system

Abstract

The system according to this embodiment aims to centrally manage everything from analyzing usage history to generating reminders, registering them in a calendar, and managing them on a dashboard. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a registration unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates reminders based on the analysis results obtained by the analysis unit. The registration unit registers the reminders generated by the generation unit in a calendar. The management unit manages the information registered by the registration unit on a dashboard.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, functions such as analysis of usage history, expenditure management, and reminder generation are not integrated, and there is a problem that efficient management is difficult.

[0005] The system according to the embodiment aims to perform unified operations from analysis of usage history to generation of reminders, registration in a calendar, and management on a dashboard.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a registration unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates reminders based on the analysis results obtained by the analysis unit. The registration unit registers the reminders generated by the generation unit in a calendar. The management unit manages the information registered by the registration unit on a dashboard. [Effects of the Invention]

[0007] The system according to this embodiment can centrally perform tasks ranging from analyzing usage history to generating reminders, registering them in a calendar, and managing them on a dashboard. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The expenditure management system according to an embodiment of the present invention is a system that uses AI to analyze and classify the usage history of an electronic payment system and provides expenditure management and reminder functions. The expenditure management system automatically classifies the usage history data of the electronic payment system using machine learning and natural language processing. Next, the expenditure management system automatically registers recurring payments and events in a calendar. This eliminates the need for users to manually record expenses. Furthermore, the expenditure management system provides a customizable dashboard that allows for easy management of expenditure status and reminders. For example, users can see their monthly expenditure status at a glance on the dashboard and grasp budget overruns in real time. The expenditure management system also works in conjunction with a calendar service to automatically send important notifications and reminders for missing items. This allows users to streamline their lives without forgetting recurring payments or important events. The expenditure management system is particularly designed for busy office workers, parents raising children, people with busy schedules, students busy with studies or part-time jobs, and the elderly—people who often find expenditure management troublesome. AI-powered automatic analysis and classification, automatic reminders for recurring payments and events, an integrated dashboard, and integration with calendar services significantly reduce the effort required for expense management, leading to more efficient living. For example, the AI ​​analyzes the purchase history of items purchased through an electronic payment system and automatically categorizes them into categories such as food, transportation, and entertainment. Next, the expense management system automatically registers recurring payments (e.g., rent, utilities) and important events (e.g., birthdays, meetings) in the calendar. Users can centrally manage this information on the dashboard and set reminders as needed. Furthermore, by integrating with calendar services, the expense management system automatically sends important notifications and reminders for missing items, preventing users from forgetting things. This reduces the effort required for expense management, allowing users to live more efficiently without forgetting recurring payments or important events. In addition, AI-powered automatic analysis and classification allows users to understand their spending situation in real time and prevent exceeding their budget. This improves users' financial management skills and enables them to live more efficiently.This allows the expense management system to streamline users' spending and improve their quality of life.

[0029] The expenditure management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a registration unit, and a management unit. The collection unit collects data. For example, the collection unit collects usage history data from an electronic payment system. The collection unit can collect data such as credit card payments and mobile payments. The collection unit can also collect usage history data in real time. The collection unit can also collect past usage history data in bulk. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using machine learning and natural language processing. For example, the analysis unit can analyze the data using a neural network. For example, the analysis unit can analyze the data using a support vector machine. For example, the analysis unit can analyze the data using morphological analysis. The generation unit generates reminders based on the analysis results obtained by the analysis unit. For example, the generation unit generates reminders for recurring payments and events. For example, the generation unit can generate reminders for monthly subscriptions. For example, the generation unit can generate reminders for recurring subscriptions. The generation unit can, for example, generate meeting reminders. The registration unit registers the reminders generated by the generation unit to a calendar. The registration unit can, for example, automatically register reminders to a digital calendar. The registration unit can also, for example, manually register reminders to a paper calendar. The registration unit can also, for example, automatically register reminders under specific conditions. The management unit manages the information registered by the registration unit on a dashboard. The management unit can, for example, centrally manage spending status and reminders on a customizable dashboard. The management unit can, for example, check monthly spending status at a glance on the dashboard. The management unit can, for example, grasp budget overruns in real time. The management unit can, for example, allow users to select the items displayed on the dashboard. Thus, the spending management system according to this embodiment can consistently perform everything from data collection and analysis to reminder generation, calendar registration, and dashboard management.

[0030] The data collection unit collects data. For example, the data collection unit collects usage history data from electronic payment systems. Specifically, it can collect data from various electronic payment methods, such as credit card payments and mobile payments. The data collection unit can collect this data in real time, allowing for immediate understanding of the details of payments made by users. For example, when a user makes a purchase with a credit card, the transaction information is sent to the data collection unit and stored in the database. It can also collect past usage history data in bulk, making it easy to obtain data for analyzing users' long-term spending patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning and natural language processing. Specifically, it can analyze data using neural networks. Neural networks have the ability to learn from large amounts of data and identify patterns and trends. For example, they can analyze user spending patterns and predict increases or decreases in spending in specific categories. It can also analyze data using support vector machines. Support vector machines excel at data classification and regression analysis, and are useful for detecting abnormal spending patterns. Furthermore, it can analyze data using morphological analysis. Morphological analysis is a technique that breaks down text data into smaller parts and extracts meaningful words and phrases, making it effective for analyzing the detailed content of spending. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of user spending patterns. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term spending trends and risk assessments. For example, it can predict fluctuations in spending over a specific period based on historical spending data and formulate future budget plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual spending patterns and abnormal transactions, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term spending management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The generation unit generates reminders based on the analysis results obtained by the analysis unit. The generation unit can generate reminders for recurring payments and events, for example. Specifically, it can generate reminders for monthly subscriptions and recurring payments. For instance, it can generate reminders for subscription services that users pay for monthly, notifying them when the payment deadline approaches. It can also generate meeting reminders. This allows users to manage important payments smoothly without forgetting them. The generation unit can generate reminders at the optimal time based on the user's spending patterns and schedule. For example, it can prioritize generating payment reminders for services and products that the user frequently uses, and notify users according to their importance. Furthermore, the generation unit can customize the content and format of reminders. For example, users can choose from email, SMS, in-app notifications, etc., for reminder notifications. This enables the generation unit to create flexible reminders tailored to user needs and support spending management.

