system

Abstract

The system according to this embodiment aims to efficiently manage the user's income and expenses and predict future life plans. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a prediction unit. The data collection unit collects data on the user's income and expenses. The analysis unit analyzes the data collected by the data collection unit and analyzes income and expense patterns. The proposal unit proposes a current operating method based on the analysis results obtained by the analysis unit. The prediction unit predicts future income and expenses based on the operating 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently manage a user's income and expenditure and predict a future life plan.

[0005] The system according to the embodiment aims to efficiently manage a user's income and expenditure and predict a future life plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a forecast unit. The data collection unit collects data on the user's income and expenses. The analysis unit analyzes the data collected by the data collection unit and analyzes income and expense patterns. The proposal unit proposes a current operating method based on the analysis results obtained by the analysis unit. The forecast unit predicts future income and expenses based on the operating method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the user's income and expenses and predict future life plans. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The life plan management and prediction system according to an embodiment of the present invention is a system in which AI proposes appropriate current management and predicts the future by managing income and expenses. This life plan management and prediction system collects data on the user's income and expenses, and the AI ​​analyzes the data to analyze income and expense patterns, thereby proposing appropriate current management methods and predicting future income and expenses. For example, the life plan management and prediction system collects data such as payment information from electronic payment systems and transaction history from bank accounts. Next, the AI ​​analyzes the collected data to analyze income and expense patterns. Based on this, it proposes appropriate current management methods. Furthermore, the AI ​​predicts future income and expenses, allowing the user to visualize their life plan concretely. Through this service, the user can efficiently manage their income and expenses and appropriately manage and predict their future life plan. For example, by reducing unnecessary expenses and increasing savings, they can prepare for large future expenses. Also, based on the AI's suggestions, they can implement methods to increase income and efficiently manage expenses. In this way, the life plan management and prediction system can efficiently manage the user's income and expenses and appropriately manage and predict their future life plan.

[0029] The life plan management and forecasting system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a forecasting unit. The data collection unit collects data on the user's income and expenses. The data collection unit can, for example, collect payment information from an electronic payment system or transaction history from a bank account. The data collection unit can, for example, collect payment information from an electronic payment system and obtain the user's expense data. The data collection unit can also collect transaction history from a bank account and obtain the user's income data. The analysis unit analyzes the data collected by the data collection unit and analyzes patterns of income and expenses. The analysis unit can, for example, analyze the collected data using time series analysis or trend analysis. The analysis unit can, for example, analyze income and expense data in a time series and identify patterns of income and expenses. The analysis unit can also analyze fluctuations in income and expenses using trend analysis. The proposal unit proposes current operating methods based on the analysis results obtained by the analysis unit. The proposal unit can, for example, provide advice on reducing unnecessary expenses. The proposal unit can, for example, identify unnecessary expenses based on the user's expense data and propose methods for reducing them. Furthermore, the proposal unit can also propose the optimal method for saving. For example, the proposal unit proposes the optimal method for saving based on the user's income data. The forecasting unit forecasts future income and expenses based on the investment method proposed by the proposal unit. For example, the forecasting unit can forecast the likelihood of future income increasing. For example, the forecasting unit forecasts whether future income will increase based on the user's income data. The forecasting unit can also make forecasts that take into account large future expenses. For example, the forecasting unit forecasts large future expenses based on the user's expense data. As a result, the life plan management and forecasting system according to this embodiment can efficiently manage the user's income and expenses and appropriately manage and forecast their future life plan.

[0030] The data collection unit can collect payment information from electronic payment systems or transaction history from bank accounts. For example, the data collection unit can collect payment information from electronic payment systems to obtain user spending data. For example, the data collection unit can collect payment information from credit cards and electronic money to obtain user spending data. The data collection unit can also collect transaction history from bank accounts to obtain user income data. For example, the data collection unit can collect deposit and withdrawal history and transfer history from bank accounts to obtain user income data. In this way, the data collection unit can accurately grasp income and spending data by collecting payment information from electronic payment systems and transaction history from bank accounts. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input payment information from electronic payment systems into AI and have AI perform the analysis of the payment information.

[0031] The suggestion department can provide advice on reducing spending. For example, the suggestion department can identify unnecessary spending based on the user's spending data and propose ways to reduce it. For example, the suggestion department can analyze the user's spending data to identify unnecessary spending. The suggestion department can also propose specific methods for reducing unnecessary spending. For example, the suggestion department can propose ways to save money and reduce waste based on the user's spending data. In this way, the suggestion department can efficiently manage the user's spending by providing advice on reducing unnecessary spending. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's spending data into AI and have the AI ​​identify unnecessary spending and propose ways to reduce it.

[0032] The suggestion unit can propose methods for saving. For example, the suggestion unit can propose the optimal method for saving based on the user's income data. For example, the suggestion unit can analyze the user's income data and identify the optimal method for saving. The suggestion unit can also propose specific methods for saving. For example, the suggestion unit can propose savings methods such as time deposits or mutual funds based on the user's income data. In this way, the suggestion unit can efficiently increase the user's savings by proposing the optimal method for saving. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's income data into AI and have the AI ​​propose the optimal method for saving.

[0033] The prediction unit can predict whether future income will increase or not. For example, the prediction unit predicts whether future income will increase or not based on the user's income data. For example, the prediction unit analyzes the user's income data and identifies the likelihood of future income increase. The prediction unit can also make predictions by considering specific factors that may contribute to income increase. For example, the prediction unit considers factors such as the user's potential for salary increases and investment returns to predict whether future income will increase or not. In this way, by predicting the likelihood of future income increase, the prediction unit allows the user to prepare for future income increases. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's income data into AI and have the AI ​​perform the income increase prediction.

[0034] The forecasting unit can make forecasts that take future expenditures into account. For example, the forecasting unit can predict large future expenditures based on the user's expenditure data. For example, the forecasting unit can analyze the user's expenditure data and identify large future expenditures. The forecasting unit can also make forecasts that take into account the specific factors of future expenditures. For example, the forecasting unit can predict large future expenditures by considering the user's mortgage or education expenses. In this way, by making forecasts that take large future expenditures into account, the forecasting unit can help the user prepare for those large future expenditures. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not using AI. For example, the forecasting unit can input the user's expenditure data into AI and have the AI ​​perform the forecast of future expenditures.

