system

Abstract

The system according to this embodiment aims to efficiently manage the household finances of dual-income couples. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects payment history. The analysis unit analyzes the payment history collected by the collection unit. The proposal unit proposes the optimal payment method based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the sharing of living expenses based on the payment method proposed by the proposal 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 prior art, there is a problem that the household management of dual-income couples is complicated, and it is difficult to efficiently propose a payment method and adjust the sharing of living expenses.

[0005] The system according to the embodiment aims to efficiently manage the household of dual-income couples.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects payment history. The analysis unit analyzes the payment history collected by the collection unit. The proposal unit proposes the optimal payment method based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the sharing of living expenses based on the payment method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the household finances of dual-income couples. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household budget management system according to an embodiment of the present invention is an AI-powered household budget management app for dual-income couples. This household budget management system works in conjunction with 2D code payment apps (e.g., QR code® payment apps) and credit card apps to support efficient household budget management while respecting the individual finances of each spouse. This household budget management system automatically analyzes the payment history of each spouse. The AI ​​analyzes the details of each payment and identifies expenditures as living expenses. For example, it automatically categorizes living expenses such as food and utilities, clearly identifying which payments are living expenses. Next, this household budget management system automatically adjusts the division of living expenses between the spouses. The AI ​​calculates the difference between each payment and automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. This simplifies household budget management between spouses and deepens their bond. Furthermore, this household budget management system suggests the most advantageous payment method for each store. The AI ​​analyzes the payment methods available at each store and suggests the most advantageous payment method. For example, it may suggest that a credit card is the most advantageous at a particular store, while 2D code payment is the most advantageous at another store. Thus, this household budget management system acts as a household budget management agent that supports household budget management in the era of dual-income households, respecting each spouse's finances while supporting efficient household budget management. This allows the system to automatically analyze each spouse's payment history, adjust the division of living expenses, and suggest the most cost-effective payment method.

[0029] The household finance management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects the payment history of each spouse. The collection unit can collect, for example, credit card statements, bank account transaction history, and electronic money usage history. The collection unit can also automatically collect payment history using AI. The analysis unit analyzes the payment history collected by the collection unit. The analysis unit uses AI to analyze the collected payment history and identify expenditures as living expenses. For example, the analysis unit automatically classifies living expenses such as food expenses and utility bills to clarify which payments are living expenses. The proposal unit proposes the optimal payment method based on the analysis results obtained by the analysis unit. The proposal unit uses AI to analyze the payment methods available at each store and proposes the most advantageous payment method. For example, the proposal unit proposes that credit cards are the most advantageous at a particular store, while electronic payments are the most advantageous at another store. The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between the spouses. For example, the adjustment unit automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. In this way, the household management system according to the embodiment can collect and analyze the payment history of each spouse, propose the optimal payment method, and adjust the division of living expenses.

[0030] The data collection unit collects the payment history of each spouse. For example, the unit can collect credit card statements, bank account transaction history, and electronic money usage history. Specifically, credit card statements are automatically retrieved from each card company's online service, and bank account transaction history is periodically downloaded via internet banking. Electronic money usage history is obtained from each electronic money service's application or website. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can also use AI to automatically collect payment history. The AI ​​automates data acquisition from various data sources and regularly collects the latest data. For example, the AI ​​automatically retrieves new data whenever credit card statements are updated and regularly checks bank account transaction history to collect the latest information. This allows the data collection unit to understand each spouse's payment history in real time and manage household finances based on the latest data. Furthermore, the data collection unit removes duplicate data and verifies data accuracy to maintain data integrity. For example, if the same payment is collected from multiple devices, the AI ​​detects this and eliminates the duplicates. Furthermore, to ensure the accuracy of the collected data, the system performs anomaly detection and data integrity checks. This allows the data collection unit to provide accurate and reliable data, improving the overall accuracy of the household finance management system.

[0031] The Analysis Department analyzes payment history collected by the Collection Department. Using AI, the Analysis Department analyzes the collected payment history to identify expenses related to living costs. Specifically, the AI ​​analyzes the content of each payment and automatically classifies them into categories such as food, utilities, communication, and entertainment. For example, payments at supermarkets are classified as food expenses, and payments to the electricity company are classified as utilities. The AI ​​determines which category each payment belongs to based on the content, amount, and recipient information of the payment. Furthermore, the AI ​​can learn from past payment history and patterns to perform more accurate classifications. For example, if payments are repeatedly made at a specific store, those payments will be classified into a specific category. The AI ​​can also detect unusual or fraudulent spending. For example, if a payment deviates significantly from the normal spending pattern, the AI ​​will detect this and notify the user. This allows the Analysis Department to quickly and accurately analyze the collected payment history and identify expenses related to living costs. Furthermore, the Analysis Department visualizes spending trends and patterns and provides this information to the user. For example, monthly spending trends and spending percentages by category can be displayed in graphs and charts, allowing users to grasp their spending situation at a glance. This enables the analytics department to help users manage their spending more easily and support healthy household finances.

