. For example, if the user is in a hurry, the analysis unit can provide a concise analysis. For example, if the user is stressed, the analysis unit can provide a to-the-point analysis. By adjusting the length of the analysis according to the user's emotions, the analysis results of an appropriate length can be provided to the user. The length of the analysis includes, but is not limited to, detailed analysis or summary provision. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also determine the priority of analysis based on the data submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. The data submission date includes, but is not limited to, real-time data and historical data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0079] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. If the user is stressed, the service provider can also provide concise advice. If the user is in a hurry, the service provider can also provide to the point. By adjusting the way advice is expressed according to the user's emotions, the service provider can provide advice that is easy for the user to understand. The way advice is expressed includes, but is not limited to, verbal explanations and written explanations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0080] The service provider can adjust the level of detail of the advice based on the importance of the data when providing advice. For example, the service provider can provide detailed advice for highly important data. For example, the service provider can also provide concise advice for less important data. The service provider can also adjust the level of detail of the advice according to the importance of the data. This allows for more efficient advice by adjusting the level of detail according to the importance of the data. The level of detail of the advice includes, but is not limited to, concise advice and detailed advice. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0081] The service provider can apply different advice algorithms depending on the data category when providing advice. For example, the service provider can apply a specific algorithm to provide advice for expenditure data. For example, the service provider can also apply a different algorithm to provide advice for asset data. The service provider can also apply the most suitable advice algorithm depending on the data category. This improves the accuracy of the advice by applying the most suitable advice algorithm depending on the data category. Advice algorithms include, but are not limited to, rule-based algorithms and machine learning algorithms. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data categories into a generating AI and have the generating AI apply the advice algorithm.

[0082] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is in a hurry, the service provider can provide concise advice. For example, if the user is stressed, the service provider can provide concise advice. By adjusting the length of the advice according to the user's emotions, the service provider can provide advice of an appropriate length for the user. The length of the advice includes, but is not limited to, short advice or detailed advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0083] The service provider can prioritize advice based on the data submission date when providing advice. For example, the service provider may prioritize advice for the most recent data. For example, the service provider may postpone providing advice for older data. The service provider can also prioritize advice based on the data submission date. This allows for prioritizing advice based on the data submission date, ensuring that the most recent data is prioritized. Prioritization of advice may include, but is not limited to, data importance and urgency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data submission date into a generating AI and have the generating AI determine the priority of advice.

[0084] The advice provider can adjust the order of advice based on the relevance of the data when providing advice. For example, the provider can prioritize providing advice for highly relevant data. For example, the provider can postpone providing advice for less relevant data. The provider can also adjust the order of advice according to the relevance of the data. This allows for more efficient advice by adjusting the order of advice according to the relevance of the data. The order of advice includes, but is not limited to, examples such as in order of relevance or in order of importance. Some or all of the above processing in the advice provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the data into a generating AI and have the generating AI adjust the order of advice.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can collect user health data in addition to user spending and asset data. For example, it can collect data from users' fitness trackers and smartwatches to understand their health status. The data collection unit can also analyze the user's health data and provide spending and investment advice based on their health status. For example, if a user is leading a healthy lifestyle, it can advise them to increase their health-related spending. Conversely, if a user's health is deteriorating, it can provide investment advice in anticipation of increased medical expenses. This allows for the provision of appropriate advice tailored to the user's health status.

[0087] The analytics department can consider users' life events when analyzing collected data. For example, if a user is about to get married, it can predict wedding-related expenses and provide investment advice. If a user plans to have children, it can provide advice that takes into account education and childcare costs. Furthermore, if a user is planning to retire, it can provide investment advice that anticipates post-retirement living expenses. This allows for the provision of appropriate advice tailored to each user's life event.

[0088] The service provider can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is stressed, the service can delay providing advice until the user is relaxed. If the user is relaxed, the service can speed up the delivery of advice, providing it in real time. If the user is in a hurry, the service can adjust the timing of advice until the user is calm. By adjusting the timing of advice according to the user's emotions, the service can reduce the user's burden.

[0089] The service provider can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is relaxed, it can provide detailed advice. If the user is stressed, it can provide concise advice. If the user is in a hurry, it can provide advice that gets straight to the point. In this way, by adjusting the content of the advice according to the user's emotions, it can provide advice that is easy for the user to understand.