[0035] The data collection unit can analyze the user's past income and expenditure data and select a collection method. For example, if the data collection unit analyzes the user's past income and expenditure data and finds that income is high at the end of the month, it will concentrate data collection at the end of the month. For example, the data collection unit will select a collection method based on the user's past income data. The data collection unit can also analyze the user's past expenditure patterns and, if expenditure is high on specific days of the week, it can collect data on those days. For example, the data collection unit will select a collection method based on the user's past expenditure data. Furthermore, if the data collection unit analyzes the user's past income and expenditure data and finds that income is irregular, it can collect data each time income is generated. For example, the data collection unit will select a collection method based on the user's past income data. This allows the data collection unit to select the optimal collection method by analyzing past data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into AI and have the AI ​​select the collection method.

[0036] The data collection unit can filter income and expenditure data based on the user's current living situation or areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. For example, the data collection unit can filter the collected data based on the user's living situation data. The data collection unit can also prioritize collecting expenditure data related to a new hobby if the user has started one. For example, the data collection unit can filter the collected data based on the user's areas of interest data. The data collection unit can also prioritize collecting moving-related expenditure data if the user is planning to move. For example, the data collection unit can filter the collected data based on the user's living situation data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's living situation data into an AI and have the AI ​​perform the filtering of the collected data.

[0037] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting income and expenditure data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of expenditure data in that region. For example, the data collection unit will filter the collected data based on the user's geographical location. The data collection unit can also prioritize the collection of expenditure data at the user's travel destination if the user is traveling. For example, the data collection unit will filter the collected data based on the user's geographical location. The data collection unit can also prioritize the collection of expenditure data related to the new address if the user is planning to move. For example, the data collection unit will filter the collected data based on the user's geographical location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's geographical location into AI and have AI perform the filtering of collected data.

[0038] The data collection unit can analyze social media activity and collect relevant data when collecting income and expenditure data. For example, if a user posts on social media about attending a specific event, the data collection unit can collect expenditure data related to that event. For example, the data collection unit can filter the collected data based on the user's social media activity data. The data collection unit can also collect expenditure data related to a hobby if a user posts on social media about starting a new hobby. For example, the data collection unit can filter the collected data based on the user's social media activity data. The data collection unit can also collect expenditure data related to the purchase of a specific product if a user posts on social media about considering purchasing that product. For example, the data collection unit can filter the collected data based on the user's social media activity data. This allows the data collection unit to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media activity data into AI and have AI perform the filtering of the collected data.

[0039] The analysis unit can adjust the level of detail of its analysis based on the priority of income and expenses during the analysis. For example, the analysis unit can perform a detailed analysis for important income and expenses and a simplified analysis for other income and expenses. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. The analysis unit can also perform a detailed analysis for months with high income and a simplified analysis for months with low income. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. The analysis unit can also perform a detailed analysis for categories with high expenses and a simplified analysis for categories with low expenses. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail of its analysis based on the importance of income and expenses. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input income and expense priority data into AI and have the AI ​​perform the adjustment of the level of detail of its analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the type of income and expenditure during analysis. For example, the analysis unit can apply an income forecasting algorithm to income categories and an expenditure reduction algorithm to expenditure categories. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. Furthermore, if income is irregular, the analysis unit can also apply an algorithm that takes income fluctuations into account. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. Furthermore, the analysis unit can apply an expenditure reduction algorithm to categories with high expenditures and a simplified analysis algorithm to categories with low expenditures. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the income and expenditure categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expenditure type data into AI and have the AI ​​execute the application of different analysis algorithms.

[0041] The analysis unit can determine the order of analysis based on the submission timing of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of months with high income and postpone the analysis of months with low income. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. The analysis unit can also prioritize the analysis of months with high expenses and postpone the analysis of months with low expenses. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. The analysis unit can also prioritize the analysis of income and expenses when the submission timings are close and postpone the analysis when the submission timings are far apart. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. This enables efficient data analysis by allowing the analysis unit to prioritize based on the submission timing of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expense submission timing data into AI and have the AI ​​determine the order of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relationship between income and expenses during the analysis. For example, if the correlation between income and expenses is high, the analysis unit will prioritize the analysis. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. The analysis unit can also postpone the analysis if the correlation between income and expenses is low. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. The analysis unit can also perform analyses in an appropriate order if the correlation between income and expenses is moderate. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. This allows the analysis unit to perform efficient data analysis by adjusting the order of analysis based on the relationship between income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationship data between income and expenses into AI and have the AI ​​perform the adjustment of the order of analysis.

[0043] The proposal department can adjust the level of detail of its proposals based on the priority of income and expenses. For example, it can provide detailed proposals for important income and expenses and concise proposals for other income and expenses. For example, it can adjust the level of detail of its proposals based on income and expense priority data. It can also provide detailed proposals for months with high income and concise proposals for months with low income. For example, it can adjust the level of detail of its proposals based on income and expense priority data. It can also provide detailed proposals for categories with high expenses and concise proposals for categories with low expenses. For example, it can adjust the level of detail of its proposals based on income and expense priority data. This allows the proposal department to provide efficient proposals by adjusting the level of detail of its proposals based on the importance of income and expenses. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input income and expense priority data into AI and have the AI ​​perform the adjustment of the level of detail of its proposals.

[0044] The proposal unit can apply different proposal algorithms depending on the type of income and expenditure when making a proposal. For example, the proposal unit can apply an income increase algorithm to income categories and an expenditure reduction algorithm to expenditure categories. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. Furthermore, if income is irregular, the proposal unit can also apply an algorithm that takes income fluctuations into account. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. Furthermore, the proposal unit can apply an expenditure reduction algorithm to categories with high expenditures and a simpler proposal algorithm to categories with low expenditures. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. In this way, the proposal unit can make more accurate proposals by applying different proposal algorithms depending on the income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input income and expenditure type data into AI and have the AI ​​execute the application of different proposal algorithms.