[0033] The registration unit registers reminders generated by the generation unit to the calendar. For example, the registration unit can automatically register reminders to a digital calendar. It can also manually register reminders to a paper calendar. For instance, if a user uses a paper calendar, they can print the generated reminders and manually add them to the calendar. Furthermore, the registration unit can automatically register reminders under specific conditions. For example, it can automatically generate a reminder and register it to the calendar whenever a specific payment occurs. This allows the registration unit to streamline the user's schedule management and ensure important payments are not forgotten. Additionally, the registration unit can customize the priority and notification methods of reminders. For example, important payment reminders can be given a high priority, and multiple notification methods can be selected to ensure the user is notified. This enables the registration unit to provide flexible reminder registration tailored to the user's needs and support expense management.

[0034] The management department manages information registered by the registration department on a dashboard. For example, the management department centrally manages spending status and reminders on a customizable dashboard. Specifically, it visually displays monthly spending status using graphs and charts so that users can see it at a glance. It can also track budget overruns in real time. For example, if a user exceeds their set budget, a warning is displayed on the dashboard to prompt appropriate action. Furthermore, the management department allows users to select the items displayed on the dashboard. For example, it can be customized to display only spending status and reminders for specific categories. This enables the management department to provide flexible management tailored to user needs and support spending management. In addition, the management department can refer to past spending data and reminder history on the dashboard. For example, it can analyze spending trends over the past few months to plan future budgets. The management department can also detect abnormal spending patterns and unprocessed reminders and issue warnings early. This allows the management department to not only grasp the situation in real time but also to handle long-term spending management and anomaly detection, improving the reliability and security of the entire system.

[0035] The data collection unit can collect usage history data from electronic payment systems. For example, the data collection unit can collect usage history data from credit card payments. The data collection unit can also collect usage history data from mobile payments. The data collection unit can also collect usage history data from electronic money payments. This makes it possible to manage spending by collecting usage history data from electronic payment systems. 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 usage history data from electronic payment systems into AI and have AI perform the data collection.

[0036] The analysis unit can analyze and automatically classify data collected by the collection unit using machine learning and natural language processing. For example, the analysis unit can analyze and automatically classify data using a neural network. The analysis unit can also analyze and automatically classify data using a support vector machine, for example. The analysis unit can also analyze and automatically classify data using morphological analysis, for example. This automates data analysis and classification using machine learning and natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the collection unit into an AI and have the AI ​​perform data analysis and classification.

[0037] The generation unit can generate reminders for recurring payments and events based on the analysis results obtained by the analysis unit. For example, the generation unit can generate reminders for monthly subscriptions. The generation unit can also generate reminders for recurring subscriptions. The generation unit can also generate reminders for meetings. This allows users to manage important payments and events without forgetting them by generating reminders based on the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​generate the reminders.

[0038] The registration unit can automatically register reminders generated by the generation unit to a calendar. The registration unit can, for example, automatically register reminders to a digital calendar. The registration unit can also, for example, manually register reminders to a paper calendar. The registration unit can also, for example, automatically register reminders under specific conditions. This eliminates the need for users to manually register reminders by automatically registering them to the calendar. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input reminders generated by the generation unit into an AI and have the AI ​​perform the registration to the calendar.

[0039] The management department can manage information registered by the registration department on a customizable dashboard, enabling centralized management of spending and reminders. For example, the management department can view monthly spending at a glance on the dashboard. The management department can also, for example, identify budget overruns in real time. The management department can also, for example, allow users to select the items displayed on the dashboard. This allows users to easily understand spending and reminders by centrally managing information on a customizable dashboard. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information registered by the registration department into the AI ​​and have the AI ​​perform management on the dashboard.

[0040] The data collection unit can analyze the user's past usage history and select the optimal data collection method. For example, the data collection unit may prioritize collecting payment methods that the user has frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past usage history. The data collection unit can also analyze the user's past usage patterns and select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past usage history. 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 usage history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can collect only the data necessary for the user based on their current lifestyle. The data collection unit can also prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also filter out unnecessary data based on the user's lifestyle and areas of interest. This allows the collection of only the necessary data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter highly relevant data based on the user's geographical location information. For example, if the user is on the move, the data collection unit can also collect the most relevant data based on the user's current location. This allows for the collection of more useful data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to the user's areas of interest from the user's social media activity. The data collection unit can also prioritize the collection of relevant data based on the content of the user's social media posts. The data collection unit can also collect relevant data based on the activity of the user's social media followers and friends. This allows the collection of relevant data based on the user's social media activity to obtain data tailored to the user's interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to food expense data. For example, the analysis unit can apply a different analysis algorithm to transportation expense data. For example, the analysis unit can apply yet another analysis algorithm to entertainment expense data. By applying different analysis algorithms depending on the data category, the analysis can be optimized for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the latest data by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The generation unit can adjust the level of detail of reminders based on the importance of the data when generating them. For example, the generation unit can generate detailed reminders for highly important data. For example, the generation unit can also generate concise reminders for less important data. The generation unit can also dynamically adjust the level of detail of reminders according to the importance of the data. This allows for the generation of detailed reminders for important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into a generation AI and have the generation AI perform the adjustment of the level of detail of the reminders.