[0032] The Proposal Department proposes the optimal payment method based on the analysis results obtained by the Analysis Department. Using AI, the Proposal Department analyzes the payment methods available at each store and proposes the most advantageous method. Specifically, the AI ​​calculates the most advantageous payment method by considering each store's point reward rate, discount campaigns, and credit card benefits. For example, if a particular supermarket offers a high point accumulation rate for credit card use, the Proposal Department will suggest using a credit card for payments at that supermarket. Similarly, if another store offers a discount for electronic payments, the Proposal Department will suggest using electronic payments at that store. Based on this information, the Proposal Department proposes specific payment methods to users, supporting them in saving money. Furthermore, the Proposal Department can learn users' payment history and preferences to provide individually optimized suggestions. For example, if a user frequently uses a particular credit card, the Proposal Department will suggest ways to maximize the benefits of that credit card. Also, if a user frequently uses a particular store, the Proposal Department will prioritize suggesting the most suitable payment method for that store. This allows the Proposal Department to propose the optimal payment method tailored to user needs, supporting efficient household budget management. Additionally, the Proposal Department can evaluate the effectiveness of its suggestions and continuously improve them. For example, the results of actually using the proposed payment method are collected and its effectiveness is analyzed. This allows the proposal department to improve the accuracy of its proposals and make more beneficial suggestions to users.

[0033] The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between spouses. Specifically, the AI ​​calculates each spouse's share of the expenses based on the amount and method of each payment. For example, it totals the food expenses paid by the husband with a credit card and the utility expenses paid by the wife with electronic money and compares their respective shares. If a difference occurs, the AI ​​calculates the difference and adjusts the division of living expenses between spouses. For example, it automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. The adjustment unit performs these adjustments automatically to ensure a fair burden between spouses. Furthermore, the adjustment unit notifies the user of the adjustment results to ensure transparency. For example, when an adjustment is made, it sends a notification to the user explaining in detail what adjustments were made. This allows the user to accurately understand their share of the expenses and the details of the adjustments. The adjustment unit also records the adjustment results and makes past adjustment history available for reference. This allows users to review past adjustments and use them as a reference when making future plans. Furthermore, the adjustment unit collects user feedback and improves the accuracy of the adjustment algorithm. For example, it reviews adjustment methods and criteria based on user opinions and requests to achieve fairer and more effective adjustments. This allows the adjustment unit to efficiently and fairly adjust the division of living expenses between spouses, improving the reliability and convenience of the entire household budget management system.

[0034] The collection unit can collect the payment history of each spouse. For example, the collection unit can collect the credit card statements of each spouse. The collection unit can also collect the bank account transaction history of each spouse. The collection unit can also collect the electronic money usage history of each spouse. This allows for the individual collection of each spouse's payment history. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the credit card statements of each spouse into a generating AI and have the generating AI perform the collection of payment history.

[0035] The analysis unit can analyze the collected payment history using AI to identify living expenses. For example, the analysis unit can analyze the collected payment history using a machine learning model. The analysis unit can also extract features from the collected payment history to identify living expenses. The analysis unit can also classify the collected payment history using a clustering algorithm to identify living expenses. This allows for the automatic identification of living expenses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected payment history into a generating AI and have the generating AI perform the identification of living expenses.

[0036] The suggestion unit can analyze the payment methods available at each store and propose the most advantageous payment method. For example, the suggestion unit can use AI to analyze the payment methods available at each store. The suggestion unit can also compare discount rates and point reward rates at each store and propose the most advantageous payment method. The suggestion unit can also consider whether or not each store charges fees and propose the most advantageous payment method. This allows it to propose the most advantageous payment method. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the payment methods available at each store into a generating AI and have the generating AI propose the most advantageous payment method.

[0037] The adjustment unit can calculate the difference in payments and adjust the division of living expenses between spouses. For example, the adjustment unit calculates the difference in the total amount of each payment item. The adjustment unit can also calculate the difference in the total amount of payments over a certain period. The adjustment unit can also use AI to calculate the difference in payments and adjust the division of living expenses between spouses. This allows for the automatic adjustment of the division of living expenses between spouses. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the difference in the total amount of each payment item into a generating AI and have the generating AI perform the adjustment of the division of living expenses.

