system

Abstract

The system according to this embodiment aims to provide individually optimized savings suggestions based on the user's purchase history, behavioral patterns, and asset status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's purchase history, behavioral patterns, and asset status. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prompts that take into account the latest financial information based on the data obtained by the analysis unit. The provision unit provides individually optimized savings suggestions based on the prompts generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to provide an individually optimized savings proposal based on a user's purchase history, behavior pattern, and asset status, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an individually optimized savings proposal based on a user's purchase history, behavior pattern, and asset status.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's purchase history, behavioral patterns, and asset status. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prompts that incorporate the latest financial information based on the data obtained by the analysis unit. The provision unit provides individually optimized savings suggestions based on the prompts generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually optimized savings suggestions based on the user's purchase history, behavioral patterns, and asset status. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal financial assistant system according to an embodiment of the present invention is a system in which AI analyzes a user's purchase history, behavioral patterns, and asset status, generates prompts that take into account the latest financial information, and provides the user with individually optimized savings suggestions. The personal financial assistant system collects the user's purchase history, behavioral patterns, and asset status, and the AI ​​analyzes the collected data to understand the user's behavioral patterns and asset status. Subsequently, it generates prompts that take into account the latest financial information and generates individually optimized savings suggestions. Based on the generated savings suggestions, it presents personalized coupons and guides the user to various financial services. As a result, the user can receive optimal savings suggestions based on their purchase history, behavioral patterns, and asset status, and can save effectively. In addition, the presentation of personalized coupons and guidance to financial services improves user convenience and increases satisfaction. Thus, the personal financial assistant system can provide optimal savings suggestions based on the user's purchase history, behavioral patterns, and asset status.

[0029] The personal financial assistant system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's purchase history, behavioral patterns, and asset status. For example, the collection unit can collect purchase history such as the type of goods purchased by the user, the date and time of purchase, and the place of purchase. The collection unit can also collect behavioral patterns such as the user's daily travel routes and online activity history. Furthermore, the collection unit can collect asset status such as the user's bank account balance, investment portfolio, and real estate ownership status. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the user's purchase history using data mining techniques to understand the user's purchasing trends. Furthermore, the analysis unit can analyze the user's behavioral patterns using statistical analysis techniques to understand the user's behavioral trends. Furthermore, the analysis unit can analyze the user's asset status using machine learning algorithms to understand the user's asset management status. The generation unit generates prompts that incorporate the latest financial information based on the data obtained by the analysis unit. The generation unit can collect financial information such as the latest stock price information, interest rate information, and economic indicators, and generate prompts based on this information. The generation unit can also generate individually optimized savings suggestions based on the user's purchase history, behavioral patterns, and asset status. The provision unit provides individually optimized savings suggestions based on the prompts generated by the generation unit. The provision unit can provide savings suggestions to the user in the form of text messages, notifications, alerts, etc. Furthermore, the provision unit can provide individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle. As a result, the personal financial assistant system according to this embodiment can provide optimal savings suggestions based on the user's purchase history, behavioral patterns, and asset status.

[0030] The data collection unit collects users' purchase history, behavioral patterns, and asset status. Specifically, it can collect purchase history such as the type of product purchased, the date and time of purchase, and the location of purchase. This includes purchase data from online shopping sites and physical stores used by the user. For example, if a user frequently purchases products from a particular brand, the purchase history for that brand will be recorded in detail. The data collection unit can also collect behavioral patterns such as the user's daily travel routes and online activity history. This includes smartphone GPS data, the history of websites visited by the user, and social media activity. Furthermore, the data collection unit can collect asset status such as the user's bank account balance, investment portfolio, and real estate ownership. This includes data obtained through APIs of financial institutions used by the user, as well as asset information manually entered by the user. The data collection unit centrally manages and updates this data in real time, allowing it to always understand the user's current situation. In addition, the data collection unit implements encryption technology and access control to protect users' personal information in order to ensure data privacy and security. This allows the data collection unit to efficiently collect diverse user data and improve the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it can use data mining techniques to analyze users' purchase history and understand their purchasing trends. For example, if a user frequently purchases products in a particular category, it can analyze their purchasing trends for products related to that category. The analysis unit can also use statistical analysis techniques to analyze users' behavioral patterns and understand their behavioral trends. This includes identifying behavioral patterns at specific times and locations based on the user's daily travel routes and online activity history. Furthermore, the analysis unit can use machine learning algorithms to analyze users' asset status and understand their asset management status. For example, it can analyze the performance of a user's investment portfolio and evaluate the balance between risk and return. Based on these analysis results, the analysis unit comprehensively evaluates users' purchasing trends, behavioral patterns, and asset management status, providing foundational data for making optimal savings suggestions to users. In addition, the analysis unit can predict future risks and opportunities by considering past data and market trends. In this way, the analysis unit can analyze user data from multiple perspectives and provide highly accurate information.

[0032] The generation unit generates prompts that incorporate the latest financial information based on data obtained by the analysis unit. Specifically, it can collect financial information such as the latest stock price information, interest rate information, and economic indicators, and generate prompts based on this information. For example, if a sharp fluctuation in stock prices or an increase in interest rates is predicted, it will generate a prompt prompting the user to review their investments. The generation unit can also generate individually optimized savings suggestions based on the user's purchase history, behavior patterns, and asset status. For example, it can generate prompts that suggest cheaper alternatives to products that the user frequently purchases. It can also suggest saving methods for specific times or locations based on the user's behavior patterns. Furthermore, the generation unit can generate prompts that suggest investment strategies to maximize returns while minimizing risk, taking into account the user's asset status. When generating these prompts, the generation unit can consider the user's individual needs and goals and make optimal suggestions. As a result, the generation unit can provide users with concrete and practical savings suggestions.

[0033] The service provider provides individually optimized savings suggestions based on prompts generated by the generation unit. Specifically, savings suggestions can be delivered to users in the form of text messages, notifications, alerts, etc. For example, notifications can be sent to the user's smartphone to inform them of discounts on specific products or ways to save money. The service provider can also provide individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle. For example, it can analyze the user's monthly spending and provide specific advice on how to reduce unnecessary expenses. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on the results of users implementing savings suggestions and use that data to make future suggestions more accurate. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide important information not only through smartphone notifications but also through email and social media. This allows the service provider to deliver savings suggestions to users quickly and reliably, supporting their financial well-being.