[0045] The proposal department can determine the order of proposals based on the submission timing of income and expenses. For example, the proposal department can prioritize proposals for months with high income and postpone proposals for months with low income. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. The proposal department can also prioritize proposals for months with high expenses and postpone proposals for months with low expenses. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. The proposal department can also prioritize proposals when the submission timings of income and expenses are close together and postpone proposals when the submission timings are far apart. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the submission timing of income and expenses. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input income and expense submission timing data into AI and have the AI ​​determine the order of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relationship between income and expenses when making proposals. For example, the proposal unit will prioritize proposals when the relationship between income and expenses is high. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. The proposal unit can also postpone proposals when the relationship between income and expenses is low. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. The proposal unit can also make proposals in an appropriate order when the relationship between income and expenses is moderate. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relationship between income and expenses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relationship between income and expenses into AI and have the AI ​​perform the adjustment of the order of proposals.

[0047] The prediction unit can adjust its prediction algorithm by referring to past income and expenditure data during the prediction process. For example, the prediction unit can optimize its algorithm for predicting future income based on past income data. The prediction unit can also optimize its algorithm for predicting future expenditures based on past expenditure data. The prediction unit can also analyze past income and expenditure patterns to optimize its algorithm for predicting future income and expenditures. For example, the prediction unit can analyze past income and expenditure patterns to optimize its prediction algorithm. By doing so, the prediction unit can optimize its prediction algorithm by referring to past data, enabling highly accurate predictions. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past income and expenditure data into AI and have AI perform the adjustment of the prediction algorithm.

[0048] The forecasting unit can apply different forecasting methods to different types of income and expenses during the forecasting process. For example, the forecasting unit can apply an income forecasting method to income categories and an expense forecasting method to expense categories. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. Furthermore, if income is irregular, the forecasting unit can also apply a forecasting method that takes income fluctuations into account. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. Furthermore, the forecasting unit can apply an expense reduction forecasting method to categories with high expenses and a simplified forecasting method to categories with low expenses. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. In this way, the forecasting unit can make more accurate forecasts by applying different forecasting methods to different income and expense categories. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income and expense type data into AI and have the AI ​​apply different forecasting methods.

[0049] The forecasting unit can determine the order of predictions based on the submission timing of income and expenses. For example, the forecasting unit can prioritize predictions for months with high income and postpone predictions for months with low income. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. The forecasting unit can also prioritize predictions for months with high expenses and postpone predictions for months with low expenses. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. The forecasting unit can also prioritize predictions for months with similar submission timings and postpone predictions for months with different submission timings. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. This allows the forecasting unit to perform efficient predictions by prioritizing based on the submission timing of income and expenses. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income and expense submission timing data into AI and have the AI ​​determine the order of predictions.

[0050] The forecasting unit can improve the accuracy of its forecasts by referring to market data for income and expenditure during the forecasting process. For example, when forecasting income, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. For example, the forecasting unit improves the accuracy of its forecasts based on income data and market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data when forecasting expenditures. For example, the forecasting unit improves the accuracy of its forecasts based on expenditure data and market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data when forecasting income and expenditures. For example, the forecasting unit improves the accuracy of its forecasts based on income, expenditure data and market data. In this way, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income, expenditure data and market data into AI and have the AI ​​perform the forecasting accuracy improvement.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The life plan management and prediction system can also be equipped with a health data collection unit. The health data collection unit collects data on the user's health status and provides it to the analysis unit. For example, the health data collection unit can collect heart rate, steps, and sleep data from the user's smartwatch or fitness tracker. The health data collection unit can also collect the user's medical records and health checkup results and provide them to the analysis unit. Based on the collected health data, the analysis unit can analyze the relationship between the user's health status and income / expenses and propose a more accurate life plan. For example, if the user's health status is good, the analysis unit can predict a reduction in future medical expenses and propose a savings plan. Conversely, if the user's health status is deteriorating, it can predict an increase in future medical expenses and propose spending reductions.

[0053] The life plan management and forecasting system may also include an environmental data collection unit. This unit collects data related to the user's living environment and provides it to the analysis unit. For example, the environmental data collection unit can collect climate data and air pollution data for the user's residential area. It can also collect energy consumption data and water usage data for the user's residence and provide it to the analysis unit. Based on this, the analysis unit can analyze the relationship between the user's living environment and income / expenses using the collected environmental data, and propose a more accurate life plan. For example, if the climate in the user's residential area is severe, the analysis unit can predict an increase in energy consumption and propose improvements to energy efficiency. Furthermore, if air pollution in the user's residential area is severe, it can propose an expenditure plan that takes health risks into consideration.

[0054] The life plan management and forecasting system can also include an education data collection unit. This unit collects data related to the user's education and provides it to the analysis unit. For example, it can collect information on the user's children's academic performance and desired schools. It can also collect information on the user's children's education expenses and scholarships and provide this information to the analysis unit. This allows the analysis unit to analyze the relationship between the user's education expenses and income / expenses based on the collected education data, enabling it to propose a more accurate life plan. For example, if the user's child is accepted into their desired school, the analysis unit can predict an increase in future education expenses and propose a savings plan. Similarly, if the user's child receives a scholarship, the analysis unit can predict a reduction in education expenses and propose an expenditure plan.

[0055] The life plan management and forecasting system can also include a hobby data collection unit. This unit collects data related to the user's hobbies and provides it to the analysis unit. For example, the hobby data collection unit can collect expenditure data and event participation data related to the user's hobbies. It can also collect purchase history of hobby-related products and usage of subscription services and provide this data to the analysis unit. This allows the analysis unit to analyze the relationship between the user's hobbies and income / expenses based on the collected hobby data, enabling it to propose a more accurate life plan. For example, if the user's hobby-related expenses are high, the analysis unit can suggest ways to reduce spending. Similarly, if the user frequently participates in hobby-related events, the analysis unit can predict future increases in spending and propose a savings plan.