[0038] The suggestion unit can suggest that using a credit card is the most advantageous option at a particular store, while electronic payment is the most advantageous option at another store. For example, the suggestion unit can analyze the discount rate and point reward rate for credit cards at a particular store. The suggestion unit can also analyze whether or not there are fees for electronic payments at another store. The suggestion unit can use AI to suggest that using a credit card is the most advantageous option at a particular store. The suggestion unit can also use AI to suggest that using electronic payment is the most advantageous option at another store. This allows the suggestion unit to suggest the most advantageous payment method for each store. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input that using a credit card is the most advantageous option at a particular store into a generating AI and have the generating AI execute the suggestion.

[0039] The data collection unit can analyze the user's past payment history and select the optimal collection method. For example, the data collection unit may prioritize collecting payment methods that the user has frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. The data collection unit can also analyze the user's past payment history and select the most efficient collection method. This allows for the selection of the optimal collection method based on past payment history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past payment history into a generating AI and have the generating AI select the optimal collection method.

[0040] The collection unit can filter payment history based on the user's current lifestyle and areas of interest when collecting it. For example, the collection unit may prioritize collecting payment history from specific categories based on the user's current lifestyle. The collection unit can also filter and collect relevant payment history based on the user's areas of interest. The collection unit can also exclude unnecessary payment history, taking into account the user's lifestyle and areas of interest. This allows for filtering of payment history based on lifestyle and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of payment history.

[0041] The collection unit can prioritize the collection of highly relevant payment history by considering the user's geographical location information when collecting payment history. For example, the collection unit can prioritize the collection of payment history from stores close to the user's current location. The collection unit can also filter and collect highly relevant payment history based on the user's geographical location information. The collection unit can also prioritize the collection of payment history from places the user frequently visits. This allows for the priority collection of highly relevant history based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant history.

[0042] The collection unit can analyze the user's social media activity and collect relevant history when collecting payment history. For example, the collection unit can prioritize collecting payment history from stores mentioned by the user on social media. The collection unit can also filter and collect relevant payment history based on the user's social media activity. The collection unit can also prioritize collecting payment history from categories that the user has shown interest in on social media. This allows for the collection of relevant history based on social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant history.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the payments. For example, the analysis unit can perform a detailed analysis for high-importance payments, and a simplified analysis for low-importance payments. The analysis unit can also determine the priority of the analysis according to the importance of the payments, thereby adjusting the level of detail of the analysis according to the importance of the payments. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of payments into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the payment category during analysis. For example, the analysis unit may apply a specific analysis algorithm to payments related to food expenses. The analysis unit may also apply a different analysis algorithm to payments related to utilities. The analysis unit can also select the optimal analysis algorithm depending on the payment category. This ensures that the most suitable analysis algorithm is applied according to the payment category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the payment category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analyses based on the payment submission dates. For example, the analysis unit can prioritize analyzing payments that are due soon. It can also postpone analyzing payments that are due far in the future. The analysis unit can also adjust the priority of analyses according to the payment submission dates. This allows the analysis priority to be determined according to the payment submission dates. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the payment submission dates into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of payments during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant payments. The analysis unit can also postpone the analysis of less relevant payments. The analysis unit can adjust the order of analysis according to the relevance of payments. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of payments into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail of a proposal based on the importance of the payment method. For example, the proposal unit can provide detailed proposals for high-importance payment methods, and simplified proposals for low-importance payment methods. The proposal unit can also determine the priority of proposals according to the importance of the payment method, thereby adjusting the level of detail of the proposal according to the importance of the payment method. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the payment method into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0048] The proposal unit can adjust the order of proposals based on the relevance of the payment methods. For example, the proposal unit will prioritize proposals for highly relevant payment methods. The proposal unit may also postpone proposals for less relevant payment methods. The proposal unit can also adjust the order of proposals according to the relevance of the payment methods. This allows the order of proposals to be adjusted according to the relevance of the payment methods. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of payment methods into a generating AI and have the generating AI perform the adjustment of the proposal order.

[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 household finance management system can also include a budget setting section. This section can automatically set monthly budgets based on the income and expenses of each spouse. For example, it can analyze past spending data and suggest appropriate budgets for categories such as food, utilities, and entertainment. It can also consider the couple's savings goals and set spending limits to reach those goals. Furthermore, it can issue alerts when spending exceeds the budget, prompting a review of spending. This allows couples to manage their household finances in a planned manner.

[0051] The household budget management system can also include a notification function. This function can provide timely notifications based on each spouse's payment history and budget status. For example, it can send reminders when payment deadlines are approaching. It can also issue warnings if there are signs of budget overruns. Furthermore, it can provide information on limited-time promotions offering special deals on specific payment methods. This allows couples to efficiently manage their finances without missing important information.