[0049] The generation unit can apply different generation algorithms depending on the data category when generating reminders. For example, the generation unit can apply a specific generation algorithm to recurring payments. For example, the generation unit can apply a different generation algorithm to events. For example, the generation unit can apply yet another generation algorithm to other reminders. This allows for the generation of the most suitable reminder for each category by applying different generation algorithms depending on the data category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into a generation AI and have the generation AI perform the application of the generation algorithm.

[0050] The generation unit can determine the priority of reminders based on the data submission date when generating reminders. For example, the generation unit can prioritize the generation of reminders based on recently submitted data. The generation unit can also, for example, postpone reminders based on older data submission dates. The generation unit can also dynamically adjust the priority of reminders based on the submission date. This allows for priority notification of the most recent reminders by determining the priority of reminders based on the data submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data submission date into a generation AI and have the generation AI determine the priority of reminders.

[0051] The generation unit can adjust the order of reminders based on the relevance of the data when generating them. For example, the generation unit can prioritize generating reminders based on highly relevant data. The generation unit can also postpone reminders based on less relevant data. The generation unit can also dynamically adjust the order of reminders based on the relevance of the data. This allows for priority notification of highly relevant reminders by adjusting the order of reminders based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the order of reminders.

[0052] The registration unit can adjust the level of detail in calendar entries based on the importance of the reminder. For example, the registration unit can perform detailed calendar entries for high-importance reminders. For example, the registration unit can also perform simple calendar entries for low-importance reminders. The registration unit can also dynamically adjust the level of detail in calendar entries according to the importance of the reminder. This allows important reminders to be registered in detail by adjusting the level of detail in calendar entries based on the importance of the reminder. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the importance of the reminder into a generating AI and have the generating AI perform the adjustment of the level of detail in calendar entries.

[0053] The registration unit can apply different registration algorithms depending on the reminder category when registering a reminder to the calendar. For example, the registration unit can apply a specific registration algorithm to recurring payments. For example, the registration unit can apply a different registration algorithm to events. For example, the registration unit can apply yet another registration algorithm to other reminders. This allows for optimal calendar registration for each category by applying different registration algorithms depending on the reminder category. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the reminder category into a generating AI and have the generating AI execute the application of the registration algorithm.

[0054] The registration unit can determine the priority of calendar registrations based on the submission date of the reminders. For example, the registration unit can prioritize recently submitted reminders. The registration unit can also, for example, postpone older reminders. The registration unit can also dynamically adjust the registration priority of reminders based on the submission date. This allows for priority registration of the most recent reminders by determining the calendar registration priority based on the submission date of the reminders. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the submission date of the reminders into a generating AI and have the generating AI determine the priority of calendar registrations.

[0055] The registration unit can adjust the registration order based on the relevance of reminders when registering them to the calendar. For example, the registration unit can prioritize registering highly relevant reminders. For example, the registration unit can also postpone registering less relevant reminders. For example, the registration unit can dynamically adjust the registration order based on the relevance of reminders. This allows for prioritizing the registration of highly relevant reminders by adjusting the registration order based on the relevance of reminders. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the relevance of reminders into a generating AI and have the generating AI perform the adjustment of the registration order.

[0056] The management unit can select the optimal display method when displaying the dashboard by referring to the user's past operation history. For example, the management unit can prioritize providing display methods that the user has frequently used in the past. For example, the management unit can also suggest the optimal display method based on the user's past operation history. For example, the management unit can analyze the user's past operation patterns and select the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0057] The management unit can customize the content displayed on the dashboard based on the user's current lifestyle. For example, if the user is busy, the management unit can display only essential information. If the user is relaxed, the management unit can also display detailed information. The management unit can also dynamically customize the content displayed based on the user's lifestyle. This allows the management unit to provide the user with the most relevant information by customizing the content based on their lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the displayed content.

[0058] The management unit can select the optimal display method when displaying the dashboard, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the management unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the management unit can also provide a concise and highly visible display method. By providing the optimal display method based on the user's device information, a user-friendly display can be achieved. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0059] The management unit can analyze the user's social media activity and customize the displayed content when the dashboard is shown. For example, the management unit can display information related to the user's areas of interest based on their social media activity. The management unit can also prioritize the display of relevant information based on the user's social media posts. The management unit can also display relevant information based on the activity of the user's social media followers and friends. In this way, by customizing the displayed content based on the user's social media activity, the management unit can provide the user with information that is highly relevant to them. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] An expense management system can analyze a user's past spending patterns and suggest optimal budget settings. For example, it can set appropriate budgets for categories in which the user has frequently spent money in the past. It can also analyze spending trends over specific periods based on the user's past spending patterns and suggest budget adjustments. Furthermore, it can predict future spending based on the user's past spending patterns and use this as a reference for budget setting. In this way, by analyzing the user's past spending patterns, it can suggest optimal budget settings.

[0062] An expense management system can provide optimal spending advice by taking into account the user's geographical location. For example, if the user is in a specific region, it can provide information on local sales and discount coupons. If the user is traveling, it can provide information on local attractions and restaurants. Furthermore, if the user is at home, it can provide information on online shopping discounts. This allows for more efficient spending by providing optimal spending advice based on the user's geographical location.

[0063] A spending management system can analyze a user's social media activity and provide relevant spending advice. For example, if a user mentions a specific product or service on social media, it can provide discount information related to that product or service. It can also provide spending advice based on the services used by the user's followers and friends. Furthermore, it can provide spending advice related to areas of interest based on the content of the user's social media posts. In this way, by providing relevant spending advice based on the user's social media activity, it can streamline the user's spending.