[0056] The life plan management and forecasting system may also include a travel data collection unit. This unit collects data related to the user's travels and provides it to the analysis unit. For example, it can collect data on the user's travel destinations, duration, and expenses. It can also collect expenditure data and travel insurance information related to the user's travels and provide it to the analysis unit. This allows the analysis unit to analyze the relationship between the user's travel and income / expenses based on the collected travel data, enabling it to propose a more accurate life plan. For example, if the user's travel expenses are high, the analysis unit can suggest ways to reduce spending. Furthermore, if the user's travel destinations are expensive, the analysis unit can predict future increases in expenses and propose a savings plan.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects user income and expenditure data. For example, it can collect payment information from electronic payment systems and transaction history from bank accounts. The collection unit collects payment information from electronic payment systems and obtains user expenditure data. It can also collect transaction history from bank accounts and obtain user income data. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze income and expenditure patterns. For example, the collected data can be analyzed using time series analysis or trend analysis. Income and expenditure data can be analyzed in time series to identify income and expenditure patterns. Trend analysis can also be used to analyze fluctuations in income and expenditure. Step 3: The proposal department proposes current operating methods based on the analysis results obtained by the analysis department. For example, it can provide advice on reducing unnecessary expenses. Based on the user's spending data, it identifies unnecessary expenses and proposes ways to reduce them. It can also propose the best way to save money. Based on the user's income data, it proposes the best way to save money. Step 4: The forecasting unit predicts future income and expenses based on the operating methods proposed by the proposal unit. For example, it can predict the likelihood of future income increasing. Based on the user's income data, it predicts whether future income will increase or not. It can also make predictions that take into account large future expenses. Based on the user's expense data, it predicts large future expenses.

[0059] (Example of form 2) The life plan management and prediction system according to an embodiment of the present invention is a system in which AI proposes appropriate current management and predicts the future by managing income and expenses. This life plan management and prediction system collects data on the user's income and expenses, and the AI ​​analyzes the data to analyze income and expense patterns, thereby proposing appropriate current management methods and predicting future income and expenses. For example, the life plan management and prediction system collects data such as payment information from electronic payment systems and transaction history from bank accounts. Next, the AI ​​analyzes the collected data to analyze income and expense patterns. Based on this, it proposes appropriate current management methods. Furthermore, the AI ​​predicts future income and expenses, allowing the user to visualize their life plan concretely. Through this service, the user can efficiently manage their income and expenses and appropriately manage and predict their future life plan. For example, by reducing unnecessary expenses and increasing savings, they can prepare for large future expenses. Also, based on the AI's suggestions, they can implement methods to increase income and efficiently manage expenses. In this way, the life plan management and prediction system can efficiently manage the user's income and expenses and appropriately manage and predict their future life plan.

[0060] The life plan management and forecasting system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a forecasting unit. The data collection unit collects data on the user's income and expenses. The data collection unit can, for example, collect payment information from an electronic payment system or transaction history from a bank account. The data collection unit can, for example, collect payment information from an electronic payment system and obtain the user's expense data. The data collection unit can also collect transaction history from a bank account and obtain the user's income data. The analysis unit analyzes the data collected by the data collection unit and analyzes patterns of income and expenses. The analysis unit can, for example, analyze the collected data using time series analysis or trend analysis. The analysis unit can, for example, analyze income and expense data in a time series and identify patterns of income and expenses. The analysis unit can also analyze fluctuations in income and expenses using trend analysis. The proposal unit proposes current operating methods based on the analysis results obtained by the analysis unit. The proposal unit can, for example, provide advice on reducing unnecessary expenses. The proposal unit can, for example, identify unnecessary expenses based on the user's expense data and propose methods for reducing them. Furthermore, the proposal unit can also propose the optimal method for saving. For example, the proposal unit proposes the optimal method for saving based on the user's income data. The forecasting unit forecasts future income and expenses based on the investment method proposed by the proposal unit. For example, the forecasting unit can forecast the likelihood of future income increasing. For example, the forecasting unit forecasts whether future income will increase based on the user's income data. The forecasting unit can also make forecasts that take into account large future expenses. For example, the forecasting unit forecasts large future expenses based on the user's expense data. As a result, the life plan management and forecasting system according to this embodiment can efficiently manage the user's income and expenses and appropriately manage and forecast their future life plan.

[0061] The data collection unit can collect payment information from electronic payment systems or transaction history from bank accounts. For example, the data collection unit can collect payment information from electronic payment systems to obtain user spending data. For example, the data collection unit can collect payment information from credit cards and electronic money to obtain user spending data. The data collection unit can also collect transaction history from bank accounts to obtain user income data. For example, the data collection unit can collect deposit and withdrawal history and transfer history from bank accounts to obtain user income data. In this way, the data collection unit can accurately grasp income and spending data by collecting payment information from electronic payment systems and transaction history from bank accounts. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input payment information from electronic payment systems into AI and have AI perform the analysis of the payment information.

[0062] The suggestion department can provide advice on reducing spending. For example, the suggestion department can identify unnecessary spending based on the user's spending data and propose ways to reduce it. For example, the suggestion department can analyze the user's spending data to identify unnecessary spending. The suggestion department can also propose specific methods for reducing unnecessary spending. For example, the suggestion department can propose ways to save money and reduce waste based on the user's spending data. In this way, the suggestion department can efficiently manage the user's spending by providing advice on reducing unnecessary spending. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's spending data into AI and have the AI ​​identify unnecessary spending and propose ways to reduce it.

[0063] The suggestion unit can propose methods for saving. For example, the suggestion unit can propose the optimal method for saving based on the user's income data. For example, the suggestion unit can analyze the user's income data and identify the optimal method for saving. The suggestion unit can also propose specific methods for saving. For example, the suggestion unit can propose savings methods such as time deposits or mutual funds based on the user's income data. In this way, the suggestion unit can efficiently increase the user's savings by proposing the optimal method for saving. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's income data into AI and have the AI ​​propose the optimal method for saving.