[0034] The provisioning unit includes a coupon presentation unit that presents personalized coupons based on the generated savings suggestions. The coupon presentation unit can, for example, provide the user with the most suitable coupons based on the user's purchase history and behavioral patterns. For example, the coupon presentation unit can provide discount coupons for products that the user frequently purchases. The coupon presentation unit can also provide coupons that can be used at specific stores or online shops based on the user's purchase history. Furthermore, the coupon presentation unit can provide coupons that can be used at specific times or days of the week based on the user's behavioral patterns. This allows the user to receive the most suitable coupons based on their purchase history and behavioral patterns, enabling them to save effectively. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can provide coupons using an AI model that takes the user's purchase history and behavioral patterns as input and outputs the most suitable coupons.

[0035] The service provision unit includes a financial service guidance unit that guides users to various financial services based on the generated savings suggestions. The financial service guidance unit can, for example, provide the user with the most suitable financial services based on the user's purchase history and behavioral patterns. The financial service guidance unit can, for example, provide the user with the most suitable investment advice based on the user's asset situation. The financial service guidance unit can also provide the user with the most suitable loan or insurance services based on the user's purchase history. Furthermore, the financial service guidance unit can provide specific financial services based on the user's behavioral patterns. This allows users to receive the most suitable financial services based on their purchase history, behavioral patterns, and asset situation, enabling them to manage their assets effectively. Some or all of the above-described processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can provide financial services using an AI model that takes the user's purchase history and behavioral patterns as input and outputs the most suitable financial services.

[0036] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from stores and online shops that the user frequently uses. The data collection unit can also analyze the user's purchasing behavior during specific time periods and collect data during those times. Furthermore, the data collection unit can analyze the user's purchasing patterns and focus on collecting data related to specific product categories. This allows the optimal data collection method to be selected based on the user's past purchase 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 purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of data related to product categories that the user is currently interested in. The data collection unit can also collect highly relevant data based on the user's lifestyle (e.g., family structure, occupation). Furthermore, the data collection unit can filter and collect the purchase history based on the user's areas of interest (e.g., health, hobbies). This allows the data collection unit to filter the purchase history 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 the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, the data collection unit can prioritize the collection of purchase history from stores in the area where the user is currently located. It can also prioritize the collection of purchase history from areas that the user frequently visits. Furthermore, the data collection unit can analyze purchasing behavior in specific areas based on the user's geographical location information and collect highly relevant data. This allows for the collection of highly relevant purchase history 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 data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0039] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history related to products and services mentioned by the user on social media. The data collection unit can also identify product categories of interest from the user's social media activity and collect their purchase history. Furthermore, the data collection unit can analyze the purchasing behavior of the user's social media followers and friends and collect relevant data. This allows for the collection of relevant purchase history based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or 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 history.

[0040] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can identify specific behavior patterns based on the user's past purchase history and optimize the analysis algorithm. The analysis unit can also focus its analysis on data related to specific time periods or days of the week based on the user's past behavior patterns. Furthermore, the analysis unit can analyze the user's past behavior patterns and reflect the purchasing trends of specific products in the analysis algorithm. This allows the analysis algorithm to be optimized based on the user's past behavior patterns. 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 user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0041] The analysis unit can improve the accuracy of its analysis based on the user's asset status. For example, the analysis unit can take the user's asset status into consideration and focus its analysis on the purchase history of expensive items. It can also focus its analysis on data related to saving money based on the user's asset status. Furthermore, the analysis unit can analyze the user's asset status and analyze data related to specific asset categories. This allows for improved accuracy of the analysis based on the user's asset status. 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 user asset status data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0042] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, the analysis unit can analyze the purchasing behavior of the user in the area where the user is currently located and grasp region-specific trends. The analysis unit can also analyze the purchasing behavior of areas that the user frequently visits and identify purchasing patterns in those areas. Furthermore, the analysis unit can analyze purchasing behavior in specific areas based on the user's geographical location information and extract relevant data. This allows analysis to be performed based on the user's geographical location information. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze data on products and services that the user has mentioned on social media. It can also identify product categories of interest from the user's social media activity and analyze that data. Furthermore, the analysis unit can analyze the purchasing behavior of the user's social media followers and friends and analyze the related data. This allows the accuracy of the analysis to be improved based on the user's social media activity. 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 user's social media activity data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0044] The generation unit can improve the accuracy of prompt generation by evaluating the reliability of the latest financial information during prompt generation. For example, the generation unit can collect the latest financial information from multiple reliable sources and evaluate its reliability. The generation unit can also compare the current financial information with historical data to check for consistency in order to evaluate its reliability. Furthermore, the generation unit can consult with experts to evaluate the reliability of the financial information. This allows the generation unit to improve the accuracy of prompt generation by evaluating the reliability of the latest financial information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input financial information data into a generation AI and have the generation AI perform the reliability evaluation.

[0045] The generation unit can customize the generated prompts based on the user's asset status. For example, the generation unit can generate prompts that consider the user's asset status and advise against purchasing expensive items. It can also generate prompts related to saving money based on the user's asset status. Furthermore, the generation unit can analyze the user's asset status and generate prompts related to specific asset categories. This allows for customization of the generated prompts based on the user's asset status. 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 user asset status data into a generation AI and have the generation AI perform the customization of the generated prompts.

[0046] The generation unit can adjust the generated content when generating prompts, taking into account the user's geographical location information. For example, the generation unit can generate prompts based on financial information for the area where the user is currently located. It can also generate prompts based on financial information for areas the user frequently visits. Furthermore, the generation unit can generate prompts that reflect financial trends in a specific area based on the user's geographical location information. This allows the generated content of prompts to be adjusted based on the user's geographical location information. 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 user's geographical location information data into a generation AI and have the generation AI perform the adjustment of the generated content.

[0047] The generation unit can optimize the generated content by referring to the user's social media activity when generating prompts. For example, the generation unit can generate prompts based on financial information mentioned by the user on social media. It can also identify financial topics of interest from the user's social media activity and generate prompts that reflect that information. Furthermore, the generation unit can analyze the financial behavior of the user's social media followers and friends and generate relevant prompts. This allows for the optimization of the generated prompt content based on the user's social media activity. 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 user's social media activity data into a generation AI and have the generation AI optimize the generated content.