[0052] The household financial management system can also include a goal-setting section. This section allows each spouse to set short-term and long-term financial goals and track their progress. For example, the goal-setting section can set savings goals for travel or major purchases. It can also set long-term goals such as early mortgage repayment or children's education funds. Furthermore, the goal-setting section can regularly report on progress toward goals and adjust them as needed. This allows couples to save systematically towards specific goals.

[0053] The household financial management system can also include an investment suggestion section. This section can analyze the financial situation of each spouse and propose appropriate investment plans. For example, it can suggest investment products such as stocks, bonds, and mutual funds based on risk tolerance and investment timeframe. Furthermore, it can periodically analyze market trends and suggest revisions to the investment portfolio. In addition, the investment suggestion section can adjust investment strategies according to the couple's life events (marriage, childbirth, retirement, etc.). This allows the couple to increase their assets while minimizing risk.

[0054] The household financial management system can also include an education section. This education section can provide each spouse with knowledge about household financial management and investment. For example, the education section can offer online courses on the basics of household financial management and saving techniques. It can also hold seminars on the fundamentals of investment and risk management. Furthermore, the education section can provide customized advice tailored to the couple's financial situation. This allows couples to deepen their knowledge of household financial management and investment, enabling them to make wiser financial decisions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects the payment history of each spouse. The data collection unit can collect, for example, credit card statements, bank account transaction history, and e-money usage history. The data collection unit can also use AI to automatically collect payment history. Step 2: The analysis department analyzes the payment history collected by the collection department. The analysis department uses AI to analyze the collected payment history and identify expenses related to living costs. For example, the analysis department automatically categorizes living expenses such as food and utilities, clearly identifying which payments are related to living costs. Step 3: The proposal department proposes the optimal payment method based on the analysis results obtained by the analysis department. The proposal department uses AI to analyze the payment methods available at each store and proposes the most advantageous payment method. For example, the proposal department might suggest that credit cards are the most advantageous at one store, while electronic payments are the most advantageous at another. Step 4: The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between the couple. For example, the adjustment unit automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account.

[0057] (Example of form 2) The household budget management system according to an embodiment of the present invention is an AI-powered household budget management app for dual-income couples. This household budget management system works in conjunction with 2D code payment apps (e.g., QR code payment apps) and credit card apps to support efficient household budget management while respecting the individual finances of each spouse. This household budget management system automatically analyzes the payment history of each spouse. The AI ​​analyzes the details of each payment and identifies expenditures as living expenses. For example, it automatically categorizes living expenses such as food and utilities, clearly identifying which payments are living expenses. Next, this household budget management system automatically adjusts the division of living expenses between the spouses. The AI ​​calculates the difference between each payment and automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. This simplifies household budget management between spouses and deepens their bond. Furthermore, this household budget management system suggests the most advantageous payment method for each store. The AI ​​analyzes the payment methods available at each store and suggests the most advantageous payment method. For example, it may suggest that a credit card is the most advantageous at a certain store, while 2D code payment is the most advantageous at another store. Thus, this household budget management system acts as a household budget management agent that supports household budget management in the era of dual-income households, respecting each spouse's finances while supporting efficient household budget management. This allows the system to automatically analyze each spouse's payment history, adjust the division of living expenses, and suggest the most cost-effective payment method.

[0058] The household finance management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects the payment history of each spouse. The collection unit can collect, for example, credit card statements, bank account transaction history, and electronic money usage history. The collection unit can also automatically collect payment history using AI. The analysis unit analyzes the payment history collected by the collection unit. The analysis unit uses AI to analyze the collected payment history and identify expenditures as living expenses. For example, the analysis unit automatically classifies living expenses such as food expenses and utility bills to clarify which payments are living expenses. The proposal unit proposes the optimal payment method based on the analysis results obtained by the analysis unit. The proposal unit uses AI to analyze the payment methods available at each store and proposes the most advantageous payment method. For example, the proposal unit proposes that credit cards are the most advantageous at a particular store, while electronic payments are the most advantageous at another store. The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between the spouses. For example, the adjustment unit automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. In this way, the household management system according to the embodiment can collect and analyze the payment history of each spouse, propose the optimal payment method, and adjust the division of living expenses.

[0059] The data collection unit collects the payment history of each spouse. For example, the unit can collect credit card statements, bank account transaction history, and electronic money usage history. Specifically, credit card statements are automatically retrieved from each card company's online service, and bank account transaction history is periodically downloaded via internet banking. Electronic money usage history is obtained from each electronic money service's application or website. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can also use AI to automatically collect payment history. The AI ​​automates data acquisition from various data sources and regularly collects the latest data. For example, the AI ​​automatically retrieves new data whenever credit card statements are updated and regularly checks bank account transaction history to collect the latest information. This allows the data collection unit to understand each spouse's payment history in real time and manage household finances based on the latest data. Furthermore, the data collection unit removes duplicate data and verifies data accuracy to maintain data integrity. For example, if the same payment is collected from multiple devices, the AI ​​detects this and eliminates the duplicates. Furthermore, to ensure the accuracy of the collected data, the system performs anomaly detection and data integrity checks. This allows the data collection unit to provide accurate and reliable data, improving the overall accuracy of the household finance management system.