[0064] The prediction unit can predict whether future income will increase or not. For example, the prediction unit predicts whether future income will increase or not based on the user's income data. For example, the prediction unit analyzes the user's income data and identifies the likelihood of future income increase. The prediction unit can also make predictions by considering specific factors that may contribute to income increase. For example, the prediction unit considers factors such as the user's potential for salary increases and investment returns to predict whether future income will increase or not. In this way, by predicting the likelihood of future income increase, the prediction unit allows the user to prepare for future income increases. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's income data into AI and have the AI ​​perform the income increase prediction.

[0065] The forecasting unit can make forecasts that take future expenditures into account. For example, the forecasting unit can predict large future expenditures based on the user's expenditure data. For example, the forecasting unit can analyze the user's expenditure data and identify large future expenditures. The forecasting unit can also make forecasts that take into account the specific factors of future expenditures. For example, the forecasting unit can predict large future expenditures by considering the user's mortgage or education expenses. In this way, by making forecasts that take large future expenditures into account, the forecasting unit can help the user prepare for those large future expenditures. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not using AI. For example, the forecasting unit can input the user's expenditure data into AI and have the AI ​​perform the forecast of future expenditures.

[0066] The data collection unit can estimate emotions and adjust the timing of income and expenditure data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect income and expenditure data at night to reduce the user's burden. For example, the data collection unit adjusts the collection timing based on the user's emotional data. Also, if the user is relaxed, the data collection unit can collect income and expenditure data in real time to provide up-to-date information. For example, the data collection unit adjusts the collection timing based on the user's emotional data. Furthermore, if the user is busy, the data collection unit can collect income and expenditure data all at once on weekends to reduce the burden during weekdays. For example, the data collection unit adjusts the collection timing based on the user's emotional data. In this way, the data collection unit can reduce the user's burden and enable efficient data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have AI adjust the timing of data collection.

[0067] The data collection unit can analyze the user's past income and expenditure data and select a collection method. For example, if the data collection unit analyzes the user's past income and expenditure data and finds that income is high at the end of the month, it will concentrate data collection at the end of the month. For example, the data collection unit will select a collection method based on the user's past income data. The data collection unit can also analyze the user's past expenditure patterns and, if expenditure is high on specific days of the week, it can collect data on those days. For example, the data collection unit will select a collection method based on the user's past expenditure data. Furthermore, if the data collection unit analyzes the user's past income and expenditure data and finds that income is irregular, it can collect data each time income is generated. For example, the data collection unit will select a collection method based on the user's past income data. This allows the data collection unit to select the optimal collection method by analyzing past data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into AI and have the AI ​​select the collection method.

[0068] The data collection unit can filter income and expenditure data based on the user's current living situation or areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. For example, the data collection unit can filter the collected data based on the user's living situation data. The data collection unit can also prioritize collecting expenditure data related to a new hobby if the user has started one. For example, the data collection unit can filter the collected data based on the user's areas of interest data. The data collection unit can also prioritize collecting moving-related expenditure data if the user is planning to move. For example, the data collection unit can filter the collected data based on the user's living situation data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's living situation data into an AI and have the AI ​​perform the filtering of the collected data.

[0069] The data collection unit can estimate emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting important income and expenditure data, delaying the collection of detailed data. For example, the data collection unit may prioritize the data to collect based on the user's emotional data. Alternatively, if the user is relaxed, the data collection unit may collect all income and expenditure data equally. For example, the data collection unit may prioritize the data to collect based on the user's emotional data. Also, if the user is busy, the data collection unit may prioritize collecting only major income and expenditure data, collecting other data later. For example, the data collection unit may prioritize the data to collect based on the user's emotional data. This allows the data collection unit to prioritize important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the data to be collected.

[0070] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting income and expenditure data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of expenditure data in that region. For example, the data collection unit will filter the collected data based on the user's geographical location. The data collection unit can also prioritize the collection of expenditure data at the user's travel destination if the user is traveling. For example, the data collection unit will filter the collected data based on the user's geographical location. The data collection unit can also prioritize the collection of expenditure data related to the new address if the user is planning to move. For example, the data collection unit will filter the collected data based on the user's geographical location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's geographical location into AI and have AI perform the filtering of collected data.

[0071] The data collection unit can analyze social media activity and collect relevant data when collecting income and expenditure data. For example, if a user posts on social media about attending a specific event, the data collection unit can collect expenditure data related to that event. For example, the data collection unit can filter the collected data based on the user's social media activity data. The data collection unit can also collect expenditure data related to a hobby if a user posts on social media about starting a new hobby. For example, the data collection unit can filter the collected data based on the user's social media activity data. The data collection unit can also collect expenditure data related to the purchase of a specific product if a user posts on social media about considering purchasing that product. For example, the data collection unit can filter the collected data based on the user's social media activity data. This allows the data collection unit to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media activity data into AI and have AI perform the filtering of the collected data.

[0072] The analysis unit can estimate emotions and adjust the income and expenditure pattern analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit performs a concise and to-the-point pattern analysis. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. The analysis unit can also perform a detailed pattern analysis if the user is relaxed. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. Furthermore, if the user is busy, the analysis unit can analyze only the main income and expenditure patterns and postpone the detailed analysis. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. In this way, the analysis unit can provide more appropriate analysis results by adjusting the pattern analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the pattern analysis method.

[0073] The analysis unit can adjust the level of detail of its analysis based on the priority of income and expenses during the analysis. For example, the analysis unit can perform a detailed analysis for important income and expenses and a simplified analysis for other income and expenses. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. The analysis unit can also perform a detailed analysis for months with high income and a simplified analysis for months with low income. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. The analysis unit can also perform a detailed analysis for categories with high expenses and a simplified analysis for categories with low expenses. For example, the analysis unit can adjust the level of detail of its analysis based on the priority data of income and expenses. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail of its analysis based on the importance of income and expenses. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input income and expense priority data into AI and have the AI ​​perform the adjustment of the level of detail of its analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the type of income and expenditure during analysis. For example, the analysis unit can apply an income forecasting algorithm to income categories and an expenditure reduction algorithm to expenditure categories. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. Furthermore, if income is irregular, the analysis unit can also apply an algorithm that takes income fluctuations into account. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. Furthermore, the analysis unit can apply an expenditure reduction algorithm to categories with high expenditures and a simplified analysis algorithm to categories with low expenditures. For example, the analysis unit applies different analysis algorithms based on the income and expenditure type data. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the income and expenditure categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expenditure type data into AI and have the AI ​​execute the application of different analysis algorithms.