[0064] An expense management system can analyze a user's past spending history and propose an optimal spending plan. For example, it can suggest an appropriate spending plan for categories in which the user has frequently spent money in the past. It can also analyze spending trends over specific periods based on the user's past spending history and suggest adjustments to the spending plan. Furthermore, it can make future spending predictions based on the user's past spending history and use them as a reference for spending planning. In this way, by analyzing the user's past spending history, it can propose an optimal spending plan.

[0065] An expense management system can provide the optimal expense management method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide an expense management method adapted to the screen size. If the user is using a tablet, it can provide an expense management method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible expense management method. In this way, by providing the optimal expense management method based on the user's device information, it is possible to achieve expense management that is easy for the user to understand.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects data. For example, it collects usage history data for electronic payment systems. The collection unit can collect data such as credit card payments and mobile payments, and can collect data in real time or collect historical usage history data in bulk. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using methods such as machine learning, natural language processing, neural networks, support vector machines, and morphological analysis. Step 3: The generation unit generates reminders based on the analysis results obtained by the analysis unit. For example, it can generate reminders for recurring payments, events, monthly subscriptions, recurring subscriptions, and meetings. Step 4: The registration unit registers the reminders generated by the generation unit to the calendar. For example, reminders can be automatically registered to a digital calendar, manually registered to a paper calendar, or automatically registered under specific conditions. Step 5: The management department manages the information registered by the registration department on a dashboard. For example, a customizable dashboard allows for centralized management of spending status and reminders, enabling users to see monthly spending at a glance, track budget overruns in real time, and select the items displayed on the dashboard.

[0068] (Example of form 2) The expenditure management system according to an embodiment of the present invention is a system that uses AI to analyze and classify the usage history of an electronic payment system and provides expenditure management and reminder functions. The expenditure management system automatically classifies the usage history data of the electronic payment system using machine learning and natural language processing. Next, the expenditure management system automatically registers recurring payments and events in a calendar. This eliminates the need for users to manually record expenses. Furthermore, the expenditure management system provides a customizable dashboard that allows for easy management of expenditure status and reminders. For example, users can see their monthly expenditure status at a glance on the dashboard and grasp budget overruns in real time. The expenditure management system also works in conjunction with a calendar service to automatically send important notifications and reminders for missing items. This allows users to streamline their lives without forgetting recurring payments or important events. The expenditure management system is particularly designed for busy office workers, parents raising children, people with busy schedules, students busy with studies or part-time jobs, and the elderly—people who often find expenditure management troublesome. AI-powered automatic analysis and classification, automatic reminders for recurring payments and events, an integrated dashboard, and integration with calendar services significantly reduce the effort required for expense management, leading to more efficient living. For example, the AI ​​analyzes the purchase history of items purchased through an electronic payment system and automatically categorizes them into categories such as food, transportation, and entertainment. Next, the expense management system automatically registers recurring payments (e.g., rent, utilities) and important events (e.g., birthdays, meetings) in the calendar. Users can centrally manage this information on the dashboard and set reminders as needed. Furthermore, by integrating with calendar services, the expense management system automatically sends important notifications and reminders for missing items, preventing users from forgetting things. This reduces the effort required for expense management, allowing users to live more efficiently without forgetting recurring payments or important events. In addition, AI-powered automatic analysis and classification allows users to understand their spending situation in real time and prevent exceeding their budget. This improves users' financial management skills and enables them to live more efficiently.This allows the expense management system to streamline users' spending and improve their quality of life.

[0069] The expenditure management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a registration unit, and a management unit. The collection unit collects data. For example, the collection unit collects usage history data from an electronic payment system. The collection unit can collect data such as credit card payments and mobile payments. The collection unit can also collect usage history data in real time. The collection unit can also collect past usage history data in bulk. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using machine learning and natural language processing. For example, the analysis unit can analyze the data using a neural network. For example, the analysis unit can analyze the data using a support vector machine. For example, the analysis unit can analyze the data using morphological analysis. The generation unit generates reminders based on the analysis results obtained by the analysis unit. For example, the generation unit generates reminders for recurring payments and events. For example, the generation unit can generate reminders for monthly subscriptions. For example, the generation unit can generate reminders for recurring subscriptions. The generation unit can, for example, generate meeting reminders. The registration unit registers the reminders generated by the generation unit to a calendar. The registration unit can, for example, automatically register reminders to a digital calendar. The registration unit can also, for example, manually register reminders to a paper calendar. The registration unit can also, for example, automatically register reminders under specific conditions. The management unit manages the information registered by the registration unit on a dashboard. The management unit can, for example, centrally manage spending status and reminders on a customizable dashboard. The management unit can, for example, check monthly spending status at a glance on the dashboard. The management unit can, for example, grasp budget overruns in real time. The management unit can, for example, allow users to select the items displayed on the dashboard. Thus, the spending management system according to this embodiment can consistently perform everything from data collection and analysis to reminder generation, calendar registration, and dashboard management.