[0060] The Analysis Department analyzes payment history collected by the Collection Department. Using AI, the Analysis Department analyzes the collected payment history to identify expenses related to living costs. Specifically, the AI ​​analyzes the content of each payment and automatically classifies them into categories such as food, utilities, communication, and entertainment. For example, payments at supermarkets are classified as food expenses, and payments to the electricity company are classified as utilities. The AI ​​determines which category each payment belongs to based on the content, amount, and recipient information of the payment. Furthermore, the AI ​​can learn from past payment history and patterns to perform more accurate classifications. For example, if payments are repeatedly made at a specific store, those payments will be classified into a specific category. The AI ​​can also detect unusual or fraudulent spending. For example, if a payment deviates significantly from the normal spending pattern, the AI ​​will detect this and notify the user. This allows the Analysis Department to quickly and accurately analyze the collected payment history and identify expenses related to living costs. Furthermore, the Analysis Department visualizes spending trends and patterns and provides this information to the user. For example, monthly spending trends and spending percentages by category can be displayed in graphs and charts, allowing users to grasp their spending situation at a glance. This enables the analytics department to help users manage their spending more easily and support healthy household finances.

[0061] The Proposal Department proposes the optimal payment method based on the analysis results obtained by the Analysis Department. Using AI, the Proposal Department analyzes the payment methods available at each store and proposes the most advantageous method. Specifically, the AI ​​calculates the most advantageous payment method by considering each store's point reward rate, discount campaigns, and credit card benefits. For example, if a particular supermarket offers a high point accumulation rate for credit card use, the Proposal Department will suggest using a credit card for payments at that supermarket. Similarly, if another store offers a discount for electronic payments, the Proposal Department will suggest using electronic payments at that store. Based on this information, the Proposal Department proposes specific payment methods to users, supporting them in saving money. Furthermore, the Proposal Department can learn users' payment history and preferences to provide individually optimized suggestions. For example, if a user frequently uses a particular credit card, the Proposal Department will suggest ways to maximize the benefits of that credit card. Also, if a user frequently uses a particular store, the Proposal Department will prioritize suggesting the most suitable payment method for that store. This allows the Proposal Department to propose the optimal payment method tailored to user needs, supporting efficient household budget management. Additionally, the Proposal Department can evaluate the effectiveness of its suggestions and continuously improve them. For example, the results of actually using the proposed payment method are collected and its effectiveness is analyzed. This allows the proposal department to improve the accuracy of its proposals and make more beneficial suggestions to users.

[0062] The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between spouses. Specifically, the AI ​​calculates each spouse's share of the expenses based on the amount and method of each payment. For example, it totals the food expenses paid by the husband with a credit card and the utility expenses paid by the wife with electronic money and compares their respective shares. If a difference occurs, the AI ​​calculates the difference and adjusts the division of living expenses between spouses. For example, it automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account. The adjustment unit performs these adjustments automatically to ensure a fair burden between spouses. Furthermore, the adjustment unit notifies the user of the adjustment results to ensure transparency. For example, when an adjustment is made, it sends a notification to the user explaining in detail what adjustments were made. This allows the user to accurately understand their share of the expenses and the details of the adjustments. The adjustment unit also records the adjustment results and makes past adjustment history available for reference. This allows users to review past adjustments and use them as a reference when making future plans. Furthermore, the adjustment unit collects user feedback and improves the accuracy of the adjustment algorithm. For example, it reviews adjustment methods and criteria based on user opinions and requests to achieve fairer and more effective adjustments. This allows the adjustment unit to efficiently and fairly adjust the division of living expenses between spouses, improving the reliability and convenience of the entire household budget management system.

[0063] The collection unit can collect the payment history of each spouse. For example, the collection unit can collect the credit card statements of each spouse. The collection unit can also collect the bank account transaction history of each spouse. The collection unit can also collect the electronic money usage history of each spouse. This allows for the individual collection of each spouse's payment history. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the credit card statements of each spouse into a generating AI and have the generating AI perform the collection of payment history.

[0064] The analysis unit can analyze the collected payment history using AI to identify living expenses. For example, the analysis unit can analyze the collected payment history using a machine learning model. The analysis unit can also extract features from the collected payment history to identify living expenses. The analysis unit can also classify the collected payment history using a clustering algorithm to identify living expenses. This allows for the automatic identification of living expenses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected payment history into a generating AI and have the generating AI perform the identification of living expenses.