[0048] The service provider can analyze the user's past spending behavior at the time of service provision to select the optimal suggestion method. For example, the service provider can select the most effective savings suggestion based on the user's past spending behavior. Furthermore, the service provider can select savings suggestions related to a specific category based on the user's past spending behavior. In addition, the service provider can analyze the user's past spending behavior and select savings suggestions related to a specific time of day or day of the week. This allows for the selection of the optimal suggestion method based on the user's past spending behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past spending behavior data into a generating AI and have the generating AI select the optimal suggestion method.

[0049] The service provider can customize the suggested content based on the user's current living situation at the time of delivery. For example, the service provider can customize savings suggestions based on the user's current living situation (e.g., family structure, occupation). The service provider can also provide savings suggestions related to specific categories based on the user's current living situation. Furthermore, the service provider can consider the user's current living situation and provide the most effective savings suggestions. This allows for the customization of suggestions based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the suggested content.

[0050] The service provider can select the optimal suggestion method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide optimal savings suggestions based on the user's spending habits in the area where the user is currently located. It can also provide optimal savings suggestions based on the user's spending habits in areas they frequently visit. Furthermore, the service provider can provide savings suggestions that reflect spending habits in specific areas based on the user's geographical location information. This allows the service provider to select the optimal suggestion method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal suggestion method.

[0051] The service provider can analyze the user's social media activity at the time of delivery to optimize the suggested content. For example, the service provider can provide savings suggestions related to products or services mentioned by the user on social media. It can also provide savings suggestions related to product categories of interest based on the user's social media activity. Furthermore, the service provider can analyze the consumption behavior of the user's social media followers and friends and provide relevant savings suggestions. This allows the service provider to optimize the suggested content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the optimization of the suggested content.

[0052] The coupon presentation unit can select the most suitable coupon by referring to the user's past purchase history when presenting a coupon. For example, the coupon presentation unit can select the most effective coupon based on the user's past purchase history. It can also select coupons related to a specific category from the user's past purchase history. Furthermore, the coupon presentation unit can analyze the user's past purchase history and select coupons related to a specific time of day or day of the week. This allows for the selection of the most suitable coupon based on the user's past purchase history. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input the user's past purchase history data into a generating AI and have the generating AI select the most suitable coupon.

[0053] The coupon presentation unit can customize coupons based on the user's current living situation when presenting them. For example, the coupon presentation unit can customize coupons based on the user's current living situation (e.g., family structure, occupation). It can also provide coupons related to specific categories based on the user's current living situation. Furthermore, the coupon presentation unit can consider the user's current living situation and provide the most effective coupon. This allows for the customization of coupons based on the user's current living situation. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input user living situation data into a generating AI and have the generating AI perform the coupon customization.

[0054] The coupon presentation unit can present the most suitable coupon by considering the user's geographical location information when presenting a coupon. For example, the coupon presentation unit can prioritize presenting coupons from stores in the area where the user is currently located. It can also prioritize presenting coupons from stores in areas the user frequently visits. Furthermore, based on the user's geographical location information, the coupon presentation unit can present coupons that reflect purchasing behavior in specific areas. This allows the system to present the most suitable coupon based on the user's geographical location information. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of presenting the most suitable coupon.

[0055] The coupon distribution unit can analyze the user's social media activity and optimize coupons when presenting them. For example, the coupon distribution unit can provide coupons related to products or services mentioned by the user on social media. It can also provide coupons related to product categories the user is interested in, based on their social media activity. Furthermore, the coupon distribution unit can analyze the purchasing behavior of the user's social media followers and friends and provide relevant coupons. This allows for the optimization of coupons based on the user's social media activity. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input the user's social media activity data into a generating AI and have the generating AI perform coupon optimization.

[0056] The financial service guidance unit can select the most suitable service by referring to the user's past asset status when guiding users to financial services. For example, the financial service guidance unit can select the most effective financial service based on the user's past asset status. The financial service guidance unit can also select financial services related to a specific category based on the user's past asset status. Furthermore, the financial service guidance unit can analyze the user's past asset status and select financial services related to a specific time of day or day of the week. This allows for the selection of the most suitable financial service based on the user's past asset status. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input the user's past asset status data into a generating AI and have the generating AI perform the selection of the most suitable service.

[0057] The financial service guidance unit can customize services based on the user's current living situation when guiding users to financial services. For example, the financial service guidance unit can customize financial services based on the user's current living situation (e.g., family structure, occupation). Furthermore, the financial service guidance unit can provide financial services related to specific categories based on the user's current living situation. In addition, the financial service guidance unit can consider the user's current living situation and provide the most effective financial services. This allows for the customization of services based on the user's current living situation. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input user living situation data into a generating AI and have the generating AI perform the service customization.

[0058] The financial service guidance unit can guide users to the most suitable services by considering the user's geographical location information during the financial service guidance process. For example, the financial service guidance unit can prioritize providing financial services in the user's current location. It can also prioritize providing financial services in areas the user frequently visits. Furthermore, based on the user's geographical location information, the financial service guidance unit can provide suggestions that reflect financial services in specific areas. This allows for the guidance of the most suitable financial services based on the user's geographical location information. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input the user's geographical location information data into a generating AI and have the generating AI execute the guidance to the most suitable services.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of data related to product categories that the user is currently interested in. The data collection unit can also collect highly relevant data based on the user's lifestyle (e.g., family structure, occupation). Furthermore, the data collection unit can filter and collect the purchase history based on the user's areas of interest (e.g., health, hobbies). This allows the data collection unit to filter the purchase history 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 the filtering.

[0061] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, the data collection unit can prioritize the collection of purchase history from stores in the area where the user is currently located. It can also prioritize the collection of purchase history from areas that the user frequently visits. Furthermore, the data collection unit can analyze purchasing behavior in specific areas based on the user's geographical location information and collect highly relevant data. This allows for the collection of highly relevant purchase history 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 data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0062] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history related to products and services mentioned by the user on social media. The data collection unit can also identify product categories of interest from the user's social media activity and collect their purchase history. Furthermore, the data collection unit can analyze the purchasing behavior of the user's social media followers and friends and collect relevant data. This allows for the collection of relevant purchase history based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or 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 history.