[0075] The analysis unit can estimate emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit adjusts the display method based on the user's emotion data. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit adjusts the display method based on the user's emotion data. The analysis unit can also provide a concise display method if the user is in a hurry. For example, the analysis unit adjusts the display method based on the user's emotion data. In this way, the analysis unit can provide a display that is easy for the user to understand by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the display method.

[0076] The analysis unit can determine the order of analysis based on the submission timing of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of months with high income and postpone the analysis of months with low income. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. The analysis unit can also prioritize the analysis of months with high expenses and postpone the analysis of months with low expenses. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. The analysis unit can also prioritize the analysis of income and expenses when the submission timings are close and postpone the analysis when the submission timings are far apart. For example, the analysis unit determines the order of analysis based on the submission timing data of income and expenses. This enables efficient data analysis by allowing the analysis unit to prioritize based on the submission timing of income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income and expense submission timing data into AI and have the AI ​​determine the order of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relationship between income and expenses during the analysis. For example, if the correlation between income and expenses is high, the analysis unit will prioritize the analysis. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. The analysis unit can also postpone the analysis if the correlation between income and expenses is low. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. The analysis unit can also perform analyses in an appropriate order if the correlation between income and expenses is moderate. For example, the analysis unit will adjust the order of analysis based on the relationship data between income and expenses. This allows the analysis unit to perform efficient data analysis by adjusting the order of analysis based on the relationship between income and expenses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationship data between income and expenses into AI and have the AI ​​perform the adjustment of the order of analysis.

[0078] The suggestion unit can estimate emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can make simple and easily understandable suggestions. For example, the suggestion unit can adjust the way suggestions are presented based on the user's emotional data. Also, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. For example, the suggestion unit can adjust the way suggestions are presented based on the user's emotional data. Also, if the user is in a hurry, the suggestion unit can make suggestions that are to the point. For example, the suggestion unit can adjust the way suggestions are presented based on the user's emotional data. In this way, the suggestion unit can make suggestions that are easy for the user to understand by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the proposal department can input user emotion data into the AI ​​and have the AI ​​adjust the way the proposal is expressed.

[0079] The proposal department can adjust the level of detail of its proposals based on the priority of income and expenses. For example, it can provide detailed proposals for important income and expenses and concise proposals for other income and expenses. For example, it can adjust the level of detail of its proposals based on income and expense priority data. It can also provide detailed proposals for months with high income and concise proposals for months with low income. For example, it can adjust the level of detail of its proposals based on income and expense priority data. It can also provide detailed proposals for categories with high expenses and concise proposals for categories with low expenses. For example, it can adjust the level of detail of its proposals based on income and expense priority data. This allows the proposal department to provide efficient proposals by adjusting the level of detail of its proposals based on the importance of income and expenses. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input income and expense priority data into AI and have the AI ​​perform the adjustment of the level of detail of its proposals.

[0080] The proposal unit can apply different proposal algorithms depending on the type of income and expenditure when making a proposal. For example, the proposal unit can apply an income increase algorithm to income categories and an expenditure reduction algorithm to expenditure categories. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. Furthermore, if income is irregular, the proposal unit can also apply an algorithm that takes income fluctuations into account. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. Furthermore, the proposal unit can apply an expenditure reduction algorithm to categories with high expenditures and a simpler proposal algorithm to categories with low expenditures. For example, the proposal unit can apply different proposal algorithms based on the income and expenditure type data. In this way, the proposal unit can make more accurate proposals by applying different proposal algorithms depending on the income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input income and expenditure type data into AI and have the AI ​​execute the application of different proposal algorithms.

[0081] The suggestion unit can estimate emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can make short, to-the-point suggestions. For example, the suggestion unit can adjust the length of suggestions based on the user's emotion data. Also, if the user is relaxed, the suggestion unit can make longer suggestions that include detailed explanations. For example, the suggestion unit can adjust the length of suggestions based on the user's emotion data. Also, if the user is in a hurry, the suggestion unit can make quick and concise suggestions. For example, the suggestion unit can adjust the length of suggestions based on the user's emotion data. In this way, the suggestion unit can make suggestions of an appropriate length for the user by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the proposal department can input user emotion data into the AI ​​and have the AI ​​adjust the length of the proposal.

[0082] The proposal department can determine the order of proposals based on the submission timing of income and expenses. For example, the proposal department can prioritize proposals for months with high income and postpone proposals for months with low income. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. The proposal department can also prioritize proposals for months with high expenses and postpone proposals for months with low expenses. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. The proposal department can also prioritize proposals when the submission timings of income and expenses are close together and postpone proposals when the submission timings are far apart. For example, the proposal department determines the order of proposals based on the submission timing data of income and expenses. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the submission timing of income and expenses. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input income and expense submission timing data into AI and have the AI ​​determine the order of proposals.

[0083] The proposal unit can adjust the order of proposals based on the relationship between income and expenses when making proposals. For example, the proposal unit will prioritize proposals when the relationship between income and expenses is high. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. The proposal unit can also postpone proposals when the relationship between income and expenses is low. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. The proposal unit can also make proposals in an appropriate order when the relationship between income and expenses is moderate. For example, the proposal unit will adjust the order of proposals based on the relationship between income and expenses. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relationship between income and expenses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relationship between income and expenses into AI and have the AI ​​perform the adjustment of the order of proposals.

[0084] The prediction unit can estimate emotions and adjust its prediction method for future income and expenses based on the estimated emotions. For example, if the user is stressed, the prediction unit will make a concise and to-the-point prediction. For example, the prediction unit will adjust its prediction method based on the user's emotional data. The prediction unit can also make a detailed prediction if the user is relaxed. For example, the prediction unit will adjust its prediction method based on the user's emotional data. Furthermore, if the user is busy, the prediction unit can only predict major income and expenses, postponing detailed predictions. For example, the prediction unit will adjust its prediction method based on the user's emotional data. In this way, the prediction unit can provide more appropriate prediction results by adjusting its prediction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the AI ​​and have the AI ​​adjust the prediction method.