[0065] The suggestion unit can analyze the payment methods available at each store and propose the most advantageous payment method. For example, the suggestion unit can use AI to analyze the payment methods available at each store. The suggestion unit can also compare discount rates and point reward rates at each store and propose the most advantageous payment method. The suggestion unit can also consider whether or not each store charges fees and propose the most advantageous payment method. This allows it to propose the most advantageous payment method. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the payment methods available at each store into a generating AI and have the generating AI propose the most advantageous payment method.

[0066] The adjustment unit can calculate the difference in payments and adjust the division of living expenses between spouses. For example, the adjustment unit calculates the difference in the total amount of each payment item. The adjustment unit can also calculate the difference in the total amount of payments over a certain period. The adjustment unit can also use AI to calculate the difference in payments and adjust the division of living expenses between spouses. This allows for the automatic adjustment of the division of living expenses between spouses. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the difference in the total amount of each payment item into a generating AI and have the generating AI perform the adjustment of the division of living expenses.

[0067] The suggestion unit can suggest that using a credit card is the most advantageous option at a particular store, while electronic payment is the most advantageous option at another store. For example, the suggestion unit can analyze the discount rate and point reward rate for credit cards at a particular store. The suggestion unit can also analyze whether or not there are fees for electronic payments at another store. The suggestion unit can use AI to suggest that using a credit card is the most advantageous option at a particular store. The suggestion unit can also use AI to suggest that using electronic payment is the most advantageous option at another store. This allows the suggestion unit to suggest the most advantageous payment method for each store. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input that using a credit card is the most advantageous option at a particular store into a generating AI and have the generating AI execute the suggestion.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of payment history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection until the user is relaxed. If the user is relaxed, the data collection unit can also immediately collect the payment history and begin analysis. If the user is in a hurry, the data collection unit can shorten the collection time and quickly collect the payment history. This allows the data collection timing to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 data collection timing.

[0069] The data collection unit can analyze the user's past payment history and select the optimal collection method. For example, the data collection unit may prioritize collecting payment methods that the user has frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. The data collection unit can also analyze the user's past payment history and select the most efficient collection method. This allows for the selection of the optimal collection method based on past payment history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past payment history into a generating AI and have the generating AI select the optimal collection method.

[0070] The collection unit can filter payment history based on the user's current lifestyle and areas of interest when collecting it. For example, the collection unit may prioritize collecting payment history from specific categories based on the user's current lifestyle. The collection unit can also filter and collect relevant payment history based on the user's areas of interest. The collection unit can also exclude unnecessary payment history, taking into account the user's lifestyle and areas of interest. This allows for filtering of payment history based on lifestyle and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of payment history.

[0071] The data collection unit can estimate the user's emotions and determine the priority of payment history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important payment history. If the user is relaxed, the data collection unit may collect all payment history equally. If the user is in a hurry, the data collection unit may prioritize collecting high-priority payment history. This allows the priority of payment history to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 determine the priority of payment history.

[0072] The collection unit can prioritize the collection of highly relevant payment history by considering the user's geographical location information when collecting payment history. For example, the collection unit can prioritize the collection of payment history from stores close to the user's current location. The collection unit can also filter and collect highly relevant payment history based on the user's geographical location information. The collection unit can also prioritize the collection of payment history from places the user frequently visits. This allows for the priority collection of highly relevant history based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant history.

[0073] The collection unit can analyze the user's social media activity and collect relevant history when collecting payment history. For example, the collection unit can prioritize collecting payment history from stores mentioned by the user on social media. The collection unit can also filter and collect relevant payment history based on the user's social media activity. The collection unit can also prioritize collecting payment history from categories that the user has shown interest in on social media. This allows for the collection of relevant history based on social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant history.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method of payment history based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can also perform a simplified analysis. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. This allows the analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the payments. For example, the analysis unit can perform a detailed analysis for high-importance payments, and a simplified analysis for low-importance payments. The analysis unit can also determine the priority of the analysis according to the importance of the payments, thereby adjusting the level of detail of the analysis according to the importance of the payments. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of payments into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the payment category during analysis. For example, the analysis unit may apply a specific analysis algorithm to payments related to food expenses. The analysis unit may also apply a different analysis algorithm to payments related to utilities. The analysis unit can also select the optimal analysis algorithm depending on the payment category. This ensures that the most suitable analysis algorithm is applied according to the payment category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the payment category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display. If the user is relaxed, the analysis unit can also provide a display that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise display. This allows the display of analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0078] The analysis unit can determine the priority of analyses based on the payment submission dates. For example, the analysis unit can prioritize analyzing payments that are due soon. It can also postpone analyzing payments that are due far in the future. The analysis unit can also adjust the priority of analyses according to the payment submission dates. This allows the analysis priority to be determined according to the payment submission dates. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the payment submission dates into a generating AI and have the generating AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of payments during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant payments. The analysis unit can also postpone the analysis of less relevant payments. The analysis unit can adjust the order of analysis according to the relevance of payments. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of payments into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. This allows the suggestion unit to adjust its presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of its suggestions.