[0063] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can identify specific behavior patterns based on the user's past purchase history and optimize the analysis algorithm. The analysis unit can also focus its analysis on data related to specific time periods or days of the week based on the user's past behavior patterns. Furthermore, the analysis unit can analyze the user's past behavior patterns and reflect the purchasing trends of specific products in the analysis algorithm. This allows the analysis algorithm to be optimized based on the user's past behavior patterns. 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 user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0064] The analysis unit can improve the accuracy of its analysis based on the user's asset status. For example, the analysis unit can take the user's asset status into consideration and focus its analysis on the purchase history of expensive items. It can also focus its analysis on data related to saving money based on the user's asset status. Furthermore, the analysis unit can analyze the user's asset status and analyze data related to specific asset categories. This allows for improved accuracy of the analysis based on the user's asset status. 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 user asset status data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects the user's purchase history, behavioral patterns, and asset status. For example, it collects purchase history such as the type of product the user purchased, the date and time of purchase, and the place of purchase. It can also collect behavioral patterns such as the user's daily travel routes and online activity history. Furthermore, it collects asset status such as the user's bank account balance, investment portfolio, and real estate ownership status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses data mining techniques to analyze users' purchase history and understand their purchasing trends. It also uses statistical analysis techniques to analyze users' behavioral patterns and understand their behavioral trends. Furthermore, it uses machine learning algorithms to analyze users' asset status and understand their asset management status. Step 3: The generation unit generates prompts that incorporate the latest financial information based on the data obtained by the analysis unit. For example, it collects financial information such as the latest stock price information, interest rate information, and economic indicators, and generates prompts based on that information. It also generates individually optimized savings suggestions based on the user's purchase history, behavior patterns, and asset status. Step 4: The providing unit provides individually optimized savings suggestions based on the prompts generated by the generating unit. For example, it provides savings suggestions to the user in the form of text messages, notifications, alerts, etc. It also provides individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle.

[0067] (Example of form 2) The personal financial assistant system according to an embodiment of the present invention is a system in which AI analyzes a user's purchase history, behavioral patterns, and asset status, generates prompts that take into account the latest financial information, and provides the user with individually optimized savings suggestions. The personal financial assistant system collects the user's purchase history, behavioral patterns, and asset status, and the AI ​​analyzes the collected data to understand the user's behavioral patterns and asset status. Subsequently, it generates prompts that take into account the latest financial information and generates individually optimized savings suggestions. Based on the generated savings suggestions, it presents personalized coupons and guides the user to various financial services. As a result, the user can receive optimal savings suggestions based on their purchase history, behavioral patterns, and asset status, and can save effectively. In addition, the presentation of personalized coupons and guidance to financial services improves user convenience and increases satisfaction. Thus, the personal financial assistant system can provide optimal savings suggestions based on the user's purchase history, behavioral patterns, and asset status.

[0068] The personal financial assistant system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's purchase history, behavioral patterns, and asset status. For example, the collection unit can collect purchase history such as the type of goods purchased by the user, the date and time of purchase, and the place of purchase. The collection unit can also collect behavioral patterns such as the user's daily travel routes and online activity history. Furthermore, the collection unit can collect asset status such as the user's bank account balance, investment portfolio, and real estate ownership status. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the user's purchase history using data mining techniques to understand the user's purchasing trends. Furthermore, the analysis unit can analyze the user's behavioral patterns using statistical analysis techniques to understand the user's behavioral trends. Furthermore, the analysis unit can analyze the user's asset status using machine learning algorithms to understand the user's asset management status. The generation unit generates prompts that incorporate the latest financial information based on the data obtained by the analysis unit. The generation unit can collect financial information such as the latest stock price information, interest rate information, and economic indicators, and generate prompts based on this information. The generation unit can also generate individually optimized savings suggestions based on the user's purchase history, behavioral patterns, and asset status. The provision unit provides individually optimized savings suggestions based on the prompts generated by the generation unit. The provision unit can provide savings suggestions to the user in the form of text messages, notifications, alerts, etc. Furthermore, the provision unit can provide individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle. As a result, the personal financial assistant system according to this embodiment can provide optimal savings suggestions based on the user's purchase history, behavioral patterns, and asset status.

[0069] The data collection unit collects users' purchase history, behavioral patterns, and asset status. Specifically, it can collect purchase history such as the type of product purchased, the date and time of purchase, and the location of purchase. This includes purchase data from online shopping sites and physical stores used by the user. For example, if a user frequently purchases products from a particular brand, the purchase history for that brand will be recorded in detail. The data collection unit can also collect behavioral patterns such as the user's daily travel routes and online activity history. This includes smartphone GPS data, the history of websites visited by the user, and social media activity. Furthermore, the data collection unit can collect asset status such as the user's bank account balance, investment portfolio, and real estate ownership. This includes data obtained through APIs of financial institutions used by the user, as well as asset information manually entered by the user. The data collection unit centrally manages and updates this data in real time, allowing it to always understand the user's current situation. In addition, the data collection unit implements encryption technology and access control to protect users' personal information in order to ensure data privacy and security. This allows the data collection unit to efficiently collect diverse user data and improve the accuracy and reliability of the entire system.

[0070] The analysis unit analyzes the data collected by the data collection unit. Specifically, it can use data mining techniques to analyze users' purchase history and understand their purchasing trends. For example, if a user frequently purchases products in a particular category, it can analyze their purchasing trends for products related to that category. The analysis unit can also use statistical analysis techniques to analyze users' behavioral patterns and understand their behavioral trends. This includes identifying behavioral patterns at specific times and locations based on the user's daily travel routes and online activity history. Furthermore, the analysis unit can use machine learning algorithms to analyze users' asset status and understand their asset management status. For example, it can analyze the performance of a user's investment portfolio and evaluate the balance between risk and return. Based on these analysis results, the analysis unit comprehensively evaluates users' purchasing trends, behavioral patterns, and asset management status, providing foundational data for making optimal savings suggestions to users. In addition, the analysis unit can predict future risks and opportunities by considering past data and market trends. In this way, the analysis unit can analyze user data from multiple perspectives and provide highly accurate information.