[0090] The service provider can estimate the user's emotions and adjust the format of the advice based on those emotions. For example, if the user is relaxed, the advice can be provided in a detailed report format. If the user is stressed, the advice can be provided in a concise bulleted list format. If the user is in a hurry, the advice can be provided in a short, to-the-point message format. By adjusting the format of the advice according to the user's emotions, the service can provide advice that is more easily accepted by the user.

[0091] The data collection unit can consider a user's hobbies and interests when collecting their spending and asset data. For example, the unit can prioritize collecting spending related to a user's specific hobbies. The unit can also collect relevant data based on the user's interests. The unit can also filter the data it collects, taking into account the user's hobbies and interests. This allows for more personalized advice by collecting data tailored to the user's hobbies and interests.

[0092] The analytics department can consider users' past behavioral patterns when analyzing collected data. For example, it can analyze users' past spending patterns to predict future spending. It can also analyze users' past investment results to provide advice on future investments. By considering users' past behavioral patterns, it can provide optimal advice. This allows for the provision of appropriate advice based on users' past behavioral patterns.

[0093] The service provider can estimate the user's emotions and adjust the frequency of advice based on those emotions. For example, if the user is relaxed, advice will be provided frequently. If the user is stressed, the frequency of advice can be reduced. If the user is in a hurry, the frequency of advice can be adjusted so that advice is provided when the user is calm. In this way, the burden on the user can be reduced by adjusting the frequency of advice according to the user's emotions.

[0094] The data collection unit can consider the user's geographical location when collecting user spending and asset data. For example, if the user is in a specific region, the data collection unit will prioritize collecting data related to that region. The data collection unit can also collect highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can also collect data based on their current location. This allows for the priority collection of highly relevant data by considering the user's geographical location.

[0095] The analytics department can consider users' social media activity when analyzing collected data. For example, the analytics department can analyze users' social media activity and collect relevant spending data. The analytics department can also collect asset data of interest from users' social media activity. The analytics department can also collect relevant data based on users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects user spending and asset data. Specifically, it collects credit card usage history, bank account balances, and payment information from electronic payment platforms to understand the user's spending trends and asset status. Step 2: The analysis unit uses a generating AI to analyze the data collected by the collection unit. The generating AI uses specific machine learning models and algorithms to analyze the data and provide advice on the user's spending trends and asset management. Step 3: The service provider provides advice to the user based on the analysis results obtained by the analysis provider. Specifically, it provides advice on asset management, recommendations for investment destinations, methods for risk management, and also includes a function to automate household financial management.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the collection unit, analysis 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 control unit 46A of the smart device 14 and collects payment information from an electronic payment platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and a generating AI analyzes the collected data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides advice to the user based on the analysis results. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis 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 control unit 46A of the smart glasses 214 and collects payment information from an electronic payment platform. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and a generating AI analyzes the collected data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides advice to the user based on the analysis results. 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.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis 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 control unit 46A of the headset terminal 314 and collects payment information from an electronic payment platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the generated AI analyzes the collected data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides advice to the user based on the analysis results. 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.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects payment information from an electronic payment platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the generated AI analyzes the collected data. The provision unit is implemented by the control unit 46A of the robot 414 and provides advice to the user based on the analysis results. 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects user spending data and asset data, The data collected by the aforementioned collection unit is analyzed by an analysis unit, and the generation AI analyzes the data. The system includes a provisioning unit that provides advice to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect payment information from electronic payment platforms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed by a generating AI, and users are notified of their daily spending trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the results of the AI's analysis, the system provides users with investment advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Integrate household budgeting app functionality to fully automate household financial management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate user sentiment and adjust the timing of spending and asset data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past spending history and 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 expenditure and asset data, 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 data 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 expenditure and asset data, the system prioritizes collecting highly relevant data 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 spending and asset data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, adjust the level of detail of the advice based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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 data collection unit that collects user spending data and asset data, The data collected by the aforementioned collection unit is analyzed by an analysis unit using a generating AI. The system includes a provisioning unit that provides advice to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect payment information from electronic payment platforms. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed by a generating AI, and the user is notified of their daily spending trends. The system according to feature 1.

4. The aforementioned supply unit is, Based on the results of the AI's analysis, the system provides users with investment advice. The system according to feature 1.

5. The aforementioned supply unit is, Integrate household budgeting app functionality to fully automate household financial management. The system according to feature 1.

6. The aforementioned collection unit is We estimate user sentiment and adjust the timing of spending and asset data collection based on the estimated user sentiment. The system according to feature 1.

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

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

Images