[0081] The proposal unit can adjust the level of detail of a proposal based on the importance of the payment method. For example, the proposal unit can provide detailed proposals for high-importance payment methods, and simplified proposals for low-importance payment methods. The proposal unit can also determine the priority of proposals according to the importance of the payment method, thereby adjusting the level of detail of the proposal according to the importance of the payment method. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the payment method into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0082] The proposal unit can adjust the order of proposals based on the relevance of the payment methods. For example, the proposal unit will prioritize proposals for highly relevant payment methods. The proposal unit may also postpone proposals for less relevant payment methods. The proposal unit can also adjust the order of proposals according to the relevance of the payment methods. This allows the order of proposals to be adjusted according to the relevance of the payment methods. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of payment methods into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The household finance management system can also include a budget setting section. This section can automatically set monthly budgets based on the income and expenses of each spouse. For example, it can analyze past spending data and suggest appropriate budgets for categories such as food, utilities, and entertainment. It can also consider the couple's savings goals and set spending limits to reach those goals. Furthermore, it can issue alerts when spending exceeds the budget, prompting a review of spending. This allows couples to manage their household finances in a planned manner.

[0085] The household budget management system can also include a notification function. This function can provide timely notifications based on each spouse's payment history and budget status. For example, it can send reminders when payment deadlines are approaching. It can also issue warnings if there are signs of budget overruns. Furthermore, it can provide information on limited-time promotions offering special deals on specific payment methods. This allows couples to efficiently manage their finances without missing important information.

[0086] The household financial management system can also include a goal-setting section. This section allows each spouse to set short-term and long-term financial goals and track their progress. For example, the goal-setting section can set savings goals for travel or major purchases. It can also set long-term goals such as early mortgage repayment or children's education funds. Furthermore, the goal-setting section can regularly report on progress toward goals and adjust them as needed. This allows couples to save systematically towards specific goals.

[0087] The household financial management system can also include an investment suggestion section. This section can analyze the financial situation of each spouse and propose appropriate investment plans. For example, it can suggest investment products such as stocks, bonds, and mutual funds based on risk tolerance and investment timeframe. Furthermore, it can periodically analyze market trends and suggest revisions to the investment portfolio. In addition, the investment suggestion section can adjust investment strategies according to the couple's life events (marriage, childbirth, retirement, etc.). This allows the couple to increase their assets while minimizing risk.

[0088] The household financial management system can also include an education section. This education section can provide each spouse with knowledge about household financial management and investment. For example, the education section can offer online courses on the basics of household financial management and saving techniques. It can also hold seminars on the fundamentals of investment and risk management. Furthermore, the education section can provide customized advice tailored to the couple's financial situation. This allows couples to deepen their knowledge of household financial management and investment, enabling them to make wiser financial decisions.

[0089] The household budget management system can further utilize an emotion estimation function to provide spending advice based on the couple's emotions. For example, the suggestion function can estimate the couple's emotions and suggest spending on relaxing entertainment when they are stressed. It can also use the emotion estimation function to suggest saving or investing for the future when the couple is relaxed. Furthermore, it can use the emotion estimation function to provide concise and quick spending advice when the couple is in a hurry. This allows the system to provide appropriate spending advice tailored to the couple's emotions.

[0090] The household budget management system can further utilize an emotion estimation function to adjust the budget based on the couple's emotions. For example, the adjustment unit can estimate the couple's emotions and, when stress levels are high, can ease the budget and increase spending on relaxing activities. It can also use the emotion estimation function to increase the budget for savings and investments when the couple is relaxed. Furthermore, it can use the emotion estimation function to quickly adjust the budget when the couple is in a hurry. This allows for flexible budget adjustments tailored to the couple's emotions.

[0091] The household budget management system can further utilize an emotion estimation function to suggest payment methods based on the couple's emotions. For example, the suggestion function can estimate the couple's emotions and suggest a payment method with lower fees when they are stressed. It can also suggest a payment method with a higher point reward rate when the couple is relaxed. Furthermore, it can suggest a method for quick payment completion when the couple is in a hurry. This allows the system to suggest the optimal payment method tailored to the couple's emotions.