[0071] The generation unit generates prompts that incorporate the latest financial information based on data obtained by the analysis unit. Specifically, it can collect financial information such as the latest stock price information, interest rate information, and economic indicators, and generate prompts based on this information. For example, if a sharp fluctuation in stock prices or an increase in interest rates is predicted, it will generate a prompt prompting the user to review their investments. The generation unit can also generate individually optimized savings suggestions based on the user's purchase history, behavior patterns, and asset status. For example, it can generate prompts that suggest cheaper alternatives to products that the user frequently purchases. It can also suggest saving methods for specific times or locations based on the user's behavior patterns. Furthermore, the generation unit can generate prompts that suggest investment strategies to maximize returns while minimizing risk, taking into account the user's asset status. When generating these prompts, the generation unit can consider the user's individual needs and goals and make optimal suggestions. As a result, the generation unit can provide users with concrete and practical savings suggestions.

[0072] The service provider provides individually optimized savings suggestions based on prompts generated by the generation unit. Specifically, savings suggestions can be delivered to users in the form of text messages, notifications, alerts, etc. For example, notifications can be sent to the user's smartphone to inform them of discounts on specific products or ways to save money. The service provider can also provide individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle. For example, it can analyze the user's monthly spending and provide specific advice on how to reduce unnecessary expenses. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on the results of users implementing savings suggestions and use that data to make future suggestions more accurate. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide important information not only through smartphone notifications but also through email and social media. This allows the service provider to deliver savings suggestions to users quickly and reliably, supporting their financial well-being.

[0073] The provisioning unit includes a coupon presentation unit that presents personalized coupons based on the generated savings suggestions. The coupon presentation unit can, for example, provide the user with the most suitable coupons based on the user's purchase history and behavioral patterns. For example, the coupon presentation unit can provide discount coupons for products that the user frequently purchases. The coupon presentation unit can also provide coupons that can be used at specific stores or online shops based on the user's purchase history. Furthermore, the coupon presentation unit can provide coupons that can be used at specific times or days of the week based on the user's behavioral patterns. This allows the user to receive the most suitable coupons based on their purchase history and behavioral patterns, enabling them to save effectively. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can provide coupons using an AI model that takes the user's purchase history and behavioral patterns as input and outputs the most suitable coupons.

[0074] The service provision unit includes a financial service guidance unit that guides users to various financial services based on the generated savings suggestions. The financial service guidance unit can, for example, provide the user with the most suitable financial services based on the user's purchase history and behavioral patterns. The financial service guidance unit can, for example, provide the user with the most suitable investment advice based on the user's asset situation. The financial service guidance unit can also provide the user with the most suitable loan or insurance services based on the user's purchase history. Furthermore, the financial service guidance unit can provide specific financial services based on the user's behavioral patterns. This allows users to receive the most suitable financial services based on their purchase history, behavioral patterns, and asset situation, enabling them to manage their assets effectively. Some or all of the above-described processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can provide financial services using an AI model that takes the user's purchase history and behavioral patterns as input and outputs the most suitable financial services.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting purchase history and collect it when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can actively collect purchase history and obtain detailed data. Furthermore, if the user is in a hurry, the data collection unit can quickly collect purchase history and obtain only the minimum necessary data. This allows the timing of purchase history collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from stores and online shops that the user frequently uses. The data collection unit can also analyze the user's purchasing behavior during specific time periods and collect data during those times. Furthermore, the data collection unit can analyze the user's purchasing patterns and focus on collecting data related to specific product categories. This allows the optimal data collection method to be selected based on the user's past purchase 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 purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of data related to product categories that the user is currently interested in. The data collection unit can also collect highly relevant data based on the user's lifestyle (e.g., family structure, occupation). Furthermore, the data collection unit can filter and collect the purchase history based on the user's areas of interest (e.g., health, hobbies). This allows the data collection unit to filter the purchase history 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 the filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize the collection of purchase history related to stress reduction. Similarly, if the user is relaxed, the data collection unit can prioritize the collection of purchase history related to relaxation. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of purchase history related to urgent purchasing behavior. This allows for the prioritization of purchase history 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 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 using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, the data collection unit can prioritize the collection of purchase history from stores in the area where the user is currently located. It can also prioritize the collection of purchase history from areas that the user frequently visits. Furthermore, the data collection unit can analyze purchasing behavior in specific areas based on the user's geographical location information and collect highly relevant data. This allows for the collection of highly relevant purchase history 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 data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0080] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history related to products and services mentioned by the user on social media. The data collection unit can also identify product categories of interest from the user's social media activity and collect their purchase history. Furthermore, the data collection unit can analyze the purchasing behavior of the user's social media followers and friends and collect relevant data. This allows for the collection of relevant purchase history based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or 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 history.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can focus on analyzing data related to stress reduction. Similarly, if the user is relaxed, the analysis unit can focus on analyzing data related to relaxation. Furthermore, if the user is in a hurry, the analysis unit can focus on analyzing data related to urgent purchasing behavior. This allows the analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can identify specific behavior patterns based on the user's past purchase history and optimize the analysis algorithm. The analysis unit can also focus its analysis on data related to specific time periods or days of the week based on the user's past behavior patterns. Furthermore, the analysis unit can analyze the user's past behavior patterns and reflect the purchasing trends of specific products in the analysis algorithm. This allows the analysis algorithm to be optimized based on the user's past behavior patterns. 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 user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The analysis unit can improve the accuracy of its analysis based on the user's asset status. For example, the analysis unit can take the user's asset status into consideration and focus its analysis on the purchase history of expensive items. It can also focus its analysis on data related to saving money based on the user's asset status. Furthermore, the analysis unit can analyze the user's asset status and analyze data related to specific asset categories. This allows for improved accuracy of the analysis based on the user's asset status. 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 user asset status data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of data related to stress reduction. Similarly, if the user is relaxed, the analysis unit can prioritize the analysis of data related to relaxation. Furthermore, if the user is in a hurry, the analysis unit can prioritize the analysis of data related to urgent purchasing behavior. This allows the analysis priority to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, the analysis unit can analyze the purchasing behavior of the user in the area where the user is currently located and grasp region-specific trends. The analysis unit can also analyze the purchasing behavior of areas that the user frequently visits and identify purchasing patterns in those areas. Furthermore, the analysis unit can analyze purchasing behavior in specific areas based on the user's geographical location information and extract relevant data. This allows analysis to be performed based on the user's geographical location information. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze data on products and services that the user has mentioned on social media. It can also identify product categories of interest from the user's social media activity and analyze that data. Furthermore, the analysis unit can analyze the purchasing behavior of the user's social media followers and friends and analyze the related data. This allows the accuracy of the analysis to be improved based on the user's social media activity. 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 user's social media activity data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0087] The generation unit can estimate the user's emotions and adjust the prompt generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate prompts related to stress reduction. It can also generate prompts related to relaxation if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate prompts related to urgent purchasing behavior. This allows the prompt generation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The generation unit can improve the accuracy of prompt generation by evaluating the reliability of the latest financial information during prompt generation. For example, the generation unit can collect the latest financial information from multiple reliable sources and evaluate its reliability. The generation unit can also compare the current financial information with historical data to check for consistency in order to evaluate its reliability. Furthermore, the generation unit can consult with experts to evaluate the reliability of the financial information. This allows the generation unit to improve the accuracy of prompt generation by evaluating the reliability of the latest financial information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input financial information data into a generation AI and have the generation AI perform the reliability evaluation.