[0085] The prediction unit can adjust its prediction algorithm by referring to past income and expenditure data during the prediction process. For example, the prediction unit can optimize its algorithm for predicting future income based on past income data. The prediction unit can also optimize its algorithm for predicting future expenditures based on past expenditure data. The prediction unit can also analyze past income and expenditure patterns to optimize its algorithm for predicting future income and expenditures. For example, the prediction unit can analyze past income and expenditure patterns to optimize its prediction algorithm. By doing so, the prediction unit can optimize its prediction algorithm by referring to past data, enabling highly accurate predictions. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past income and expenditure data into AI and have AI perform the adjustment of the prediction algorithm.

[0086] The forecasting unit can apply different forecasting methods to different types of income and expenses during the forecasting process. For example, the forecasting unit can apply an income forecasting method to income categories and an expense forecasting method to expense categories. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. Furthermore, if income is irregular, the forecasting unit can also apply a forecasting method that takes income fluctuations into account. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. Furthermore, the forecasting unit can apply an expense reduction forecasting method to categories with high expenses and a simplified forecasting method to categories with low expenses. For example, the forecasting unit can apply different forecasting methods based on income and expense type data. In this way, the forecasting unit can make more accurate forecasts by applying different forecasting methods to different income and expense categories. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income and expense type data into AI and have the AI ​​apply different forecasting methods.

[0087] The prediction unit can estimate emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-read display method. For example, the prediction unit adjusts the display method based on the user's emotion data. The prediction unit can also provide a display method that includes detailed information if the user is relaxed. For example, the prediction unit adjusts the display method based on the user's emotion data. The prediction unit can also provide a concise display method if the user is in a hurry. For example, the prediction unit adjusts the display method based on the user's emotion data. In this way, the prediction unit can provide a display that is easy for the user to read by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the AI ​​and have the AI ​​adjust the display method.

[0088] The forecasting unit can determine the order of predictions based on the submission timing of income and expenses. For example, the forecasting unit can prioritize predictions for months with high income and postpone predictions for months with low income. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. The forecasting unit can also prioritize predictions for months with high expenses and postpone predictions for months with low expenses. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. The forecasting unit can also prioritize predictions for months with similar submission timings and postpone predictions for months with different submission timings. For example, the forecasting unit determines the order of predictions based on the submission timing data of income and expenses. This allows the forecasting unit to perform efficient predictions by prioritizing based on the submission timing of income and expenses. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income and expense submission timing data into AI and have the AI ​​determine the order of predictions.

[0089] The forecasting unit can improve the accuracy of its forecasts by referring to market data for income and expenditure during the forecasting process. For example, when forecasting income, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. For example, the forecasting unit improves the accuracy of its forecasts based on income data and market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data when forecasting expenditures. For example, the forecasting unit improves the accuracy of its forecasts based on expenditure data and market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data when forecasting income and expenditures. For example, the forecasting unit improves the accuracy of its forecasts based on income, expenditure data and market data. In this way, the forecasting unit can improve the accuracy of its forecasts by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input income, expenditure data and market data into AI and have the AI ​​perform the forecasting accuracy improvement. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and prediction unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects payment information from an electronic payment system and transaction history from a bank account via the communication I / F 44 of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze income and expenditure patterns. The proposal unit proposes current operating methods by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future income and expenditures by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects payment information from an electronic payment system and transaction history from a bank account via the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze income and expenditure patterns. The proposal unit proposes current operating methods by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future income and expenditures by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and prediction unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects payment information from an electronic payment system and transaction history from a bank account via the communication I / F 44 of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze income and expenditure patterns. The proposal unit proposes current operating methods, for example, by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future income and expenditure, for example, by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects payment information from an electronic payment system and transaction history from a bank account via the communication I / F 44 of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze income and expenditure patterns. The proposal unit proposes current operating methods, for example, by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future income and expenditures, for example, by the specific processing unit 290 of the data processing unit 12.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The life plan management and prediction system can also be equipped with a health data collection unit. The health data collection unit collects data on the user's health status and provides it to the analysis unit. For example, the health data collection unit can collect heart rate, steps, and sleep data from the user's smartwatch or fitness tracker. The health data collection unit can also collect the user's medical records and health checkup results and provide them to the analysis unit. Based on the collected health data, the analysis unit can analyze the relationship between the user's health status and income / expenses, and propose a more accurate life plan. For example, if the user's health status is good, the analysis unit can predict a reduction in future medical expenses and propose a savings plan. Conversely, if the user's health status is deteriorating, it can predict an increase in future medical expenses and propose ways to reduce spending.

[0092] The life plan management and forecasting system may also include an environmental data collection unit. This unit collects data related to the user's living environment and provides it to the analysis unit. For example, the environmental data collection unit can collect climate data and air pollution data for the user's residential area. It can also collect energy consumption data and water usage data for the user's residence and provide it to the analysis unit. Based on this, the analysis unit can analyze the relationship between the user's living environment and income / expenses using the collected environmental data, and propose a more accurate life plan. For example, if the climate in the user's residential area is severe, the analysis unit can predict an increase in energy consumption and propose improvements to energy efficiency. Furthermore, if air pollution in the user's residential area is severe, it can propose an expenditure plan that takes health risks into consideration.

[0093] The life plan management and forecasting system can also include an education data collection unit. This unit collects data related to the user's education and provides it to the analysis unit. For example, it can collect information on the user's children's academic performance and desired schools. It can also collect information on the user's children's education expenses and scholarships and provide this information to the analysis unit. This allows the analysis unit to analyze the relationship between the user's education expenses and income / expenses based on the collected education data, enabling it to propose a more accurate life plan. For example, if the user's child is accepted into their desired school, the analysis unit can predict an increase in future education expenses and propose a savings plan. Similarly, if the user's child receives a scholarship, the analysis unit can predict a reduction in education expenses and propose an expenditure plan.