[0070] The data collection unit collects data. For example, the data collection unit collects usage history data from electronic payment systems. Specifically, it can collect data from various electronic payment methods, such as credit card payments and mobile payments. The data collection unit can collect this data in real time, allowing for immediate understanding of the details of payments made by users. For example, when a user makes a purchase with a credit card, the transaction information is sent to the data collection unit and stored in the database. It can also collect past usage history data in bulk, making it easy to obtain data for analyzing users' long-term spending patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning and natural language processing. Specifically, it can analyze data using neural networks. Neural networks have the ability to learn from large amounts of data and identify patterns and trends. For example, they can analyze user spending patterns and predict increases or decreases in spending in specific categories. It can also analyze data using support vector machines. Support vector machines excel at data classification and regression analysis, and are useful for detecting abnormal spending patterns. Furthermore, it can analyze data using morphological analysis. Morphological analysis is a technique that breaks down text data into smaller parts and extracts meaningful words and phrases, making it effective for analyzing the detailed content of spending. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of user spending patterns. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term spending trends and risk assessments. For example, it can predict fluctuations in spending over a specific period based on historical spending data and formulate future budget plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual spending patterns and abnormal transactions, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term spending management and anomaly detection, thereby improving the reliability and security of the entire system.

[0072] The generation unit generates reminders based on the analysis results obtained by the analysis unit. The generation unit can generate reminders for recurring payments and events, for example. Specifically, it can generate reminders for monthly subscriptions and recurring payments. For instance, it can generate reminders for subscription services that users pay for monthly, notifying them when the payment deadline approaches. It can also generate meeting reminders. This allows users to manage important payments smoothly without forgetting them. The generation unit can generate reminders at the optimal time based on the user's spending patterns and schedule. For example, it can prioritize generating payment reminders for services and products that the user frequently uses, and notify users according to their importance. Furthermore, the generation unit can customize the content and format of reminders. For example, users can choose from email, SMS, in-app notifications, etc., for reminder notifications. This enables the generation unit to create flexible reminders tailored to user needs and support spending management.

[0073] The registration unit registers reminders generated by the generation unit to the calendar. For example, the registration unit can automatically register reminders to a digital calendar. It can also manually register reminders to a paper calendar. For instance, if a user uses a paper calendar, they can print the generated reminders and manually add them to the calendar. Furthermore, the registration unit can automatically register reminders under specific conditions. For example, it can automatically generate a reminder and register it to the calendar whenever a specific payment occurs. This allows the registration unit to streamline the user's schedule management and ensure important payments are not forgotten. Additionally, the registration unit can customize the priority and notification methods of reminders. For example, important payment reminders can be given a high priority, and multiple notification methods can be selected to ensure the user is notified. This enables the registration unit to provide flexible reminder registration tailored to the user's needs and support expense management.

[0074] The management department manages information registered by the registration department on a dashboard. For example, the management department centrally manages spending status and reminders on a customizable dashboard. Specifically, it visually displays monthly spending status using graphs and charts so that users can see it at a glance. It can also track budget overruns in real time. For example, if a user exceeds their set budget, a warning is displayed on the dashboard to prompt appropriate action. Furthermore, the management department allows users to select the items displayed on the dashboard. For example, it can be customized to display only spending status and reminders for specific categories. This enables the management department to provide flexible management tailored to user needs and support spending management. In addition, the management department can refer to past spending data and reminder history on the dashboard. For example, it can analyze spending trends over the past few months to plan future budgets. The management department can also detect abnormal spending patterns and unprocessed reminders and issue warnings early. This allows the management department to not only grasp the situation in real time but also to handle long-term spending management and anomaly detection, improving the reliability and security of the entire system.

[0075] The data collection unit can collect usage history data from electronic payment systems. For example, the data collection unit can collect usage history data from credit card payments. The data collection unit can also collect usage history data from mobile payments. The data collection unit can also collect usage history data from electronic money payments. This makes it possible to manage spending by collecting usage history data from electronic payment systems. 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 usage history data from electronic payment systems into AI and have AI perform the data collection.

[0076] The analysis unit can analyze and automatically classify data collected by the collection unit using machine learning and natural language processing. For example, the analysis unit can analyze and automatically classify data using a neural network. The analysis unit can also analyze and automatically classify data using a support vector machine, for example. The analysis unit can also analyze and automatically classify data using morphological analysis, for example. This automates data analysis and classification using machine learning and natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the collection unit into an AI and have the AI ​​perform data analysis and classification.

[0077] The generation unit can generate reminders for recurring payments and events based on the analysis results obtained by the analysis unit. For example, the generation unit can generate reminders for monthly subscriptions. The generation unit can also generate reminders for recurring subscriptions. The generation unit can also generate reminders for meetings. This allows users to manage important payments and events without forgetting them by generating reminders based on the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​generate the reminders.

[0078] The registration unit can automatically register reminders generated by the generation unit to a calendar. The registration unit can, for example, automatically register reminders to a digital calendar. The registration unit can also, for example, manually register reminders to a paper calendar. The registration unit can also, for example, automatically register reminders under specific conditions. This eliminates the need for users to manually register reminders by automatically registering them to the calendar. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input reminders generated by the generation unit into an AI and have the AI ​​perform the registration to the calendar.

[0079] The management department can manage information registered by the registration department on a customizable dashboard, enabling centralized management of spending and reminders. For example, the management department can view monthly spending at a glance on the dashboard. The management department can also, for example, identify budget overruns in real time. The management department can also, for example, allow users to select the items displayed on the dashboard. This allows users to easily understand spending and reminders by centrally managing information on a customizable dashboard. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information registered by the registration department into the AI ​​and have the AI ​​perform management on the dashboard.