[0089] The generation unit can customize the generated prompts based on the user's asset status. For example, the generation unit can generate prompts that consider the user's asset status and advise against purchasing expensive items. It can also generate prompts related to saving money based on the user's asset status. Furthermore, the generation unit can analyze the user's asset status and generate prompts related to specific asset categories. This allows for customization of the generated prompts based on the user's asset status. 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 user asset status data into a generation AI and have the generation AI perform the customization of the generated prompts.

[0090] The generation unit can estimate the user's emotions and determine the priority of prompts based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize generating prompts related to stress reduction. Similarly, if the user is relaxed, the generation unit can prioritize generating prompts related to relaxation. Furthermore, if the user is in a hurry, the generation unit can prioritize generating prompts related to urgent purchasing behavior. This allows the system to prioritize prompts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The generation unit can adjust the generated content when generating prompts, taking into account the user's geographical location information. For example, the generation unit can generate prompts based on financial information for the area where the user is currently located. It can also generate prompts based on financial information for areas the user frequently visits. Furthermore, the generation unit can generate prompts that reflect financial trends in a specific area based on the user's geographical location information. This allows the generated content of prompts to be adjusted based on the user's geographical location information. 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 user's geographical location information data into a generation AI and have the generation AI perform the adjustment of the generated content.

[0092] The generation unit can optimize the generated content by referring to the user's social media activity when generating prompts. For example, the generation unit can generate prompts based on financial information mentioned by the user on social media. It can also identify financial topics of interest from the user's social media activity and generate prompts that reflect that information. Furthermore, the generation unit can analyze the financial behavior of the user's social media followers and friends and generate relevant prompts. This allows for the optimization of the generated prompt content based on the user's social media activity. 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 user's social media activity data into a generation AI and have the generation AI optimize the generated content.

[0093] The service provider can estimate the user's emotions and adjust the way it provides savings suggestions based on those emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand savings suggestions. If the user is relaxed, the service provider can provide detailed savings suggestions. Furthermore, if the user is in a hurry, the service provider can provide savings suggestions that can be implemented quickly. This allows the service provider to adjust the way it provides savings suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can analyze the user's past spending behavior at the time of service provision to select the optimal suggestion method. For example, the service provider can select the most effective savings suggestion based on the user's past spending behavior. Furthermore, the service provider can select savings suggestions related to a specific category based on the user's past spending behavior. In addition, the service provider can analyze the user's past spending behavior and select savings suggestions related to a specific time of day or day of the week. This allows for the selection of the optimal suggestion method based on the user's past spending behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past spending behavior data into a generating AI and have the generating AI select the optimal suggestion method.

[0095] The service provider can customize the suggested content based on the user's current living situation at the time of delivery. For example, the service provider can customize savings suggestions based on the user's current living situation (e.g., family structure, occupation). The service provider can also provide savings suggestions related to specific categories based on the user's current living situation. Furthermore, the service provider can consider the user's current living situation and provide the most effective savings suggestions. This allows for the customization of suggestions based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the suggested content.

[0096] The service provider can estimate the user's emotions and prioritize savings suggestions based on those emotions. For example, if the user is stressed, the service provider can prioritize savings suggestions related to stress reduction. Similarly, if the user is relaxed, the service provider can prioritize savings suggestions related to relaxation. Furthermore, if the user is in a hurry, the service provider can prioritize savings suggestions related to urgent purchasing. This allows the service provider to prioritize savings suggestions 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 service provider may be performed using AI, or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The service provider can select the optimal suggestion method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide optimal savings suggestions based on the user's spending habits in the area where the user is currently located. It can also provide optimal savings suggestions based on the user's spending habits in areas they frequently visit. Furthermore, the service provider can provide savings suggestions that reflect spending habits in specific areas based on the user's geographical location information. This allows the service provider to select the optimal suggestion method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal suggestion method.

[0098] The service provider can analyze the user's social media activity at the time of delivery to optimize the suggested content. For example, the service provider can provide savings suggestions related to products or services mentioned by the user on social media. It can also provide savings suggestions related to product categories of interest based on the user's social media activity. Furthermore, the service provider can analyze the consumption behavior of the user's social media followers and friends and provide relevant savings suggestions. This allows the service provider to optimize the suggested content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the optimization of the suggested content.