[0092] The household budget management system can further utilize an emotion estimation function to determine spending priorities based on the couple's emotions. For example, the collection unit can estimate the couple's emotions and postpone less important expenses when stress levels are high. It can also use the emotion estimation function to collect all expenses equally when the couple is relaxed. Furthermore, it can use the emotion estimation function to prioritize collecting high-priority expenses when the couple is in a hurry. This allows for the determination of spending priorities in accordance with the couple's emotions.

[0093] The household budget management system can further utilize an emotion estimation function to review spending based on the couple's emotions. For example, the analysis unit can estimate the couple's emotions and review spending when stress levels are high, reducing unnecessary expenses. It can also use the emotion estimation function to review savings and investments for the future when the couple is relaxed. Furthermore, it can quickly review spending when the couple is in a hurry. This allows for appropriate spending adjustments tailored to the couple's emotions.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection unit collects the payment history of each spouse. The data collection unit can collect, for example, credit card statements, bank account transaction history, and e-money usage history. The data collection unit can also use AI to automatically collect payment history. Step 2: The analysis department analyzes the payment history collected by the collection department. The analysis department uses AI to analyze the collected payment history and identify expenses related to living costs. For example, the analysis department automatically categorizes living expenses such as food and utilities, clearly identifying which payments are related to living costs. Step 3: The proposal department proposes the optimal payment method based on the analysis results obtained by the analysis department. The proposal department uses AI to analyze the payment methods available at each store and proposes the most advantageous payment method. For example, the proposal department might suggest that credit cards are the most advantageous at one store, while electronic payments are the most advantageous at another. Step 4: The adjustment unit adjusts the division of living expenses based on the payment method proposed by the proposal unit. The adjustment unit uses AI to calculate the difference in payments and adjust the division of living expenses between the couple. For example, the adjustment unit automatically adjusts the division of living expenses by transferring the difference from person A's account to person B's account.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and adjustment 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 credit card statements and bank account transaction history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected payment history to identify expenditures as living expenses. The proposal unit is implemented by the control unit 46A of the smart device 14 and analyzes the payment methods available at each store and proposes the most advantageous payment method. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the difference in payments and adjusts the division of living expenses between spouses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and adjustment 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 credit card statements and bank account transaction history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected payment history to identify expenditures as living expenses. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the payment methods available at each store and proposes the most advantageous payment method. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the difference in payments and adjusts the division of living expenses between spouses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and adjustment 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 credit card statements and bank account transaction history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected payment history to identify expenditures as living expenses. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the payment methods available at each store and proposes the most advantageous payment method. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the difference in payments and adjusts the division of living expenses between spouses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and adjustment 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 credit card statements and bank account transaction history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected payment history to identify expenditures as living expenses. The proposal unit is implemented by the control unit 46A of the robot 414 and analyzes the payment methods available at each store and proposes the most advantageous payment method. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the difference in payments and adjusts the division of living expenses between spouses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A collection unit that collects payment history, An analysis unit analyzes the payment history collected by the collection unit, A proposal unit that proposes the optimal payment method based on the analysis results obtained by the aforementioned analysis unit, The system includes an adjustment unit that adjusts the distribution of living expenses based on the payment method proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the payment history of each spouse. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected payment history is analyzed using AI to identify spending related to living expenses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze the payment methods available at each store and suggest the most advantageous payment method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Calculate the difference in payments and adjust the division of living expenses between spouses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, This suggests that credit cards are the most advantageous at certain stores, while electronic payments are the most advantageous at others. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of payment history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting payment history, 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 10) The aforementioned collection unit is It estimates the user's sentiment and determines the priority of payment history to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting payment history, the system prioritizes collecting the most relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting payment history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the user's emotions and adjust the payment history analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the payments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the payment category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on the payment submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the payments. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the payment method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the payment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects payment history, An analysis unit analyzes the payment history collected by the collection unit, A proposal unit that proposes the optimal payment method based on the analysis results obtained by the aforementioned analysis unit, The system includes an adjustment unit that adjusts the distribution of living expenses based on the payment method proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the payment history of each spouse. The system according to feature 1.

3. The aforementioned analysis unit is The collected payment history is analyzed using AI to identify spending related to living expenses. The system according to feature 1.

4. The aforementioned proposal section is, We analyze the payment methods available at each store and suggest the most advantageous payment method. The system according to feature 1.

5. The adjustment unit is, Calculate the difference in payments and adjust the division of living expenses between spouses. The system according to feature 1.

6. The aforementioned proposal section is, This suggests that credit cards are the most advantageous at certain stores, while electronic payments are the most advantageous at others. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of payment history collection based on those estimated emotions. The system according to feature 1.

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

Images