[0080] The management department can integrate with the calendar service to automatically send important notifications and reminders for missing items. For example, the management department can automatically send notifications for payment deadlines. The management department can also automatically send notifications for important meetings. The management department can also automatically send reminders for out-of-stock items or required documents. This enables the automatic sending of important notifications and reminders for missing items by integrating with the calendar service. Some or all of the above processes in the management department may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the management department can integrate with the calendar service to input important notifications and reminders for missing items into the generative AI and have the generative AI execute the automatic sending of reminders.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to enable more accurate analysis. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0082] The data collection unit can analyze the user's past usage history and select the optimal data collection method. For example, the data collection unit may prioritize collecting payment methods that the user has frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past usage history. The data collection unit can also analyze the user's past usage patterns and select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past usage history. 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 usage history data into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can collect only the data necessary for the user based on their current lifestyle. The data collection unit can also prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also filter out unnecessary data based on the user's lifestyle and areas of interest. This allows the collection of only the necessary data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0084] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if the user is in a hurry, the data collection unit may quickly collect highly important data. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter highly relevant data based on the user's geographical location information. For example, if the user is on the move, the data collection unit can also collect the most relevant data based on the user's current location. This allows for the collection of more useful data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0086] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to the user's areas of interest from the user's social media activity. The data collection unit can also prioritize the collection of relevant data based on the content of the user's social media posts. The data collection unit can also collect relevant data based on the activity of the user's social media followers and friends. This allows the collection of relevant data based on the user's social media activity to obtain data tailored to the user's interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to food expense data. For example, the analysis unit can apply a different analysis algorithm to transportation expense data. For example, the analysis unit can apply yet another analysis algorithm to entertainment expense data. By applying different analysis algorithms depending on the data category, the analysis can be optimized for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the user's emotions, analysis results can be provided that are appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the latest data by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The generation unit can estimate the user's emotions and adjust the reminder generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed reminder. For example, if the user is in a hurry, the generation unit can also generate a concise reminder. For example, if the user is excited, the generation unit can also generate a visually stimulating reminder. In this way, by adjusting the reminder generation method based on the user's emotions, the system can provide the user with the most appropriate reminder. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the reminder generation method.

[0094] The generation unit can adjust the level of detail of reminders based on the importance of the data when generating them. For example, the generation unit can generate detailed reminders for highly important data. For example, the generation unit can also generate concise reminders for less important data. The generation unit can also dynamically adjust the level of detail of reminders according to the importance of the data. This allows for the generation of detailed reminders for important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into a generation AI and have the generation AI perform the adjustment of the level of detail of the reminders.

[0095] The generation unit can apply different generation algorithms depending on the data category when generating reminders. For example, the generation unit can apply a specific generation algorithm to recurring payments. For example, the generation unit can apply a different generation algorithm to events. For example, the generation unit can apply yet another generation algorithm to other reminders. This allows for the generation of the most suitable reminder for each category by applying different generation algorithms depending on the data category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into a generation AI and have the generation AI perform the application of the generation algorithm.

[0096] The generation unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the generation unit may postpone notifications for less important reminders. For example, if the user is relaxed, the generation unit may prioritize notifications for detailed reminders. For example, if the user is in a hurry, the generation unit may quickly notify notifications for high-priority reminders. This allows important reminders to be notified preferentially by determining the priority of reminders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of reminders.

[0097] The generation unit can determine the priority of reminders based on the data submission date when generating reminders. For example, the generation unit can prioritize the generation of reminders based on recently submitted data. The generation unit can also, for example, postpone reminders based on older data submission dates. The generation unit can also dynamically adjust the priority of reminders based on the submission date. This allows for priority notification of the most recent reminders by determining the priority of reminders based on the data submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data submission date into a generation AI and have the generation AI determine the priority of reminders.

[0098] The generation unit can adjust the order of reminders based on the relevance of the data when generating them. For example, the generation unit can prioritize generating reminders based on highly relevant data. The generation unit can also postpone reminders based on less relevant data. The generation unit can also dynamically adjust the order of reminders based on the relevance of the data. This allows for priority notification of highly relevant reminders by adjusting the order of reminders based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the order of reminders.

[0099] The registration unit can estimate the user's emotions and adjust the calendar registration method based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple calendar registration method. For example, if the user is relaxed, the registration unit can also provide a detailed calendar registration method. For example, if the user is in a hurry, the registration unit can also provide a method for quick calendar registration. In this way, by adjusting the calendar registration method based on the user's emotions, the system can provide the user with the most optimal calendar registration method. 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 registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the calendar registration method.

[0100] The registration unit can adjust the level of detail in calendar entries based on the importance of the reminder. For example, the registration unit can perform detailed calendar entries for high-importance reminders. For example, the registration unit can also perform simple calendar entries for low-importance reminders. The registration unit can also dynamically adjust the level of detail in calendar entries according to the importance of the reminder. This allows important reminders to be registered in detail by adjusting the level of detail in calendar entries based on the importance of the reminder. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the importance of the reminder into a generating AI and have the generating AI perform the adjustment of the level of detail in calendar entries.

[0101] The registration unit can apply different registration algorithms depending on the reminder category when registering a reminder to the calendar. For example, the registration unit can apply a specific registration algorithm to recurring payments. For example, the registration unit can apply a different registration algorithm to events. For example, the registration unit can apply yet another registration algorithm to other reminders. This allows for optimal calendar registration for each category by applying different registration algorithms depending on the reminder category. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the reminder category into a generating AI and have the generating AI execute the application of the registration algorithm.

[0102] The registration unit can estimate the user's emotions and determine the priority of calendar entries based on the estimated emotions. For example, if the user is stressed, the registration unit will postpone the registration of low-priority reminders. For example, if the user is relaxed, the registration unit may prioritize the registration of detailed reminders. For example, if the user is in a hurry, the registration unit may quickly register high-priority reminders. In this way, important reminders can be registered preferentially by determining the priority of calendar entries 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 registration unit may be performed using AI or not using AI. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform the determination of calendar entry priorities.