[0099] The coupon presentation unit can estimate the user's emotions and adjust the coupon presentation method based on the estimated emotions. For example, if the user is stressed, the coupon presentation unit can present a simple and easy-to-understand coupon. If the user is relaxed, the coupon presentation unit can present a more detailed coupon. Furthermore, if the user is in a hurry, the coupon presentation unit can present a coupon that can be used quickly. This allows the coupon presentation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon presentation unit may be performed using AI, or not using AI. For example, the coupon presentation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The coupon presentation unit can select the most suitable coupon by referring to the user's past purchase history when presenting a coupon. For example, the coupon presentation unit can select the most effective coupon based on the user's past purchase history. It can also select coupons related to a specific category from the user's past purchase history. Furthermore, the coupon presentation unit can analyze the user's past purchase history and select coupons related to a specific time of day or day of the week. This allows for the selection of the most suitable coupon based on the user's past purchase history. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input the user's past purchase history data into a generating AI and have the generating AI select the most suitable coupon.

[0101] The coupon presentation unit can customize coupons based on the user's current living situation when presenting them. For example, the coupon presentation unit can customize coupons based on the user's current living situation (e.g., family structure, occupation). It can also provide coupons related to specific categories based on the user's current living situation. Furthermore, the coupon presentation unit can consider the user's current living situation and provide the most effective coupon. This allows for the customization of coupons based on the user's current living situation. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input user living situation data into a generating AI and have the generating AI perform the coupon customization.

[0102] The coupon presentation unit can estimate the user's emotions and determine the priority of coupons based on the estimated emotions. For example, if the user is feeling stressed, the coupon presentation unit can prioritize coupons related to stress reduction. Similarly, if the user is relaxed, the coupon presentation unit can prioritize coupons related to relaxation. Furthermore, if the user is in a hurry, the coupon presentation unit can prioritize coupons related to urgent purchasing behavior. This allows the coupon priority to be determined 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 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 coupon presentation unit may be performed using AI, or not using AI. For example, the coupon presentation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The coupon presentation unit can present the most suitable coupon by considering the user's geographical location information when presenting a coupon. For example, the coupon presentation unit can prioritize presenting coupons from stores in the area where the user is currently located. It can also prioritize presenting coupons from stores in areas the user frequently visits. Furthermore, based on the user's geographical location information, the coupon presentation unit can present coupons that reflect purchasing behavior in specific areas. This allows the system to present the most suitable coupon based on the user's geographical location information. Some or all of the above processing in the coupon presentation unit may be performed using AI, for example, or without AI. For example, the coupon presentation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of presenting the most suitable coupon.

[0104] The coupon distribution unit can analyze the user's social media activity and optimize coupons when presenting them. For example, the coupon distribution unit can provide coupons related to products or services mentioned by the user on social media. It can also provide coupons related to product categories the user is interested in, based on their social media activity. Furthermore, the coupon distribution unit can analyze the purchasing behavior of the user's social media followers and friends and provide relevant coupons. This allows for the optimization of coupons based on the user's social media activity. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input the user's social media activity data into a generating AI and have the generating AI perform coupon optimization.

[0105] The financial service guidance unit can estimate the user's emotions and adjust the method of guiding users to financial services based on the estimated emotions. For example, if the user is stressed, the financial service guidance unit can provide simple and easy-to-understand financial services. If the user is relaxed, the financial service guidance unit can also provide detailed financial services. Furthermore, if the user is in a hurry, the financial service guidance unit can provide financial services that can be used quickly. This allows the method of guiding users to financial services to be adjusted 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 financial service guidance unit may be performed using AI, for example, or not using AI. For example, the financial service guidance unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The financial service guidance unit can select the most suitable service by referring to the user's past asset status when guiding users to financial services. For example, the financial service guidance unit can select the most effective financial service based on the user's past asset status. The financial service guidance unit can also select financial services related to a specific category based on the user's past asset status. Furthermore, the financial service guidance unit can analyze the user's past asset status and select financial services related to a specific time of day or day of the week. This allows for the selection of the most suitable financial service based on the user's past asset status. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input the user's past asset status data into a generating AI and have the generating AI perform the selection of the most suitable service.

[0107] The financial service guidance unit can customize services based on the user's current living situation when guiding users to financial services. For example, the financial service guidance unit can customize financial services based on the user's current living situation (e.g., family structure, occupation). Furthermore, the financial service guidance unit can provide financial services related to specific categories based on the user's current living situation. In addition, the financial service guidance unit can consider the user's current living situation and provide the most effective financial services. This allows for the customization of services based on the user's current living situation. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input user living situation data into a generating AI and have the generating AI perform the service customization.

[0108] The financial service guidance unit can estimate the user's emotions and determine the priority of financial services based on the estimated emotions. For example, if the user is stressed, the financial service guidance unit can prioritize providing financial services related to stress reduction. Similarly, if the user is relaxed, the financial service guidance unit can prioritize providing financial services related to relaxation. Furthermore, if the user is in a hurry, the financial service guidance unit can prioritize providing financial services related to urgent purchasing behavior. This allows for the prioritization of financial services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the financial service guidance unit may be performed using AI, or not. For example, the financial service guidance unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The financial service guidance unit can guide users to the most suitable services by considering the user's geographical location information during the financial service guidance process. For example, the financial service guidance unit can prioritize providing financial services in the user's current location. It can also prioritize providing financial services in areas the user frequently visits. Furthermore, based on the user's geographical location information, the financial service guidance unit can provide suggestions that reflect financial services in specific areas. This allows for the guidance of the most suitable financial services based on the user's geographical location information. Some or all of the above processing in the financial service guidance unit may be performed using AI, for example, or without AI. For example, the financial service guidance unit can input the user's geographical location information data into a generating AI and have the generating AI execute the guidance to the most suitable services.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can focus on analyzing data related to stress reduction. Similarly, if the user is relaxed, the analysis unit can focus on analyzing data related to relaxation. Furthermore, if the user is in a hurry, the analysis unit can focus on analyzing data related to urgent purchasing behavior. This allows the analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0112] The service provider can estimate the user's emotions and adjust the way it provides savings suggestions based on those emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand savings suggestions. If the user is relaxed, the service provider can provide detailed savings suggestions. Furthermore, if the user is in a hurry, the service provider can provide savings suggestions that can be implemented quickly. This allows the service provider to adjust the way it provides savings suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize the collection of purchase history related to stress reduction. Similarly, if the user is relaxed, the data collection unit can prioritize the collection of purchase history related to relaxation. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of purchase history related to urgent purchasing behavior. This allows for the prioritization of purchase history 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 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 using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0114] The generation unit can estimate the user's emotions and adjust the prompt generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate prompts related to stress reduction. It can also generate prompts related to relaxation if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate prompts related to urgent purchasing behavior. This allows the prompt generation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0115] The coupon presentation unit can estimate the user's emotions and adjust the coupon presentation method based on the estimated emotions. For example, if the user is stressed, the coupon presentation unit can present a simple and easy-to-understand coupon. If the user is relaxed, the coupon presentation unit can present a more detailed coupon. Furthermore, if the user is in a hurry, the coupon presentation unit can present a coupon that can be used quickly. This allows the coupon presentation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon presentation unit may be performed using AI, or not using AI. For example, the coupon presentation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0116] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of data related to product categories that the user is currently interested in. The data collection unit can also collect highly relevant data based on the user's lifestyle (e.g., family structure, occupation). Furthermore, the data collection unit can filter and collect the purchase history based on the user's areas of interest (e.g., health, hobbies). This allows the data collection unit to filter the purchase history 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 the filtering.