[0094] The life plan management and forecasting system can also include a hobby data collection unit. This unit collects data related to the user's hobbies and provides it to the analysis unit. For example, the hobby data collection unit can collect expenditure data and event participation data related to the user's hobbies. It can also collect purchase history of hobby-related products and usage of subscription services and provide this data to the analysis unit. This allows the analysis unit to analyze the relationship between the user's hobbies and income / expenses based on the collected hobby data, enabling it to propose a more accurate life plan. For example, if the user's hobby-related expenses are high, the analysis unit can suggest ways to reduce spending. Similarly, if the user frequently participates in hobby-related events, the analysis unit can predict future increases in spending and propose a savings plan.

[0095] The life plan management and forecasting system may also include a travel data collection unit. This unit collects data related to the user's travels and provides it to the analysis unit. For example, it can collect data on the user's travel destinations, duration, and expenses. It can also collect expenditure data and travel insurance information related to the user's travels and provide it to the analysis unit. This allows the analysis unit to analyze the relationship between the user's travel and income / expenses based on the collected travel data, enabling it to propose a more accurate life plan. For example, if the user's travel expenses are high, the analysis unit can suggest ways to reduce spending. Furthermore, if the user's travel destinations are expensive, the analysis unit can predict future increases in expenses and propose a savings plan.

[0096] The analysis unit can estimate the user's emotions and adjust the income and expenditure pattern analysis method based on the estimated emotions. For example, if the user is stressed, it can perform a concise and to-the-point pattern analysis. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. The analysis unit can also perform a detailed pattern analysis if the user is relaxed. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. Furthermore, if the user is busy, the analysis unit can analyze only the main income and expenditure patterns, postponing the detailed analysis. For example, the analysis unit adjusts the pattern analysis method based on the user's emotional data. In this way, the analysis unit can provide more appropriate analysis results by adjusting the pattern analysis method based on the user's emotions.

[0097] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, it can present simple and easily understandable suggestions. For example, the suggestion function adjusts the way it presents suggestions based on the user's emotional data. Also, if the user is relaxed, the suggestion function can present suggestions that include detailed information. For example, the suggestion function adjusts the way it presents suggestions based on the user's emotional data. Furthermore, if the user is in a hurry, the suggestion function can present suggestions that get straight to the point. For example, the suggestion function adjusts the way it presents suggestions based on the user's emotional data. In this way, by adjusting the way it presents suggestions based on the user's emotions, the suggestion function can provide suggestions that are easy for the user to understand.

[0098] The prediction unit can estimate the user's emotions and adjust its prediction method for future income and expenses based on those estimated emotions. For example, if the user is stressed, it can make a concise and to-the-point prediction. For example, the prediction unit adjusts its prediction method based on the user's emotional data. The prediction unit can also make a detailed prediction if the user is relaxed. For example, the prediction unit adjusts its prediction method based on the user's emotional data. Furthermore, if the user is busy, the prediction unit can only predict major income and expenses, postponing detailed predictions. For example, the prediction unit adjusts its prediction method based on the user's emotional data. In this way, the prediction unit can provide more accurate prediction results by adjusting its prediction method based on the user's emotions.

[0099] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting important income and expenditure data, delaying the collection of more detailed data. For example, the data collection unit prioritizes the data to collect based on the user's emotional data. Alternatively, if the user is relaxed, the data collection unit can collect all income and expenditure data equally. For example, the data collection unit prioritizes the data to collect based on the user's emotional data. Furthermore, if the user is busy, the data collection unit can prioritize collecting only key income and expenditure data, collecting other data later. For example, the data collection unit prioritizes the data to collect based on the user's emotional data. This allows the data collection unit to prioritize important data by prioritizing data based on the user's emotions.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. For example, the analysis unit adjusts the display method based on the user's emotional data. Also, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit adjusts the display method based on the user's emotional data. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, the analysis unit adjusts the display method based on the user's emotional data. In this way, the analysis unit can provide a display that is easy for the user to understand by adjusting the display method based on the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The collection unit collects user income and expenditure data. For example, it can collect payment information from electronic payment systems and transaction history from bank accounts. The collection unit collects payment information from electronic payment systems and obtains user expenditure data. It can also collect transaction history from bank accounts and obtain user income data. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze income and expenditure patterns. For example, the collected data can be analyzed using time series analysis or trend analysis. Income and expenditure data can be analyzed in time series to identify income and expenditure patterns. Trend analysis can also be used to analyze fluctuations in income and expenditure. Step 3: The proposal department proposes current operating methods based on the analysis results obtained by the analysis department. For example, it can provide advice on reducing unnecessary expenses. Based on the user's spending data, it identifies unnecessary expenses and proposes ways to reduce them. It can also propose the best way to save money. Based on the user's income data, it proposes the best way to save money. Step 4: The forecasting unit predicts future income and expenses based on the operating methods proposed by the proposal unit. For example, it can predict the likelihood of future income increasing. Based on the user's income data, it predicts whether future income will increase or not. It can also make predictions that take into account large future expenses. Based on the user's expense data, it predicts large future expenses.

[0103] 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.

[0104] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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. 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.

[0121] 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.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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. 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.

[0154] 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.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user income and expenditure data, An analysis unit analyzes the data collected by the aforementioned collection unit and analyzes the patterns of income and expenditure, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the current operating method, The system includes a forecasting unit that predicts future income and expenses based on the operating method proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect payment information from electronic payment systems or transaction history from bank accounts. The system according to feature 1.

3. The aforementioned proposal section is, We provide advice on how to reduce expenses. The system according to feature 1.

4. The aforementioned proposal section is, I propose methods for saving money. The system according to feature 1.

5. The prediction unit, Predicting whether future income will increase or not. The system according to feature 1.

6. The prediction unit, Make forecasts that take future expenditures into account. The system according to feature 1.

7. The aforementioned collection unit is We estimate emotions and adjust the timing of income and expenditure data collection based on those estimated emotions. The system according to feature 1.

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

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