[0103] The registration unit can determine the priority of calendar registrations based on the submission date of the reminders. For example, the registration unit can prioritize recently submitted reminders. The registration unit can also, for example, postpone older reminders. The registration unit can also dynamically adjust the registration priority of reminders based on the submission date. This allows for priority registration of the most recent reminders by determining the calendar registration priority based on the submission date of the reminders. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the submission date of the reminders into a generating AI and have the generating AI determine the priority of calendar registrations.

[0104] The registration unit can adjust the registration order based on the relevance of reminders when registering them to the calendar. For example, the registration unit can prioritize registering highly relevant reminders. For example, the registration unit can also postpone registering less relevant reminders. For example, the registration unit can dynamically adjust the registration order based on the relevance of reminders. This allows for prioritizing the registration of highly relevant reminders by adjusting the registration order based on the relevance of reminders. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the relevance of reminders into a generating AI and have the generating AI perform the adjustment of the registration order.

[0105] The management unit can estimate the user's emotions and adjust the dashboard display based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible display. For example, if the user is relaxed, the management unit can also provide a display that includes detailed information. For example, if the user is in a hurry, the management unit can provide a display that gets straight to the point. By adjusting the dashboard display based on the user's emotions, the management unit can provide the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the dashboard display.

[0106] The management unit can select the optimal display method when displaying the dashboard by referring to the user's past operation history. For example, the management unit can prioritize providing display methods that the user has frequently used in the past. For example, the management unit can also suggest the optimal display method based on the user's past operation history. For example, the management unit can analyze the user's past operation patterns and select the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0107] The management unit can customize the content displayed on the dashboard based on the user's current lifestyle. For example, if the user is busy, the management unit can display only essential information. If the user is relaxed, the management unit can also display detailed information. The management unit can also dynamically customize the content displayed based on the user's lifestyle. This allows the management unit to provide the user with the most relevant information by customizing the content based on their lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the displayed content.

[0108] The management unit can estimate the user's emotions and adjust the dashboard's operating procedures based on the estimated emotions. For example, if the user is tense, the management unit can provide simple and intuitive operating procedures. For example, if the user is relaxed, the management unit can also provide detailed operating procedures. For example, if the user is in a hurry, the management unit can also provide procedures that allow for quick operation. In this way, by adjusting the dashboard's operating procedures based on the user's emotions, the optimal operating procedures can be provided to the user. 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 management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operating procedures.

[0109] The management unit can select the optimal display method when displaying the dashboard, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the management unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the management unit can also provide a concise and highly visible display method. By providing the optimal display method based on the user's device information, a user-friendly display can be achieved. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0110] The management unit can analyze the user's social media activity and customize the displayed content when the dashboard is shown. For example, the management unit can display information related to the user's areas of interest based on their social media activity. The management unit can also prioritize the display of relevant information based on the user's social media posts. The management unit can also display relevant information based on the activity of the user's social media followers and friends. In this way, by customizing the displayed content based on the user's social media activity, the management unit can provide the user with information that is highly relevant to them. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] An spending management system can estimate a user's emotions and provide spending advice based on those emotions. For example, if a user is stressed, it can suggest spending on entertainment or vacations to help them relax. If a user is excited, it can provide saving advice to help them avoid impulse purchases. Furthermore, if a user is relaxed, it can provide advice on future investments and savings. By providing appropriate spending advice based on the user's emotions, this system can improve the user's ability to manage their finances.

[0113] An expense management system can analyze a user's past spending patterns and suggest optimal budget settings. For example, it can set appropriate budgets for categories in which the user has frequently spent money in the past. It can also analyze spending trends over specific periods based on the user's past spending patterns and suggest budget adjustments. Furthermore, it can predict future spending based on the user's past spending patterns and use this as a reference for budget setting. In this way, by analyzing the user's past spending patterns, it can suggest optimal budget settings.

[0114] An expense management system can estimate a user's emotions and prioritize spending based on those emotions. For example, if a user is stressed, less important expenses can be postponed. Conversely, if a user is relaxed, detailed spending plans can be prioritized. Furthermore, if a user is in a hurry, high-priority expenses can be processed quickly. This allows for efficient spending management by prioritizing expenses based on the user's emotions.

[0115] An expense management system can provide optimal spending advice by taking into account the user's geographical location. For example, if the user is in a specific region, it can provide information on local sales and discount coupons. If the user is traveling, it can provide information on local attractions and restaurants. Furthermore, if the user is at home, it can provide information on online shopping discounts. This allows for more efficient spending by providing optimal spending advice based on the user's geographical location.

[0116] An spending management system can estimate a user's emotions and provide spending feedback based on those emotions. For example, if a user is stressed, it can suggest reviewing spending or saving. If a user is relaxed, it can share successful spending experiences and provide motivating feedback. Furthermore, if a user is excited, it can provide feedback to encourage calm decision-making. In this way, by providing appropriate spending feedback based on the user's emotions, it can improve the user's ability to manage their finances.

[0117] A spending management system can analyze a user's social media activity and provide relevant spending advice. For example, if a user mentions a specific product or service on social media, it can provide discount information related to that product or service. It can also provide spending advice based on the services used by the user's followers and friends. Furthermore, it can provide spending advice related to areas of interest based on the content of the user's social media posts. In this way, by providing relevant spending advice based on the user's social media activity, it can streamline the user's spending.

[0118] An expense management system can estimate a user's emotions and adjust spending reminders based on those emotions. For example, if a user is stressed, the frequency of reminders can be reduced to lessen their burden. Conversely, if a user is relaxed, detailed reminders can be provided. Furthermore, if a user is in a hurry, concise reminders can be offered. By adjusting reminders based on the user's emotions, the system can provide the most appropriate reminders for each user.