[0117] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, the data collection unit can prioritize the collection of purchase history from stores in the area where the user is currently located. It can also prioritize the collection of purchase history from areas that the user frequently visits. Furthermore, the data collection unit can analyze purchasing behavior in specific areas based on the user's geographical location information and collect highly relevant data. This allows for the collection of highly relevant purchase history 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 data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0118] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history related to products and services mentioned by the user on social media. The data collection unit can also identify product categories of interest from the user's social media activity and collect their purchase history. Furthermore, the data collection unit can analyze the purchasing behavior of the user's social media followers and friends and collect relevant data. This allows for the collection of relevant purchase history based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or 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 history.

[0119] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during analysis. For example, the analysis unit can identify specific behavior patterns based on the user's past purchase history and optimize the analysis algorithm. The analysis unit can also focus its analysis on data related to specific time periods or days of the week based on the user's past behavior patterns. Furthermore, the analysis unit can analyze the user's past behavior patterns and reflect the purchasing trends of specific products in the analysis algorithm. This allows the analysis algorithm to be optimized based on the user's past behavior patterns. 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 user's past behavior pattern data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0120] The analysis unit can improve the accuracy of its analysis based on the user's asset status. For example, the analysis unit can take the user's asset status into consideration and focus its analysis on the purchase history of expensive items. It can also focus its analysis on data related to saving money based on the user's asset status. Furthermore, the analysis unit can analyze the user's asset status and analyze data related to specific asset categories. This allows for improved accuracy of the analysis based on the user's asset status. 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 user asset status data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the user's purchase history, behavioral patterns, and asset status. For example, it collects purchase history such as the type of product the user purchased, the date and time of purchase, and the place of purchase. It can also collect behavioral patterns such as the user's daily travel routes and online activity history. Furthermore, it collects asset status such as the user's bank account balance, investment portfolio, and real estate ownership status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses data mining techniques to analyze users' purchase history and understand their purchasing trends. It also uses statistical analysis techniques to analyze users' behavioral patterns and understand their behavioral trends. Furthermore, it uses machine learning algorithms to analyze users' asset status and understand their asset management status. Step 3: The generation unit generates prompts that incorporate the latest financial information based on the data obtained by the analysis unit. For example, it collects financial information such as the latest stock price information, interest rate information, and economic indicators, and generates prompts based on that information. It also generates individually optimized savings suggestions based on the user's purchase history, behavior patterns, and asset status. Step 4: The providing unit provides individually optimized savings suggestions based on the prompts generated by the generating unit. For example, it provides savings suggestions to the user in the form of text messages, notifications, alerts, etc. It also provides individually optimized savings suggestions based on the user's income, spending patterns, and lifestyle.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavior patterns using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates prompts based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated prompts to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavioral patterns using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates prompts based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated prompts to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavioral patterns using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates prompts based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the generated prompts to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavior patterns using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates prompts based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated prompts to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects users' purchase history, behavioral patterns, and asset status, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a prompt that incorporates the latest financial information based on the data obtained by the analysis unit, The system includes a providing unit that provides individually optimized savings suggestions based on prompts generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It includes a coupon display unit that presents personalized coupons based on the generated savings suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It includes a financial services guidance unit that directs users to various financial services based on the generated savings suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of purchase history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting purchase history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's asset status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts how prompts are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating prompts, the reliability of the latest financial information is evaluated to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating prompts, customize the generated content based on the user's asset status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and determines the priority of prompts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating prompts, the generated content is adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating prompts, the system optimizes the generated content by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts how savings suggestions are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing a service, the system analyzes the user's past purchasing behavior to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the suggestions will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and prioritizes savings suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal suggestion method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity to optimize the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coupon display section is, The system estimates the user's emotions and adjusts how coupons are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned coupon display section is, When presenting a coupon, the system selects the most suitable coupon by referring to the user's past purchase history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned coupon display section is, When presenting a coupon, customize the coupon based on the user's current lifestyle. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned coupon display section is, The system estimates user sentiment and prioritizes coupons based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned coupon display section is, When presenting a coupon, the system will consider the user's geographical location to display the most suitable coupon. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned coupon display section is, When a coupon is presented, the system analyzes the user's social media activity to optimize the coupon. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned financial services guidance unit is We estimate user sentiment and adjust how financial services are guided based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned financial services guidance unit is When guiding users to financial services, the system selects the most suitable service by referring to the user's past asset history. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned financial services guidance unit is When guiding users to financial services, the service will be customized based on the user's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned financial services guidance unit is It estimates user sentiment and prioritizes financial services based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned financial services guidance unit is When guiding users to financial services, the system takes into account the user's geographical location to guide them to the most suitable service. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 users' purchase history, behavioral patterns, and asset status, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a prompt that incorporates the latest financial information based on the data obtained by the analysis unit, The system includes a providing unit that provides individually optimized savings suggestions based on prompts generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, It includes a coupon display unit that presents personalized coupons based on the generated savings suggestions. The system according to feature 1.

3. The aforementioned supply unit is, It includes a financial services guidance unit that directs users to various financial services based on the generated savings suggestions. The system according to feature 1.

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

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

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

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on the estimated user emotions. The system according to feature 1.

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

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