[0119] An expense management system can analyze a user's past spending history and propose an optimal spending plan. For example, it can suggest an appropriate spending plan for categories in which the user has frequently spent money in the past. It can also analyze spending trends over specific periods based on the user's past spending history and suggest adjustments to the spending plan. Furthermore, it can make future spending predictions based on the user's past spending history and use them as a reference for spending planning. In this way, by analyzing the user's past spending history, it can propose an optimal spending plan.

[0120] The spending management system can estimate the user's emotions and adjust the way spending notifications are delivered based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a more detailed notification. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point notification. By adjusting the notification method based on the user's emotions, it can provide the most optimal notification method for the user.

[0121] An expense management system can provide the optimal expense management method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide an expense management method adapted to the screen size. If the user is using a tablet, it can provide an expense management method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible expense management method. In this way, by providing the optimal expense management method based on the user's device information, it is possible to achieve expense management that is easy for the user to understand.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects data. For example, it collects usage history data for electronic payment systems. The collection unit can collect data such as credit card payments and mobile payments, and can collect data in real time or collect historical usage history data in bulk. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using methods such as machine learning, natural language processing, neural networks, support vector machines, and morphological analysis. Step 3: The generation unit generates reminders based on the analysis results obtained by the analysis unit. For example, it can generate reminders for recurring payments, events, monthly subscriptions, recurring subscriptions, and meetings. Step 4: The registration unit registers the reminders generated by the generation unit into the calendar. For example, reminders can be automatically registered to a digital calendar, manually registered to a paper calendar, or automatically registered under specific conditions. Step 5: The management department manages the information registered by the registration department on a dashboard. For example, a customizable dashboard allows for centralized management of spending status and reminders, providing an at-a-glance overview of monthly spending, real-time tracking of budget overruns, and allowing users to select the items displayed on the dashboard.

[0124] 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, registration unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects usage history data of the electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning and natural language processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates reminders based on the analysis results. The registration unit is implemented by the control unit 46A of the smart device 14 and registers the generated reminders in the calendar. The management unit is implemented by the control unit 46A of the smart device 14 and manages spending status and reminders on a dashboard. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The 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.

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

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 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.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the 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.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, registration unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects usage history data of the electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning and natural language processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates reminders based on the analysis results. The registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the generated reminders in the calendar. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages spending status and reminders on a dashboard. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The 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.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, registration unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects usage history data of the electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning and natural language processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates reminders based on the analysis results. The registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the generated reminders in the calendar. The management unit is implemented by the control unit 46A of the headset terminal 314 and manages spending status and reminders on a dashboard. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, registration unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects usage history data of the electronic payment system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning and natural language processing. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates reminders based on the analysis results. The registration unit is implemented by, for example, the control unit 46A of the robot 414 and registers the generated reminders in the calendar. The management unit is implemented by, for example, the control unit 46A of the robot 414 and manages the expenditure status and reminders on a dashboard. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates reminders based on the analysis results obtained by the analysis unit, A registration unit that registers the reminders generated by the generation unit to the calendar, The system includes a management unit that manages the information registered by the registration unit on a dashboard. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect usage history data for electronic payment systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using machine learning and natural language processing, the data collected by the collection unit is analyzed and automatically classified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results obtained by the aforementioned analysis unit, reminders for recurring payments and events are generated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned registration unit is The reminders generated by the generation unit are automatically registered in the calendar. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, The information registered by the aforementioned registration unit is managed on a customizable dashboard, allowing for centralized management of spending status and reminders. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, It integrates with calendar services to automatically send important notifications and reminders for missing items. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts how reminders are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating reminders, adjust the level of detail of the reminders based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating reminders, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating reminders, the priority of reminders is determined based on when the data should be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating reminders, adjust the order of reminders based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned registration unit is It estimates the user's emotions and adjusts the calendar registration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned registration unit is When adding a reminder to your calendar, adjust the level of detail based on the importance of the reminder. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned registration unit is When registering a reminder to the calendar, a different registration algorithm is applied depending on the reminder category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned registration unit is It estimates the user's emotions and determines the priority of calendar entries based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned registration unit is When registering for a calendar event, the priority of registration is determined based on the timing of the reminder submission. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned registration unit is When adding reminders to your calendar, adjust the order of registration based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When displaying the dashboard, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, When displaying the dashboard, customize the displayed content based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, It estimates the user's emotions and adjusts the dashboard's operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, When displaying the dashboard, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, When displaying the dashboard, analyze the user's social media activity and customize the displayed content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 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 data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates reminders based on the analysis results obtained by the analysis unit, A registration unit that registers the reminders generated by the generation unit to the calendar, The system includes a management unit that manages the information registered by the registration unit on a dashboard. A system characterized by the following features.

2. The aforementioned collection unit is Collect usage history data for electronic payment systems. The system according to feature 1.

3. The aforementioned analysis unit, Using machine learning and natural language processing, the data collected by the collection unit is analyzed and automatically classified. The system according to feature 1.

4. The generating unit is Based on the analysis results obtained by the aforementioned analysis unit, reminders for recurring payments and events are generated. The system according to feature 1.

5. The aforementioned registration unit is The reminders generated by the generation unit are automatically registered in the calendar. The system according to feature 1.

6. The aforementioned management department, The information registered by the aforementioned registration unit is managed on a customizable dashboard, allowing for centralized management of spending status and reminders. The system according to feature 1.

7. The aforementioned management department, It integrates with calendar services to automatically send important notifications and reminders for missing items. The system according to feature 1.

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

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

10. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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